Automated capillary electrophoresis : instrumental and ...Automated capillary electrophoresis : instrumental and methodological aspects Citation for published version (APA): ... The

Automated capillary electrophoresis : instrumental andmethodological aspectsCitation for published version (APA):Wanders, B. J. (1993). Automated capillary electrophoresis : instrumental and methodological aspects.Technische Universiteit Eindhoven. https://doi.org/10.6100/IR404408

DOI:10.6100/IR404408

Document status and date:Published: 01/01/1993

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AUTOMATED CAPILLARY ELECTROPHORESIS

INSTRUMENTAL AND METHODOLOGICAL ASPECTS

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de Rector Magnificus, prof. dr. J.H. van Lint, voor een commissie aangewezen door het College van Dekanen in het openbaar te verdedigen op

dinsdag 2 november 1993 om 16.00 uur

door

BART JAN WANDERS geboren te Geleen

druk; wibro dissertatiedrukkerij, helmond.

Dit proefschrift is goedgekeurd door de promotoren:

prof.dr.ir. F.M. Everaerts

en

prof.dr.ir. C.A.M.G. Cramers

co pro motor:

dr. B.G.M. Vandeginste

VEN/, VIDI, VICI ?

To Lisa, Jessica and Alexandra

CONTENTS

1 GENERAL INTRODUCTION 1.1 Brief history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 1.2 About this thesis ..................................... 3 References ............................................. 4

2 PRINCIPLES OF CAPILLARY ELECTROPHORESIS Abstract ............................................... 5 2.1 Principles of electrophoretic mobility . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Electroosmotic flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Different modes of Capillary Electrophoresis . . . . . . . . . . . . . . . . 8

2.3.l Capillary Zone Electrophoresis (CZE) ................ 10 2.3.2 Capillary Moving Boundary Electrophoresis (CMBE) ..... 10 2.3.3 Capillary lsotachophoresis (CITP) ................... 10 2.3.4 Capillary Isoelectric Focusing (CIEF) . . . . . . . . . . . . . . . . 11 2.3.5 Micellar Electrokinetic Capillary Chromatography (MECC) . 11 2.3.6 Capillary Gel Electrophoresis (CGE) . . . . . . . . . . . . . . . . . 12

2.4 Some theoretical aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5 Factors influencing performance ........................ 15

2.5.1 Injection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.2 Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.5.3 Joule heating ................................. 16 2.5 .4 Electroosmosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.5.5 Electrodispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5.6 Surface Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.6 Some experimental considerations . . . . . . . . . . . . . . . . . . . . . . . 18 2.6.1 Injection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6.2 Calculating results from experimental parameters . . . . . . . . 20

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

CONTENTS

3 INSTRUMENTATION Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2.1 Injection .................................... 25 3.2.2 Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3 Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.l Injection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.2 Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3.3 The entire system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.4 Reagents and Materials .......................... 29 3.3.5 Sample Pretreatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Injection .................................... 30 3.4.2 Detection .................................... 31 3.4.3 Sample Pretreatment ............................ 34

3.S Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. .. .. . . 34 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4 AMINE REACTIVE FLUORESCENT DYES

ii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2 Theory ........................................... 38 4.3 Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.3.l Derivatization procedure for FITC .................. 42 4.3.2 Derivatization procedure for DTAF .................. 43 4.3.3 Derivatization procedure for FSE ................... 44 4.3.4 Derivatization procedure for NBD-F . . . . . . . . . . . . . . . . . 45 4.3.5 Derivatization procedure for CBQCA ................ 46 4.3.6 General labelling information ...................... 47

4.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5 ELECTROOSMOTIC FLOW Abstract .............................................. 59 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2.l Indirect measurement of the electroosmotic flow ......... 63 5.2.2 Direct measurement of the electroosmotic flow .......... 64

5.2.2.l Weighing ............................. 64 5.2.2.2 Conductivity cell . . . . . . . . . . . . . . . . . . . . . . . . 64

5.3 Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.3.l Indirect measurement of the electroosmotic flow ......... 65 5.3.2 Direct measurement of the electroosmotic flow .......... 67

5.3.2.l Weighing ............................. 67 5.3.2.2 Conductivity cell ........................ 68

5.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.4.1 Indirect measurement of the electroosmotic flow ......... 70 5.4.2 Direct measurement of the electroosmotic flow .......... 71

5.4.2.l Weighing ............................. 71 5.4.2.2 Conductivity cell . . . . . . . . . . . . . . . . . . . . . . . . 73

5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

6 DATA ANALYSIS IN CAPILLARY ELECTROPHORESIS Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 6.2 Description of the algorithm ............................ 78

6.2.1 Baseline construction . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.2.2 Cluster detection ............................... 81 6.2.3 Cluster border adjustment . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.4 Peak detection and check for overlapping peaks . . . . . . . . . 83 6.2.5 Integration of peak areas . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.2.6 The iteration process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.2. 7 Existing peak detection algorithm . . . . . . . . . . . . . . . . . . . 86

6.3 Description of the tests used . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6.3. I Simulated electropherograms . . . . . . . . . . . . . . . . . . . . . . 87 6.3.2 Actual electropherograms . . . . . . . . . . . . . . . . . . . . . . . . . 89

iii

CONTENTS

6.4 Results and discussion ......................... ,, . . . . . 89

6.4. l Effectiveness of the iteration process . . . . . . . . . . . . . . . . . 89

6.4.2 Comparison using simulated electropherograms . . . . . . . . . 92

6.4.3 Effect of decoupling of the baseline construction . . . . . . . . 94

6.4.4 Comparison using actual electropherograms . . . . . . . . . . . . 99 6.5 Conclusions ....................................... 102

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 SAMENVATTING .......................................... 107 List of Symbols and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Dankwoord en Curriculum Vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Authors publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

iv

CHAPTER 1

GENERAL INTRODUCTION

1.1 BRIEF HISTORY

Electrophoresis and especially Capillary Electrophoresis (CE) have received increasing attention in recent years, as can be seen from the exponential growth in the number of papers on the subject over the last two and a half decades. This development does not stem from a breakthrough in the understanding or the theoretical foundations for the techniques, as the principals were already known as early as the beginning of last century [1-4] and a theoretical basis was formulated by Kohlrausch as early as 1897 [5]. The recent development of the technique as a qualitative and quantitative analytical tool is merely the result of the vast improvement in instrumentation that proved to be possible and to which the group of Prof.dr.ir. F.M. Everaerts at the Technical University of Eindhoven has contributed substantially. This thesis is part of that work.

Electrophoretic techniques are based on the principle that charged particles migrate under the influence of an external electrical field. Under stationary conditions the velocity of certain species is proportional to the field strength, the relevant proportionality constant being known as the electrophoretic mobility. The electrophoretic mobility is dependent on the medium in which the experiment is performed and the properties of the compound. Components with sufficiently different effective mobilities can be separated experimentally. Electrophoresis, until recently, was most often used for separating proteins because their electrophoretic mobility strongly depends on the properties of the medium, especially the pH, making this technique rather suitable for their separation.

Application of an electric field will draw a current across a solution generating heat. Often gels have been used as anti-convective medium to limit the solute band dispersion caused by the heat generated. Detection in gel electrophoresis was mostly performed by staining. This method can be very sensitive, but the techniques are labour intensive, not reproducible and not real suitable for quantitative analyses.

1 GENERAL INTRODUCTION

The use of narrow bore tubes and capillaries was an obvious solution; the beneficial effects on diffusion broadening were already known from chromatography. For use in electrophoresis, improved temperature control was an added advantage. Giddings [6] showed that, theoretically, very efficient separations can be obtained if longitudinal diffusion would be the only cause for dispersion in a capillary system. Experimentally this was verified by Everaerts et al. [7] using teflon capillaries and Virtanen [8] who used glass capillaries. However, from these experiments, it became clear that further reduction of the capillary diameter would only be possible if suitable instrumentation for injection and detection of very small samples could be developed.

The detection problem was circumvented by Everaerts et al. [9] by using an electrophoretic technique known as Isotachophoresis (ITP). In this technique, the use of a double electrolyte system, which sandwiches the sample, produces relatively narrow zones with self correcting properties increasing the detection limits. However, the rather complicated buffer system selection involved and the elaborate instrumentation, prevented wide spread acceptance as a routine tool.

More progress in the field of zone electrophoresis was made by Mikkers et al. [10,11], who showed a rapid and highly efficient separation of organic acids, almost reaching the theoretical predictions made by Giddings.

An important breakthrough was brought about by Jorgenson et al. [12-15] in 1981, by using a fluorescence detector in combination with open glass capillaries of 75 µm l.D. He showed that the theoretical efficiencies predicted by Giddings could be reached.

An obvious drawback of CE stems from the fact that in principle only charged species can be separated. This situation can be corrected by using Micellar Electrokinetic Capillary Chromatography (MECC) as described by Terabe et al. [16,17]. He introduced a separate phase by adding a surfactant in concentrations greater than the critical micelle concentration, whereby micelles are formed. The micelles are charged and migrate electrophoretically in the capillary. Neutral compounds may then be separated on the basis of their differing affinity for the micellar phase. A number of other approaches have been used to effect or enhance electrophoretic separations by using different physical parameters as described in Chapter 2. Since 1989, commercial instruments have become available introducing CE to many laboratories and bringing about further growth in the use of this versatile technique.

2

1.2 ABOUT THIS THESIS

After the initial phase in its development, where the focus was mainly on fundamental studies and development of first generation instrumentation, CE now enters the second phase in which it bas to proof itself as a viable analytical tool for routine analyses in areas such as quality control in the pharmaceutical industry. Out of this first phase came two potential shortcomings, standing in the way of a general acceptance of CE as a routine analytical tool: lack of sensitivity and poor reproducibility. This thesis addresses different aspects of these two shortcomings, both from a fundamental and a practical point of view.

Chapter 2 discusses the principles of Capillary Electrophoresis. The relevant terms, used throughout this thesis, e.g. electrophoretic mobility and electroosmotic flow, are defined and different modes of operation are discussed. Furthermore, different effects are discussed contributing to zone broadening in Capillary Zone Electrophoresis (CZE).

Chapter 3 and Chapter 4 address the issue "lack of sensitivity". Chapter 3 addresses this issue from an instrumental point of view. It describes a home made CE instrument for the analysis of fluorescent water tracers. The instrument utilizes laser induced :fluorescence as a detection technique to improve sensitivity. The performance of the instrument is discussed and feasibility is shown for the use of CZE for water tracer analysis. In order to use the sensitivity of a laser induced :fluorescence detector for a wider range of applications, chemical modification of analytes (attachment of a fluorescent label) is necessary, because most components do not show native :fluorescence. Chapter 4 describes the use of amine reactive fluorescent dyes for the labelling of amino acids and peptides. Reactions conditions, linearity and limitations are discussed for five different dyes, which all can be excited by the 488nm laserline of a Argon Ion laser.

An important phenomenon in Capillary Electrophoresis is the Electroosmotic Flow (EOF). The EOF influences not only the resolution and speed of an analysis, but also allows the detection of positive ions, negative ions, and even neutral molecules within the same run. Although often useful, EOF fluctuations can introduce extra variances into the overall reproducibility of the analysis. Changes in the EOF can even lead to loss of sample. It is, therefore, important that the EOF is known for each individual separation system. Chapter 5 addresses this problem. After a theoretical discussion of the EOF, this

3

I GENERAL INTRODUCTION

chapter describes different off-line and on-line methods to determine this flow; different

options for the ultimate goal: an on-line monitoring and control system for the EOF are discussed.

Another, often underestimated, contributor to the overall reproducibility of a CE method is the data analysis. Limitations of High Performance Liquid Chromatography (HPLC) based peak detection algorithms in currently available CE packages, often lead to an unacceptably high contribution of the data analysis to the overall reproducibility of the method. In Chapter 6, a new iterative peak detection algorithm is discussed for the analysis of electrophoretic data. This algorithm, which uses a different approach for detecting and integrating peaks, improves both the reproducibility and accuracy of the data analysis; as is shown in a comparison with three commercially available CE data

analysis packages.

REFERENCES

1. F. von Reuss, Comment. Soc. Phys. Univ. Mosquencem, 1, (1808) 141. 2. Lodge, 0., Brit. Ass. Adv. Sci. Rep., 56 (1886) 389. 3. Whetman, W.C.D., Phil. Trans. Roy. Soc. London, Ser. A., 184 (1893) 337. 4. Whetman, W.C.D., Phil. Trans. Roy. Soc. London, Ser. A., 184 (1893) 507. 5. Kohlrausch, F., Ann. Phys. (Leipzig), 62 (1897) 209. 6. Giddings, J.C., Sep. Sci., 4 (1969) 181. 7. Everaerts, F.M., Hoving-Keulemans, W.M.L., Sci. Tools, 17 (1970) 25. 8. Virtanen, R., Acta. Polytech. Scand., 123 (1974) 1. 9. F.M. Everaerts, J.L. Beckers, Th.P.E.M. Verheggen, lsotachophoresis: Theory,

Instrumentation and Applications (Journal of Chromatography Library, Vol. 6), Elsevier,

Amsterdam, 1976. 10. Mikkers, F.E.P., Verheggen, Th.P.E.M., Everaerts, F.M., J. Chromatogr., 169 (1979) 1. 11. Mikkers, F.E.P., Verheggen, Th.P.E.M., Everaerts, F.M., J. Chromatogr., 169 (1979) 11. 12. Jorgenson, J.W., Lukacs, K.D., Anal. Chem., 53 (1981) 1298. 13. Jorgenson, J.W., Lukacs, K.D., J. Chromatogr., 218 (1981) 209.

14. Jorgenson, J.W., Lukacs, K.D., Clin. Chem., 27 (1981) 1551. 15. Jorgenson, J.W., Lukacs, K.D., J. High Res. Chromatogr., 4 (1981) 230. 16. Terabe, S., Otsuka, K., Ichikawa, A., Tsuchiya, A., and Ando, T., Anal. Chem., 56

(1984) 111. 17. Terabe, S., Otsuka, and Ando, T., Anal. Chem., 57 (1985) 843.

4

CHAPTER2

PRINCIPLES OF CAPILLARY ELECTROPHORESIS

ABSTRACT

This thesis deals with some instrumental aspects of Capillary Electrophoresis (CE) geared at improvement of reproducibility and sensitivity. In this chapter, the necessary theoretical concepts of electrophoresis will be discussed together with another phenomenon playing an important role in a capillary electrophoretic separation: the electroosmotic flow.

2.1 PRINCIPLES OF ELECTROPHORETIC MOBILITY

The term Electrophoresis (from Greek: ~opeeo0cu = to be dragged) is used for a collection of separation techniques based on the difference in the velocity of charged particles, when subjected to an external electric field. Under given experimental conditions the velocity (v) of each particle proves to be proportional to the strength (E) of the electric field:

v = µE (2.1)

The proportionality constant µ is called the effective electrophoretic mobility.

The electrophoretic mobility for a given ion strongly depends on the environment in which the ion is present. Therefore, a theoretical absolute mobility µ0 is defined as the mobility under conditions of infinite dilution and complete ionisation, where the species are unaffected by the solvent environment.

Under these conditions, an expression for the electrophoretic mobility can be obtained by equating, under steady state conditions, the force exerted on a particle by an electric field with the opposing frictional force leading to:

5

2 PRINCIPLES OF CAPILLARY ELECTROPHORESIS

qE = fcv (2.2)

in which q is the electrical charge of the particle, E the electric field and fc the friction factor.

According to Stokes, the friction factor for a spherical particle is -given by:

fc = 61tTjr (2.3)

in which ri is the viscosity of the solvent and r the radius of the species.

This leads to the following expression for the absolute mobility (µ0):

(2.4)

At finite dilution the situation alters because each ion will be surrounded by a cloud of counter ions which tends to move in the "wrong" direction; thereby lowering the mobility. A correction factory is used to relate the absolute mobility to the mobility at finite dilution [I]:

(2.5)

Separation of two species will be possible if the effective mobilities are sufficiently

different. After t minutes the difference in migration distance (Ad) will be:

(2.6)

An important factor determining the mobility of the species is the dissociation degree of the species in situations where there is only partial ionization, e.g. in the case of weak acids and bases. In this case, the species will only be in the ionized form part of the time (dynamic equilibrium) and, therefore, the effective mobility µA of species A will depend on the mobility of the ion and on the dissociation constant. According to Tiselius [2] the effective mobility can be calculated from the summation of all products of the dissociation constant «, the correlation factor y and the mobility at infinite dilution µ0

:

(2.7)

6

For a weak monovalent acid A the effective mobility µA can be calculated using:

_ c [A -1 c k,._ m,._ - µ,._ - µ

[HA]+ [A-] - A (H+] +k,._ (2.8)

where

k,._ [H+][A 1

[HA] (2.9)

For two species A and B the separation will depend on the difference in effective mobilities and the pH.

(2.10)

The optimum pH for separation is then given by [3]:

(2.11)

2.2 ELECTROOSMOTIC FLOW

As CE is often performed in uncoated silica capillaries, the interaction of surface with the solvent buffer is of utmost importance. Especially at high pH, silanol groups will be formed on the surface of the capillary, making the surface negatively charged with respect to the solvent buffer. In the absence of an external electric field, an electric double layer will be formed as described more fully in chapter 5. This double layer consists of a positively charged immobile part tightly bound to the surface of the capillary and a also positively charged mobile part. The boundary between the stationary and the mobile phase is called the "plane of shear" and the potential at this plane is called the ((zeta)-potential. When an electric field is applied over the length of the capillary, the mobile part will start to move in the direction of the negative electrode; the ions dragging the solvent along. This flow is called the Electroosmotic Flow (EOF).

7


The velocity of the electroosmotic flow is, analogous to Equation 2.1, given by:

(2.12)

in which µEOF• analogous to the mobilities of ions, is called the electroosmotic mobility.

For a given combination of capillary and solvent buffer µEoF is a constant which can be expressed as:

µ = ~ EOF T]

(2.13)

in which ( is the zeta-potential, e the dielectric constant of the medium and TJ the viscosity of the medium.

It can be shown that the radial variance of electroosmotic flow is limited to the thickness of the double layer, giving the EOF a plug like profile as long as the thickness of the double layer is small compared to the inner diameter of the capillary. This is an advantage because this flow type will contribute much less to the dispersion of the zones, compared to the parabolic velocity profiles created by applying pressure (e.g.

HPLC).

An important aspect of the EOF is that it is superimposed on the electrophoretic migration of the species to be separated. If the sample contains both positive and negative species that are of interest and the absolute value of mEoF is greater than the absolute value of the electrophoretic velocity of the most mobile species, than all species move in the same direction and can be detected in one run.

2.3 DIFFERENT MODES OF CAPILLARY ELECTROPHORESIS

All based on the same electrophoretic principles, four basic modes of operation can be distinguished, depending on the choice of electrolyte system:

8

Capillary Zone Electrophoresis (CZE) Capillary Moving Boundary Electrophoresis (CMBE) Capillary Isotachophoresis (CITP) Capillary Isoelectric Focusing (CIEF)

These techniques are shown schematically in Figure 2.1.

BGE

BGE

LB

LB CITP ............. -··=TE!======;;;·;;;;;··;;;;:;;1;;:::1 =L=B =::::::;

CIEF

~·· ---,pH gradient

Figure 2.1 Schematic overview of the four basic modes of Capillary Electrophoresis. BGE: Background Electrolyte; LE: Leading

Electrolyte; TE: Terminating Electrolyte. A and B: sample components. This overview does not show the co-ions and counter-ions present in the different sample zones. For instance, in CZE this is equal to the composition of the BGE, in ITP only the counter-ions are present.

There are also hybrid techniques in which the electrophoretic principle is combined with other techniques:

Capillary Gel Electrophoresis (COE) combines CZE with the principle of molecular sieving. Micellar Electrokinetic Capillary Chromatography (MECC) combines CZE with the phase partition in normal chromatography.

Although named as different modes for capillary electrophoresis, these principles can and are also used in other separation media (e.g. zone electrophoresis and isoelectric focusing in slab gels).

9


2.3.1 Capillary Zone Electrophoresis (CZE) Capillary Zone Electrophoresis (CZE) is the most basic and mature method of operation. The capillary is filled with a buffer known as the background electrolyte. The sample is introduced as a small band into the background electrolyte and an electric field is applied. The components in the sample will start migrating, each with its own velocity, depending on its effective mobility under the experimental conditions. After an appropriate amount of time and given that the difference in effective mobility is large enough, the components will migrate in separate zones, giving the technique its name. The electrophoretic migration causes anions and cations to move in opposite directions. The superimposed electroosmotic flow (if present) pushes all the analytes in the direction of the cathode (for glass capillaries). As a result, the zone containing the cations with the highest mobility will be detected first and the zone containing the anions with the highest mobility will be detected last.

2.3.2 Capillary Moving boundary Electrophoresis (CMBE) Capillary Moving Boundary Electrophoresis (CMBE) has limited value as an analytical tool, but the principle applies to every electrophoretic technique in the initial phase. In this technique the sample is continuously fed into the capillary which contains the socalled leading electrolyte. The leading electrolyte contains an ion with the same charge sign as the components to be separated, but with a higher effective mobility. When an electric field is applied, the components will start migrating. After a given amount of time the component with the highest mobility, say A, will have formed a pure zone at the front of the sample, followed by a mixed zone containing A and B.

