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MSc Analytical Chemistry - N. Blakiston 1
FACULTY OF SCIENCE
MSc DEGREE IN
Analytical Chemistry
Neil Blakiston
K0429387
Development of a Near-Infrared Spectroscopic Method for the
Determination of Water in Prozac
September 2007Supervisor: Dr R. J. Singer
MSc Analytical Chemistry - N. Blakiston 2
1. Acknowledgements
First, I would like to thank my Supervisors, Dr Richard Singer and Dr Steve Barton
whose support, encouragement and feedback on both practical and written work was
invaluable to the completion of this thesis.
My gratitude also goes to Amanda Hibberd for her assistance and training on the NIR
spectrophotometer, to Jim Martindale, Ian Anderson, Yousuf Jeetoo, Ian Ross, Andy
Firth, Russell Freeman and Teresa Coyle for their review and input to the technical
reports and authorisation of the practical work.
I am also grateful to Eli Lilly & Co. Ltd. for their financial support and Jon Marks
whom supported my application, gave encouragement and technical guidance.
Finally, I would like to thank my family, especially my wife Jane, daughters
Hermione and Hannah for their continued encouragement and patience throughout all
of my studies.
MSc Analytical Chemistry - N. Blakiston 3
2. Abstract
The application of near-infrared (NIR) spectroscopy as an alternative analytical
procedure for analysis has been a controversial subject with both the analytical
community and regulatory authorities. This is due, in part, to the belief that the
International Conference on Harmonisation (ICH) Guidelines on the Validation of
Analytical Procedures could not be applied to NIR spectroscopy.
In this study Prozac was chosen to determine if NIR spectroscopy could replace the
more time consuming, costly and hazardous method of water determination by
volumetric Karl Fischer Titration (reference method).
This work demonstrated that a NIR method for determination of water was not only
feasible, but with only a few data points could produce results statistically comparable
to the existing method. During investigation the reference method required
development due to its unforeseen poor performance.
The guidelines of the International Conference on Harmonisation (ICH) for the
submission requirements of the pharmaceutical industry regulatory approval are also
discussed in relation to NIR methods.
The method proved suitable for the measurement of water concentrations from
approximately 2 to 9 % H2O with a maximum absolute error of 1.2 % H2O from the
average replicate value of the reference method. This is of a similar magnitude to the
error between replicate analysis by the reference method.
MSc Analytical Chemistry - N. Blakiston 4
Contents Page
FACULTY OF SCIENCE..............................................................................................11. Acknowledgements................................................................................................22. Abstract..................................................................................................................3Contents Page.................................................................................................................43. Contents of Figures................................................................................................74. Contents of Tables and Attachments......................................................................85. Abbreviations.........................................................................................................96. General Introduction............................................................................................10
6.1. Aims and Objectives....................................................................................106.2. Background..................................................................................................106.3. Water Determination....................................................................................12
6.3.1. Definition:............................................................................................126.3.2. Methods for the determination of water...............................................13
7. Choosing a Suitable Method................................................................................167.1. The Product of Choice.................................................................................167.2. The Method of Choice.................................................................................17
8. Introduction to the drug product PROZAC®........................................................189. Sample Source......................................................................................................2010. Introduction to Karl Fischer Volumetric Titration...........................................21
10.1. Karl Fischer Titration - History and Development..................................2110.2. Conditions for Karl Fischer Titration.......................................................2510.3. Hydranal® Composite 5 Titrant................................................................2610.4. Solvent......................................................................................................2710.5. pH.............................................................................................................2810.6. Standard Sodium Tartrate Dihydrate.......................................................2910.7. Automated Analysis.................................................................................2910.8. Orion Turbo2TM Volumetric Karl Fisher Titrator.....................................3110.9. Limitations of KF.....................................................................................34
11. An introduction to Near-infrared spectroscopy................................................3611.1. NIR Spectroscopy - History and Development........................................3611.2. Theory......................................................................................................3811.3. Instrumentation........................................................................................4411.4. Mathematical Pre-Treatment and Processing...........................................4611.5. Bruker Opus NIR Spectroscopic Quant-Software...................................4611.6. Theoretical Background...........................................................................4711.7. Mulitvariate Calibration...........................................................................4811.8. Cross Validation.......................................................................................4911.9. Processing the Data..................................................................................50
12. Experimental Analysis Karl Fischer................................................................5212.1. Chemical Safety (CoSHH, Risk and MSDS)...........................................5212.2. Sample Preparation..................................................................................5312.3. Loss on Drying Determination.................................................................5412.4. Good Manufacturing Practice (GMP) Requirements...............................5412.5. Execution of Technical Reports...............................................................55
12.5.1. QCL-TR-014: Loss on Drying of Prozac Powder Blend.....................5512.5.2. QCL-TR-015: Water Determination on Dried Prozac Powder Blend by Karl Fischer Titration and Near-Infrared.............................................................56
MSc Analytical Chemistry - N. Blakiston 5
12.6. Investigation of Failed Execution: QCL-TR-015....................................5612.7. Review of method AP1043-01.................................................................59
12.7.1. Performance checks.............................................................................6012.7.2. Review of Site Certification Report B07766.......................................6112.7.3. Influence of solvent on the Karl Fischer reaction................................6212.7.4. Sample Transfer to the Karl Fischer Instrument..................................6212.7.5. QCL-TR-016: Water Determination on Dried Prozac Powder Blend by Karl Fischer Titration and Near-Infrared II.........................................................64
12.8. Evaluation of Karl Fischer Data...............................................................6512.8.1. Data Acceptance Criteria.....................................................................6512.8.2. Review of Variance between Replicates..............................................6912.8.3. Background Sample Analysis..............................................................6912.8.4. Summary..............................................................................................70
13. Experimental Analysis NIR.............................................................................7013.1. Performance Calibration check................................................................7113.2. Sample Analysis.......................................................................................7213.3. Calibration Models...................................................................................7213.4. Mathematical Pre-treatment and Processing............................................7313.5. Calibration of Data Set R1.......................................................................7413.6. Calibration of Data Set R2.......................................................................7513.7. Calibration of Data Set R3.......................................................................7613.8. Comparison of R1, R2 and R3.................................................................7713.9. Repeatability Five Replicates - Between Methods..................................81
13.9.1. Repeatability between methods............................................................8113.9.2. Review of water replicates by KF........................................................8213.9.3. Review of water replicates by NIR......................................................8213.9.4. Comparison of variance between methods..........................................83
13.10. Repeatability 16 Replicates - Within Method..........................................9013.10.1. Repeatability NIR measurements.....................................................9013.10.2. ANOVA on 16 Replicates................................................................9213.10.3. Outlier Check for Repeatability.......................................................9313.10.5. Review of Residuals for Repeatability.............................................95
14. Discussion of Results.....................................................................................10014.1 Further Discussion on Results....................................................................103
14.1.1 Cost....................................................................................................10314.1.2 Speed..................................................................................................10414.1.3 Non-destructive..................................................................................10514.1.4 Containment and Safety.....................................................................106