2.3.3 Capillary Isotachophoresis (CITP) In Capillary Isotachophoresis (CITP) [3] a discrete amount of sample is introduced at the interface between two electrolyte systems: the leading electrolyte and terminating electrolyte. Both these electrolytes contain one ionic species with the same charge sign as the components to be separated. The leading ion has a higher effective mobility and the terminating ion has a lower effective mobility than any of the components to be separated. As a practical consequence, only anions or cations can be separated in a single run. When the system has reached equilibrium, all the ions migrate with the same speed, but they are separated into a discrete number of zones which are in direct contact with each

10

other and are arranged according to their effective mobility. As all the zones move with constant and identical speed, a stepwise voltage gradient over the consecutive zones is established, each step being proportional to the effective mobility of the sample component in that zone. This potential gradient can be used for detection. According to Kohlrausch [4], the concentration in each zone is directly related to the concentration of the leading ions. Another important aspect of this arrangement is that the zones have self-correcting properties with respect to zone broadening by dispersion. Within a zone, the concentration is constant so the length of the zone provides quantitative information. The step height, which is determined by the effective mobility of the component and the leading ion, contains both quantitative and qualitative information [5].

2.3.4 CapiUary Isoelectric Focusing (CIEF) In Capillary Isoelectric Focusing (CIEF) amphoteric analytes, such as proteins, are separated on the basis of their isoelectric point (pl). In this technique the capillary is filled with the background electrolyte containing ampholytes that span the desired pH range. When an electric field is applied, the analytes will migrate until they reach the pH, in the electrolyte, that equals their pl. When all the components have reached their pl point, the conductivity will be minimal and their net velocity is zero. After this focusing step, the sample ions must be moved past the detector window, e.g. by pressure or change in pH of the electrolyte. For use in CIEF, the capillaries must be coated in order to prevent elution by the EOF.

2.3.5 Micellar Electrokinetic Capillary Chromatography (MECC) In Micellar Electrokinetic Capillary Chromatography (MECC) [6,7] a surfactant (e.g.

SodiumDodecylSulfate (SDS)) is added to the background electrolyte in a concentration above the Critical Micellar Concentration (CMC). The negatively charged micelles formed migrate at slow speeds towards the anode. In the case that the velocity of the EOF exceeds the absolute velocity of the micelles, the micelle phase will have a net velocity towards to cathode. In fact two mobile phases are present: a fast moving aqueous hydrophillic phase and a slower moving hydrophobic phase within the micelles (see Figure 2.2). This setup allows the separation of neutral solutes, based on the difference in affinity for the hydrophobic phase inside the micelle. It may also enhance the separation of charged analytes, because of · this added separation power based on

11


chromatographic principles. For all particles, ions as well as neutral species, the binding

to the micelles reduces the migration velocity. Zone broadening, however is minimal,

due to the high rate of association and dissociation.

~me --EO-...F __ ,, ..

Figure 2.2 Schematic view of the two mobile phases in a MECC separation, using a negatively charged swfactant and negatively charged capillary wall.

2.3.6 Capillary Gel Electrophoresis (CGE) In Capillary Gel Electrophoresis (CGE) the selectivity of the separation is enhanced by

filling the capillary with a polymer gel. In conventional gel electrophoresis, the gel

functions as a matrix to prevent zone broadening produced by thermal convection. In

CGE the matrix has other functions too, because the micropores in the gel allow

separations based on molecular size. When polyacrylamide gels are used, the pore size

can be influenced by altering the ratio of monomer/cross linking agent so that the gel

can be geared to the size of the molecules to be analyzed.

CGE has a definite advantage over conventional gel electrophoresis; much higher

voltages can be applied because joule heat is easily dissipated through the relatively

large surface to volume ratio of narrow bore capillaries, allowing shorter run times. On

the other hand, because of the intrinsic charge of the gel, it will tend to expel itself from

the capillary during the electrophoretic process. To prevent this, the material of the gel

is covalently bound to the walls of the capillary.

12

2.4 SOME THEORETICAL ASPECTS

The most important quantity measured in CZE is migration time t..,, the time a sample

component (zone) needs to migrate the distance 1 from injection point to detector.

t,,. l

v+vEOF (2.14)

in which l is the effective length of the capillary (from injection side to the detector),

v the electrophoretic velocity of the component and v00p the velocity of the osmotic flow.

Using Equations 2.1 and 2.12 this expression becomes

I tm = -(µ_+_µ_EOF-_)E- (2.15)

With present day apparatus the use of capillaries with a very small bore is possible,

making longitudinal diffusion the main source of peak broadening. In the absence of

other types of broadening the variance 0 21 of the zone after time t, is given by the

Einstein equation:

2 l o1 = 2Dt = 2D----(µ +µEOF)E

(2.16)

in which D is the molecular diffusion coefficient of the solute in the zone.

Giddings [8] postulated that the efficiency of a chromatographic system, or in this case an electrophoretic system, can be expressed in terms of a number of theoretical plates,

N, defined as:

12 N= -

2 a, (2.17)

Combining Eqn. 2.16 and 2.17, and by using E =VIL, where Vis the applied voltage and Lis the total length of the capillary, Equation 2.18 can be obtained:

13


N "' (µ. + llEOF) VI 2DL

The resolution Rs of two zones in CZE is, according to Giddings, given by:

R = ./N Av s 4 v

(2.18)

(2.19)

in which 11 v is the velocity difference between the two zones, and v is the average velocity of the two zones.

Combining Eqn. 2.19 and 2.1 leads to:

R = ./N 11µ. s 4 µ.

For systems with an EOF Equation 2.21 can be obtained:

After combining Equations 2.18 and 2.21 the result is:

0.1811µ Vl

(2.20)

(2.21)

(2.22)

From this equation it can be seen that the resolution depends on the difference in electrophoretic mobility of the sample components, the applied voltage and the molecular diffusion coefficient of the solute. Also the sum of the electroosmotic flow and the average electrophoretic migration velocity influence the resolution. In case m and lllsoF are equal but of opposite sign, the resolution is maximal but the net velocity through the capillary will be zero, which leads to extremely long (infinite) run times.

14

2.5 FACTORS INFLUENCING PERFORMANCE

In using the Einstein equation 2.16 it was supposed that only longitudinal diffusion was

present. In actual practice a number of other factors influence zone broadening. In capillary electrophoresis, the total variance can be assumed, as a first approximation, to be a summation of the following statistical independent variances [9,10]:

(2.23)

in which the suffixes in the right hand term represent the contributions of injection (I), diffusion (D), Joule heating (J), electrodispersion (E), electroosmotic flow (EOF) and

other (0) effects.

All variances, except the injection, are proportional to the analysis time and can be expressed similar to the Einstein equation for diffusion. The injection variance is only determined by the initial condition. Its relative contribution to the overall variance will, therefore, decrease for longer analysis times.

The overall variance as shown in eqn. 2.23 can now be rewritten as a summation of an analysis time dependent and independent term:

(2.24)

in which D0 , D1, D6 , DEOF• D0 are the dispersion coefficients of diffusion, joule heating, electrodispersion, electroosmotic flow and other effects.

2.5.1 Injection In the ideal case, where a narrow pulse (width w) is injected the variance caused by injection can be described as:

w2

12 (2.25)

As can be seen, the shorter the injection plug, the lower the injection variance. Practical reasons (e.g. a more sensitive detector which is needed to detect these smaller amounts), however, put a limit on the minimum injection volume.

15


Another limitation of eqn. 2.25 is that it is only valid in cases where the ionic strength of the sample zone is equal to that of the background electrolyte. In practice, however, this is often not the case, and additional effect like sample stacking and peak broadening caused by joule heating will result in deviations from this equation.

2.5.2 Diffusion The Einstein equation (2.16) describes the relation between the peakbroadening caused by diffusion and the diffusion coefficient D. In case this coefficient is not known, it can be estimated from the mobility by the Nernst-Einstein equation for dilute solutions

D RTµ zF

(2.26)

in which R is the universal gas constant, T the absolute temperature, z the valence of the ion and F the Faraday constant.

A small mobility will give the optimum situation with respect to band broadening.

2.5.3 Joule heating The electric field applied during an electrophoretic experiment will draw a current and generate heat depending on the conductivity of the solvent buffer. This is produced uniformly over the entire volume of the capillary, but can only be dissipated at the capillary wall, resulting in a radial temperature gradient. For capillary columns, this profile can be approximated by a parabolic function [11-13]:

llT = Wr2 4,\T

(2.27)

in which W is the electric power per unit of volume and ,\T is the thermal conductivity of the solvent buffer.

As the heat is generated over the entire volume of the capillary, the dispersion arising from joule heating, is dependent on the applied voltage and the radius of the capillary.

16

Virtanen (12] derived an equation for the dispersion coefficient showing that:

(2.28)

It is clear that a low field strength and a small capillary bore are beneficial. However, a lower field tends to lengthen separation time and the reduction in capillary diameter will increase instrumental problems with injection and detection.

According to Jones et al. (13] there is no significant influence on the efficiency of a separation if the radial temperature gradient in the bore of the capillary is less than l.5'C, corresponding to heat generation rates of over 10 W/m. This means that under typical separation conditions, the contribution of the radial temperature gradient (joule heating) on the peak broadening in CE is negligible.

However, another result arising from the constant generation of heat in the bore of the capillary, is that the absolute temperature of the capillary can be significantly higher than the passive or active cooling environment. Terabe et al. (14] showed that even in the narrow-bore capillaries the actual temperature in the bore can rise over 70°C without a proper cooling method. This dramatic temperature increase in the bore of the capillary influences chemical equilibria, electrolyte viscosity and pH, sample stability, and most of the other physical and chemical parameters relevant in a electrophoretic separation. This means that to ensure optimal usage of the advantages inherent in CE separations, good reproducibility and repeatability, the temperature of the electrolyte and column have to be accurately and precisely controlled.

2.5.4 Electroosmosis The driving force of electroosmotic flow, causing the migration of the solvent buffer, is exerted on very thin cylinders along the wall with the thickness of the electric double layer, causing a characteristic plug like flow. Only within that cylinder, where the velocity drops off to zero, there will be a gradient.

Therefore, in an open tubular system, where the thickness of the double layer is negligible compared to the capillary diameter, the contribution to dispersion will be zero. Longitudinal variations in the double layer, however, may occur as a consequence of experimental conditions or e.g. introduction of the sample, leading to a mismatch in

17


osmotic flows and causing dispersion [15]. In general, however, the contribution of these effects to band broadening will be small.

2.5.S Electrodispersion Electrodispersion may occur in cases where the ionic strength of the sample is of the same order of magnitude as the background electrolyte and at the same time the effective mobilities of the two ions are different [16]. Under these conditions "tailing" occurs either forwards or backwards depending on which ion has the highest mobility. The process is comparable to the effects occurring in the moving boundary method. As long as the concentration of the sample ion is kept low enough compared to background ion (s::0,01) the effect of electrodispersion is negligible.

2.S.6 Surface Interaction The inner surface of the capillary used is of great importance to the success of the electrophoretic separations, constituting an important parameter to influence the course of the experiment. Capillary material (glass, teflon) and capillary coating can be varied to minimize or even exclude electroosmotic flow. Interaction between solute and surface will have an effect on zone broadening; both electrostatic and "Van der Waals" attraction are possible. The broadening can be influenced by choosing the optimum setup for the experiment e.g. by choosing the pH of the background electrolyte in such a way that capillary wall and the ions to be separated have the same charge sign resulting in the ions being repelled from the wall.

2.6 SOME EXPERIMENTAL CONSIDERATIONS

2.6.1 Injection Injection volumes in CE are often extremely small typically ranging from 10 to 50 nl, comparable to a moisture droplet in fog being approximately l 0 nl. Therefore, special techniques, e.g. hydrodynamic (pressure or vacuum) injection, hydrostatic (gravity) injection and electrokinetic injection are used.

Hydrodynamic injection Hydrodynamic injection is a versatile tool and often used with CZE. The volume

18

injected (V) will be a function of capillary diameter (d), the applied pressure (AP), the injection time (t) and the viscosity of the buffer (11) and can be calculated using the Hagen-Poiseuille equation [17]:

V = l!P d 4 1t t

128 11 L (2.29)

The most difficult parameter to obtain is the AP, because atmospheric conditions and aging of pressure or vacuum lines etc. may influence the precision. Compared to the fixed loop injection system used in High Performance Liquid Chromatography (HPLC), this can potentially lead to a higher run-to-run and especially day-to-day variability.

Hydrostatic injection

Hydrostatic injection is a special case of pressure injection. The sample is introduced under the influence of the hydrostatic pressure obtained by raising the sample level an amount Ah above the liquid level at the other end of the capillary, causing the sample to enter by syphoning. Hydrostatic injection tends to be reproducible because most of the disturbing factors mentioned above are eliminated. However, the' pressure range is rather limited. The injected volume can be calculated using:

d4 1C t V= ~~---

128 '1 L (2.30)

in which Ah is the height difference, p the density of the electrolyte and g the gravitational constant. This equation is similar to eqn. 2.29, but with a different expression for AP.

Electrokinetic injection

With electrokinetic injection, analytes are brought into the capillary by applying an electric field. They can enter the capillary in two ways: passively by electroosmotic flow, if present, and actively by the electrophoretic mobility of the ions themselves. As a first approximation, the injection volume can be derived from:

(2.31)

19


This volume will be different for all analytes and will lead to a biased representation of the sample in the capillary; a high value for mEOF will decrease this bias. However, in practice, the calculation of injection volumes is very difficult, because that volume is dependent on the concentration and mobility of all the other ions in the system. For instance, variation in the ionic strength of the sample matrix greatly influences the electric field (E) in the sample during injection and as a result the injection volumes of the different analytes.

2.6.2 Calculating results from experimental parameters

Qualitative results At the end of the experiment, the migration times of the analytes are known and their apparent mobilities can be calculated from:

app _ IL µ - Vt

m (2.32)

in which I represents the effective length of the capillary (from injection point to

detection point), lin the migration time, V the applied voltage and L the total length of the capillary.

From µ•PP the effective electrophoretic mobility can be obtained by subtracting µEoF·

(2.33)

The µEoF can be obtained by using a so called EOF-marker. This marker, a neutral molecule which also doesn't show any interaction with the capillary wall, will be dragged to the detector at a velocity equal to that of the EOF. From its migration time the µEOF can then be calculated using eqn. 2.32. In chapter 5 other methods for determining the EOF will be discussed. The effective mobility m is a characteristic quantity for an analyte under the given experimental conditions, and may therefore be used for identification purposes.

Quantitative results Quantitative information can be obtained from an electropherogram by integrating the

20

signal over time, analogue to HPLC. However, there is a fundamental difference between the calculated peak areas in CE and HPLC. In contrast to HPLC detection in CE is performed on-line. This means that the peak area will be dependent on the migration time, because different analytes pass the detector at different speeds. As a result, variation in migration times will cause variation in peak areas. To correct for this dependency, Hjerten suggested the use of the corrected peak area (Ac;), which is equal to the peak area (A) divided by the migration time (t.,,):

A A = -c t m

(2.34)

Separation efficiency To characterize the separation efficiency, the theoretical plate number of the system may be calculated. Assuming Gaussian type peaks, the theoretical plate number may be derived from the peakwidth found after the analyte has travelled a certain distance in the capillary. In practice, it is convenient to take the peakwidth at half peak height (w~) at the time the peak passes through the detector (t.,,), both expressed in time units, as a

measure for N [18]:

N = 5.54(.!!!!-)2

Wv.

(2.35)

REFERENCES

I. Falkenhagen, H., Electrolyte, Verlag von S. Hirzel, Leipzig, 1932. 2. Tiselius, Nova Acta Reg. Soc. Sve. Sci., Upsala, 4, 7, no. 4 (1930).

3. F.M. Everaerts, J.L. Beckers, Th.P.E.M. Verheggen, Isotachophoresis: Theory,

Instrumentation and Applications (Journal of Chromatography Library, Vol. 6), Elsevier, Amsterdam, 1976.

4. Kohlrausch, F., Ann. Phys. Chem., 62 (1897) 209. 5. Beckers, J.L., and Everaerts, F.M., J. Chromatogr., 470 (1989) 277.

6. Terabe, S., Otsuka, K., Ichikawa, A., Tsuchiya, A., and Ando, T., Anal. Chem., 56 (1984) 111.

7. Terabe, S., Otsuka, and Ando, T., Anal. Chem., 57 (1985) 843.

8. Giddings, J.C., Sep. Sci., 4 (1969) 181. 9. Foret, F., Demi, M., and Bocek, P., J. Chromatogr., 452 (1988) 601.

10. Foret, F., and Bocek, P., Capillary Electrophoresis, in Advances in Electrophoresis, Vol.

21

2 PRINCIPLES OF CAPil..LARY ELECTROPHORESIS

3, Editors: Chrambach, A., Dunn, M.J. and Radola, B.J., VCH Verlag Gesellschaft, Weinheim, Germany, 1989.

11. Hjerten, S., Chromatogr. Rev., 9 (1967) 122.

12. Virtanen, R., Acta Polytech. Scand., No. 123 (1974) 11.

13. Jones, A.E. and Grushka E., J. Chromatogr., 466 (1989) 219.

14. Terabe, S., Otsuka, K. and Ando T., Anal Chem., 61 (1989) 251.

15. Burgi, D.S., and Chien, R.L., Anal. Chem. 63 (1991) 2042.

16. Mikkers, F.E.P., Verheggen, Th.P.E.M .• Everaerts, F.M., J. Chromatogr., 169 (1979) 1.

17. Sabersky, R.H., Acosta, A.J. and Hauptmann, Fluid flow, 2nd ed., MacMillan Pub!. Co.,

New York, 1971. 18. Poole, C.F., Poole, S.K., Chromatography today, Elsevier, Amsterdam, The Netherlands,

1991.

22

CBAPTER3

INSTRUMENTATION

ABSTRACT

To verify and calibrate models which describe the dispersal of water from the river

Rhine in the North Sea and North-East (N.E.) Atlantic ocean, tracers experiments have

to be peif ormed. Both cost and environmental pollution limit the amount of tracer that

can be used in each of those experiments. Hence, making these experiments usefut a

sensitive analysis technique is required, because of the limited amount of tracer combined with the enormous dilution factor in the ocean.

In this chapter, a home-made instrument for Capillary Electrophoresis, built for this purpose, will be discussed. This system utilizes vacuum to peiform hydrodynamic

injections, has a ten position autosampler and utilizes laser induced fluorescence as the

detection technique. The system, however, lacks a capillary temperature control system.

Instrument control, data acquisition and data analysis are peif ormed by a laboratory

written software package.

3.1 INTRODUCTION

In hydrographic research, mathematical flow and transport models are used for the assessment of the quality of water in the North Sea and Atlantic ocean [l-6]. The accuracy of these models is very important (e.g. for policy making and predictions in case of environmental accidents) and, therefore, tracer experiments have to be performed for verification and calibration of the mathematical models.

One of the projects (project number MAST 90.051C) supported by the European Community (EC) was the "Determination of the dispersal of Rhine water in the North Sea and the North-East Atlantic by measurement of fluorescent xenobiotic river substances (XTRANS)". XTRANS combines a program for optimalization of transport models and experimental work on improving the analysis techniques of fluorescent xenobiotic tracers.

23

3 INSTRUMENTATION

Strongly fluorescent, chemically stable and relatively polar components are most

frequently used as model compounds for tracing studies. Such molecules are soluble in

water, malting them suitable for flow and transport studies. To date, rhodamine B has

been used in numerous tracer experiments. Rhodamine B has fluorescent properties in the orange region (590-610 run) of the spectrum far enough away from the high

fluorescent background in seawater, the blue-green region (430-530 nm) [7,8].

However, there are two problems with the use of rhodamine B as a water tracer. First

of all, rhodamine B is photodegradable. This problem can partly be solved by adjusting

the mathematical models to take into account this photodegradation. The combination of these models with the transport models then enables the determination of the age and

percentage of river water at different locations.

The second problem for the use of rhodamine B is the fact that most of the examined major european rivers, especially the Rhine, and even parts of the North Sea and N.E.

Atlantic contain a relatively high background of this rhodamine, limiting the use of this

dye to small scale studies. Therefore, for large scale studies a non-native dye, rhodamine

WT, is used. This dye, with similar spectral properties as rhodamine B, is only produced

in the United States, and is not present at detectable levels in the waters covered by the

XTRANS project. It is also less toxic than the other rhodamines and water soluble, making it a suitable dye for the large scale tracer experiments. However, its high price

prevents it from replacing rhodamine B in small scale tracer experiments.

The method currently used for the analysis of rhodamines is High Pressure Liquid

Chromatography (HPLC) [9]. In combination with an off-line preconcentration step [IO],

detection limits of 10-100 pg/I seawater were obtained for 1000 ml samples [ll].

The main goal of this study was the development of a sensitive analysis method for the quantitative analysis of rhodamine WT at concentration levels below 1 pg/I, because the

current HPLC method is sensitive enough for the small scale tracer experiments. For this purpose a Capillary Electrophoresis (CE) instrument was built. The high

resolution and fast analysis times make it an interesting analysis technique for this application. For detection, Laser Induced Fluorescence (LIF) was used, which already had been demonstrated to achieve low detection limits 00·14M) for rhodamine 6G in aqueous solutions [12].

24

3.2 DESIGN CONSIDERATIONS

3.2.1 Injection The most used injection technique in CE is hydrodynamic injection (which includes pressure, vacuum and gravity injection techniques). The injection is based on the fact that if a pressure difference is applied across the capillary, a liquid flow will occur (see Chapter 2, Eqn. 2.29).

In pressure and vacuum injection systems, the pressure difference is generated by a separate pressure source; in gravity injection the pressure difference is created by raising the sample level a certain height above the liquid level at the other end of the capillary. All three injection techniques have their advantages and disadvantages. - Gravity injection is reproducible, but its pressure range is limited. This system is,

therefore, not suitable as general injection/rinse system. - Pressure injection has the big advantage that the pressure range is almost unlimited.