15 Conclusion......................................................................................................10816 A Review of Method Validation....................................................................110
16.1 Specificity..................................................................................................11116.2 Linearity.....................................................................................................11116.3 Range..........................................................................................................11116.4 Accuracy....................................................................................................11116.5 Precision.....................................................................................................11116.6 Repeatability..............................................................................................11216.7 Intermediate Precision................................................................................11216.8 Reproducibility...........................................................................................11216.9 Detection Limit..........................................................................................11216.10 Quantitation Limit..................................................................................113
MSc Analytical Chemistry - N. Blakiston 6
16.11 Robustness/Ruggedness.........................................................................11316.12 System Suitability Testing.....................................................................113
17 Reference Review..........................................................................................11817.1 Applications of NIR Spectroscopy.............................................................118
17.1.1 Qualitative Determinations................................................................11817.1.2 Quantitative Determinations..............................................................121
18 Brief History of Water Analysis by NIRS.....................................................12419 Future Work...................................................................................................12720 Reference to Raw Data...................................................................................128
20.1 Bench Books..............................................................................................12820.2 Analytical Data Wallet...............................................................................128
21 References......................................................................................................129
MSc Analytical Chemistry - N. Blakiston 7
3. Contents of Figures
Figure 4.3.1 The various forms of water storage..............................................13Figure 8.1 Three representations of the structure for fluoxetine;....................19Figure 9.1 MG2 Supermatic Capsule Filling Machine.....................................20Figure 10.5 Departure of the reaction rate constant K on the pH;....................28Figure 11.1a Diagram of the electromagnetic spectrum...................................37Figure 11.1b Full Electromagnetic Spectrum19..................................................37Figure 11.2a Vibrational energy levels for a diatomic molecule38....................40Figure 10.2b Representation of different modes of bond vibration;................41Figure 11.3a Diagram of diffuse reflectance and transmittance;.....................45Figure 12.8.2. Replicate Variance;........................................................................69Figure 13.3a An overlay of all 41 sample spectra;.............................................72Figure 13.3b Primary water peak at 5180cm-1 (combination band).................73Figure 13.8a Calibration plot for Model R1......................................................77Figure 13.8b Calibration and Validation plot for Model R2............................78Figure 13.8c Calibration and Validation plot for Model R3............................78Figure 13.10.4 Frequency plots for calibration model R1, R2 and R3...........94Figure 13.10.5 Matched pair plots for calibration model R1, R2 and R3......97
MSc Analytical Chemistry - N. Blakiston 8
4. Contents of Tables and Attachments
Table 12.7.2 Site Certification of Method B07766...........................................61Table 12.8a Karl Fischer Results: Method acceptance criteria..........................67Table 12.8b Karl Fischer Results: Background Titre......................................68Table 13.8 Comparison of calibration models R2 and R3................................79Table 13.9.1 Repeatability data for KF and NIR comparison.........................81Table 13.9a F-test between KF and R1 for five replicate results........................84Table 13.9b F-test between KF and R2 for five replicate results....................84Table 13.9c F-test between KF and R3 for five replicate results........................84Table 13.9d t-test between KF and R1 for five replicate results.....................86Table 13.9e t-test between KF and R2 for five replicate results.........................86Table 13.9f ANOVA: Single Factor......................................................................87Table 13.10a Predicted NIR values Vs KF - Repeatability...............................91Table 13.10b ANOVA: Single Factor - R1:R2:R3.............................................92Table 13.10c ANOVA: Single Factor - R2:R3....................................................93Table 13.10c Dixon’s Q-test applied to NIR repeatability study......................93Table 13.10d Residual analysis for predicted versus true for NIR...................96Attachment 1: Tabulated Overview moisture determination methods 30...........132Attachment 2: Orion Turbo2TM Volumetric Karl Fisher Titrator......................133Attachment 3: Bruker PQ Test Protocol...............................................................134Attachment 4: Pay-Off Matrix for Water Determination A................................135Attachment 5: Pay-Off Matrix for Water Determination B................................136Attachment 6: Pay-Off Matrix for Water Determination C................................137Attachment 7: Bruker MPA FT-NIR Default Parameter Settings.....................138Attachment 8: Equipment.......................................................................................138Attachment 8: Equipment.......................................................................................139Attachment 9: Method AP-1043-01........................................................................140Attachment 10: Technical Protocols.......................................................................141
MSc Analytical Chemistry - N. Blakiston 9
5. Abbreviations
ANOVA Analysis of VarianceAPI Active Pharmaceutical IngredientBP British PharmacopoeiacGMP Current Cood Manufacturing PracticeCV Coefficient of VariationFT Fourier-transformHPLC High Performance Liquid ChromatographyICH International Conference on HarmonisationIR InfraredKF Karl FischerMLR Multiple Linear RegressionNIR Near-infraredPASG Pharmaceutical Analytical Sciences GroupPCA Principal Components AnalysisPhEur European PharmacopoeiaPLS Partial Least SquaresPLSR Partial Least Squares RegressionQCL Quality Control Laboratory(s)R2 Multiple Correlation Coefficient %RSD Percentage Residual Standard DeviationRSEP Relative Standard Error of Prediction RSS Residual Sum of Squaress Standard Deviationse Standard ErrorSEC Standard Error of CalibrationSEE Standard Error of EstimationSEP Standard Error of Prediction%SEP Percentage Standard Error of Prediction SNV Standard Normal VariateTR Technical ReportUSP United States PharmacopeiaUV Ultravioletµ Mean of the Population σ Standard Deviation of the Population
MSc Analytical Chemistry - N. Blakiston 10
6. General Introduction
6.1. Aims and Objectives
The primary aim of this thesis was to conduct a feasibility study, investigating the use
of near-infrared (NIR) spectroscopy as a replacement analytical technique for Karl
Fischer volumetric analysis, for the determination of water in pharmaceutical product.
To achieve this end goal a suitable multicomponent material was required, which
would have suitable range of water contents, so as to produce a calibration set for the
NIR software to manipulate. The objective was to apply multivariate and
chemometeric tools to produce a good fit calibration from which the precision and
accuracy of the NIR method could be assessed.
6.2. Background
Water can have an adverse effect to the stability profile of an active pharmaceutical
product, through degradation of the sample over time. Both the physical and
chemical properties may be altered, which could influence the safety, strength,
quality, performance and shelf life of the manufactured product. An example of a
chemical change due to the quantity of water present in a sample is the increased rate
of degradation pathways which could lead to raised levels of related substances
(degradation products) above regulated limits. A physical example would be the
change to friability of a tablet or reduced performance of gelatine capsules.
The impact of these changes may result in a costly product recall, which may lead to
customer, share holder and inspectorate loss of faith.
MSc Analytical Chemistry - N. Blakiston 11
To support the regulatory commitment of product release and to demonstrate shelf life
stability various analytical techniques are conducted on the pharmaceutical product.
These include both in-process monitoring and finished product analysis.
Water determination on finished product within Eli Lilly and company limited is
routinely conducted by Karl Fischer volumetric determination. This analytical
technique is destructive and time consuming. It also requires the use of various
organic solvents. Due to the nature of these solvents the analysis requires fume
cupboard or bench extraction and appropriate containment.
A risk of skin contact with the solvents used presents a hazard to the analyst, hence
Personal Protective Equipment (PPE) must be worn for analysis at all times. In
implementing any risk reduction to a process containment and PPE should be the last
consideration according to industry hierarchy. Continued use of even the most hyper-
allergenic nitrile gloves poses its own health issues and can cause irritation and
sensitisation. Eliminating the risk or re-engineering the process should be the first
consideration.
NIR spectroscopy offers a safe alternative where, solvents and reagents are eliminated
and samples require no significant preparation. NIR offers a non-destructive analysis,
so the sample can be stored or used for further analysis and no waste is generated
other than the sample.
MSc Analytical Chemistry - N. Blakiston 12
6.3. Water Determination
6.3.1. Definition:
“The moisture contained in a material comprises all those substances which vaporize
on heating and lead to weight loss of the sample... According to this definition,
moisture content includes not only water, but also other mass losses such as
evaporating organic solvents…, aromatic components, as well as decomposition and
combustion products” 30.
Within the industry the term moisture and water are often interchangeable, even if not
correct. This is evident within Eli Lilly as many global analytical methods are titled
“Moisture determination by Karl Fischer…”
There are several different types of moisture
Free Moisture; Readily available, dissolved, homo- or heterogeneous e.g.
perfume, beverages or solvents.
Surface Moisture; Readily available, covers surface of solids, heterogeneous
e.g. sugar, polymers.
Trapped Moisture; this must be liberated for analysis, heterogeneous e.g. cells,
food products, polymers.
Capillary Moisture; Must be liberated for analysis, heterogeneous e.g. soil,
rocks, concrete.
Water of Crystallisation; chemically bound, homogeneous e.g. soil, rocks,
concrete.
MSc Analytical Chemistry - N. Blakiston 13
Figure 4.3.1 The various forms of water storage
6.3.2. Methods for the determination of water
Thermogravimetric methods
Drying oven with balance
IR drying and direct weighing
Microwave drying and direct weighing
Halogen drying and direct weighing
Thermal Gravimetric Analysis
Azeotropic distillation and weighing
Phosphorous Pentoxide P2O5 method
Spectroscopic Methods
Near Infrared spectroscopy
Microwave spectroscopy
NMR
Chemical/Other
MSc Analytical Chemistry - N. Blakiston 14
Karl Fischer
Calcium carbide method
Density determination
Refractory determination
Conductivity
Gas Chromatography
Distillation
Each method has its own advantages and disadvantages and each will be discussed in
turn.
Gravimetric methods
The methods used for drying the sample to a predetermined or constant weight are
only suitable if the chemistry of the sample is known, as volatisation will remove not
only water, but other volatiles present. There is also the problem of thermal
decomposition of the sample. The samples required are generally large for most
drying techniques, which give a good composite result, but not suitable if the sample
size is small. Although analysis time can be long, for example oven drying times can
be several hours or even days, sample handling can be quite large depending on oven
size. Halogen, infrared, and microwave offer benefits as they do not suffer from
lengthy analysis times.
Thermogravimetric analysis suffers from only being able to handle small sample
quantities, so is generally not suitable in the analysis of, for example, a batch of
pharmaceutical product. The analysis tends to be slow and has limited suitability for
MSc Analytical Chemistry - N. Blakiston 15
liquids, but a wide application for solid samples. These techniques are generally used
in the pharmaceutical industry for in-process manufacturing controls, for example
during wet granulation or use of fluid bed dryers, where a certain end point of
moisture content is required. They are also used in the analysis of excipient material
following pharmacopoeia15 methods.
Spectroscopic methods
These have fast analysis times, can be easily automated and can handle large sample
volumes. Microwave and NIR analysis both need to be calibrated to a specific
substance and suffer interference from particle size and bulk density variation. The
instruments require high capital investment, especially NMR and a significant amount
of knowledge and time is required to develop methods and manipulate the data.
Chemical/Other
Karl Fischer has a wide application and is relatively cheap, but can not be automated
and analysis times vary depending on the substance analysed. The coulometric
method deals with trace analysis, while the volumetric deals with water contents of 1-
100%.
Refractometry and density measurement is a fast analysis technique, requires little
training and is highly mobile, but is suitable only for clearly defined samples.
The calcium carbide method is favourably priced, but is prone to forming explosive
materials and requires specialist training and relevant safety precautions. Gas
Chromatography is suitable for multicomponent analysis, but requires specialist
MSc Analytical Chemistry - N. Blakiston 16
knowledge in development and in daily operation. Automation proves useful for
large sample volumes, but set up time is lengthy if only one sample is required.
Attachment 1 gives a tabulated overview of the typical measurement range, accuracy
and water selectivity of a range of analytical techniques. Attachment 4, 5 and 6
indicates a pay-off matrix for the various techniques.
7. Choosing a Suitable Method
7.1. The Product of Choice
As this was my first introduction to NIR analysis it was suggested that a product with
few ingredients should be chosen. This would reduce the complexity of any spectra
obtained and should reduce any interferences with the water band(s) of interest.
The product chosen would need to present a range of water contents that could be
determined to construct a calibration set for the NIR software or it needed to be in a
form that could be processed in the laboratory to give the same. Any result generated
as part of this product could not jeopardise the release of product from the site or any
batch on stability.
The product that met these requirements was Prozac. Prozac has a relatively simple
manufacturing recipe containing just Fluoxetine Hydrochloride, Starch Flowable and
Silicone fluid (350 centistokes) 0.96%. The active ingredient represents
approximately 10%, by weight, in the bulk powder. The powder blend is filled into
gelatine capsules with a running weight of approximately 230mg to give the
equivalent of 20mg active fluoxetine as the free base.
MSc Analytical Chemistry - N. Blakiston 17
Prozac is manufactured by Basingstoke Manufacturing Operations, Eli Lilly and Co
Ltd under licence.
As Prozac is a granular/powder blend it was suitable for drying or doping to give
various concentrations of water content. The sample could also be collected in such a
way or adulterated by environmental conditions so that it would not represent any
released material.
The batch of Prozac powder blend used in this thesis was dispensed on the 30th May
2007 and the product was released by qualified person approval on the 13th June 2007.
The batch size was approximately 1.5million doses and review of the batch
documentation indicated no processing issues or any laboratory test result failures.
7.2. The Method of Choice
Due to the very stable nature of Prozac in the presence of water this product does not
have any regulatory release limits for water content. Interrogation of the Laboratory
Information Management System (LIMS) revealed that Global Method B07766
(Turbo2TM Karl Fischer Assay of Water in Fluoxetine Hydrochloride Capsules
Equivalent to 10 and 20 mg Fluoxetine) had been developed for the measurement of
water in Prozac formulations. This method had been used on the Basingstoke site for
the analysis of stability samples for a short period and is no longer performed. Global
Method B07766 was imbedded into Basingstoke Analytical Procedure (AP) AP1043-
01 (see Attachment 9).
MSc Analytical Chemistry - N. Blakiston 18
8. Introduction to the drug product PROZAC®
In a healthy individual serotonin is released, in the body, into the space between the
"sending" nerve cell and the "receiving" nerve cell. When serotonin is received on the
surface of the "receiving" cell, it stimulates or activates serotonin receptors.
Stimulation of these receptors generates an impulse and allows messages to move
forward.
When a person suffers from depression, there may be a problem with the balance of
the serotonin system. It is thought that this imbalance occurs when serotonin is
released from the "sending" nerve cell and is reabsorbed by an uptake pump. By
blocking the serotonin uptake pump, Prozac increases the amount of active serotonin
that can be delivered to the "receiving" nerve cell. This helps message transmission
return to normal.
Pharmacotherapy is currently the only proven method for treating major depressive
disorder and Prozac (active ingredient - Fluoxetine hydrochloride (HCl) Figure 8.1) is
one of the world's most widely prescribed antidepressants; it has been prescribed for
more than 54 million people worldwide.
Prozac is approved by the FDA (Food and Drugs Administration) for the treatment of
the following disorders;
Major Depressive Disorder, Obsessive-Compulsive Disorder, Bulimia
Nervosa and Panic Disorder in adults.
Major Depressive Disorder and Obsessive-Compulsive Disorder in paediatric
patients (children and adolescents).
MSc Analytical Chemistry - N. Blakiston 19
Prozac was a first-in-class antidepressant, which lost U.S. market exclusivity in 2001
and experienced huge generic competition.
Unlike other treatments in this field Prozac is not associated with withdrawal
symptoms of a somatic or psychological nature and has a relatively benign side-effect
profile.
Figure 8.1 Three representations of the structure for fluoxetine;
The active ingredient in Prozac;
(N-Methyl-g-[4-trifluoromethyl)phenoxy]benzenepropanamine).