However, from a design standpoint a pressure system in combination with an autosampler is relatively difficult because it requires a pressure seal for every vial in the autosampler.

- Vacuum injection, on the other hand, allows the use of one sealed outlet vial, with no special requirements on the inlet (autosampler) side. However, the pressure range is limited, compared to the pressure injection.

Based on these considerations, a vacuum injection system was selected for this CE instrument. Vacuum injection combines relative ease of implementation with an acceptable pressure range for the target application (water tracer analysis in aqueous buffer systems).

3.2.2 Detection The target application for this instrument is the analysis of water tracers at very low concentration levels ( < 1 pgll). Standard UV detection, with detection limits in most optimal cases of 10~. is not suitable for this application. Laser induced fluorescence as detection technique is the most logical choice, since the water tracers have native fluorescence at wavelengths close to available laserlines. See chapter 4 for a more detailed discussion of fluorescence.

25

3 INSTRUMENTATION

3.3 EXPERIMENTAL

3.3.1 Injection Figure 3.1 shows a schematic drawing of the home made injection system. The system consists of two ballast tanks (1 litre each) and five electromagnetic valves, connected by 1/8" tubing.

low pressure vessel

restriction

® electromagnetic valve

I pressure sensor air

high pressure vessel

outlet vial

cap• ary

Figure 3.1 Schematic view of the vacuum injection/ rinse system.

This system was used for both high pressure rinsing and low pressure injection. To perform an injection the following sequence of steps was executed: first the Low Pressure Vessel (LPV) was brought to a pressure slightly below the target injection pressure by opening valves VI and V2 and starting the vacuum pump. A pressure sensor connected to the LPV was used to monitor the pressure. After closing valve VI, the tank was brought to the exact injection pressure by pulsed opening of valve V3, allowing a limited airflow into the system through a restrictor. An injection was then performed by opening valve V5 which connects the injection ballast tank to the outlet vial. Typical injection pressures used were 10-50 mBar below atmospheric pressure. The pressure change after opening valve V5 is negligible because the volume of the injection ballast tank is large in comparison to the outlet vial and all the tubing. At the end of the injection the outlet vial was opened to air to release the pressure The High Pressure Vessel (HPV) was used in a similar way to perform high pressure rinses. Typical rinse pressures used were 400-600 mBar below atmospheric pressure.

26

3.3.2 Detection Figure 3.2 gives a schematic sideview of the home-made laser induced fluorescence detector.

Capillary Pholo

Multiplier

Readout

TO INTERFACE

Figure 3.2 Schematic sideview of the laser induced fluorescence detector.

A watercooled tunable Argon Ion laser (model 2020-03, Spectra Physics, Mountain View, CA, USA) was employed as the excitation source. A special set of reflectors (Spectra Physics, Mountain View, CA, USA) in this laser allowed the use of the relatively weak 528.9 nm laserline at 70 mW. The laserlight was reflected upwards using a reflecting mirror (Oriel, Stratford, CT, USA) and focused on the capillary window using a quartz focusing lens with a focal length of 19 mm (Oriel, USA). The fluorescence light was collected at a 90'C angle using a 60x microscope objective with a numerical aperture of 0.7 and a focal length of 200 µm (Euromex, Arnhem, The Netherlands). The collimated beam then passed through a set of optical bandpass filters before being detected by a photomultiplier tube. The two 585 nm bandpass filters with a bandwidth of 30 nm (Oriel, USA) were contained in a filterholder (Oriel, USA) mounted in between the microscope objective and the cooled housing (model C659, Hamamatsu, Toyooka Vill., Iwata Gun, Japan) of the photomultiplier tube (model R928, Hamamatsu, Japan). Both the reflective mirror and the focus lens were mounted on optical XYZ tables (Newport, USA) for alignment purposes. Besides step-injections of a relatively concentrated rhodamine WT solution, scatter patterns on the ceiling were used to align the optical parts. The current signal coming from the photomultiplier tube was converted to voltage by a Read-Out (Oriel, USA) with adjustable gain.

27

3 INSTRUMENTATION

3.3.3 The entire system Figure 3.3 shows the entire instrument setup, including the described injection and detection system.

Figure 3.3 Schematic view of the entire CE-UF instrument.

The autosampler consisted of a 10 position sample tray, holding standard 0.5 ml microcentrifuge tubes. This tray could be rotated, moved up and down to allow for a fixed capillary and electrode position. High voltage was applied to the inlet electrode using a High Voltage Power Supply (HVPS) (HNC-35-35000, FuG, Rosenheim., Germany) while the outlet electrode was kept at ground potential. The outlet vial was sealed and directly connected to the injection system. The volume of this vial was 20 ml, which allowed up to 50 runs before contaminants, buffer depletion and/or electrode reactions started influencing the separation. A home-made 12 bits ND-DIA convertor with 10 digital input and output channels was used to connect the injection system, autosampler and high voltage power supply to a IBM compatible computer (Tulip AT386/25, 's-Hertogenbosch, The Netherlands). Instrument control and data-acquisition were performed using a laboratory written computer program (Turbo Pascal 6.0, Borland, Fountain Valley, CA USA). This program allowed fully automated analyses by running user programmed methods consisting of any combination of rinse, injection and/or voltage steps. Subsequent data-analysis was performed using Caesar for Windows V3.0 (CE Solutions, Long Branch, NJ, USA) using the iterative peak detection algorithm described in Chapter 6.

28

3.3.4 Reagents and Materials The structural formulas of the different rhodamines are shown in Figure 3.4.

Figure 3.4 Structural formulas for rhodamine B (top left), rhodamine WT (top right), sulpho

rhodamine B (bottom left) and sulphorhodamine G (bottom right).

Standards were supplied by Rijkswaterstaat Tidal Waters Division (Middelburg, The Netherlands). Chemicals to prepare the electrolyte systems were of analytical grade and obtained from Sigma (St. Louis, MO, USA). Stock solutions and electrolyte systems were prepared using purified water (Milli-Q, Millipore, Bedford, MA, USA). Separations were carried out in untreated fused silica capillaries (Siemens, Germany) with an inner diameter of 100 µm and a total length of 75 cm (55 cm to detector). Before first use, the capillaries were rinsed with O. lM sodium hydroxide for 5 minutes at 500 mBar pressure, followed by electrolyte for 25 min at 500 mBar.

3.3.5 Sample pretreatment Sample pretreatment was necessary for two different reasons, matrix clean-up and concentration. The matrix clean-up mainly consisted of the removal of salt from the seawater samples, which otherwise would interfere with the electrophoretic separation. The Solid Phase Extraction (SPE) used for the sample preparation was based on the existing HPLC method [10,11] and utilizes a Sep-pak Cl8 cartridge (Waters, Bedford, MA, USA) as solid phase. The cartridge was first activated with 2 ml of methanol and

29

3 INSTRUMENTATION

then rinsed with 20 ml of deionized (DI) water. After absorption of the components of interest from the seawater sample (100 or 1000 ml), the matrix residues were removed by an additional flush of 20 ml DI water. After desorption of the rhodamines with 2 ml of methanol, the methanol was evaporated and the sample was redissolved in 100 µI DI

water.

3.4 RESULTS AND DISCUSSION

3.4.1 Injection To test the performance of the injection system, a solution of 10·1°M of rhodamine WT was injected. The injection plug was then rinsed to the detector at a 50 mBar rinse pressure. The reproducibility was tested by repeating 10 injections at 50 mBar for 5 seconds. The linearity was tested by repeating an injection at 50 mBar at 7 different injection times.

Figure 3.5 shows the result of the reproducibility and linearity test of the vacuum injection system.

Figure 3.5 Result of A: reproducibility (n=JO) and B: linearity test of the homemade vacuum injection system.

The relative standard deviation (RSD) over 10 injections was 0.96%, while the linearity over an injection time range of 3 to 21 s showed a correlation coefficient of 0.9999. These numbers show that with a relatively simple injection system, good reproducibility and linearity may be obtained. However, the injected volume was typically in the order of 50-500 nl, and initial experiments at lower injection volumes (1-10 nl) showed an increase in RSD to 3-5%. This increase was mainly caused by the inaccuracy of the pressure monitoring using only 12 bits resolution over the full injection range, and small leaks in the pressure system.

30

For the target application, the analysis of water tracers, an injection at 50 mBar for 1 Os (±100 nl) was used, because the detection limit and not the resolution nor the efficiency was the primary goal.

3.4.2 Detection A pH-scan of the separation of the different rhodamines showed that a complete separation could be obtained at a pH of 3 - 4.5. In this pH range the rhodamine B is positively charged and the rhodamine WT, sulphorhodamine B and G negatively charged. The electrolyte system finally chosen for the separation was a 0.01 Me-amino caproic acid at pH 4.0. At this pH the maximum signal to noise ratio (SIN) was achieved for rhodamine WT. Figure 3.6 shows the analysis of a mixture of rhodamine B, rhodamine WT and sulphorhodamine G each at a concentration of 10·12M, showing the good performance of this home-made detector as far as sensitivity is concerned.

0.80 ~--------------~

0.60

0.20

EOF o.oo~---~

o.oo 10.00 20.00

Time(min) 30.00 40.00

Figure 3.6 Separation of rhodamine B (1), rhodamine WT (2) and sulphorhodamine G (3) each at a concentration of 10-12M in O.OJM e-amino caproic acid pH 3.9. Running voltage: 20 kV. injection volume JOO nl. Other conditions see text.

Using a 3: 1 ratio as lowest acceptable SIN level, the detection limit for rhodamine WT and sulphorhodamine G was determined at 10·12M (500 pg/I), the detection of rhodamine B at 5x10·12M (2.5 ng/l). Figure 3.7 shows the calibration curve for rhodamine WT. A correlation coefficient of 0.999 was found, which was acceptable for this project.

31

3 INSTRUMENTATION

20

SE-JO IE-9 Concentration (M)

Figure 3.7 Calibration curve for rhodamine WT with a co"elation coefficient (R) of 0.999.

However, the 12 bits resolution of the data acquisition system limits the practical linear dynamic range of the detector to 2 orders of magnitude.

One of the other objectives in this project was a feasibility study of the usefulness of CE as a separation technique for a shipborne instrument to replace the existing shipborne HPLC system [14J. At first glance CE is suitable because it has no moving parts. However, because of the small inner diameters of the capillary, optical alignment of the detector parts is more critical then in HPLC detectors. By overfilling the detection window in the capillary with the excitation laserlight (purposely misalignment of the focusing lens), the alignment of the laserbeam to the capillary could be made relatively insensitive without losing to much signal. The alignment of the capillary to the microscope objective, however, is sensitive to misalignment, as shown in Figure 3.8a&b and Table 3.1. In these figures and table, the y-axis is defined as the optical axis of the microscope objective, and the z-axis is defined as the vertical axis, perpendicular to the y axis (see Figure 3.2).

The sensitivity to alignment in the x-direction (parallel to the capillary) is normally not a problem, as long as the size of the detection window (=length over which the polyimide coating of the capillary has been removed) is large compared to the illuminated length.

32

Y..axis

-200 0 200 Dil!tllnce from optimal point (pm) Distmtc from optimal point (µm)

Figure 3.8 Sensitivity for misalignment of the capillary to the

microscope objective. See text for definition of y- and z-a:xis.

Table 3.1 Sensitivity for misalignment of the capillary to the microscopy objective. See text for definition of yand z-a:xis.

Direction Distance for specific loss of signal (o/o max signal)

(11m)

90"/o 50"/o

y 4 18

z 59 163

As can be seen, the y-axis is more than ten times as sensitive to misalignment as the zaxis. The reason for this relative insensitivity to misalignment in the z-direction can be explained as follows; although out of the focal point, the detection window is still in the focal plane of the microscope objective. As a result part of the fluorescence light is still collected and collimated by the objective, only the collimated beam will be slightly off axis. But, because the dimensions of the bandpass filters and the photosensitive area of the PMT are large compared to the beam diameter and the off-axis angle, only relatively large misalignments will lead to significant loss in signal.

33

3 INSTRUMENTATION

3.4.3 Sample pretreatment The sample pretreatment as described in the experimental section, results in concentration factors of 1000 for a 100 ml sample and 10000 for a 1000 ml sample. Together with the 10·12M detection limit for the rhodamines this should theoretically result in an absolute detection limit of 50-500 fg/l seawater. However, it was shown that the preconcentration method introduces contaminants which comigrate with rhodamine B, sulphorhodamine B and G [15,16]. Although not positively identified as rhodamines, identical electrophoretic properties make this a valid assumption. The levels of these rhodamine contaminants were determined at 5-10 pg/l for rhodamine B and sulphorhodamine B and 2-5 pg/l for sulphorhodamine G (15,16]. Part of this contamination was traced back to the methanol used in the sample preparation [15).

As a result the absolute detection limit for rhodamine B, sulphorhodamine B and sulphorhodamine G were limited to the pg/l range. For rhodamine WT detection limits below l pg/I were achieved, because none of the contaminant peaks interfered with the rhodamine WT peak.

3.S CONCLUSIONS

As shown in this chapter, the relatively simple instrumental requirements make Capillary Electrophoresis (CE) a suitable technique for home-made instrumentation. The sensitivity of the home made Laser Induced Fluorescence (LIF) detector can compete with the, at this point, commercially available instrumentation. However, the accurate temperature control and other instrument optimalizations, make the commercial instruments more reproducible and linear. Therefore, looking at the diversity of instruments available today, a commercial instrument should be the first choice for application research, in which the instrument is solely used as an analytical tool. For fundamental and instrumental research the flexibility and modularity of home-made systems still have potential. The CE-LIF combination showed to have potential for the water tracer analysis. The fast · separation, and good sensitivity in combination with an off-line sample preconcentration step brings the current detection limit of rhodamine WT down 2 orders of magnitude. Further optirnalizations of the LIF detector and the sample preconcentration step make another order quite feasible. Problems with the current method for sample concentration, however, limit the gain for the other rhodamines to less than l order of magnitude. CE also has potential of replacing the current shipborne HPLC, as long as the LIF

34

detector design takes into account the tight tolerances for the alignment of the capillary

to the microscope objective [17].

REFERENCES

1. Van Dam, G.C., Proc. Symp. Systems and Models in Air and Water Pollution, (1976) 11.1-11.9.

2. Davies, A.M., Proc. of the 8th International Liege Cont on Ocean Hydrodynamics., 19 (1977) 27-48.

3. Davies, A.M., Computer meth. in appl. mech. and eng., 22 (1980) 187-211. 4. Davies, A.M., Dt. hydrog. Z., 33 (1980) 19-37. 5. Van Dam, G.C., Neth. J. Sea Res. Puhl. Series, 13 (1986) 66-67. 6. Van Dam, O.C., Internal report GWA0-88.043 (1988). Rijkswaterstaat (Netherlands), Tidal

Waters Division. 7. Loane, R. et. al., Neth. J. Sea Res., 15, 1 (1981) 88-99. 8. Smart, P.H., and Smith, D.I., J. Hydrod., 30 (1976) 179-195. 9. Suylen, J.M. and Van Leussen, W., Estuarine water Quality Measurement, Monitoring,

Modelling and Research, Coastal and Estuarine Studies, Vol. 36, 181-188 (1990). 10. Laane, R.W., Manuals, M.W. and Staal, W., Water Research, 18 (1984) 163-165. 11. Hofstraat, J.W, Steendijk M., Vriezekolk, G., Schreurs, W., Broer, G.J.A.A. and Wijnstok,

N., Water Research, 25 (1991) 883-890. 12. Hahn, J.H. et. al., Appl. Spectrosc., 45 (1991) 743-745. 13. Sabersky, R.H., Acosta, A.J. and Hauptmann, Fluid flow, 2nd ed., MacMillan Publ. Co.,

New York, 1971. 14. Suijlen, J.M., Staal, W., Houpt, P.M. and Draaier, A., submitted. 15. Rutten, T.P.A., Graduation report, Eindhoven University of Technology, Eindhoven, The

Netherlands, 1993. 16. Loo, I., MS Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands, 1993,

ISBN 90-5282-248-4. 17. Martens, J.H.P.A., Graduation report, Eindhoven University of Technology, Eindhoven, The

Netherlands, 1993.

ACKNOWLEDGEMENTS Part of this research was funded by the European Community under project MAST-90.05 IC.

35

36

CHAPTER4

AMINE REACTIVE FLUORESCENT DYES

ABSTRACT

Although the interest in Capillary Electrophoresis (CE) has grown rapidly in the last decade, there are still issues which need to be addressed before CE will be accepted as a routine analytical tool in such areas as quality control. One of these issues is the reported lack of sensitivity. Derivatization of analytes with fluorescent dyes, in combination with a laser induced fluorescence detector, has the potential of greatly enhancing sensitivity. Several amine reactive fluorescent dyes were investigated and compared in areas such as coupling efficiency, background and linearity. It appeared that the concentration limit of detection is not governed by the sensitivity of the detection method and hardware, but by chemical limitations as coupling efficiency and dye purity at low analyte concentrations.

4.1 INTRODUCTION

The last few years have seen a rapidly spreading acceptance of Capillary Electrophoresis (CE) in its various separation modes expanding the range of analytical applications. Some of the capabilities of CE which account for its growing use are its high separation efficiency, short analysis times and low minimum detectable masses. The monitoring method in the vast majority of analytical CE applications is UV absorbance, which combines wide applicability with useful sensitivity as well as ease of implementation and cost effectiveness. Minimum detectable concentrations for UV absorbance are typically 1 o-sM with 1 o·6M achievable under favourable conditions. While this sensitivity is adequate for many purposes, greater detection sensitivity, i.e. lower minimum detectable concentrations, would allow further extensions of the application range of CE. Laser Induced Fluorescence (LIF) detection is one of the possible solutions for this need. Minimum detectable concentrations have been reported to be 2-5 orders of magnitude lower than UV detection [l-3). Even the analysis of solutes from a single cell (albeit a

37


large cell) has been demonstrated [4]. This improvement in sensitivity results from an inherent advantage of fluorescence

over absorbance which is the specifity of signal origin. The use of fluorescent labels,

which absorb strongly at specific wavelengths and emit intensely at higher specific

wavelengths, are far removed from the background absorbance and intrinsic fluorescence of the system and sample. Sensitivity is also improved by the selectivity

of derivatization which not only reduces background, e.g. potential interferants which lack a primary amine will not be seen in a sample tagged with a label that only

reacts with primary amines, but also endows the assay with an overall specificity

which is often useful. In this chapter different amine-reactive fluorophores will be

evaluated for the analysis of amino acids and peptides using a commercial CE-LIF

system.

4.2 THEORY

When a substance is struck by electromagnetic radiation, different phenomena have the potential to occur, such as reflection, refraction, diffraction, Rayleigh and Raman

scattering, absorption, fluorescence, phosphorescence and photodegradation. These phenomena can be divided into two basic groups: radiative and non-radiative. Some

of these phenomena (reflection, refraction and diffraction) were explained by

Maxwell using a wave model description of light. Others were explained by Planck's

quantum theory [5]. Figure 4.1 gives a schematical representation of some of these

phenomena. In organic molecules, electrons occupy Molecular Orbitals (MO's), with specific energy levels and a specific angular momentum. In the ground state, most electrons

are in the singlet state (S0) with a net angular momentum of zero (two electrons with

opposite spin occupy a MO). When one electron reaches a higher energy level due to the absorption of a quantum of energy, the net angular momentum normally remains zero (singulet state S1, S2 etc.). However, if the spin of the electron changes the net

angular moment becomes one and the electron reaches the triplet state (T1, T2 etc.),

which is an unstable condition.

When an excited electron returns back to its ground state, it emits a photon with

energy equal to the energy difference (dE) between the two states. In cases where the angular momentum doesn't change (S 1 -> S0) we talk about fluorescence; in cases

where the angular momentum changes (T1 -> S0) we talk about phosphorescence.

38

2 S2 1

0

3

so 2 l 0

-- -.-.-...-,---·~

.,, YR

"'

SI

A

IC ->A I

2 I

I

l 0

A

!

I

YR 3

I

-I

' ' I ' ' :ic

F ' '

' I I

I

' ~

"

VR

' I

' I

I

I

ISC: ' I

I

I

I

I

I

~

' '

p

"

I Tl 0

Figure 4.1 Energy diagram showing radiative (solid line) and nonradiative (dotted line) processes in an organic molecule. A: absorbance, F: fluorescence, P: phosphorescence, VR: vibrational relaxation, /SC: inter system crossing, IC: internal conversion.

The wavelength (1) of the emitted photon is equal to:

A.=!!£ AE

(4.l)

in which h is Planck's constant, c the velocity of light and AE the energy of the photon.

Non-radiative enuss10n of energy may also occur (e.g. vibrational relaxation and photo-chemical modification). In these processes, a portion of the absorbed energy is emitted as non-radiative energy, mainly due to vibrational relaxation. As a result, the energy of the emitted light photon in radiative emission processes, fluorescence and phosphorescence, is lower than the absorbed energy. This means that the emission wavelength is higher than the excitation wavelength (Eqn. 4.1 ). The difference between these two wavelength is called the Stokes shift. With a large enough Stokes shift, long-pass, bandpass or dicbroic filters can be used to distinguish between the fluorescence and excitation light.

39


The total fluorescence intensity F may be described by the following equation:

(4.2)

in which Io is the intensity of the excitation light, e the excitation coefficient and c the concentration of the analyte, d the optical depth of the solution and lb the quantum efficiency.