MSc Analytical Chemistry - N. Blakiston 20
9. Sample Source
The sample was taken from the powder bed of an Italian MG2 Supermatic Capsule
Filling Machine (Figure 9.1) capable of filling up to a 60,000 capsules per hour.
Figure 9.1 MG2 Supermatic Capsule Filling Machine.
This is a high performance encapsulation machine, which consists of a capsule-
containing hopper that delivers capsules into feeding tubes, having vertical motion
and an orienting drum. The capsules are separated, filled and rejoined before passing
a metal check and de-duster area. Finally they are air fed into fibre board drums
ready for packaging. At the end of the operation the filling bed is separated and
several kilograms of powder blend are left behind, which is removed as waste.
This material was collected and placed inside a double lined plastic bag. The sample
contained both powder blend and waste gelatine capsules. To prepare the sample for
analysis the powder was poured into a shallow dish and the individual capsule shells
were removed with tweezers.
Powder Bed
Capsule gravity feed
Capsule delivery tubes
MSc Analytical Chemistry - N. Blakiston 21
This was not an ideal pure sample as it was not possible to remove all of the capsule
fragments. These fragments were present in a relatively small quantity as most
capsule shells were intact. It is not known if this has had any impact on the course of
the analysis carried out.
The sample was divided into several HDPE screw cap bottles, which provide a
relatively good barrier against water ingress.
10. Introduction to Karl Fischer Volumetric Titration
10.1. Karl Fischer Titration - History and Development
When required to determine the water content in an organic compound, considerations
of heat stability, volatility and the time factor involved make it difficult or impossible
to apply physical test methods.
In 1935 Karl Fischer initiated a new method for the determination of water. He
considered that the reaction involving iodine, sulphur dioxide and water, originally
investigated by Bunsen1 and later applied to the determination of sulphur dioxide in
flue gases, might form the basis for the estimation of small quantities of water.
The reaction may be represented by;
I2 + SO2 + 2H2O ↔ 2HI + H2SO4 (1)
MSc Analytical Chemistry - N. Blakiston 22
Karl Fisher dissolved the iodine (0.33 mol/l) and sulphur dioxide (0.50 mol/l) in
anhydrous methanol and added pyridine (1.67 mol/l) to displace the reaction to the
right by removing the acidic products of the reaction; he assumed, at the time, this had
no bearing on the course of the reaction, so that one mole of iodine would be
equivalent to two moles of water.
In the presence of a suitable base, the reaction consumes two molecules of water for
one molecule of iodine (Equation 1) and therefore there is a direct relationship
between water present and the consumption of iodine. Smith, et al.2 however, showed
that his assumption was unjustified, since both the pyridine and the methanol take
active part in the reaction process. In its simplest form they represented the reaction
as follows;
(2)
(3)
Therefore each molecule of water is equivalent to one molecule of iodine. This has
been universally accepted within the analytical community.
The early Karl Fischer reagents were not completely stable, due to the hygroscopic
nature of the constituents and other reactions, which take place even in the absence of
water. An established early procedure for reagent preparation was detailed in the
European Pharmacopoeia3 as follows;
MSc Analytical Chemistry - N. Blakiston 23
“In a 750 ml combustion flask mix 400 ml of anhydrous methanol and 80 g of
anhydrous pyridine. Immerse the flask in ice and bubble dried sulphur dioxide
through the mixture until the weight of the flask and contents has increased by 20 g.
Add 45 g iodine and shake the flask until solution is complete. Allow the solution to
stand for 24 hours before use”.
Several references suggest adding stabilising agents to the Karl Fisher titrant, such as
pyridinium iodide4 to absorb the hydrogen iodide and sulphuric acid formed; sodium
and zinc acetates5 can be added to eliminate side reactions, while addition of bromine6
allowed determination, iodometrically, of iodic acid from the oxidation of any
hydrogen iodide. The methanol present in the reagent can also promote side reactions
with the sample, which can increase its instability. Early reagents often substituted
the methanol with other more stable constituents’; 2-methoxy-ethanol7,
dimethylformamide8 and ethanediol9. Although methanol is routinely used as the
sample solvent care must be taken that unwanted side reactions do not occur with the
sample. For example; carbonyls, particularly aldehydes and ketones often need to be
converted into the cyanohydrins2 to avoid interfering reactions and the production of
water.
Two classes of compound therefore give anomalous results when titrated with Karl
Fischer reagent;
1. Those reacting with the iodine-sulphur dioxide portion of the reagent, which
include; ascorbic acid, quinols, per-acids, diacyl peroxides (and other
MSc Analytical Chemistry - N. Blakiston 24
oxidising agents), amines, mercaptans (thiols and other reducing agents) and
easily oxidised substances.
2. Those reacting with the components of the reagent, which result in water
formation. These include formic and acetic acids, which slowly form esters
with methanol in the reagent, basic oxides and salts of weak oxy-acids18. The
lower aldehydes and ketones similarly can form acetals and ketals7.
Modern instrumentation performs analysis at a rate, which reduces the extent of these
unwanted reactions. However if a potential problem exists, then alternative reagent
components and sample solvents may be required. For example British Standard
2511 :1970 recommends a modified reagent and pyridine-methanol solvent for the
determination of water in ketones.
The early Karl Fischer reagents also suffered from rapid decay, especially in sunlight,
which lead to erroneous results. The unpleasant smell and high toxicity of pyridine
opened the way to replace this undesirable component with a more suitable base, with
at least the same or better role in the reaction process.
Dr Eugen Scholz12/13 led development of the method in 1979 to eliminate pyridine as
the base. Investigation involved testing amines with a higher basicity and greater
affinity for methyl sulfite. Aliphatic amines and several other heterocyclic
compounds proved suitable replacements. However the base of choice proved to be
imidazole (C3H4N2), which shifts the equilibrium completely to the right, producing a
rapid reaction speed and stable end point (1980). Imidazole is the main base
component in Hydranal® Composite 5 KF reagent as supplied by Riedel-deHaën.
MSc Analytical Chemistry - N. Blakiston 25
With the development of new reagents, the stoichiometry of the reaction required
verification. It was found that earlier equations did not fully explain the course
previously described. Scholz and his team published the following accepted
representation;
CH3OH + SO2 + RN [RNH]SO3CH3 (4)
Intermediate Methylsulfite
H2O + I2 + [RNH]SO3CH3 + 2RN [RNH]SO4CH3 + 2[RNH]I (5)
Oxidation-Reduction step (rapid) RN=base
Sulphur dioxide reacts with methanol to form an ester, which is neutralised by the
base. The anion of the alkyl sulphorus acid is present in the KF reagent and is the
reactive component. The oxidation of the alkyl sulfite anion to alkyl sulphate by the
iodine consumes water in a 1:1 ratio with iodine.
10.2. Conditions for Karl Fischer Titration
Normally the water under analysis is not pure, but fixed onto a certain matrix and
certain conditions must be met for the analysis to proceed;
The substance must dissolve in a suitable solvent or readily give up its water.
It must not change the optimum working pH.
Side reactions with the KF components must not take place.
MSc Analytical Chemistry - N. Blakiston 26
It is not always possible for these criteria to be met and therefore experimental
modification may be required. For the Analytical Procedure (AP) used in this thesis
certain changes to the basic Karl Fischer method have been employed to aid the speed
of titration, clarity of end point and release of water from the matrix. These are
discussed in turn below;
10.3. Hydranal® Composite 5 Titrant
Pyridine in the original Karl Fischer titrant is present to buffer the reaction process,
but is not present as a true reactant. The rate of the reaction is dependant on the base
chosen as it neutralises the intermediate CH3SO3H. The base activity rate of pyridine
is very slow and the end point is unstable due to its weak basicity, which can not
neutralise completely the methyl sulphurous acid. This can lead to poor repeatability;
hence pyridine can be replaced by bases which are superior for the application.
The Hydranal® Composite titrant is a one component reagent containing all of the
required elements for the reaction (imidazole, sulphur dioxide and iodine dissolved in
diethylene glycol monoethyl ether). It has been proven to reduce titration speed from
approximately 10 minutes, using conventional pyridine reagent, to about 4 minutes.
A feature of the Karl Fischer titration is the improved stability of the end point as the
time required for titration is shortened (Figure 10.3.).
MSc Analytical Chemistry - N. Blakiston 27
Figure 10.3 The course of three titrations of 40 mg water; Using; A conventional one component system (C), Hydranal Composite 5 one component reagent (B), and Hydranal two component reagent (solvent and titrant) (A).
(Reidel-deHaën)11
The titrant has been adjusted to give a titre of about 5 mg H2O/ml and the minimum
quantity of water detectable is approximately 0.5 mg. The titrant has a shelf life of
two years and a low decay rate of approximately 0.2 mg H2O/ml per year.
10.4. Solvent
The solvent chosen for application must be able to dissolve the products of the
titration reaction, for this purpose methanol is the most frequently used solvent. The
solvent must give complete esterification of the sulphur dioxide (equations (4) and
(5)) to ensure a stoichiometric course for the KF reaction. When using methanol in
the working medium the concentration must not fall below 25%, otherwise the end
point can be shifted.
Formamide improves the solubility of polar substances and readily mixes with
methanol (the methanol content of the solvent should be greater than 50%) and is
good for the detection of water in proteins, carbohydrates and inorganic salts. It is
especially good in aiding the release of water bound in such matrix as lactose and
starch, and increases the course of the reaction. Formamide requires special
precaution when handling as it has shown to have teratogenic action, so women of
child bearing age must avoid inhalation and skin contact.
MSc Analytical Chemistry - N. Blakiston 28
10.5. pH
Karl Fischer has an optimum pH range of 4-7 (Figure 10.5), in which the reaction
runs quickly and stoichiometrically. At high pH side reactions are known to consume
iodine slowly and an end point reversal is observed. In strong acid conditions the rate
constant of the Karl Fischer reaction decreases, hence the course of the titration is
slower.
Figure 10.5 Departure of the
reaction rate constant K on the
pH;
according to Verhof and Barendrecht 14
To balance any shift in the pH the addition of a weak acid or base is suitable to
neutralise the medium. An alternative approach is to add a buffering reagent to the
working medium. For method AP-1043-01 a 2% Hydranal buffer in methanol,
combined 50/50 v/v with Formamide is stated for the solvent medium. Review of the
method, chemistry and method validation indicates the reason for buffering the
solution, is due to the N-H group on the Fluoxetine API (Active Pharmaceutical
Ingredient) and the slight basicity of the starch excipient. The buffer is also available
as a base or acid reagent. However the method does not indicate which should be
used. As other methods in the laboratory use the Hydranal acid buffer, this was
chosen as the buffering reagent. The benefit of the buffer could be further
investigated through comparison of pH measurements during a titration using the
solvent, as prepared by the method in question, versus one run using 50/50 v/v
formamide and methanol.
MSc Analytical Chemistry - N. Blakiston 29
10.6. Standard Sodium Tartrate Dihydrate
Sodium tartrate forms a dehydrate, which is stable under normal conditions, does not
effloresce or absorb moisture. It therefore maintains a relatively constant water
content of 15.66%. It is used in KF determination for calibration or standardisation
purposes. However prior to use a loss on drying check may be periodically required
to ensure that no change of water content has occurred and also requires storage in a
desiccator. Due to any variability in its water content, a much cheaper reagent is now
routinely used, this being deionised water. This allows direct calibration at the level
of interest and can be administered quickly and directly into the titration vessel by
micro syringe.
10.7. Automated Analysis
Volumetric analysis is used to determine relatively high concentrations of water and
can be conducted using a single or two component titration system. The end point in
modern equipment is determined when either a defined potential (bipotentiometric
indication), or current (biamperometric indication) is not only reached, but remains
stable for a period of time. This is termed the “dead stop” method10 based on zero
current flow between two polarised electrodes enclosed in the titration vessel and is
employed with the Orion Turbo2TM Karl Fischer (as discussed below). An alternative
approach is the ‘drift stop’ method where the end-point is determined by a pre-
determined drift value measured in the titration cell. The measurement is detected by
changes across two platinum electrodes produced by the first stable appearance of free
iodine. As long as the added iodine reacts with the water present a high voltage is
required to maintain the specified polarization current across the electrodes. As free
MSc Analytical Chemistry - N. Blakiston 30
iodine becomes available it causes ionic conduction and the voltage is reduced to keep
the current constant. When the voltage drops below a defined value the titration is
terminated.
One of the highest sources of error in water determination is due to the ingress of
atmospheric moisture. For example one litre of air may contain about 20 mg of water
and to put this into perspective, one drop of Hydranal titrant (0.01 ml) corresponds to
0.05 mg of water, which is equivalent to 2.5 ml of extraneous air. For this reason the
stock reagents and vessel need adequate protection from the atmosphere. This is
provided by suitable seals or grease, which must be regularly checked and replaced as
needed. Generally, control of further water ingress is by a suitable hygroscopic
molecular sieve (drying tube) through which air is drawn to relieve any pressure
differential.
The sample addition of powder is usually conducted via back weighing addition from
a ‘long-spout’ glass weigh boat directly to the Karl Fischer cell. The sample transfer
process takes approximately 10 seconds, for the ‘Turbo2’, through a small stoppered
aperture; hence the exposure of the cell to the atmosphere is kept to a minimum.
The sample size is estimated according to the amount of water content in the sample.
Method AP-1043-01 gives guidance to the amount of sample required, which will be
discussed later. Literature values10 state that a sample size of approximately 500mg
should be taken if the water content is 10%.
MSc Analytical Chemistry - N. Blakiston 31
The results of the analysis are calculated from the consumption of reagent used in ml
(a), the water equivalency of the reagent in mg H2O/ml (WE) and the weight of the
sample in g (e), so that;
mg H2O = a . WE (6)
and
% H2O = a . WE (7) 10 . e
10.8. Orion Turbo2TM Volumetric Karl Fisher Titrator
The Turbo2 titrator combines microprocessor technology with an efficient in-built
multi-speed blender, capable of running at 7500rpm. This effectively homogenises
the sample, increasing the penetration of titrant and the release of bound water is
greatly improved. This function allows the handling of a greater range of sample
matrix eliminating the need to pre-grind samples, which can introduce moisture or
thermally degrade the sample. The titration vessel is large and can accommodate
about 750ml of solvent and sample, reducing the requirement to empty and refill. The
titrant is introduced with a high precision peristaltic pump system employing a stepper
drive motor with detachable head and tubing platen16. The reaction vessel has an
automatic vacuum waste extraction with a 3cm sample port, which is sealed with a
solid plastic stopper.
The Titrator is calibrated daily following local procedure; BPD-071003-007 (Orion
Turbo 2 Karl Fischer-operation of) and during analysis guidance in Analytical
Procedure; AP-1043-01 (Turbo2TM Karl Fischer Assay of Water in Fluoxetine
Hydrochloride Capsules Equivalent to 10 and 20 mg Fluoxetine) is followed.