For low analyte concentrations, which is true for most analytical conditions, this equation reduces to:

(4.3)

As can be seen from Equation 4.3, the intensity of the fluorescence signal is linear to the energy of the excitation light, meaning that the use of high intensity excitation sources (like lasers, which combine high output powers with great spectral purity) can greatly improve the sensitivity. However, there is a limit to the power levels which can be used for detection. Figure 4.2 shows the dependency of the fluorescence signal on the intensity of the excitation light. In the first stage, the fluorescence signal is linear to the excitation intensity.

40

II III

___ _,,,. Intensity

Figure 4.2 Fluorescence signal as a function of excitation intensity. I: linear; II: exponential , Ill: constant (see text for more information).

At a given intensity level, the ground state becomes depopulated resulting in a non

linear relationship between signal and intensity (stage II). Even higher power levels

will totally depopulate the ground state, resulting in a plateau value for the signal

(stage ill). At these high intensity levels, photodegradation may occur resulting in a

decrease in signal (arrow).

Two other effects play a role in the use of laserlight as an excitation source for

fluorescence detection in CE: Raman and Rayleigh scattering. Rayleigh scattering

occurs when an organic molecule is struck by radiation, which frequency does not

match their resonance frequency. The emitted radiation is of the same energy as the

source. The radiation may also be modified related to the vibrational energy levels of the molecule. This radiation, called Raman scattering, is either of lower energy (Stokes-Raman scattering) or higher energy (anti Stokes-Raman scattering).

When using high energy light sources (such as lasers), especially the Stokes-Raman

scattering of solvents (e.g. buffers in CE) is a source of background noise, because it

can interfere with the emission wavelength of the analyte.

4.3 EXPERIMENTAL

A selection of amino acids and peptides were labelled with different dyes and analyzed using a P/ACE 2100 CE system with LIF detector (Beckman Instruments

Inc., Fullerton, CA, USA). In this study a fixed wavelength, 488 nm, air-cooled

argon ion laser was used as the excitation source. Therefore, the selection of amine

reactive dyes was limited to those with an excitation maximum close to this laserline. Fluorescein isothiocyanate (FITC), 4-( 4,6-dichloro-s-triazin-2-ylamino )fluorescein

(DTAF) and 5-carboxyfluorescein succinimidyl ester (FSE) have excitation maxima

very close to the 488 nm Iaserline and both 4-fluoro-7-nitrobenzofurazan (NBD-F) and 3-(4-carboxybenzoyl)-2-quinolinecarboxaldehyde (CBQCA) have wide enough excitation spectra to be excited by the 488 nm laserline.

Five natural amino acids, selected for there different properties: Arginine (basic primary amine), Phenylalanine (aromatic primary amine), Proline (secondary amine), Glycine (aliphatic primary amine) and Glutamate (acidic primary amine) and four

standard peptides, Substance P, Oxytocin, Neurotensin 1-11 and Angiotensin I were used.

41


4.3.1 Derivatization procedure for FITC

s II

N=C=S H-N-C-NH-R

+R-NHz

HO HO

Figure 4.3 Reaction of FITC with a primary amine.

FITC reacts with both primary and secondary amines [6]. The derivatization procedure is given in Table 4.1, the reaction in Figure 4.3.

FITC derivatized amine groups have an excitation maximum at 492 nm and an emission maximum at 520 nm.

42

Table 4.1 Derivatii.ation procedure for FITC.

Solution A: 2.5 mM FITC in ethanol Solution B: 0.2 M N~C03 in water

Mix 50 µl of the amino acid solution, 100 µl of solution A and 50 µl of solution B and vortex for 30 seconds. React for 4 hours at room temperature. Dilute, as may be required, prior to injection.

4.3.2 Derivatization procedure for DT AF

Cl

NAN

H-N AN~CI

+R·NJ-Ii -

HO 00 0

Figure 4.4 Reaction of DTAF with a primary amine.

DTAF, like FITC, is a fluorescein derivative, and reacts with both primary and secondary amines [7,8]. However, the leaving group is more reactive, and makes DT AF more water soluble. The reaction product is very similar to FITC derivatizations and the spectral properties are almost identical. The derivatization procedure is given in Table 4.2, the reaction in Figure 4.4.

DT AF derivatized amine groups have an excitation maximum at 492 nm and an emission maximum at 518 nm.

Table 4.2 Derivatillltion procedure for DTAF.

Solution A: 10 mM DT AF in water Solution B: 0.1 M Borate buffer; pH 8.5

Mix 50 µl of the amino acid solution, 50 µI of solution A and 100 µl of solution B and vortex for 30 seconds. React for I hour at room temperature. Dilute, as may be required, prior to injection.

43


4.3.3 Derivatization procedure for FSE

0

}-0-N~ I 0 +R-Nffi

0 II C-NH-R

HO 0 HO

Figure 4.5 Reaction of FSE with a primary amine.

FSE is also a fluorescein derivative. Like FITC and DTAF, it reacts with both primary and secondary amines [9,10]. The succinimidyl ester is relatively reactive and water soluble. The derivatization procedure is given in Table 4.3, the reaction in Figure 4.5.

FSE derivatized amine groups have an excitation maximum at 494 nm and an emission maximum at 525 nm.

44

Table 4.3 Derivatization procedure for FSE.

Solution A: I mM FSE in 0.1 M/0.05 M borate/phosphate buffer at pH 8.0

Mix 50 µI of the amino acid solution and 50 µI of solution A and vortex for 30 seconds .. React for 1 hour at room temperature. Dilute, as may be required, before injection.

4.3.4 Derivatizatlon procedure for NBD-F

NO~F -n . N....._ /N

0

+R-NHi

Figure 4.6 Reaction of NBD-F with a primary amine.

Both NBD-F and NBD-Cl are commonly used in the analysis of primary and secondary amines in thin layer chromatography and HPLC [11-16]. NBD-F is preferred over NBD-Cl because it is more reactive and has fewer side reactions. The derivatization procedure is given in Table 4.4, the reaction in Figure 4.7.

NBD-F derivatized amine groups have an excitation maximum at 480 nm and an emission maximum at 552 nm.

Table 4.4 Derivatization procedure for NBD-F.

Solution A: I 0 mM NBD-F in ethanol Solution B: 0.1 M/0.05 M borate/phosphate buffer; pH 8.0

Mix 50 µl of the amino acid solution, 50 µl of solution A and 50 µI of solution B and vortex for 30 seconds. React for 1 hour at room temperature. Dilute sample, as may be required, prior to injection.

45


4.3.5 Derivatization procedure for CBQCA

COOH

Figure 4.7 Reaction of CBQCA with a primary amine.

CBQCA is a relatively new amine reactive molecule and is only reactive with primary amines [17-21). The reaction involves an intramolecular ringclosure. The unreacted dye is non fluorescent. Another advantage of this dye is the large Stoke shift, from 468 nm to 560 nm. The derivatization procedure is given in Table 4.5, the reaction in Figure 4.7.

CBQCA derivatized amine groups have an excitation maximum at 468 nm and an emission maximum at 560 nm.

46

Table 4.5 Derivatiwtion procedure for CBQCA.

Solution A: 10 mM CBQCA in methanol Solution B: 20 mM KCN in water.

Mix 50 µl of the amino acid solution, 50 µl of solution A and 100 µI of solution B and vortex for 1 minute. React for 1 hour at room temperature. Dilute sample, as may be required, prior to injection.

4.3.6 Gi!neral labelling information To ensure complete reaction, even in unknown samples, a standard amount of dye was added to the samples independent of the analyte concentration. At low analyte concentrations, where the excess of dye can be orders of magnitude, fluorescent contaminants, fluorescent side products and/or unreacted fluorescent dye can cause serious background problems limiting the overall sensitivity of the method.


The main purpose to date of fluorescent labelling and LIF detection is to achieve a lower concentration detection limit. It is worth noting that many factors other than the intrinsic spectral properties of dyes affect detection limits of a separation/detection system in practice. For example, minimum detectable concentrations reported in research articles [1-3] are often determined by dilution of a compound labelled at high concentration (ideal sensitivity). Such reported values are somewhat misleading to what can be achieved in practice. Missing from these values are factors often present in practice such as: reduced coupling efficiency at low analyte concentrations, background resulting from using a large excess of label, contaminants in the dye or produced by side reactions, and interferants present in the sample. These factors vary with circumstances, but in unfavourable cases can raise the minimum detectable concentration 1000 fold or more, e.g. a labelled analyte detectable at 10·11M under ideal conditions, may not be measurable in practice at concentrations less than 1 o..aM. These kinds of differences are illustrated by the electropherograms in Figure 4.8.

DTAF Figure 4.8 shows the analysis of a 10"9M of DTAF labelled amino acids. In Figure 4.8A, the derivatization was performed at a concentration of 2x104 M of the amino acids and 10 mM of the DTAF solution. In Figure 4.8B the same derivatization was performed, however, the starting concentration of the amino acids solutions was 2.5x10·1M. Both solutions were subsequently diluted to 10·9M prior to injection. Clearly the dramatic increase in background can be observed resulting from the 1000 times higher dye concentration in the final sample. Another important observation is that the peaks are not equal in size in both cases, although the amino acid concentration in both sample is the same. This points to an incomplete reaction at the

47


lower concentration levels. The reason for this concentration dependency is the fact that the rate determining step in the derivatization is a second order reaction. As a result, at low analyte concentrations, the reaction kinetics slow down and the reaction becomes incomplete in the same time frame.

48

20 • A

~15

~ 8 10

~ 1:l g 5 Ii:

O+--~~-• -~--+-~~----+-~~-.;~~~

0 2

9 B

~ 7

~ 5

"' 3 ~ g Ii:

.J 0 2

4 Time (min)

4

3

2 4

Time(min)

6 8 10

6 8 10

Figure 4.8 Electropherogram of DTAF labelled amino acids. Peak identification: (1)

Arg, (2) Phe, (3) Pro, (4) Gly and (5) Glu. A. Labelling and detection concentrations 2xl04 M and 10'9M respectively. B. Labelling and detection concentrations 2xl0-7M and 10-9M respectively. Labelling conditions as described in the text. Capillary

dimensions: internal diameter 75 µm; length 57 cm (50 cm to detector). Buffer: 50125 mM borate/phosphate pH 9.5. Running voltage: 25 kV.

CBQCA A similar experiment to DTAF was performed using CBQCA, shown in Figure 4.9. The only difference is that the two samples were equally diluted, resulting in a 1000 fold lower amino acid concentration in Figure 4.9B.

300

250 A

~: i 100

I 2 j 50 I I 4

0 ~ r.:.;

-50 0 2 4 6 8 lO

Time(min)

0.3

0.25 B

~ 0.2

i 0.15 0.1

~ 0.05 4 Ii:

0

-0.05 +---~

0 2 4 6 8 10 Time(min)

Figure 4.9 Electropherogram of CBQCA labelled amino acids. Peak identification:

(1) Arg, (2) Phe, (3) Gly and (4) Glu. A. Labelling and detection concentrations

2.5xlo-'M and 2.5xHT5M respectively. B. Labelling and detection concentrations

2.5xl0-7M and 2.5xlo-'M respectively. Labelling conditions as described in the text.

Capillary dimensions: internal diameter 75 µm; length 57 cm (50 cm to detector).

Buffer: 50/25 mM borate/phosphate pH 9.5. Running voltage: 25 kV.

49


Note that the peakheights in Figure 4.9B are 1000 fold lower than those in Figure 4.9A, corresponding to the 1000 times lower concentration of the amino acids. This indicates a complete reaction even at low analyte concentrations. Another important observation is the relatively clean background, allowing lower minimum detectable concentrations.

The reason for this concentration independent reaction rate for CBQCA is that the reaction, which forms highly fluorescent isoindole derivatives, involves a rate determining ring closure reaction. The first phase of the reaction is a fast formation of a Schiff-base between the primary amine and the aldehyde group of CBQCA. Subsequently, a relatively slow intramolecular reaction with the ketone group closes the ring. Other advantages of this ring closure are the large resulting Stokes shift and large increase in fluorescent emission (the unreacted dye is virtually non-fluorescent under the conditions of measurement).

However, the ring closure involved in the reaction has one major disadvantage in that the reactivity of CBQCA is strongly analyte dependent. The relative reactivities for several amino acids are illustrated in Table 4.6.

Table 4.6 Normalized peakheights for CBQCA labelled amino acids. Labelling concentration

2.5xla4M. Injection concentration 2xl<Y'M.

Amino Norm. Amino Norm Amino Norm Amino Norm. Acids Height Acids Height Acids Height Acids Height

Ala 0.11 Glu 0.099 Leu 0.410 Thr 0.020

Arg 0.20 Gly 1.000 Lys 0.739 Trp 0.000

Asp 0.05 His 0.325 Phe 0.206 Tyr 0.197

~ 0.()04 Ilu 0.044 Ser 0.118 Val 0.036

It is not practical to reduce this variable reactivity for different amino acids by adjusting reaction times due to competing side reactions of CBQCA occurring at a

50

lower but comparable rate which yield fluorescent side products. However, the linearity of an analysis is not adversely affected as shown in Figure 4.10, because the variability which arises in the cyclization step does not depend on analyte concentration. The sensitivity (limit of detection), on the other hand, can be orders of magnitude different for different analytes.

IE-10 16-0') 16-08 16-07 IE-06 IE-05 IE-04

Concentralion (M)

Figure 4.10 Calibration curve for CBQCA labelled amino acids.

Quantitative amino acid analysis using CBQCA is a practical procedure if the specific reactivities for the individual amino acids are accounted for. Its reactivity and almost four decade linear response range enable detecting impurities at 0.1 % levels (Figure 4.11).

Minimum detectable concentrations of labelled peptides are often 10 to 100 times higher than can be achieved with free amino acids (see Table 4.7). This decrease in sensitivity is believed to be due to reduced accessibility of the amino groups in peptides.

51

4 AMINE REACTNE FLUORESCENT DYES

NBD-F

140

5' ~~

<I.> (.)

5 (.)

"' e ]

40

t..r...

-lO 0 lO

Time(min)

Figure 4.11 Example of a typical quality control requirement of 0.1% impurity detection. The sample is a mixture of 10·3M PhenylAlanine (1) and 10_,,M Glycine (0.1%) (2). The sample was diluted 10 times prior to injection. Labelling conditions for CBQCA as described in the text. Capillary I.D.: 75 µm, capillary length: 57 cm (50 cm to detector). Buffer: 50125 mM borate/phosphate buffer pH 9.0. Running voltage 18.8 kV.

The large increase in fluorescence of NBD-F upon reaction with amines permits using a large excess of dye during coupling to increase coupling efficiency while avoiding problems with high background. The fast reaction rate ensures complete reaction even at low analyte concentration levels as shown in Figure 4.12.

The minimum detectable concentration of NBD-F labelled analytes in CE is generally higher Oower sensitivity) than CBQCA derivatives due to the influence of the highly polar environment of typical CE buffers on its spectral properties. In contrast to CBQCA, NBD-F produces a more uniform detector response for the different amino acids. Also, the minimum detectable concentration for peptides is only 1-10 times higher than those for free amino acids (see Table 4.7). Like CBQCA, NBD-F's reactivity and 3 decade linear response range enable detecting impurities at 0.1 % levels.

52

100

10

~ ~ 0.1

~ 0.01

0.001

0.0001 IE-07

+ Phe

IE-06 IE-05

Concenlration (M)

IE-04

Figure 4.12 Calibration curve for NBD-F labelled amino acids.

FSE, DTAF and FITC

IE-03

Of FITC, DT AF and FSE, the three highly fluorescent fluorescein derivatives, FSE has the widest range of applicability and seems best suited for use in CE because of its high reactivity for both primary and secondary amines and its availability in relative high purity. In particular, FSE is superior to FITC which reacts relatively slowly leading to poor linearity. In addition, FITC may yield numerous (up to 40) interfering impurity peaks in the electropherogram. Although DT AF gives reasonable detection limits and linear detector responses, it is not readily available in as high degree of purity as FSE (compare Figure 4.13 with Figure 4.8b). A linear range of 3 orders of magnitude is achievable with FSE enabling impurity detection at 0.1 %

(Figure 4.14), but, as also can be seen in the Figure 4.14, fluorescent impurities in FSE preparations may limit its usefulness for such applications.

53


54

!)()

RO

10

~ 60

50

i 40

3-0·

~ 20

J~~ £ IO

0

·10 0 10 15

Time(min)

Figure 4.13 Electropherogram of FSE labelled amino acids. Peak

identification: (1) Arg, (2) Phe, ( 3) Gly. Labelling and detection

concentration JU7M. Labelling conditions as described in the text.

Capillary l.D.: 75 µm, capillary length: 47 cm (40 cm to detector).

Buffer: 50125 mM borate/phosphate pH 10.0. Voltage: 14.l kV.

.JO.;. ----t--- 1----1 -0 ; 10 15 20 25

Time(min)

Figure 4.14 Example of a 0.1% impurity detection using FSE.

Sample is a mixture of Ja'M PhenylAlanine (1) and J0"7M Glycine

(0.1%) (2). The sample was diluted JO times prior to injection. For

labelling conditions see text and for running conditions see Figure

4.13.

FSE, like NBD-F and in contrast to CBQCA, produces a nearly uniform detector signal for the various amino acids. This is due to the separation of the fluorescent group from its point of attachment to the analyte by a carbonyl group as well as the coupling reaction which is less dependent on analyte structure. But, as is the case with CBQCA, FSE has apparent lower reactivity for peptides (see Table 4.7), probably also because of less accessibility of peptide amino groups. Separation conditions can sometimes be optimized to separate label impurities from analytes.

Table 4.7 Practical concentration limits of detection.

Label Amino Substance Oxytocin Neuro- Angio-Acids1) p tensin tensin I

1-11

UV (214 nm) 5x10-5M 10"5M 10-6M 10"6M rn-sM

CB QC A 5x10"8M 10"6M I0-5M SxlO"'M w-''M

NBD-F 5xl0"7M 5x10~ 10-6M 5xl0-6M 5xl0-1M

FSE lO"'M l0"8M I0-7M 10-7M l0"8M

I) Averaged value over the five standard amino acids.

A special opportunity arises when analyzing compounds whose molecular weight is much higher than the fluorescent dye, e.g. protein analysis. In these cases, the labelled analyte is so much larger that an additional sample preparation step, such as gel filtration, can be employed to separate the compounds of interest from the unreacted dye and smaller fluorescent interferants.

4.5 CONCLUSIONS

The use of amine reactive fluorescent dyes, in combination with laser induced fluorescence, can clearly enhance the sensitivity of the analysis method. Standard solutions of amino acids and peptides show a 10 to 100 fold advantage over UV absorbance detection.

55


No single dye can be qualified as general dye, because too many factors determine the linearity and concentration limit of detection. CBQCA has fast kinetics, good linearity and sensitivity, but its large variation in reactivity makes the usefulness of this dye very application dependent. FSE, on the other hand, combines reasonable sensitivity and linearity with an almost uniform reactivity for different amino acids and peptides. However, the fact that the dye itself and also its impurities and side products are highly fluorescent, makes a good separation of the analytes of interest from the interferants necessary. In cases where extremely low detection limits are not required, NBD-F is a useful dye because of its fast kinetics, good linearity and clean background.

The concentration limit of detection of the overall analysis is governed by the derivatization chemistry and not the hardware. Therefore, alternative detection methods such as fluorescence detection in the low UV region (native fluorescence) should be seriously considered. In this case derivatization would often not be necessary, because more compounds show native fluorescence at lower wavelengths. Sample stacking techniques, utilizing moving boundary or isotachophoretic principles, also offer sensitivity improvements of 0.5-2 orders of magnitude [22-25].

The detection of compounds is based on the fluorescent properties of the label (identical for all peaks) and not the intrinsic properties of the analytes itself, which greatly complicates peak: identification. This is especially true for biological samples which have· many amine containing compounds in the matrix and leads to electropherograms with an unsolvable puzzle of peaks.

REFERENCES

1. Cheng, Y.F. and Dovichi, NJ., J. Chromatogr., 480 (1989) 141. 2. Dovichi, N.J. and Cheng, Y.F., Am. Biotechnol. Lab., 10 (1989) 12. 3. Cheng, Y.F. and Dovichi, N.J., ASTM Spec.Tech. Puhl., 1066 (1990) 151. 4. Kennedy, R.T., Oates, M.D., Cooper, B.R., Nickerson, B., and Jorgenson, J.W., Science,

246 (1989) 57. 5. Longair, M.S., Theoretical concepts in physics, Cambridge University Press, England,

1984. 6. Reisher and Du, Anal. Biochem., 26 (1968) 178. 7. Blakseley et. al., J. lmmunol. Meth., 13 (1976) 305.