MSc Analytical Chemistry - N. Blakiston 32
Calibration requires a consecutive calibration test using 25μl water injections to give
three results that fall within a 1.0% correlation variation (C.V.) limit. No more than
five injections are permitted to conduct an acceptable calibration. The results of three
acceptable calibration samples are used to calculate a calibration factor, which is
stored in the memory for determination of analytical samples. The instrument has a
literature precision of 0.5% at 25mg H2O.
The instrument has several programmable variables;
1. End-point Time: At the end of a titration, the addition of excess titrant will
temporarily drive the system into an over-titration situation from which it will
need time to recover. This feature provides a variable wait time before the
‘dead stop’ reading is taken. The end-point time can be set between 1-30
seconds with 10 seconds being the default.
2. End-point Level: This variable value (5-23 arbitrary units) changes the
conductance measured at the electrode. By varying the value the end-point
will be moved along the titration curve. By lowering the value the end-point
reaction will move to an over-titration condition.
3. Step Level: This has a direct relationship to the end-point level and has
arbitrary units of 1-18. The value determines the point along the titration
curve where the peristaltic dispenser slows down and enters a pulse mode until
the final end-point is reached. The relationship between end-point level and
MSc Analytical Chemistry - N. Blakiston 33
step level should be maintained and too low a level gives rapid titrations, but
precision may be lost.
4. Turbo Speed: The action of the blender head has three speed settings;
Speed 1: (approx 1000 rpm) is for liquid samples and fine powders.
Speed 2: (approx 3000 rpm) is for granules and viscous materials.
Speed 3: (approx 7500 rpm) is for breaking down solid materials.
5. Turbo time: The time period for homogenising prior to the start of titration.
6. Titration mode: There are four modes available for analysis; ‘Sample Only’,
where the sample is titrated directly after addition to the vessel. This is
generally used for fully miscible samples. ‘Background Sample’, used for
insoluble solids using high speed blending and a pre-titration to determine the
background water level. This is automatically subtracted from the sample
analysis. ‘Background - Sample - Background’, determines the moisture level
before and after analysis and the average is subtracted from the sample.
Finally the ‘Search’ mode allows analysis of samples with an unknown water
release profile. It will alternately blend and titrate until a set criterion is
reached. For example, a point at which the water content is lower than the
background, two consecutive results fail to increase the cumulative result by
1% H2O, a total of 30 minutes Turbo time elapses etc.
A schematic of the instrument can be seen in Attachment 2.
MSc Analytical Chemistry - N. Blakiston 34
10.9. Limitations of KF
1. As previously discussed the hydroscopicity of the reagents means that routine
standardisation/calibration is required. The period of which may be weekly or
daily dependant on operating procedures and instrument validation.
2. Changes in room temperature of the laboratory will cause fluctuations in the
titre, as the reagents contain organic solvents; they are more prone to
significant thermal expansion compared to aqueous solutions. Generally, a
temperature increase of 1˚C will result in a titre decrease of about 0.1%.
3. When conducting method development a sound knowledge of the chemistry,
nature of the matrix, available reagents, environmental parameters and
equipment operating conditions is an essential requirement.
4. The new reagents used in the Karl Fischer, although less harmful than the
older generation, still pose particular safety issues.
5. The main safety issue is that of containing the inhalation of volatile substances
and for this reason the technique should be undertaken in a fume cupboard or
under an extraction unit.
6. The biggest draw back is the lack of automation and the time it takes for each
analysis. Analysis times are generally between 5 to 20 minutes (some take
longer). This not only limits the analyst to the number of samples that can be
run in a day, but also involves a lot of non-value added time waiting for the
MSc Analytical Chemistry - N. Blakiston 35
titration to finish. The short wait period means it is very difficult to conduct
other activities between sampling.
7. The KF instrument requires connection to a balance or the balance weights
need to be manually entered. If the balance is connected, stability of the
balance may be a problem due to the flow of air from extraction. Manual
transfer of weights increase the time per analysis and could lead to
transcription errors.
8. The Karl Fischer technique is well contained, but a number of reagent
transfers do occur; the stopper from the sample port is removed each time a
sample is added and the vessel needs to be emptied, cleaned and refilled
routinely. All these actions lead to possible spillage and staining of equipment
with KF reagent. The drying tubes, seals, and delivery tubes need routine
maintenance and replacement, so ownership of the system is essential.
Near-infrared spectroscopy offers an attractive solutions to these limitations if a
viable method can be developed to give equivalent or better measurements compared
to the Karl Fischer technique of water determination.
MSc Analytical Chemistry - N. Blakiston 36
11. An introduction to Near-infrared spectroscopy
11.1. NIR Spectroscopy - History and Development
NIR radiation was first discovered by the English astronomer Sir William F Herschel
in 1800. His area of interest lay in identifying which colour of the visible spectrum
was responsible for the heat in sunlight (“Experiments on the Refrangibility of the
invisible rays of the Sun”). Using a prism he separated sunlight into its constituent
visible light spectrum and by means of a thermometer measured the temperature
associated with each colour. No discernible increase in temperature was observed
until the thermometer was placed just outside the red end of the spectrum. At this
location there was no light visible to the naked eye so Herschel named this region
infrared, the prefix being Latin for below.
Although some investigative work continued after this discovery (a full history of
works are reference by Workman37) there was no significant volume of work
published until the 1960’s, when a prolific series of papers were released. It was only
in 1973 when P. Williams reported the use of a commercial NIR grain analyser for
analyses of cereal products that the technique established a strong interest.
NIR is comprised of two sub-regions, 780 to 1100 nm and 1100 to 2500 nm, the
former being referred to as the Herschel region. This lies between the red end of the
visible spectrum and what is now called the mid infrared region (Fig 11.1a and 11.1b)
of the electromagnetic spectrum. This region was not widely investigated as a useful
analytical application due to scepticism arising from the apparent lack of sharp peaks,
MSc Analytical Chemistry - N. Blakiston 37
loss of baseline resolution and lack of sensitivity that is typically two or three orders
of magnitude less relative to the infrared region.
X-Ray UV VIS NIR Mid IR Radio Waves
200 380 780 2500 25000 nm
Figure 11.1a Diagram of the electromagnetic spectrum
Figure 11.1b Full Electromagnetic Spectrum19
Band assignments are also difficult due to the
numerous overtone and combinations of
vibrations observed in the mid infrared region.
The consequence of this is that NIR absorptions
are less intense, broader and more overlapping
than the parent absorptions.
Recently this initial reluctance to investigate
applications of NIR spectroscopy has been compensated, due to instrumental
breakthroughs that include developments in detectors; fast-scan and Fourier-transform
(FT) instruments, fibre-optics and increased mathematical processing capabilities of
modern computers. In addition, unique combination bands provide information not
available in the infrared region, and these reduced intensities allow for direct
measurements on undiluted samples.
MSc Analytical Chemistry - N. Blakiston 38
Although NIR spectroscopy is not useful for trace analysis, the benefits of NIR
spectroscopy are that it is rapid, non-destructive and in general, there is little or no
sample preparation required. NIR spectra can therefore be used to both identify and
quantify samples. NIR spectra also contain information relating to the physical
properties of the sample, such as particle size and compaction density which can be
measured with this technique. As an analytical technique, its penetration potential is
far greater than that of many other spectral techniques. This allows determination
deep into a bulk material and can even be conducted through outer packaging
components and manufacturing inspection windows.
11.2. Theory
Vibrational spectroscopy utilises the concept that atom-to-atom bonds within
molecules vibrate with frequencies according to the laws of physics. When these
molecular vibrators absorb light of a particular frequency, they are excited to a higher
energy level. In order to absorp infrared radiation, a molecule must undergo a net
change in dipole moment as a consequence to its vibrational or rotational motion.
Generally at room temperature most molecules are at the zero energy level so are
vibrating at the least energetic state allowed by quantum mechanics.
Infrared spectroscopy is used to investigate the molecular vibrational properties of a
sample by interpretation of the resultant absorption bands. The infrared radiation
absorbed by a molecule causes individual bonds to vibrate in a similar fashion to a
diatomic oscillator. Therefore, in the simplest vibrating model of a diatomic molecule
the molecule behaves as a harmonic oscillator and Hooke’s law is obeyed
(Equation (8)).
MSc Analytical Chemistry - N. Blakiston 39
F = ky (8)
Where F is the restoring force and is proportional to the displacement, k is the force
constant of a spring (equivalent to the measure of a diatomic molecule bond strength)
and y is the distance from the equilibrium position38. In this model, as the atoms are
displaced from the equilibrium position, the potential energy of the vibrating system
increases.
This results in a symmetrical parabolic curve about the equilibrium position or bond
length. This works well for the fundamental vibrations of simple diatomic molecules
and is not too far from the average value of a two-atom stretch within a polyatomic
molecule36.
In real molecules the electron withdrawing/donating properties of neighbouring atoms
and groups influence the bond strength and length and thus the frequency of the
diatomic bonds. The harmonic oscillator model therefore has limits similar to those
of a spring attached to two masses. As one mass approaches the other compression
forces are fighting against the bulk of the spring. As the spring stretches, it eventually
loses its shape and fails to return.
In molecules the electron clouds around two bound atoms, together with the nuclear
charges, limit the approach of the nuclei during the compression step. In addition, at
the extension of the stretch the bond eventually reaches breaking point when the
vibrational energy level reaches the dissociation energy. Therefore, in practise, a
better model for the potential energy in a diatomic model is the anharmonic oscillator
illustrated in Figure 11.2a.
MSc Analytical Chemistry - N. Blakiston 40
Figure 11.2a Vibrational energy levels for a diatomic molecule38.
As can be seen from this graphic representation, the energy levels in the anharmonic
oscillator are not equal, the levels being slightly closer as the energy increases. An
expression for the deviation away from the simple harmonic model is the ‘Morse
function’, which is an empirical equation where the allowed energy levels, E, for this
anharmonic oscillator are given by;
E = (v + 0.5) hf (v + 0.5)2 hfx (9)
Where h is Planck’s constant, v is the vibrational quantum number, f is the
equilibrium frequency of oscillation and x is the anharmonicity constant.
The vibrational quantum number is an integral value so the anharmonicity constant is
a measure of how the potential energy curve deviates from Hooke’s law. The
selection rules for the anharmonic oscillator are Δv = ± 1, ± 2, ± 3 etc. and therefore
the following transitions are allowed v1 ← 0, v2 ← 0, v3 ← 0, v2 ← 1 etc.
MSc Analytical Chemistry - N. Blakiston 41
In practice, only transitions starting at v = 0 are observed and these are referred to as
the fundamental transitions, first overtone, second overtone etc. respectively. These
transitions occur at frequencies, which approximate to f, 2f, 3f etc. due to the
anharmonicity constant being, for practical purposes, typically less than 5%36.
The fundamental transitions typically occur in the mid infrared region, while the
overtones bands which are 10 to 1000 times weaker than the fundamental vibrations
generally occur in the NIR region and this therefore is the basis of NIR spectroscopy.
In polyatomic molecules, there are different vibrational modes each of which has an
associated frequency and energy. These include symmetric stretching, asymmetric
stretching, scissoring, rocking, wagging and twisting as illustrated in Figure 11.2b.
Figure 10.2b Representation of different modes of bond vibration; the + sign
indicating the plane perpendicular to the page towards the reader and the ─ sign the
opposite. The principle fundamental vibration bands of water are at wavelengths
5150 cm-1 (Combination Band = Stretch + Scissoring ≈ 5352 cm-1), 3756 cm-1
MSc Analytical Chemistry - N. Blakiston 42
(Asymmetric Stretch) and 1596 cm-1 (Scissoring) 63. These bands are not only strong,
but are typically well resolved from absorption bands of other compounds. The
diffuse reflected intensity is measured, which is proportional (in a non-linear relation)
to the water concentration.
There is the potential for simultaneous changes in the energies of two or more
vibrational modes that results in a frequency that is the sum of the individual
frequencies, referred to as combination bands. These combination bands are typically
weak with a low probability of occurrence.
Typically the near infrared bands are broad and therefore spectra can be very difficult
to interpret with regards to specific chemical components. Multivariate (multiple
wavelength) calibration techniques are generally performed to clean the spectra and to
extract the chemical information required. This requires significant understanding in
the application of mathematical manipulations, such as principle component analysis
(PCA) and partial least squares (PLS) when building calibration sets.
To conduct analysis the instrument must be taught how to interpret spectra to build up
a calibration set. A calibration set is a set of numbers which convert the signal from
the detector into a predicted value of constants. The constants in the case of a NIR
instrument are assigned to each of the filters or grating slits, which are used to
conduct the scan. These applied constants are then multiplied by the absorbance
reading attained. The sum of these calculations is then plotted to give a representative
spectra. Initially a multiple linear regression is conducted (Equation (10)) which uses
various terms to describe a straight line;
MSc Analytical Chemistry - N. Blakiston 43
y = C0 + S(C1L1 + …+ CnLn) (10)
The term C0 is the intercept value and represents the S slope, the values for C are the
constants applied and have to be determined for each parameter that requires
measurement. The terms in the equation are log values and represent the output
information regarding the sample, where A is the apparent absorbance and is generally
plotted against the log function. The log values include the reflectance value R (the
amount of light reflected) and is described by;
A = log (1/R) (11)
These calculations are automatically applied by the instrument controller.
The most prominent absorption bands occurring in the NIR region are related to the
overtone and combination bands of the fundamental molecular vibrations of
-CH, -NH and -OH functional groups observed in the infrared spectral region.
Measurement is undertaken on the principle that the number of photons absorbed is
proportional to the concentration, but a strict adherence to Beer’s law is not the
general case when dealing with NIR spectra. This is not only due to matrix effects
such as particle size and compaction, but also to the complex nature of overlapping
bands in the spectra. A paper written by Miller (1993) 64 reviews a number of sources
that can cause non linearity in NIR methods including detectors, stray light and
chemical interactions.
MSc Analytical Chemistry - N. Blakiston 44
A good overview of NIR spectroscopy can be read in the paper by Morisseau et al,
(1995) 65.
11.3. Instrumentation
NIR radiation can be measured by either reflectance (R) or transmittance (T).
Reflectance is the ratio of the intensity of the radiation reflected by the sample (Ir) to
the intensity of the incident radiation (I0), equation (12). The instrument was used in
Diffuse Reflectance Mode for sample analysis of the powder blend samples.
R = Ir / I0 (12)
In diffuse reflectance, the radiation penetrates the surface layers, to an estimated depth
of 0 to 5 mm51 into the sample, and undergoes multiple reflections before re-emerging
after undergoing various characteristic absorptions.
The penetration depth depends on particle size, particle shape, degree of compaction
and the chemical nature of the material. In order to concentrate on the chemical
information, mathematical transformations such as spectral derivitisation can be used
to effectively remove the physical effects.
The radiation undergoes many internal reflections before emerging in all directions. A
schematic illustration of diffuse reflectance and transmittance is in Figure 11.3a.
MSc Analytical Chemistry - N. Blakiston 45
Figure 11.3a Diagram of diffuse reflectance and transmittance;
Showing a few of the many paths taken by the radiation.