56

8. Mahoney et. al., J. Biochem., 243 (1987) 569. 9. Lu-Steffes, M., et. al., Clinical Chem., 28 (1982) 2278. 10. Brinkley, M., Bioconjugate Chem., (1992) 3. 11. Watanabe Y., Imai K., Anal. Biochem., 116 (1981) 471. 12. Watanabe Y., Imai K., J. Chromatogr., 239 (1982) 723. 13. Miyano, H., Toyo'oka, T. and Imai, K., Anal. Chim. Acta, 170 (1985) 81. 14. Toyo'oka, T., Miyano, H., Imai, K., Peptide Chem., (1985) 403. 15. Toyo'oka, T., Miyano, H., Imai, K., Biomed. Chrom., 1 (1986) 15. 16. Kotaniguchi H., Kawakatsu M., Toyo'oka T., Imai K., J. Chromatogr., 420 (1987) 141. 17. Novotny, M., J. Microcolumn. Sep, 2 (1990) 7. 18. Liu J., Hsieh Y-Z., Wiesler D. and Novotny M., Anal. Chem., 63 (1991) 408. 19. Liu J., Shirota, 0., Wiesler, D. and Novotny, M., Proc. Natl. Acad. Sci. USA, 88 (1991)

2302. 20. Van de Goor, A.A.A.M., Wanders, B.J. and Henzel, W.J., Beckman Technical

Information Note DS-824, 1992. 21. Wanders, B.J., Van de Goor, A.A.A.M., Beckman Technical Information Note DS-826,

1992. 22. Burgi, D.S. and Chien, R.L., Anal. Chem., 63 (1991) 2042. 23. Chien, R.L. and Burgi, D.S., Anal. Chem., 64 (1992) 1046. 24. Foret, F., Szoko, E. and Karger, B.L., J. Chromatogr., 608 (1992) 3. 25. Schwer, C. and Lottspeich, F., J. Chromatogr., 623 (1992) 345.

51

58

CHAPTERS

ELECTROOSMOTIC FLOW

ABSTRACT

An intrinsic and important phenomenon in capillary electrophoretic separations is the

electroosmoticflow (EOF). The EOF influences not only the resolution and speed of an

analysis, but also allows the detection of positive, negative ions and even neutral

molecules within the same run. Although often useful, EOF fluctuations may introduce

extra variances in the overall reproducibility of the analysis. Changes in the EOF may

even lead to loss of sample. It is, therefore, important that the EOF is known for each

individual separation system. In this chapter, different methods are investigated to measure the EOF (or the streaming potential) both off-line and on-line. The off-line methads are useful for fast screening

of EOF values in different separation systems; the on-line methods are necessary to

enable correction for EOF fluctuations during a run and from run to run.

5.1 INTRODUCTION

In Capillary Electrophoresis (CE), the electroosmotic flow (EOF) serves multiple purposes. It has been used as a liquid pump in both electrophoresis and electrokinetic chromatography [1,2]. One of the major benefits for electrokinetic chromatography is the flat velocity profile of the flow compared to the parabolic velocity profile of a pneumatic flow created by mechanical pumps. This will often decrease the broadening of peaks, resulting in higher plate numbers [3,4]. The EOF is also used to generate a "bulk flow" to sweep all ions towards the detector, independent of their charge. Furthermore the EOF, when orientated in opposite direction of the migration velocity of the compounds of interest, will increase the effective separation length of the capillary, resulting in improved resolution [5,6], at the cost of time. Because of this strong influence of the EOF on the apparent mobility of the components in a separation process, fluctuations in this flow will directly influence the reproducibility of the analysis.

59

5 ELECTROOSMOTIC FLOW

E:Iectroosmotic flow in capillaries originates from an electric double layer between the capillary wall and the electrolyte. When an electric field is applied across a capillary, the radial charge distribution in the double layer will result in a liquid flow, the electroosmotic flow. Simultaneously, other electrokinetic effects will occur and influence the electroosmotic flow: e.g. surface conductance, electrophoresis, electrolyte/wall interactions. Therefore, in order to predict, monitor and/or control the electroosmotic flow, measuring techniques are necessary which resemble closely the experimental separation conditions.

Over the years, a lot of research has been done on measurement of the electroosmotic flow. A general method is the use of a neutral marker molecule [7-9]. This marker will be dragged to the detector at a velocity equal to that of the EOF. The disadvantage of this method is that it only gives an averaged value of the electroosmotic flow. Another problem is finding a suitable molecule which shows no migration and retention behaviour. The technique of using streaming potentials to predict electroosmotic flow values in capillaries, was first introduced by Reijenga et al. [10]. Although this is an indirect technique, the method is relatively simple, and suitable for fast characterization of different capillary/electrolyte combinations. It also allows the study of electroosmotic flow fluctuations as a function of time. Altria et al. [11,12] introduced a method based on weighing the amount of electrolyte leaving the capillary. While the advantage is that the setup closely resembles the experimental conditions, the disadvantage is that only time averaged results are obtained. Huang et al. [13] replaced the electrolyte in one of the reservoirs by a similar electrolyte with one of slightly different conductivity. This will result in a continuous change of the resistance over the capillary, while the interface between the two buffers moves through the capillary, enabling calculation of the EOF.

One purpose of measuring the EOF is to check if the desired value is achieved for the particular experiment and, if possible, to correct for fluctuations from run to run to improve reproducibility. Several ways may be used to change the flow: e.g. buffer additives [14], capillary coatings, viscosity and ionic strength changes (buffers) [15], or an external electric field. This field influences the charge on the capillary wall, which is directly related to the EOF [16-18]. One step further would be to monitor the electroosmotic flow in real-time, and either correct for fluctuations in post-run data

60

analysis, or use a direct feedback to a dynamic EOF control system (e.g. an external electric field) [19,20].

In this chapter, several new/modified methods will be discussed for direct and indirect measurement of the electroosmotic flow [21]. The streaming potential method was automated and used to characterize the influence of pH and buffer composition on the electroosmotic flow. It was also used to study some dynamic effects. The weighing method was modified and implemented as an on-line EOF detector with improved time resolution. A new approach will also be discussed which utilizes an on-line conductivity cell [22].

5.2 THEORY

The phenomenon electroosmotic flow in capillaries originates from an electric double layer between the capillary wall and the electrolyte present in the capillary. This double layer is caused by the fact that the surface of the capillary wall, when it comes in contact with an electrolyte, gets charged, in the case of fused silica capillaries most likely by the passing of light cations, e.g. hydrogen or alkali metal, into solution, leaving a negative charge at the surface. The charge on the capillary wall will repel co-ions and attract counter ions, resulting in the formation of a double layer with a radial potential gradient. The most widely supported view on the electric double layer is the model of Stern [23], in which he combines the essential characteristics of the two previous models of Von Helmholtz and Gouy. According to Stem the double layer consists of two parts: the first part, which approximately has the thickness of a single ion, remains almost fixed to the surface, and has a sharp fall of potential. The second part, is diffuse and extends some distance into the liquid. In this part of the double layer the ions can move freely, but the distribution between cations and anions is not uniform, since the residual negative charge at the surface will preferentially attract positive ions. As a result, this diffuse part of the double layer has a gradual fall of potential into the bulk of the liquid where the charge distribution is uniform. Figure 5.1 gives a schematic view of this model:

61


i -l ca n ·~

~ If ~

Figure 5.1 Schematic representation of the electric double I.ayer. Shaded portion represents capill.ary wall, the vertical broken the extent of the fixed part of the double layer. The ( (zeta)-potential represents the potential fall in the diffuse part of the double layer.

Two potential differences are important in the description of the double layer: II, the potential difference between the capillary wall and the bulk of the liquid and (, the potential drop in the diffuse part of the double layer.

If we assume that the diffuse part of the double layer is equivalent to an electrical condensor then it is known from electrostatics that [24):

' = dq E

(5.1)

in which d is the distance between the plates, q the charge density on the p1ates and E

the dielectric constant of the electrolyte.

If now an axial electric field is applied over the capillary, the ions in the diffuse part of the double layer will move, dragging with them the electrolyte. This results in a bulk liquid flow, called electroosmotic flow (EOF), or electroosmosis.

If we assume that the v.0 is the velocity of the EOF, then v.jd can be taken as the velocity gradient in the diffuse part of the double layer.

62

Because the EOF occurs at an uniform rate the force due to frictional effects must be equal to the electrical force causing the EOF, in formula:

1'1Veo Eq=

d

in which E is the applied electric field and 11 the viscosity of the electrolyte.

(5.2)

Combining Eqn. 5.1 and 5.2 leads to the following expression for the velocity of the EOF:

e{E v =--

eo '11 (5.3)

This equation is also known as the Helmholtz-Smoluchowski equation.

The assumption that the double layer is equivalent to an electrical condensor with parallel plates limits the use of this equation to cases where the inner diameter of the capillary is considerably larger than the double layer thickness [25]. With typical capillary I.D's of >50 µm and a double layer thickness of <0.01 µm, this is normally the case in CE.

To estimate the velocity of the electroosmotic flow in a capillary, two approaches can be taken. The first, and most logical approach, is direct measurement of the EOF. The second is an indirect method, in which the zeta-potential is determined, which, with Eqn. 5.3, can be used to estimate the electroosmotic flow.

S.2.1 Indirect measurement of the electroosmotic flow To estimate the electroosmotic flow indirectly the streaming potential, another electrokinetic phenomenon, can be used. The streaming potential is the potential difference between the inlet and outlet of a capillary, which arises when a pressure gradient is applied over that capillary. This is basically the reverse phenomenon from the electroosmotic flow, where a flow is created by applying a voltage gradient over the capillary.

63


J;be streaming potential Esp like the electroosmotic flow, can be correlated to the (potential [25]:

E = APeC ST rpc

(5.4)

in which AP is the pressure difference applied over the capillary, and K the specific

conductance of the electrolyte.

The (-potential obtained can be used to estimate the electroosmotic flow using Eqn. 5.3.

5.2.2 Direct measurement of the electroosmotic flow

5.2.2.1 Weighing The mass of the flow leaving the capillary can be used to calculate the electroosmotic flow. By determining the weight change of the outlet via1 (AW) in a certain time interval (At), the velocity of the EOF can be calculated using:

AW v =--

•0 AtpA (5.5)

in which A is the cross-sectional area of the capillary and p the density of the

electrolyte.

This method is, in principal, suitable for on-line monitoring of the EOF, by placing the outlet vial on a micro-balance and registering the weight change during a electrophoretic separation.

5.2.2.2 Conductivity Cell Another approach for direct measurement of the EOF is to replace the outlet vial with a conductivity cell filled with water, or diluted electrolyte. When an electric field is applied across the capillary the EOF will transport undiluted electrolyte into the outlet vial leading to an increase in conductivity with time. In order to convert this into actual velocity of the EOF, the system has to be calibrated using the weighing method.

64

5.3 EXPERIMENTAL

For the experiments for direct and indirect measurement of the electroosmotic flow both

teflon (PTFE) (Habia, Breda, The Netherlands) and fused silica (FS) capillaries (Siemens, Germany) were used. The PTFE capillaries had an inner diameter of 300 µm

and a length of 15 and 20 cm for the indirect measurements, and a length of 48 cm for

the direct measurements of the electroosmotic flow. The FS capillaries had an inner diameter of 75 µm and a constant length of 50 cm.

All reagents (Merck, Darmstadt, Germany) were of analytical grade. The water used for preparing the electrolyte solutions was purified using a Milli-Q purification system

(Millipore, Bedford, MA, USA). The electrolyte systems used were O.OlM chloride

solutions at different pH values as given in Table 5.1. The viscosity of all these buffers was 0.()01 kg/ms, the dielectric constant 695xt0·12 F/m and the density 1000 kg/m3

•

The specific conductivity values in Table 5.1 were measured using a conductivity meter (CDM83, Radiometer, Copenhagen, Denmark).

Table 5.1. Electrolyte solutions (JO mM hydrochloric acid).

System

I II III IV v VI

pH

3.0 3.5 4.8 6.0 8.2 9.0

Buffer ion

P-Alanine P-Alanine Creatinine Histidine Tris Ammediol

5.3.1 Indirect measurement of the electroosmotic flow

0.1219 0.1000 0.0908 0.0893 0.0853 0.0863

The setup used for streaming potential measurements is similar to the method used by Reijenga et al. [10], and is shown in Figure 5.2.

65


electrodes

electrode vessels

capillary

Figure 5.2 Setup for the streaming potential experiments.

valves

~-gas

Two sealed electrolyte vessels were used which contained a gas inlet and a 40xl mm Ag/AgCI electrode, made according to the method of Thomson [26]. The two gas inlets were connected to a nitrogen tank through three-way magnetic valves. The valves were controlled by a Analytical Interface (Perkin Elmer, Norwalk, CT, USA), which in its turn was controlled by a laboratory written program running on an Apple Ile computer (Apple Computer Inc., Cupertino, CA, USA).

Streaming potentials were measured by direct connection of the electrodes to a high input impedance voltmeter (Philips PW9414, Eindhoven, The Netherlands). The signal from the voltmeter was recorded on a strip chart recorder (Kipp BD41, Kipp & Zonen, Delft, The Netherlands).

The determination of the potential differences was performed in three cycles. During the first cycle of 20 seconds, vessel A was pressurized while vessel B was open to air. In the next 20 s cycle the pressure was switched from vessel A to B, and vessel A was opened to air to relieve the pressure. During the last 20 s cycle both vessels were connected to open air, and the registered potential difference was used as a reference. The reason for switching the pressure back and forth between the vessels A and B was to keep the liquid levels in both vessels constant, in order to minimize the contributions

66

of syphoning effects, and correct for any asymmetries in the system. The streaming potential was calculated by averaging the two potential differences (in

reference to the potential value collected in the third cycle).

5.3.2 Dired measurement of the electroosmotic flow

5.3.2.1 Weighing To measure the electroosmotic flow using the weighing method, the grounded electrolyte vessel was placed on a microbalance (MP8-l, Sartorius GmbH, Gottingen, Germany),

as depicted in Figure 5.3.

balanced electrode

microbalance

capillary

electrode vessels

power supply

Figure 5.3 Setup for measuring the electroosmotic flow using a micro balance.

To prevent any disturbances caused by moving of the electrode or capillary, both were

mounted in such a way that there was no contact with the vessel. Placing a spring at the beginning of the electrode decreased these disturbances further. The capillary had to be electrically insulated from the balance and tightly held in place to prevent current

leakage and suppress movement of the capillary when the electric field was applied. The voltage gradient was applied with a high voltage power supply model HN30000- l (Heinzinger, Rosenheim, Germany) in the constant voltage mode. The change in weight was read directly from the display.

Before starting a measurement the liquid in both vessels were brought to the same level.

Next a voltage was applied, causing the electroosmotic flow. Increase or decrease of the weight in the vessel was monitored every five minutes. Shorter intervals lead to

67


inaccurate data, because of the small weight change compared to the weight fluctuations (noise). Next pressure was used to bring the grounded vial back to the same starting weight. The measurement was now repeated, but with reversed polarity to correct for any asymmetry in the system (syphoning). Under the assumption that the contribution of syphoning(= gravity flow) is constant throughout both runs (a valid assumption because both electroosmotic flow and gravity flow are very small compared to the total volume of the vessels), the average value of the two measurements will give a syphoning corrected value of the electroosmotic flow.

5.3.2.2 Conductivity cell The setup for measurement of the EOF using the conductivity cell is shown in Figure 5.4.

I

I I I I

I I I I I_ -

HVPS DETECTOR

Figure 5.4 Setup for measuring the electroosmotic flow using a conductivity cell.

The conductivity cell, used as the grounded reservoir in this setup, was home made: a 1 cm hole was drilled in a perspex rod (diameter 3 cm). In the center of this hole, a stainless steel rod (diameter 5 mm) was placed. Two platinum wires were tightly

68

wrapped around the rod and led through a hole in the side. The hole was then filled with Insulcaat 510 (Permagil Industries Inc., Plainview, NY, USA) and subsequently polymerized. After hardening, the steel rod was removed, leaving a cylindrical space with the two circular electrodes mounted in the wall. This reservoir was then mounted on top of an air driven stirring mechanism, to ensure homogeneous mixing. To measure the conductivity, the electrodes were connected to a conductivity meter (CMD 83, Radiometer, Copenhagen, Denmark). Disturbances from the applied separation voltage were minimized by electrically shielding (DC shielding) the conductivity meter from the conductivity cell and ground electrode using metallized film capacitors (2222-344-41225 MKT DC250V, Philips, Eindhoven, The Netherlands). This setup of the conductivity cell as grounded reservoir is shown in more detail in Figure 5.5.

CAPILLARY

STIRRER

AIR INLET/OUTLET

_l_ HIGH VOLTAGE

DCSIIlELDING

AC CONDUCTIVITY

METER

Figure 5.5 Detailed view of the conductivity celVground reservoir used for on-line measurement of the electroosmotic flow.

Before each experiment, the capillary and inlet reservoir were filled with undiluted electrolyte while the conductivity cell was filled with diluted electrolyte. After the high voltage is applied, the EOF will transport undiluted electrolyte to the grounded reservoir (= conductivity cell), resulting in a continuous increase in conductivity. The rate of change of the conductivity in time is a measurement for the EOF.

69


SA RESULTS AND DISCUSSION

S.4.1 Indirect measurement of the electroosmotic Dow Figure 5.6 shows the streaming potential as function of the applied pressure. As predicted by Eqn. S.4, a linear relationship was found (R = 0.99996). The (-potential, as expected, is independent of the applied pressure. To ensure maximum resolution a AP of 1 atm. was used for all further experiments.

70

20

~ • • • • • • 16 ,..... > 12 g • !

] • • I 8 .l

-~ .l

4 • • • 0

.l -·---

0 0.2 0.4 0.6 0.8

Pressure ( atm)

Figure 5.6 Streaming potential versus pressure.

20...---··~----·~~·----.

10

~ O+--~•~----------;

2 4 6 pH

8 10

Figure 5.7 'Zeta potential as fanction of the pH for PTFE capillaries.

Figure 5.7 shows the (-potential as a function of pH for a PTFE capillary. The results are based on experiments with the buffers I to IV (Table 5.1). As can be seen, there is a strong dependency of the (-potential and consequently the electroosmotic flow on the pH. It can also be seen that the electroosmotic flow will change direction around pH 3.5. The obtained values for the '-potential are in agreement with previously published literature values [2,10). Besides the strong dependency on the pH, the streaming potential measurements showed that, when changing electrolytes or replacing the capillary, considerable time is needed before the '-potential stabilizes (Figure 5.8). When a new FS capillary was filled with buffer IV (pH 6, Table 5.1) and the '-potential was measured at one minute intervals (line I), a period of twenty minutes was required before the system stabilized. Line Il in Figure 5.8 shows the time dependent (-potential change when switching from buffer I (pH 3.0) to ID (pH 4.8). In this case it took almost two hours before the equilibrium was reached.

~ -10 '-'

~ -20

~ ,.. >J' -30

30 60

Time(min)

90 120

Figure 5.8 Zeta potential as function of time for FS capillaries (see text for further details).

S.4.2 Direct measurement of the electroosmotic flow

5.4.2.1 Weighing The weighing method was used to determine the relationship between the electroosmotic flow and the applied voltage gradient (Figure 5.9). Theoretically a linear relationship is expected. However, this is only the case up to 4 kV; at higher voltages a deviation from

71

5 ELECfROOSMOTIC FLOW

~at line was observed. This may be explained by the increasing temperature in the bore of the capillary at higher voltages. The temperature increase leads to a lower viscosity, and thus higher electroosmotic flow values (Eqn. 5.1). At even higher voltages the system becomes thermally instable.

] ,.t • • • • • ! .ro - • • U' -80 I I I

0 2 4 6 Voltage (kV)

Figure 5.9 Measurement of the zeta potential and electroosrrwtic flow as function of the applied voltage, using the on-line balance setup.

8

At low voltages the observed velocity is lower than expected, and the regression line does not go through the origin. The reason for this could be the fact that a certain minimal force is needed to overcome the resistance of the capillary.

The {-potential calculated from the averaged electroosmotic flow values are also shown in Figure 5.9. Comparing the values obtained from the weighing method (-45 mV) with those of the streaming potential method (-35 mV} and literature values, the weighing method seems to overestimate the (-potential. It is, however, difficult to compare these values, because no data was available on the temperature in the capillary and its influence on the viscosity of the buffer.

Although the weighing is an on-line method for measuring the EOF, its general usefulness is limited because of the low time resolution (1 datapoint every 5 minutes).

72

5.4.2.2 Conductivity cell Figure 5.10 shows the results of the conductivity method. Line I shows the increase of conductivity with time, for different applied voltages. Line II is a calibration line (gravity flow) created by raising the inlet vial. Using the weighing method this flow was determined to be 6.0 nl/s. A run at 0 kV was performed as a reference to correct for any siphoning in the system.

20~~·······-·······-.L-~-• -~~· -····'~~~

0 100 200 300 400 Time(s)

Figure 5.10 Increase in conductivity as a function of voltage and gravity, using the on-line conductivity cell.

Using the calibration gravity flow (line II) the EOF values can be calculated for the different voltages (Table 5.2).

Table 5.2 Conductivity change as a function of voltage using the conductivity cell method.

Voltage Conductivity EOF (kV) (µS/s) (nl/s)

0 0 0 5 0.019 3.4 10 0.034 6.1 15 0.049 8.8 20 0.082 14.7

73


A;& expected, an increase in EOF is observed at increasing voltages. However, the results deviate from linearity especially at higher voltages, as was also seen in the weighing method experiments. In this case, the temperature increase (with voltage) leads to a higher than expected value for the EOF because of the decreasing viscosity. However, non-linearity may also result from the conductivity change in the cell due to electrode reactions. When experiments were run for a long time, a deviation from the theoretical line was observed even at lower voltages. A possible solution for this problem is the separation of the conductivity cell and capillary from the electrode by means of a membrane. This system allows conductance of electricity, but prevents products from electrode reactions to enter the conductivity cell itself.

5.5 CONCLUSIONS

To characterize capillaries the streaming potential method is the most suitable because temperature effects are minimal. The time resolution of this method is also high enough to study dynamic effects. Using this method, it was shown that when replacing capillaries, or changing buffers systems, a considerable amount of time (up to two hours) is required before the system reaches equilibrium conditions. It should, therefore, be expected that in the first 1-2 hours after changing the separation conditions, some variance (drift) in the results is obtained.

The weighing method closest resembles the actual separation conditions, so the EOF values will correspond to the actual numbers. Although the measurement is on-line, the time resolution is too low to make it suitable for direct feedback to an EOF control method.

The conductivity cell is the most promising method for on-line measurement of EOF, with a high enough time resolution to allow direct feedback to EOF control methods. However, more research is necessary to solve the linearity problem caused by electrode reactions. Separation of the cell and capillary from the electrode by a membrane is one of the possible solutions for this problem.

All the described methods, however, should be seen as a "work-around" and not a real solution. A better understanding of the EOF, resulting in an elimination of the fluctuations, is to be preferred. In this case, the neutral marker (to) technique is a viable

74

method for obtaining EOF information. This method can then also be used for long term drift correction and capillary-to-capillary variances.

REFERENCES

1. Pretorius, V., Hopkins, B.J. and Schieke, J.D., J. Chromatogr., 99 (1974) 23.

2. Jorgenson, J.W., Lukacs, K.D., Anal. Chem., 53 (1981) 1298.

3. Martin, M.M. and Guiochon, G., Anal. Chem., 56 (1984) 614.

4. Martin, M.M., Guiochon, G., Walbroehl, Y. and Jorgenson, J.W., Anal. Chem., 51 (1985)

559.

5. Everaerts, F.M., Verheggen, Th.P.E.M. and Van de Venne, J.L.M., J. Chromatogr., 123

(1976) 139.

6. Vacik, J. and Zuska, J., J. Chromatogr., 91 (1974) 795.

7. Stevens, T.S. and Cortes, H.J., Anal. Chem., 55 (1983) 1365.

8. Lukacs, K.D. and Jorgenson, J.W., J. HRC&CC, 8 (1986) 166.

9. Lauer, H.H. and McManigill, D., Anal. Chem., 58 (1986) 166. 10. Reijenga, J.C., Aben, G.V.A., Verheggen, Th.P.E.M. and Everaerts, F.M.,

J. Chromatogr., 260 (1983) 241.

11. Altria, K.D. and Simpson, C.F., Anal. Proc., 23 (1986) 453.

12. Altria, K.D. and Simpson, C.F., Chromatographia, 24 (1987) 527.

13. Huang, X., Gordon, M.J. and Zare, R.N., Anal. Chem., 60 (1988) 1837.

14. Tsuda, T., J. Liq. Chromatogr., 12 (1989) 2501.

15. Schwer, C. and Kenndler, E., Anal. Chem., 63 (1991) 1801.

16. Lee, C.S., Blanchard, W.C., and Wu, C.T., Anal. Chem., 62 (1990) 1550.

17. Lee, C.S., McManigill, D., Wu, C.T., and Patel, B., Anal. Chem., 63 (1991) 1519.

18. Hayes, M.A., and Ewing, A.G .. Anal. Chem., 64 (1992) 512.

19. Wanders, B.J., Van de Goor, A.A.A.M., and Everaerts, F.M., J. Chromatogr., 470 (1989)

89.

20. Wanders, B.J., Van de Goor, A.A.A.M., Everaerts, F.M. and Cramers, C.C., Neth. Pat.

Appl., 1990.

21. Van de Goor, A.A.A.M., Wanders, BJ. and Everaerts, F.M., J. Chromatogr., 470 (1989) 95.

22. Wanders, BJ., Van de Goor, A.A.AM., and Everaerts, F.M., J. Chromatogr., (25 134),

accepted for publication.

23. Stern, Z Elek., 30 (1924) 508.

24. Hudson, A., and Nelson, R., University Physics, Harcourt Brace Jovanovich, Inc., New York, USA, 1982, Chapter 23, 563.

75


25. Glasstone, S., Textbook of Physical Chemistry, 2nd ed., Van Nostrand Company Inc.,

New York, 1946, Chapter XIV, 1219. 26. Janz, G.J. in Ives, D.J.G. and Janz, G.J., Reference Electrodes: Theory and Practice,

Chapter 4, Academic Press, New York, USA, 1961.

76

CHAPTER 6

DATA ANALYSIS IN CAPILLARY ELECTROPHORESIS

ABSTRACT

An iterative peak detection algorithm for the integration of electrophoretic data is

discussed. In this algorithm a baseline is constructed before the actual peak detection

is performed. These two steps may then be repeated; in every additional pass the results

from the previous pass are used to optimize the baseline construction and peak detection. The separate construction of the baseline under a peak, independent of the

positioning of the start and end points of that peak, results in more accurate and

reproducible peak integration, compared to existing algorithms. Also baseline drift and

even baseline shifts no longer present major problems. Another advantage of this

algorithm is that both positive and negative peaks can be integrated simultaneously. The iterative character of the algorithm also makes it more user friendly; with the

default parameters a wide range of electropherograms can be analyzed.

6.1 INTRODUCTION

Although popular in the academic world for almost a decade, Capillary Electrophoresis (CE) has only gained interest from the industry (e.g. quality control) in the last few years. One of the problems standing in the way of the general acceptance of CE as a routine analytical tool is its reported lack of reproducibility. In spite of this the CE market is growing at a rate of about 40% per year and as a result most analytical instrument manufacturers now have completely automated CE instruments on the market.

One of the underestimated contributors to the overall accuracy and reproducibility of a CE analysis is the data analysis. The data analysis part of the software supplied with the CE instruments is often based on already existing High Performance Liquid Chromatography (HPLC) algorithms, limiting changes to the addition of some CE specific calculations (e.g. corrected peak areas). However, there are some fundamental

77

6 DATA ANALYSIS IN CAPILLARY ELECTROPHORESIS

differences between electropherograms and chromatograms. The major difference is the typical lower signal to noise ratio of CE peaks compared to those in HPLC. The reason for this is the inherent lower sensitivity of CE as compared to HPLC, which often forces the analyst to challenge the detection limit, in order to analyze concentration levels considered nonnal in HPLC. Electropherograms also tend to have a higher baseline drift, sometimes even baseline shifts. Furthennore the peaks are typically sharper, more asymmetric and can be positive or negative (e.g. indirect detection) even within one run.

As a result of these differences, the commercial HPLC based peak detection algorithms have problems analyzing electropherograms automatically, both in the actual peak picking and in the accuracy and reproducibility of peak integration. This often results in an unacceptably high contribution of the data analysis to the overall reproducibility of the analysis. Another problem of these algorithms is that the outcome is dependent on a number of (user adjustable) parameters, leading to a poor user-to-user reproducibility. Unfamiliarity with the algorithm, caused by the limited information in the software manuals, makes optimization of the parameters a tedious 'trial and error' effort. Most packages offer features to correct some of these problems manually, but this requires input from the analyst and is time consuming; therefore, not desirable for routine data analysis.

This chapter will discuss a new approach in analyzing electropherograms, which tries to address these problems. Furthermore, a comparison will be made between the presented algorithm and three commercially available CE data analysis packages.

6.2 DESCRIPTION OF THE ALGORITHM

The peak detection algorithm presented here consists of two parts; the construction of a baseline and the actual peak detection/integration. Figure 6.1 shows a flow diagram

of the algorithm. The first step of the data analysis is the construction of the baseline. The objective of this baseline construction is to effectively "remove" the peaks but accurately follow any other disturbances, e.g. baseline drift and shifts, in the electropherogram. The next step is the so-called cluster detection. Clusters are defined as data subsets in the electropherogram where there is a certain minimal difference in signal between the actual data and the constructed baseline. In this step, no distinction is made between a single peak or multiple, not baseline separated, peaks. After the

78

cluster detection, the actual peak detection is performed by adjustment of the cluster start and end points and a search for not baseline separated peaks within one cluster.

A further substantial improvement of the results is obtained using an iterative approach.

no ___ P_e_ak-Detecti~~

User adjustable parameters:

Window Size Percentiles Point

Minimwn Peakheight (poslneg)

Peakwidth Slope Treshold

or Baseline OffSet

Overlapping Peaks Check Peak Threshold

~--

Integration ·~··

Figure 6.1 Flow diagram of peak detection algorithm.

6.2.1 Baseline construction The first step in the analysis of an electropherogram is the construction of a baseline. For this construction, a technique called object subtraction is used. In this case, the objects to subtract are the peaks of interest and the background is the (noisy, drifting and shifting) baseline. The tool used for this object subtraction is a modified moving median filter. This digital filter is applied to the raw data of the electropherogram and removes impulse characteristics (e.g. spikes, and in this case also peaks), but accurately preserves low frequency baseline drifts and baseline shifts. The use of moving median filtering for data analysis was already suggested in the early l 970's and is now mostly used in image processing to remove impulse noise [l ~6]. Its

79


potential use for the analysis of chromatographic and electrophoretic data was first demonstrated by Wanders et al. [7]. Also, Moore et al. [8] described its use for removal of low-frequency drift in chromatographic data.

The median of a dataset is defined as the center point of an array which contains the datapoints of the original dataset sorted in ascending order. A moving median filter processes a dataset by replacing each point in that dataset by the median of a data subset centred around that datapoint. If the original dataset contains peaks as in an electropherogram, these peaks will either be sorted towards the high end (positive peaks) or low end (negative peaks) of the data subset. This means that as long as the size of the moving median filter (Window Size) is at least twice the baseline peakwidth, datapoints of that peak will never reach the

center -median- of the data subset and the peak will, therefore, be removed.

Figure 6.2 gives a graphical representation of the baseline construction.

80

Jl Sorted data set

Figure 6.2 Graphical representation of the baseline construction using moving median filtering.

Mathematically the construction of the baseline using a moving median filter may be described as follows:

B(i) MedianfE(x)I i-r~ x !>i+r} (6.1)

in which with B represents the constructed baseline, E the electropherogram, and r the filterrank.

Besides the center point in the sorted data subset (the median), other points (percentile points) can be used to determine the value of the filter output. For instance, in an electropherogram containing only positive peaks a percentile point below the median can be used. This leads to the following general equation for the construction of the baseline:

B(i) • p

E. [-r+l] I 50

(6.2)

in which E;' is the array which contains the sorted data subset of size 2r+ I (Window Size) centred around point i of the original electropherogram and P is the percentile point.

Using a lower percentiles point, bas the advantage that with the same window size, wider peaks are removed from the original electropherogram.

6.2.2 Cluster detection After the baseline construction, the actual peak detection is started. The first step of this process is called cluster detection. Clusters are defined as regions, data subsets, in the electropherogram where there is a significant difference (positive or negative) between the raw data and the constructed baseline (Figure 6.3). The parameter used to find these regions is the Minimum Peakheight. The Minimum Peakheight is an imaginary line parallel to the constructed baseline, which marks the minimal height a peak must have (in reference to the constructed baseline) to be detected.

81


• initial cluster endpoint

2

Minimum Peakheight threshold );

constructed baseline

3

Figure 6.3 Cluster detection using the Minimum Peak.height threshold.

The identified regions can contain either one peak (Figure 6.3, peaks 1 and 2) or multiple, not baseline separated, peaks (Figure 6.3, peaks 3 and 4). In either case the region is called a cluster. Initially, the cluster start and end points are marked as the points where the raw datapoints and the Minimum Peak:height threshold line intersect (see Figure 6.3).

6.2.3 Cluster border adjustment After the cluster detection, the exact locations of the start and end points of the clusters are determined. Two, user selectable, criteria may be used for this purpose; the first criterion locates the start and end points of a cluster at the first point (in both directions, starting from the centre of the cluster) were the raw data and the constructed baseline intersect. The second criterion determines the start and end points of a cluster using the first derivative and two additional parameters "Peakwidth" and "Slope Threshold". In this case the start and end points of a peak are located at the points where the absolute value of the first derivative has remained below the value of the "Slope Threshold" for a period equal to "Peakwidth". Starting at the centre of the cluster, the actual start and end points of a cluster are found at the first points where one of these two criteria is met. The user can disable one of the two criteria forcing the location of the cluster end points based on only one of the two criteria.

82

6.2.4 Peak detection and check for overlapping peaks

After the adjustment of the cluster borders, an (optional) check for overlapping peaks

may be performed. This step is only necessary in cases where peaks which are not baseline separated occur (see Figure 6.3, peaks 3 and 4). If the user selects not to execute this step, all detected clusters are treated as peaks and subsequently integrated. If a check for overlapping peaks is requested, the number of maxima (in a positive cluster) or the number of minima (in a negative cluster) is determined. using the first derivative. In the cases where more than one maximum (or minimum) is found, the cluster is split up into the underlying peaks, using the valley between the two maxima (or minima) as border between the two peaks.

6.2.5 Integration of peak areas Now that the peak picking is done, the peaks can be integrated. The peak area is calculated using the following equation:

pr

L (E(i)-B(i)) area = _i-,_p1 ___ _

(6.3)

f

in which E represents the electropherogram, B the constructed baseline, pl and pr the left and right border of the peak and f the sample frequency.

6.2.6 The iteration process As pointed out before, the successful removal of peaks during baseline construction, is dependent on the filterrank and base peakwidth. In some cases it is not easy, or sometimes even impossible, to find values for the filterrank and the percentile point for optimal removal of all peaks in the electropherogram. This is especially the case in electropherngrams combining a wide variety of peakwidths (requiring a large filterrank) with a very unstable baseline (requiring a small filterrank to accurately preserve these relative high frequency disturbances). Figure 6.4A shows the effect of too small a filterrank on the results of the baseline construction.

83


A

• Initial borders 0 Exact borders

B

(1 11 I\ I I I I I 1 .t

I~

I \ I I I I

I I I ! I I I I

I···\ u uuuu~-uuLl--~ c IA

I \ I I I . I \

II I

I \

\ I \ 1--- --- -

Figure 6.4 Analysis of a simulated electropherogram with three gaussian peaks ( o = 1, 2 and 4). Window Size = 40 s. A: 1 pass; B: 2 passes; C: 3 passes.

Peak 1 is removed, but in both peaks 2 and 3, the constructed baseline starts following the peak. Without any further modifications to the algorithm, this would result in inaccurate peak integration. To solve this problem the algorithm was made iterative. This

84

means that multiple passes through the algorithm are possible, where in every additional

pass the results from a previous pass are used to optimize the baseline construction and

peak detection. The whole iteration process is based on the observation that even though

the baseline constructed during the first pass was not optimal, normally most of the

clusters are still detected (see Figure 6.4A). However, relevant peak information, e.g. the

peak area, will be inaccurate.

The principle of the iteration process is that the cluster information obtained in the

previous pass is used to optimize the baseline construction in the current pass.

In every subsequent pass, similar to the first pass, a moving median filter is used for the

baseline construction. However, before determining the baseline, a check is made for

overlaps between the clusters detected in the previous pass and the current filter

position. If an overlap is detected, the overlapping points are deleted from the sorted data subset, leaving virtually only datapoints corresponding to baseline points in the

original electropherogram. The value for the baseline is now determined by taking the

median of this new shortened data subset. All additional passes through the algorithm

use the median to calculate the value of a baseline point, overruling the setting of the

percentiles point in the first pass. The use of another percentiles point is no longer

necessary because virtually all datapoints belonging to either positive or negative peaks

are removed from the sorted data subset before the value of the baseline is determined. The value of the percentiles point, however, still plays an important role in the results

of the cluster detection in the first pass. Figure 6.4B shows the new baseline calculated after two passes. The baseline under peak

2 is now correct but the baseline under peak 3 is still incorrect. Note, however, that the

exact (and also the initial) cluster start and end points in peak 3 have shifted

considerably closer to their actual position. A third pass, therefore, will remove a larger

part of the peak from the sorted data subset leading to an improved baseline as shown

in Figure 6.4C.

Both the initial cluster borders and the exact cluster borders may be used in the iteration process. In most cases this will lead to almost identical results. However, in cases where

the base peakwidth is larger than the filterrank, the use of initial cluster borders will not result in an optimal baseline. The reason for this is that the time difference between the

location of the initial and exact cluster border is of the same order as the filterrank. This

means that while the median filter is moving through this part of the peak, no peak overlap is detected, potentially resulting in an incorrect baseline.

85


6.2.7 Existing peak detection algorithms The evaluated commercially available CE data analysis packages all use a similar algorithm for peak detection [9-11]. In this algorithm, the peak detection is based on changes in the first derivative of the detector signal. Figure 6.5 shows this peak detection algorithm graphically.

E(t)

Figure 6.5 Graphical representation of the peak detection algorithm used in the three commercial packages. For more information see text.

A peak is detected when the first derivative exceeds the value of the parameter Slope Threshold (l:i); the actual start time of the peak is set at the point where the first derivative departs from zero (t1). On the downside of the peak, (the first derivative crossed zero) the end of the peak is detected when the absolute value of the first derivative remains below the Slope Threshold value for a certain amount of time (t3 to t4). Besides the SJope Threshold another parameter plays an important role in this peak detection algorithm. This parameter, PeakWidth, has two functions. First and most important, is that Peak Width determines the rank of the filter used for the first derivative calculations. The second function of PeakWidth is in the detection of the end of a peak. The amount of time the first derivative has to remain below the Slope Threshold is equal to the value of the parameter PeakWidth. The perfonnance of this algorithm is dependent on correct values for the Slope Threshold and PeakWidth parameters.

86

6.3 DESCRIPTION OF THE TESTS USED

To evaluate the above described algoritlun and compare it to existing CE analysis software, a program was written in C (Turbo C++ for Windows V3.l, Borland, Scotts Valley, CA, USA). This program, Caesar, which runs under Microsoft Windows V3.l (Microsoft, Redmond, WA, USA) implements the above described algoritlun and offers additional features: manual integration, data overlay and several CE specific calculations. The performance of Caesar was compared to the following conunercially available CE data analysis packages: System Gold V7.12 (Beckman Instruments Inc., Fullerton, CA, USA), Millennium Vl.10 (Waters, Millford, MS, USA) and Biorad Integrator V3.0lb (Biorad, Hercules, CA, USA). The comparison was carried out using both simulated and actual electropherograms.

6.3.1 Simulated Electropberograms As a first comparison a set of simulated electropherograms was analyzed using the four data analysis packages. The simulations were created using Matlab 4.0 for Windows (The Mathworks Inc., Natick, MS, USA). The simulated electropberograms contained 10 identical gaussian peaks and a gaussian noise (rms = 5 µAU, peak-to-peak= ±25 µAU) was added to make them more realistic (Figure 6.6).

$ 0.0002

:$

1 r;:j O.IXXJJ

5 10 Time(min)

Figure 6.6 Example of a simulated electropherogram (nr 7, see Table 6.1). Peakheight = 250 µAU, rms-noise = 5 µAU. standard deviation o = 4 s.

87


A total of 16 electropherograms were created with varying peak.widths and peakheights. Table 6.1 shows the relevant information of these electropherograms.

Table 6.1 Overview of simulated electropherograms used in the comparison. See text for explanation of simulation type. SIN is the signal to peak-to-peak noise ratio.

Nr Height 0 SIN Nr Height 0 SIN (pAU) (s) (pAU) (s)

2500 100 9 2500 4 100

2 250 10 10 250 4 10

3 125 5 11 125 4 5

4 50 2 12 50 4 2

2500 8 100

250 8 10

125 8 5

50 8 2

The reproducibility of an algorithm was evaluated by calculating the percentage relative standard deviation (%RSD) of the peak areas of the 10 peaks in the simulated electropherograms. The accuracy was tested by comparing the mean of the 10 peaks to the true area (AT) of a gaussian peak which is given by:

(6.3)

in which h is the peakheight and a the standard deviation of the gaussian peak.

The variable dArea represents the percentage deviation of the calculated peak area from the true peak area:

(6.4)

88

in which Ac is the calculated area and AT is the theoretical peak area (Eqn. 6.3).

6.3.2 Actual Electropherograms A second test was the analysis of some real data. The data analyzed were six sets of

nine electropherograms. The sample used was a mixture of Benzoic Acid (BA) and phydroxy PhenylAcetic Acid (PAA) dissolved in running buffer (25 mM Borax, pH 9.2) at different concentrations. PAA was used as an internal standard to correct for injection variances, and its concentration was constant in all samples, 500 µg/ml. The concentration of benzoic acid (BA) was 2, 4, 10, 20, 40 and 100 µg/ml respectively for

the six different sets.

The analyses were performed on a Beckman P/ACE 2100 (Beckman Instruments Inc, Fullerton, CA, USA) CE instrument using UV detection at 214 run. All chemicals used were analytical grade and obtained from Sigma Chemical Company (St. Louis, MO, USA). Stock solutions and electrolyte systems were prepared using purified water (MilliQ, Millipore, Bedford, MA, USA).

Separations were carried out in untreated fused silica capillaries (Beckman Instruments Inc., Fullerton, CA, USA) with an inner diameter of 75 µm and a total length of 57 cm (50 cm to detector).


In paragraph 6.4.1 the effectiveness of the iterative process is tested using 16 simulated electropherograms. In paragraph 6.4.2 through 6.4.4. Caesar is compared with the three commercially available data analysis packages.

6.4.1 Effectiveness of the iteration process Table 6.2 show the results of the data analysis of simulations 1-16, with and without iteration. Figures 6.7 gives a graphical presentation of this data.

89


Table 6.2 ·Influence of iteration on reproducibility and accuracy. Electropherograms: Simulation number 1-16. For additional information about electropherograms see Table 6.1. Analysis parameters: Window Size: 50.0 s, Percentiles point: 50%, End point detection using the baseline crossing criterion.

1 Pass 2 Passes 3 Passes 4 Passes S Passes

Nr RS dArea RS dArea RS dAre D

(%) D

(%) D a

(%) (%) (%) (%)

0.08 -0.12 0. 0.09 0.05 0.09 0.05

2 l.08 -0.99 l.24 -0.06 1.24 -0.06

3 2.00 -1.44 2.12 0.22 2.12 0.26 2.12 0.26 2.12 0.26

4 2.45 -2.72 2.65 0.00 2.65 0.00 2.65 0.00 2.65 0.00

5 0.08 -0.32 0.08 0.01 0.09 0.03 0.09 0.03 O.Q9 0.03

6 0.89 -2.39 1.12 -0.53 1.19 -0.48 1.19 -0.48 1.19 -0.48

7 1.56 -3.66 2.02 -0.51 2.22 -0.30 2.22 -0.30 2.

8 2.17 -8.32 -1.76 3.

0.22 -2.05 -0.05 0.08 -0.05

0.84 -5.96 -0.69 0.65 -0.69

0.74 -9.41 -0.89 0.94 -0.89

l.98 -16.41 -4.03 3.31 -4.03

0.30 -47.98 -0.13 0.05 -0.13

1.85 -47.92 1.89 -7.75 0.30 -1.50 0.31 -1.46 0.31 -1.46

15 1.39 -48.73 2.37 -10.93 l.03 -4.03 0.38 -2.61 0.38 -2.61

16 5.73 -51.01 2.06 -19.59 2.03 -9.74 2.21 -9.47 2.21 -9.47

90

31':]1 2 t

:L*~·· 1 JO 100

SIN

t1 ~L .. 1 10 100

SIN

Reproducibility (%RSD)

~J\ '1 IJ l 10 100 I JO 100

SIN SIN

Accuracy (%dArea)

~u;~21. ;04 ·2 t .l I .g .l 4 .l I .l .() • ·12

·8 " . ·16 " .JO ·20 ............... .

l JO 100 l JO 100 SIN SIN

6FJ : .•. 0 +

I 10 100 SIN

OD •• : . " " " .()0

I IO 100 SIN

Figure 6. 7 Influence of iteration on reproducibility and accuracy. Electropherograms:

Simulation number 1-16. Number in top right comer of each graph represents the

standard deviation of the JO gaussian peaks. For additional information about the

electropherograms see Table 6.I. Analysis parameters: Window Size: 50.0 s, Percentiles

point: 50%, End point detection using the baseline crossing criterion.

Table 6.2 and Figure 6.7 show the improvement of the iteration on the accuracy of the peak area calculation when the window size is less than twice the size of the base peakwidth. However, another important finding is that even at small peakwidths (standard deviation I and 2 s) the accuracy of the peak integration improves during the iteration process. The reason for this accuracy improvement for small peaks is the following; as soon as the moving median filter encounters a peak, the points belonging to this peak will end up in either the beginning (negative peak) or the end (positive peak) of the data subset (see Theory section). The median of this data subset now no longer corresponds to the median of the baseline points, because part of the subset is occupied by points from a peak. As a result the constructed baseline will not go through the center of the noise resulting in accuracy loss for peak area calculations. The median calculated in the second pass, however, does correspond to the actual median of the noise, because the points belonging to a peak are first deleted from the data subset (the remaining data set which is used to calculate the median, contains virtually only baseline points).

91


6.4.2 Comparison using simulated electropherograms Table 6.3 shows the results of the data analysis of the different simulated electropherograms with the four different packages. Figure 6.8 gives a graphical presentation of ,this data.

92

Table 6.3 Comparison of the four data analysis packages. Electropherogroms:

Simulation number 1-16. For additional infonnation about electropherograms see Table 6.1. Caesar parameters: Window Size: 50.0 s, Percentiles point: 50%, End point

detection using the baseline crossing criterion. Iteration: 4 passes.

Caesar Integrator G I .

Nr RSD dArea RSD dArea RSD dArea RSD dAre

(%) (%) (%) (%) (%) (%) (%) a

(%)

1 0.09 0.05 0.68 0.85 0.32 -0.15 0.30 0.82

2 1.24 -0.06 3.86 6.00 2.25 0.12 1.74 7.73

3 2.12 0.26 5.64 0.70 5.40 -1.05

4 2.65 0.00 8.73 36.15 9.84 2.14 10.78 29.19

0.09 0.03 0.32 0.47 0.31 0.30 0.15 0.72

l.19 -0.48 5.22 0.69 2.09 1.35 2.24 3.28

7 2.22 -0.30 8.38 6.28 5.59 3.36 4.45 15.37

8 3.64 -1.76 11.51 13. 12.16 12.61 9.35 36.48

9 0.08 -0.05 0.44 0 0.22 0.33 0.43

10 0.65 -0.69 3.21 -0.17 1.27 5.67

11 0.94 -0.89 8.65 5.56 5.86 0.65 5.10 6.03

12 3.31 -4.03 15.33 18.37 12.97 3.86 7.80 16.11

13 0.05 -0.13 0.32 -0.30 0.19 -0.55 0.09 0.79

14 0.31 -1.46 4.34 -2.88 2.82 -3.45 2.06 -7.04

15 0.38 -2.61 7.49 2.84 3.78 -1.46 4.75 -2.44

16 2.21 -9.47 11.86 30.23 9.62 -5.12 9.81 7.50

93


12[] • 1 9 I 6 •

: .. ·i . I 10 100

SIN

30 • 20

Reproducibility (RSD(%)) IS~---~ 16

I! ! e l:f ~~ ! 4 6 t• T 3 .. .. • 4'.. • • o~-= .. -- o• .u. A

]~ ~! T1 I 10 100 I IO 100

SIN SIN 1 IO 100

SIN ·m Caesar I • 1n1egra1or 1 + Sysiem Gold Accuracy (dArea(%))

30 • 406:]1 10 •

.): t ·~ 400 l±jMillennium

~1· o I+

.JO Jb. J }t;;: i

J 10 100 SIN

10 100 I IO 100 SIN SIN

I 10 JOO SIN

Figure 6.8 Comparison of the four data analysis packages. Electropherograms:

Simulation number 1-16. Number in top right comer of each graph is standard deviation of peak For additional information about electropherograms see Table 6.1. Caesar

parameters: Window Size: 50.0 s, Percentiles point: 50%, End point detection using the

boseline crossing criterion. Iteration: 4 passes.

Figure 6.8 shows that Caesar has the best reproducibility of all 4 packages in all 16 simulations. Comparison of RSDs using the F-test shows that in most cases the differences With the other packages are statistically significant. Analysis of Caesar's accuracy shows that in most cases no systematic bias can be found. Only in the simulations in which a large peak width is combined with a low signal-tonoise ratio, some negative bias occurs. Analysis of the accuracy of the other packages shows that also with Integrator and System Gold little bias can be found. Only Millennium displays a statistically significant tendency to overestimate the peak area in most simulations.

These differences in performance can be explained by looking at the way the baseline is reconstructed by the different packages. In the three commercial packages, which all use approximately the same peak detection algorithm, the peak area is determined by calculating the area between the raw data and an imaginary baseline. This imaginary baseline is a sttaight line between the previously determined start and end points of the peak (Figure 6.9).

94

As can be seen in Figure 6.9 the value for the peak area is extremely dependent on the way this baseline is drawn. Caesar, which draws the baseline independent of the actual start and end points of the peak, does not have this dependency and is, therefore, more

reproducible.

1 3

Figure 6.9 Illustration of the positioning of the imaginary baseline.

Solid line: Caesar; Peak 1: Integrator; Peak 2: System Gold: Peak 3:

Millennium.

Millennium (peak 3) uses the simplest approach to draw a baseline. The baseline starts and ends at an actual datapoint. However, before drawing the baseline, the start and end

points of the peaks are adjusted to local minima in the raw data. The position of the baseline drawn by Millennium is, therefore, normally in the lower region of the noise. This leads to a reasonable reproducibility, but also to a systematic overestimation of the peak area, especially at low signal to noise levels (see Table 6.3 and Figure 6.8). Both System Gold and Integrator seem to allow the start and end point of a baseline to be at a different value than the actual value of those points. Although both packages display this feature, only System Gold shows improvement in the reproducibility and accuracy

of the peak area calculations (Table 6.3 and Figure 6.8). However, as a result of this feature both packages show a lower reproducibility than Millennium and Caesar.

6.4.3 Effect of decoupling of the baseline construction Caesar's decoupling of the baseline construction from the actual peak detection has several other advantages over the existing algorithms. It removes most of the problems for the second step: actual peak detection. The part of the algorithm performing the actual peak detection only has to check for peaks in data subsets where there is a

95


significant difference in signal value between the constructed baseline and the electropherogram (=cluster). Limiting the actual peak detection to these clusters, not only speeds up the analysis time, but also prevents problems caused by baseline drift and shifts. Another benefit of the constructed baseline is that the peak detection part of the algorithm "knows" beforehand, if it should expect a positive (cluster above baseline) or negative (cluster below baseline) peak. This allows for the simultaneous automatic detection of positive and negative peaks, without requiring user input. Unlike Caesar,

Integrator, System Gold and Millennium require the user to enter a directive into a time table to switch between analyzing positive and negative peaks. Figure 6.10 shows a simulated electropherogram (simulation number 6B) which was analyzed by the four different packages to illustrate these differences.

0.0006-

0.0004- yi J~r1r ,..... ::::> :S

-0.0004, 5 ' 0 10

Time(min)

Figure 6.10 Simulated electropherogram number 6B with 10 'identical' gaussian peaks (height: 250 µAU, u: 2 s) on a drifting baseline with three baseline shifts (+150 µAU, -300 µAU and+ 150 µAU).

Table 6.4 shows the results of the data-analysis of simulation number 6B shown in Figure 6.10. The first row in the table shows the overall results, the second and third row show the separate results of the five positive and negative peaks respectively. The fourth row includes for comparison the results of the analysis of simulation number 6, which is the identical electropherogram without the baseline drift and shifts.

96

To analyze simulation 6B, both in System Gold and Millennium a directive must be set which tells the algorithm to look for and analyze negative peaks. Integrator also requires the setting of this directive, but it does not function properly, resulting in unusable peak detection results. For Caesar the same parameters were used as described in Table 6.2, except for the Window Size which was reduced to 20.0 s.

Table 6.4 Results of data analysis of simulated number 68 (Figure 6.10). Row 1 (AU) shows overall RSD and dArea, Rows 2 and 3 show the RSD and dArea for the five positive and the five negative peaks respectively. Line 4 shows, for comparison, the results of the analysis of simulation number 6.

Caesar Integrator Gold Millennium

Nr Peaks RSD dArea RSD dArea RSD dArea RSD dAre

(%) (%) (%) (%) (%) (%) (%) a

(%)

6B ALL 0.70 -0.13 - - 4.97 2.20 1.43 2.97

6B POS 0.78 -0.08 - - 2.50 2.69 1.26 2.37

6B NEG 0.60 -0.19 - - 6.57 1.72 1.36 3.57

6 ALL 1.19 -0.48 5.22 0.69 2.09 1.35 2.24 3.28

For both Caesar and Millennium the results of simulations 6B and 6 are similar, even better. System Gold, on the other hand, shows a decrease in performance. To determine the cause of this performance decrease, the values for RSD and dArea were calculated separately for the five positive and five negative peaks (see Table 6.4, rows 2 and 3). Caesar and Millennium do not show a significant difference in how positive and negative peaks are analyzed. However, System Gold shows worse results when analyzing negative peaks. The reason for this could be that the System Gold algorithm correctly locates the negative peaks, but falsely detects a number of positive peaks directly adjacent to these negative peaks. This leads to more variation in the imaginary baseline, resulting in a decrease in reproducibility.

97


Both Millennium and System Gold detected 3 additional peaks in the electropherogram caused by the three baseline shifts. Caesar was the only package which did not mistake these shifts for peaks. Initially, System Gold detected approximately 15 additional peaks (little positive peaks directly adjacent to the negative peaks as described above), but the setting of another directive "Minimal Peak Height" to a value above the noise level, solved this problem.

As mentioned before, the Window Size for the analysis of simulation number 6B using Caesar had to be reduced from 50.0 s (default value) to 20.0 s showing both the advantage and disadvantage of the iteration. Figure 6.11 shows the results of the baseline construction after 1,2 and 4 passes, using the default window size of 50.0 s. As can be seen the constructed baseline after one pass is reasonable and the analysis results (RSD: 0.79% and dArea: 2.57%) are close to the analysis results after one pass for simulation number 6 in Table 6.2. One additional cluster/peak is found at the third baseline shift. However, multiple passes lead to a worse baseline.

98

-0.0003 -0

-- !pass .......... 2 passes

------ 4 passes

5 Time(min)

JO

Figure 6.11 The results of the iterative baseline construction for simulation number 6B using a Window Size of 50.0 s.

This worsening of the baseline stems from the fact that every baseline shift in simulation number 6B is directly preceded by a peak. One pass through the algorithm will nonnally result in an accurate preservation of these shifts, with the exception of a positive shift directly preceded by a positive peak, or a negative shift directly preceded by a negative peak. In these two cases, the constructed baseline will not accurately preserve the actual shift and show a shift earlier in time (see Figure 6.11). The reason for this is that the points belonging to the positive peak directly preceding the positive shift are sorted

towards the same side as and mixed with the baseline points after the actual shift. As a result the points representing the baseline value after the actual shift will reach the center (median) of the sorted data subset before the filter reaches the actual shift. The case where a positive shift is directly preceded by a negative peak (or a negative shift is directly preceded by a positive peak) will not show this problem because the points belonging to this peak will not be sorted towards the same side of the data subset as the baselinepoints after the shift. However, the second pass will delete the points of this peak from the sorted data subset before calculating the median value. As a result, the shift in the constructed baseline will show up at an earlier time, leading to the detection of an additional negative cluster. In the third pass both the original negative peak and the falsely detected negative cluster are deleted from the data subset, resulting in a larger time shift and a larger falsely detected cluster. This process will continue until the negative peak and the positive shift melt together as one big negative cluster. A similar effect occurs in the second baseline shift in Figure 6.1 l where a negative shift is directly preceded by a positive peak.

To solve this problem a window size was chosen which was small enough to prevent the positive peak and the negative baseline shift to be in the filter window at the same time. Figure 6.12 shows the result of the baseline construction after 1 and 4 passes using a smaller Window Size, 20.0 s. The baseline shifts no longer give problems for the iteration. After one pass, the constructed baseline still follows the peaks a little, but after four passes the constructed baseline is good and the data analysis leads to excellent reproducibility and accuracy (see Table 6.4).

Although the iteration process ruined the baseline at a window size of 50.0 s (disadvantage), it enabled accurate integration of peak areas at a window size of 20.0 s.

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0.0003 - l - ! r ~ II : ::;;; 0.0000 ~"""", §i . rn

-0.0003 -0

- !pass .......... 4 passes

5 Time(min)

10

Figure 6.12 The results of the iterative baseline construction for simulation number 6B using a Window Size of 20.0 s.

6.4.4 Comparison using actual electropherograms Figure 6.13 shows the separation of Benzoic Acid (BA) and p-hydroxy Phenyl Acetic Acid (PAA) at the highest (100 µg/ml, Fig 6.13A) and lowest (2 µg/ml, Fig. 6.13B) concentration of BA in the set of six samples. As described in the experimental section, the concentration of PAA was constant in all samples at 500 µg/ml.

100

0.1000 -A

O.OOIO B

O.OSOO -2

0.0600 0.0005 -

k 0.0400 -

OD200 0.0000 -lo,/\\.JV'->~ 0.0000

-0.0200 ' -0.0005 ' ' 0 5.0 55 M 6.5 7.0

Figure 6.13 Separation of p-hydrox:y phenylacetic acid (1) and benzoic acid (2). Running buffer: 25 mM Borax, pH=9.2. Injection: Pressure 5 s. Voltage: 25 kV. Temperature: 25'C. UV detection at 214 nm. Untreated FS capillary: 50/57 cm. l.D. 75 µm A: PAA: 500 µg/ml, BA: 100 µglml. B: PAA: 500 µg/ml, BA: 2 µg/ml.

One of the boundary conditions used during the analysis of the actual electropherograms was that a different set of parameters could be used for the six different samples. However, the nine repeats for each sample had to be analyzed with the same set of parameters, to more closely resemble the 'real world' of e.g. quality control. Biorad's Integrator had the most trouble fulfilling this boundary condition with 4 out of the 6 samples passing. All other packages passed. The second boundary condition used in this comparison was that the parameters would be optimized for reproducibility, because of the tight requirements for reproducibility compared to e.g. linearity by the current Food and Drug Administration (FDA) regulations

Table 6.5 and Figure 6.14 show the reproducibility results of the analysis of the six samples. Each sample was injected nine times. To correct for injection variances, the peak area of BA was divided by the peak area of the internal standard PAA, before the calculation of the relative standard deviations (RSD).

Table 6.5 Relative standard deviation (RSD) of the corrected peak area of benzoic acid as function of the concentration.

- - I -mennium Cone Caesar

(µglml) RSD (%)

100 0.15 0.28 0.28 0.23

40 0.23 0.36 0.40 0.53

20 1.09 1.18 1.55 1.17

10 1.60 2.37 2.94 2.64

4 2.31 . 5.95 4.69

2 3.761) - 10.44 5.92

1lPercentile Point: 25%

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Figure 6.14 Relative standard deviation (RSD) of the corrected peak area of benzoic acid as function of the concentration.

Both Table 6.5 and Figure 6.14 show that in general the relative standard deviation is dependent on the sample concentration, as also found by Watzig et al. [12]. They also show improved reproducibility of Caesar compared to the other packages, especially at the lower signal to noise levels.

The difference between the packages, however, is not as great as in the theoretical comparison using the simulated electropherograms. Two causes were identified to play a role in this difference. First, the shape/frequency of the noise. Figure 6.15 shows an enlarged plot of the baseline of a simulated and an actual electropherogram.

102

B A(\, /cl 11\f\J vv \ I \J\ I \J ,

I

Figure 6.15 Baseline noise for a simulated electropherogram (A) and an actual electropherogram (B).

The noise in the actual electropherogram is of equal magnitude but smoother and contains only the lower frequencies. This effect is caused by the 1 s risetime filter in the Beckman P/ACE system, which filters out most of the high frequency noise (colored noise). As a result, the variance in locating the position of the startpoint and end point

of the peak and imaginary baseline decreases, leading to a more similar and higher performance for the three commercial packages.

Another reason for the difference between the simulated and actual data is that the RSD's shown in Table 6.5 and Figure 6.14 is total variance of the analysis, which is a summation of partial contributions of the data analysis variance and several other variances (see Chapter 2). In case of the simulated electropherograms the calculated variances were pure data-analysis variances.

The total variance of a CE analysis may be written as:

0 2 = 02 + 2 2 + 2 + 02 + 02 + 2 tot inj 0 dif + 0 heat 0 dlsp •of SUlfac• 0 analysis

(6.5)

in which the suffixes refer to partial contributions of injection, diffusion, joule heating, electric dispersion, osmotic flow, surface interaction and data analysis.

Some of these variances are closely related to the hardware performance of the CE

instrument (e.g. injection and joule heating) or column chemistry (surface interaction and osmotic flow). When these variances are better controlled in next generation instruments and columns, the contribution of the data analysis to the overall variance of the CE analysis will increase. As a result, the performance differences between the different peak detection algorithms will be more pronounced.

6.5 CONCLUSIONS

The iterative peak detection algorithm presented in this chapter showed to have great potential for analyzing electropherograms. The fundamental difference between this algorithm and the algorithms used in commercially available CE data analysis software, is the fact the construction of the imaginary baseline under a peak is decoupled from the actual determination of the startpoint and end point of that peak. Especially at the lower

signal to noise levels, this decoupling results in an improvement in both accuracy and

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reproducibility. The iterative character of the algorithm, allowing multiple passes, where in each pass the results from the previous pass are used to optimize the baseline construction and

peak detection, make the algorithm very powerful and user friendly at the same time. One set of parameters can be used to analyze a wide range of electropherograms. Also the modifications made to the moving median filter used for the baseline construction, which include the use of other percentiles points besides the median, contribute to the flexibility of the algorithm. Typical improvements in reproducibility and accuracy over three commercially available CE data analysis packages, were a factor of 2-5 for simulated electropherograms, and 1.5 - 3 times for actual data. Another advantage of the presented algorithm is the fact that all, with the exception of one, electropherograms in this chapter were analyzed using the same (default) set of parameters; whereas the three commercial packages required a tedious process of adjusting different parameters to optimize the integration. This improved user interaction not only decreases the integration variances and accuracy from run to run, but also from user to user, facilitating and improving inter-laboratory data exchange.

REFERENCES

I. Rabiner, L.R., Sambur, M.R., and Schmidt, C.E., IEEE Transactions ASSP, 23 (1975) 552. 2. Justusson, B.I., in Two-Dimensional Digital Signal Processing II, Transfonns and Median

Filters, Springer-Verlag, New York, (1981) 161. 3. Gallagher, N.C., and Wise, G.L., IEEE Transactions ASSP, 29 (1982) 1136. 4. Lee, Y.H., and Kassam, S.A., IEEE Transactions ASSP, 33 (1985) 672.

5. Haddan, R.A., and Parsons, T.W., Digital Signal Processing - Theory, Applications and

Hardware, W.H. Freeman and Co., New York, 1991, ISBN 0-1767-8206-5. 6. Maisel, J.E., PE&IN, Feb (1993) 61. 7. Wanders, B.J., Van de Goor, A.A.A.M., and Everaerts, F.M., Poster, 11th Int. Symp. on

Capillary Chromatography, 1990, Monterey, USA.

8. Moore, A.W., Jorgenson, J.W., Anal. Chem., 65 (1993) 188. 9. System Gold V7.12, System Operations Manual, Beckman Instruments, Fullerton, CA,

USA. JO. Millennium Vl.10, User's Manual, Waters, Millford, MA, USA. 11. Integrator V3.0, User's Manual, Biorad, Hercules, CA, USA. 12. Watzig, H., and Dette, C., J. Chromatogr., 636 (1993) 31.

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SUMMARY

After the initial phase in its development, where the focus was mainly on fundamental studies and development of first generation instrumentation, CE now enters the second phase in which it has to proof itself as a viable analytical tool for routine analyses in areas such as quality control in the pharmaceutical industry. Out of this first phase came two potential shortcomings, standing in the way of a general acceptance of CE as a routine analytical tool: lack of sensitivity and poor reproducibility. This thesis addresses different aspects of these two shortcomings, both from a fundamental and a practical point of view. One of the potential shortcomings is the "lack of sensitivity". Different approaches are possible but in this thesis the work was focused on improving the detection sensitivity. For this purpose a fully automated Capillary Electrophoresis (CE) instrument was build utilizing Laser Induced Fluorescence (LIF) as the detection technique to improve sensitivity. Detection limits as low as 10·12M for strongly fluorescent compounds were reached. This detection limit in combination with an offline sample concentration step showed the potential of the CE-LIF for water tracer analysis. Rhodamine WT, a xenobiotic water tracer suitable for large scale studies, was detected at concentrations below 1 pg/I seawater, a 100 fold improvement over the currently used High Performance Liquid Chromatography (HPLC) method (Chapter 3).

In order to use the sensitivity of a LIF detector for a wider range of applications, chemical modification of analytes (attachment of a fluorescent label) is necessary, because most components do not show native fluorescence. Different amine reactive fluorescent dyes for the labelling of amino acids and peptides were evaluated. Reactions conditions, linearity and limitations are discussed for five different dyes, which all can be excited by the 488nm laserline of a Argon Ion laser. Even though under ideal conditions the detection limit of labelled amino acids was as low as 10-"M, in practice, chemical noise (caused by impurities and side reactions) and incomplete reactions (caused by slow kinetics at low concentration levels) increased this limit to 10-s -10-6M. This lead to the conclusion that the concentration limit of detection is not governed by the sensitivity of the detection method and hardware, but by chemistry limitations as coupling efficiency, dye purity and analyte concentrations (Chapter 4).

105

SUMMARY

Another, and probably more important, shortcoming of CE which to date prevents a general acceptance of CE as routine analytical tool, is the reported poor reproducibility. Two aspects believed to be major contributors to the overall variances were addressed: the electroosmotic flow and the data analysis. The electroosmotic flow (EOF) is an important phenomenon in CE. The EOF influences the resolution and speed of an analysis, but can also introduce extra variances into the overall reproducibility. It is, therefore, important that the EOF is known for each individual separation system. Different off-line and on-line methods to determine this flow were developed and evaluated. It was found that the streaming potential method is suitable for characterization of separation systems and dynamic studies. Using this method, it was shown that when replacing capillaries, or changing buffers systems, a considerable amount of time (up to two hours) is required before the system reaches equilibrium conditions. It should, therefore, be expected that in the first 1-2 hours after changing the separation conditions, some variance (drift) in the results is obtained. For an on-line system, keeping in mind the ultimate goal of an on-line monitoring system coupled to an on-line control system, the conductivity cell seemed to be the best approach. But more research is needed to decrease the influence of ion depletion and/or electrode reactions on the accuracy of the measurements (Chapter 5).

Another contributor to the overall reproducibility of a CE method, the data analysis, is often underestimated. Limitations of HPLC based peak detection algorithms in currently available CE packages, often lead to an unacceptably high contribution of the data analysis to the overall reproducibility of the method. A new iterative peak detection algorithm was developed and evaluated, and it was shown that with this algorithm, optimized for analyzing electrophoretic data, the overall reproducibility of a routine CE analysis can be improved by at least a factor of 1.5 to 2, compared to three commercial CE data analysis packages. Typical improvements in reproducibility and accuracy over three commercially available CE data analysis packages, were a factor of 2 to 5 for simulated electropherograms, and 1.5 to 3 times for actual data. This and the improved user-interaction (no tedious process of adjusting parameters to optimize the integration) not only improves the integration reproducibility and accuracy from run to run, but also from user to user, facilitating and improving interlaboratory data exchange (Chapter 6).

106

SAMENV ATTING

Na de beginfase van de ontwikkeling van de CapiUaire Electroforese (CE), waarin de nadruk vooral lag op fundamentele studies en de ontwikkeling van de eerste generatie instrumenten, begint nu de tweede fase waarin CE zich zal moeten bewijzen als een bruikbare techniek voor routine-analyses in gebieden wals kwaliteitscontrole in de fannaceutische industrie.

In de eerste fase kwamen een tweetal beperkingen naar voren die een algemene acceptatie van CE als routinematig bruikbare techniek in de weg stonden t.w. een kleine gevoeligheid en een slechte reproduceerbaarheid. In dit proefschrift wordt aan een aantal aspecten van deze twee tekortkomingen aandacht besteed zowel vanuit een fundamenteel als een praktisch oogpunt. Een van de genoemde tekortkomingen is een te kleine gevoeligheid. Hoewel dit gebrek op een aantal manieren kan worden aangepakt, wordt in dit proefschrift vooral aandacht gegeven aan de verbetering van de gevoeligheid van de detectie. Daartoe is een volledig geautomatiseerd CE instrument gebouwd, waarin ter vergroting van de gevoeligheid laser geinduceerde fluorescentie (Laser Induced Fluorescence, LIF) als detectiemethode wordt toegepast. Met dit instrument zijn detectiegrenzen van 10·12M bereikt voor sterk fluorescerende verbindingen. Deze grote gevoeligheid, gecombineerd met een "off-line" monster voorbewerkingsstap toonde de bruikbaarheid van de CE-LIF aan voor de bepaling van water "tracers". Voor rhodamine WT, een xenobiotische water "tracer", die gebruikt wordt voor studies op grote schaal, werden detectielimieten bereikt van< 1 pg/I zeewater. Dit betekent een verbetering met een factor I 00 in vergelijking met de thans in gebruik zijnde "High Performance Liquid Chromotography" (HPLC) methode (Hoofdstuk 3).

Teneinde de zeer gevoelige LIF-detector voor een breder applicatiegebied te kunnen gebruiken, is bet noodzakelijk om de te analyseren stoffen chemisch te modificeren (bet aanhaken van een fluorescerend label), omdat de meeste verbindingen niet van nature fluorescentie vertonen. Ben aantal fluorescerende stoffen met affiniteit voor de aminegroep werd onderzocht op hun bruikbaarheid voor de labeling van aminozuren en peptiden. Voor een vijftal van deze stoffen (labels), die aangeslagen kunnen worden met behulp van de 488 nm laserlijn van een Argon Ion laser, werden de reactiecondities, lineariteit en beperkingen in de toepassing onderzocht. Hoewel onder ideate omstandigheden de detectiegrenzen van de gelabelde

107

SAMENVATTING

aminozuren op bet niveau lagen van 10·11M, bleek in de praktijk dat cberniscbe ruis (veroorzaakt door onzuiverheden en nevenreacties) en onvolledige omzetting (veroorzaakt door een lage reactiesnelheid bij lage concentraties) deze lirnieten verhoogde tot 1 o-8-10.oM. Hieruit blijkt dat de detectiegrenzen niet zo zeer bepaald worden door de gevoeligheid van de detectiemethode en het gebruikte instrumentarium, maar veeleer door chernische beperkingen, zoals de effectiviteit van de koppeling, de zuiverheid van de gebruikte labels en de concentraties van de te bepalen stoffen (Hoofdstuk 4).

Een andere en wellicht belangrijkere tekortkorning van CE, die tot op heden de toepassing als routine analysetechniek heeft tegengehouden, is de slechte reproduceerbaarheid. In dit proefschrift wordt aan een tweetal aspecten van CE, die de grootste bijdrage leveren aan deze slecbte reproduceerbaarheid aandacht besteed, t. w. de electroosmose en de data-analyse. De electroOsmose (EOF) is een belangrijk fenomeen in CE. De EOF bei:nvloedt bet scheidend vermogen en de snelheid van de analyse, maar kan ook varianties introduceren die leiden tot een verlaging van de reproduceerbaarbeid. Het is daarom van groot belang <lat de EOF voor de verschillende scbeidingssystemen bekend is. Een aantal "off-line" en "on-line" methoden voor de bepaling van de EOF werden onderzocht. Hierbij is gebleken dat de· bepaling van de strorningspotentiaal een goede mogelijkheid biedt voor de karakterisering van scbeidingssystemen en voor zgn. "dynamische studies". Uit dit onderzoek bleek verder dat, na vervanging van een capillair of na verandering van bet buffersysteem, er een aanzienlijke tijd (tot ca. 2 uren) verloopt alvorens bet systeem opnieuw in evenwicbt is. Gedurende de eerste twee uren na het veranderen van de scheidingscondities moet dus rekening worden gehouden met een "drift" in de resultaten. Voor toepassing in "on line"-systemen, waarbij tevens rekening wordt gehouden met bet uiteindelijke doe] de methode te kunnen gebruiken als een "on-line" meet-en-regel systeem, bleek de geleidbaarheidscel de beste resultaten op te leveren. Er zal ecbter nog nader onderzoek moeten worden verricht om de negatieve invloed van de "ion depletion" en/of electrodereacties op de nauwkeurigheid van de metingen te beperken (Hoofdstuk 5).

Een andere, belangrijke factor leidend tot de lage reproduceerbaarbeid van CE, de uitwerking van de verkregen gegevens (de data-analyse) wordt vaak onderscbat. Beperkingen van de thans gebruikte HPLC piek detectie algoritmen leiden vaak tot een onacceptabel hoge bijdrage van de data analyse aan de totale reproduceerbaarbeid

108

van de analysemethode. In dit proefschrift wordt een nieuw piek detectie algoritme beschreven en geevalueerd. Hierbij kon worden aangetoond dat met behulp van dit algoritme, toegesneden op de analyse van electroferogrammen, de totale repro

duceerbaarheid van een routinematige CE-analyse met tenminste een factor 1,5 tot 2 kan worden verbeterd in vergelijking met een drietal commercieel verkrijgbare CE data analyse-pakketten. De aangetoonde verbetering in reproduceerbaarheid en nauwkeurigheid van dit nieuwe algoritme in vergelijking met de drie commerciele pakketten bedroeg een factor 2 tot 5 bij bet gebruik van gesimuleerde elect;roferogrammen en een factor 1,5 tot 3 bij het gebruik van echte electroferogrammen. Dit feit en de sterk vergrote gebruikersvriendelijkheid (bet manueel invoeren of optimaliseren van parameters is vrijwel overbodig) verbetert niet alleen de reproduceerbaarheid en nauwkeurigheid van experiment tot experiment, maar ook van gebruiker tot gebruiker, waardoor de uitwisseling van gegevens tussen laboratoria wordt vereenvoudigd en verbeterd (Hoofdstuk 6).

109

110

LIST OF SYMBOLS AND ABBREVIATIONS

Symbols A cross sectional area of the capillary m2

peak area m2

Ac corrected peak area m2

AT theoretical peak area m2

B the constructed baseline

c velocity of light m.s·1

concentration of analyte mol.r1

D molecular dispersion coefficient m2,s·t

d dissociation degree

capillary diameter m dArea the percentage deviation of the calculated peak area

from the theoretical peak area

E electric field strength V.m·1

energy J E* the sorted data subset of size 2r+ 1

E the electropherogram

EsT streaming potential v f the sample frequency s·I

f,, friction factor kg.s·I

F Faraday constant (9.64*100) C.mor1

total fluorescence intensity

g gravitational constant

~h height difference

h Planck's constant

peakheight

I intensity of the excitation light

point in which the window size is centred in the original

electropherogram

L path length in the capillary m 1 distance between injection point and detection point m M overall mobility

N theoretical plate number

n number of experiments p pressure differential across the capillary Pa

111

SYMBOLS & ABBREVIATIONS

percentiles point

AP applied pressure Pa

pl left border of a peak

pr right border of a peak

q electrical charge of the particle

R. resolution of two zones

R universal gas constant (8.314) J.K.mo1·1

correlation coefficient

r radius of a species m

filterrank

s. electrons are in singlet x state

T absolute temperature K T, electrons are in the triplet x state

t time s injection time s

ti start time of a peak s

ti slope threshold s

v applied voltage v volume injected m3

v velocity m.s·1

v• velocity of x m.s·1

w electric power per unit volume w AW weight change of an outlet vial g

z valency of the ion

6 thickness of the electric double layer m

E dielectric constant of the medium c2r1m-1

excitation coefficient

' zeta-potential v

11 viscosity of a solvent Pa.s

'IC specific conductivity of the electrolyte S.m·1

,\ wavelength nm

Lr thermal conductivity W.m·1.K·1

µ effective ( electrophoretic) mobility m1v-1s-1

µo (electrophoretic) mobility at infinite dilution m1v·1s·1

µ" (electrophoretic) mobility at finite dilution m2v·1s·1

µapp apparent (electrophoretic) mobility m2v·1s·1

µOOF mobility of the electroosmotic flow m2v-'s·'

p the density of a sample 02 variance m2 or s2

112

a standard deviation of the gaussian peak s orm

a2inJ variance of injection s2 or m2

02 to! total variance of CE analysis s2 or m2

02mf variance of diffusion s2 or m2

a2disp variance of dispersion s2 or m2

'b quantum efficiency

Abbreviations amt amount Arg arginine

Asp aspartimine

BA benzoic acid

CB QC A 3-(4-carboxy-benzoyl)-2-quinolinecarboxaldehyde

CE capillary electrophoresis

CIEF capillary isoelectric focusing

CITP capillary isotachophoresis

CGE capillary gel electrophoresis

CMBE capillary moving boundary electrophoresis

Cys cystine

CZE capillary zone electrophoresis

DI deionized water

DTAF 4-( 4,6-dichloro-s-triazin-2-ylamino )fluorescein

EOF electroosmotic flow

EC European Community eqn equation FDA food and drug administration

FITC fluorescein isothiocyanate

FS fused silica

FSE 5-carboxyfluorescein succinimidyl ester

Glu glutamic acid Gly glycine His histidine HPLC high performance liquid chromatography HPV high pressure vessel HVPS high voltage power supply IC internal conversion

Ile isoleucine ISC inter system crossing

113

SYMBOLS & ABBREVIATIONS

i.d. ITP KCN LIF LPV Leu Lys MECC

MO NBD-F PAA Phe PMT Pro

PTFE

nns RSD Ser SIN SPE Thr Trp Tyr Val VR

114

internal diameter (m) isotachophoresis potassium cyanide laser induced fluorescence low pressure vessel leucine lysine micellar electrokinetic capillary chromatography molecular orbital 4-fluoro-7-nitrobenzofuran p-hydroxy phenylacetic acid phenylalanine photo multiplier tube proline teflon root mean squared relative standard deviation serine signal to noise solid phase extraction threonine trypsin tyrosine valine vibrational relaxation

DANKWOORD EN CURRICULUM VITAE

DANKWOORD

V elen hebben direct en/of indirect bijgedragen aan het tot stand komen van dit proefschrift. Met name wil ik hier noemen: mijn promotor Frans Everaerts, die mij de mogelijkheid heeft geboden om in dit interessante vakgebied promotieonderzoek te verrichten; mijn collega Tom van de Goor voor zijn enthousiaste medewerking in diverse projecten; mijn afstudeerders Han Martens en Thomas Rutten voor bun

significante inbreng. Verder gaat mijn dank uit naar alle andere medewerkers, die op uiteenlopende wijzen, moreel en fysiek, hebben bijgedragen aan de totstandkoming van dit werk. Veel dank gaat ook uit naar mijn ouders, voor hun niet-aflatende steun.

Last but most definitely not least, I want to thank my wife Lisa, who in the last two years has become a major part of my life, not just a partuer, but a friend and a

soulmate.

CURRICULUM VITAE

6 april 1965 juni 1983

september 1983 - februari 1988

februari 1988 - oktober 1992

oktober 1992 - heden

Geboren te Geleen Bind.examen Gymnasium-p, Michiel lyceum, Geleen Scheikundige Technologie Technische Universiteit Eindhoven Promotie onderzoek vakgroep Instrumentele Analyse, Technische Universiteit Eindhoven Senior Scientist, Beckman Instruments Inc. Fullerton, CA, USA

115

AUTHOR'S PUBLICATIONS

Authors publications:

Data acquisition in capillary isotachophoresis. B.J. Wanders, A.A.G. Lemmens, F.M. Everaerts and M.M. Gladdines, J. Cbromatogr., 470 (1989) 79-88.

Methods for on line determination and control of electroendosmosis in capillary electrochromatography and electrophoresis. B.J. Wanders, A.A.A.M van de Goor and F.M. Everaerts, J. Chromatogr., 470 (1989) 89-93.

Modified methods for off- and on-line determination of electrosmosis in capillary electrophoretic separations. A.A.A.M. van de Goor, B.J. Wanders and F.M. Everaerts, J. Cbromatogr., 470 (1989) 95-104.

Applications of a Laser Induced Fluorescence Detector for Capillary Electrophoresis. B. Wanders, T. van de Goor, R. Palmieri and N. Cooke, Poster presentation, HPCE '92, Amsterdam, The Netherlands.

High-Sensitivity peptide mapping with CE using laser induced fluorescence detection. A.A.A.M. van de Goor, B.J. Wanders and W.J. Henzel, Am. Biotechn. Lab., 7 (1992), Int. Biotechn. Lab., 7 (1992), Beckman Technical Information Note DS-824.

Derivatization of Amino Acids with CBQCA for Ultrasensitive Detection by P/ACE Capillary Electrophoresis Laser Induced Fluorescence Detection. B.J. Wanders, and A.A.A.M. van de Goor, Beckman Technical Information Note DS-826 (1992).

116

AUTHOR'S PUBLICATIONS

Enhanced separation of DNA restriction fragments by capillary gel electrophoresis using field strength gradients. A. Guttman, B. Wanders and N. Cooke, Anal. Chem., 64 (1992) 2348-2351.

On-line measurement of electroosmosis in capillary electrophoresis using a conductivity cell. BJ. Wanders, A.A.AM van de Goor, and F.M. Everaerts, J. Chromatogr., (1993) accepted for publication.

Isotachophoresis in CE. BJ. Wanders and F.M. Everaerts, Chapter in "Handbook of Capillary Electrophoresis: Principles, Methods and Practice", CRC Press Inc., Boca Raton, Ann Arbor, Boston, USA, 1993, in press.

117

STELLING EN

behorende bij bet proefschrift

AUTOMATED CAPILLARY ELECTROPHORF.sIS

INSTRUMENTAL AND METHODOWGICAL ASPECTS

van

Bart Jan Wanders

L Capillaire Zone Electroforese heeft nu een fase in zijn ontwikkeling bereikt, waarin deze techniek zijn bruikbaarheid voor routine analyses rnoet gaan bewijzen. Het gebruik van commerciele instrumenten, geoptimaliseerd voor reproduceerbaarheid en nauwkeurigheid, dient, voor de benodigde valideringsstudies, de voorkeur te krijgen boven zelfbouw apparaten. Dit proeftchrift, hoofdstuk 3.

2. De detectielirniet voor gelabelde arninozuren en peptides wordt rneestal niet bepaald door de gevoeligheid van de gebruikte instrumentatie, maar door chemische beperkingen als "chemische ruis" (veroorzaakt door onzuiverheden in bet Jabel en nevenreacties) en door onvolledige omzetting als gevolg van Jage monsterconcentraties. Dit proeftchrift, hoofdsruk 4.

3. De huidige methodes voor het on-line regelen van de electroosmose zijn van het type "trial and error". Ze zouden nuttiger en bruikbaarder worden door ze te koppelen aan een on-line electroosmose meetsysteem. Blanchard, W.C. and Lee, C.S., US Patent 9112073 (1991).

4. Het woord "Monitoring" in het artikel "Electroosmotic Flow Control and Monitoring with an Applied Radial Voltage for Capillary Zone Electrophoresis" van M.A. Hayes and A.G. Ewing is misleidend. Hayes, M.A., and Ewing, A.G., Anal. Chem., 64 (1992) 512.

5. Een gedeelte van de aan Capillaire Zone Electroforese toegedichte matige reproduceerbaarheid moet worden toegeschreven aan de data-analyse en niet de analysemethode zelf. Watzig, H., and Dette, C., J. Chromarogr., 636 (1993) 31. Dit proeftchrift, hoofdstuk 6.

6. Piekdetectie algorithmes, geoptimaliseerd voor de analyse van electroferogrammen, kunnen de reproduceerbaarbeid van routine CZE analyses verbeteren met een factor 1,5 tot 2. Dit proefschrift, hoofdstuk 6.

7. "Without data you're just another opinion" bevat veel waarbeid; toch vormt in veel gevallen bet destilleren van een opinie uit de gegevens een moeilijkere opgave.

8. Tegen de "Not Invented Here"-ziekte is in veel Amerikaanse bedrijven nog geen geneesmiddel gevonden.

9. Ziekenbuizen gespecialiseerd in de bebandeling van kinderen, zijn een zegen voor de ouders en leveren een niet te onderscbatten bijdrage aan bet genezingsproces van emstig zieke kinderen.

10. Non-stop vlucbten bestaan niet.

Bart Wanders Eindboven, 2 november 1993

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Automated capillary electrophoresis : instrumental and ...Automated capillary electrophoresis : instrumental and methodological aspects Citation for published version (APA): ... The