Spectrophotometers are based on filter, grating, FT or acousto-optical tuneable filter
systems (AOTF), and diode arrays. Tungsten-halogen lamps are used as the energy
source whilst silicon, lead sulphide, indium gallium arsenide and deuterated triglycine
sulphate are common materials used in detectors.
The samples are placed on a turntable, which places each sample in turn over the
radiation source for measurement (Figure 11.3b). The NIR radiation in this
instrument is separated into discrete wavelengths by dispersing the light with a
holographic diffraction grating. The radiation is split equally into two beams by an
optical interference beam splitter and on to two mirrors mounted perpendicular to
each other, one of which is moveable parallel to the NIR radiation beam. The mirrors
reflect and recombine the two beams which can interfere constructively or
destructively depending on the position of the moveable mirror resulting in an
MSc Analytical Chemistry - N. Blakiston 46
interferogram. Spectral information can be obtained by combining the interferograms
and applying the mathematical function Inverse Fourier transform.
Figure 11.3b Sphere detector; The detector beneath
the sample is a sphere detector and will receive a high
amount of reflectance, so increasing the signal to noise
ratio.
11.4. Mathematical Pre-Treatment and Processing
Near-Infrared spectra can be very complicated with multiple components present in
the sample, overlapping bands, matrix effects and other physical properties of the
sample. For this reason complex mathematical manipulation can be conducted to
simplify the spectra by reducing the ‘Noise’ caused by light scattering.
11.5. Bruker Opus NIR Spectroscopic Quant-Software
The Quant software supplied by Bruker was used to acquire and analyse spectroscopic
data for this thesis. It was specifically designed for NIR spectral acquisition,
chemometric model development and routine analysis.
It comprised a graphical, user-friendly, interface and provided tools for instrument
performance assessment to the recommendations of the European Pharmacopoeia, the
United States Pharmacopeia Chapter 1119 on NIR analysis and fully 21 CFR Part 11
MSc Analytical Chemistry - N. Blakiston 47
(21 Code of Federal Regulations Electronic Records; Electronic Signatures) 41
compliant.
A full set of diagnostic tools were provided to conduct instrument performance tests
and the software was designed to ease method development in a regulatory
environment.
A tutorial manual and CD demonstrated hands on methods with sample spectra being
provided to facilitate understanding of the key features available.
The software included six spectral pre-treatments, multiple sample selection methods,
library identification and qualifications methods, regression methods and routine
analysis operations.
11.6. Theoretical Background
In general the aim of a NIR quantitative analytical method is to apply a suitable
mathematical solution to produce values as near to the ‘True’ value as possible. The
following gives a brief overview of the various steps undertaken to apply these
solutions.
The most demanding task in developing a NIR spectroscopic method is the
preparation of samples for calibration and the vast number of samples required to
develop a good calibration set. In developing a chromatographic or U.V.
spectroscopic method it is easy to produce standards covering the range of interest.
Interference analysis of excipients is routine and scaling dilutions to suit the detection
method is straight forward. This may be one of the draw backs that have stopped NIR
MSc Analytical Chemistry - N. Blakiston 48
analysis from exploding in popularity. For most pharmaceutical companies there is a
race to get the product from discovery to the shelf. The patents start ticking; hence
the revenues are greater the longer the market share is held.
In the early stages of development batch sizes are small and infrequent so a NIR
method is probably not appropriate and so HPLC, GC or U.V. methods are developed
and routinely used. However there is a great benefit in replacing these methods once
full production commences. By running the methods in parallel a NIR spectroscopic
technique can be developed with little effort. Once enough data has been collected
the replacement method can be fully developed reducing analysis time, consumables
and reagent costs.
In an ideal scenario the manufacturing samples could be read directly during
processing, reducing lead times and the risk of forward processing a poor quality
batch. The instrumentation and methods, once validated are simple to operate for
routine analysis reducing the requirement for highly trained operatives.
11.7. Mulitvariate Calibration
Most analytical test methods are conducted using a univariate calibration set. There
are several draw backs to this method of analysis;
Outliers caused by additional unknown components are not recognized.
Statistical fluctuations caused by detector noise are directly reflected by the
concentration values.
MSc Analytical Chemistry - N. Blakiston 49
Peaks used for the analysis of multicomponent systems must be well
separated.
The analysis of multicomponent systems assumes the validity of the Beer
Lambert Law.
Multivariate calibrations will take into account spectral features over a wide range,
thus these deficiencies can be eliminated. This method assumes that systematic
variations observed in the spectra are a consequence of the concentration change of
the components and the change in the infrared signal does not need to be linear.
The down side of multivariate analysis is the large number of samples that are
required to produce the required validation (hundreds or thousands of data points may
be required).
To develop a chemometric method the NIR instrument is taught the relationship to
analyte concentration using these calibration samples. To validate the test set two
methods can be used; “Cross Validation” and “Test Set Validation”. The former is
generally used for small sample sets and was chosen for the purpose of this project.
11.8. Cross Validation
With any NIR method the sample used for validating the system must not be part of
the calibration set. However in any calibration set each data point can be taken out in
turn and used to validate the calibration. The system will conduct this automatically
comparing the expected result to the actual result. To produce the validation set the
samples must be independently analysed quantitatively on a reliable test method.
MSc Analytical Chemistry - N. Blakiston 50
This will then generate a set of ‘True’ values, which can be assigned to the data points
in the NIR instrument. The calibration samples should cover at least the expected
range of the samples intended to be tested routinely. Ideally this should be beyond
any regulatory or control limits and the sample values should be homogenously
spaced across the concentration range.
Once the calibration samples have been acquired the known concentration values can
be applied ready for data manipulation and the application of statistical tools.
11.9. Processing the Data
There are various data pre-treatments that reduce light scattering effects due to surface
effects, sample compaction and changes in particle size. These scattering effects can
cause problems in analysis as they lead to base line shifts between spectra. It is
therefore advantageous to remove these effects prior to performing any data analysis.
When pre-processing the data a good correlation between the spectral data and the
concentration values is required. The first step is usually the application of Partial
Least Squares (PLS) regression 42. This is a method for constructing predictive
models when the factors are many and collinear. PLS was developed in the 1960’s by
Howard Wold as an econometric technique. It is used to extract multiple overlapping
components from samples containing various concentrations once true values have
been applied. It is generally used to predict a set of dependant variables from a (very)
large set of independent variables.
MSc Analytical Chemistry - N. Blakiston 51
A combination of the following methods can also be applied to improve the
correlation from the initial linear regression67, 68:
Linear Offset Subtraction: shifts the spectra in order to set the y-minimum to
zero.
Straight Line Subtraction: fits a straight line to the spectrum and sub tracts it.
Vector Normalization: normalizes a spectrum by first calculating the average
intensity value and subsequent subtraction of this value from the spectrum.
Then the sum of the squared intensities is calculated and the spectrum is
divided by the square root of this sum.
Min-max Normalization: first subtracts a linear offset and then sets the y-
maximum to a value of 2 by multiplication with a constant.
Multiplicative Scatter Correction: performs a linear transformation of each
spectrum for it to best match the average spectrum of the whole set.
First Derivative: calculates the first derivative of the spectrum. This method
emphasizes steep edges of a peak. It is used to emphasize pronounced, but
small features over a broad background. Spectral noise is also enhanced.
Second Derivative: similar to the first derivative, but with a more drastic
result.
These mathematical formulations can be chosen and applied manually from a drop
down list within the system or the software can automatically run through all
connotations to derive successful rules to aid a good linear fit. Review of the
statistical correlation is then required by the operator before deciding on the required
mathematical application.
MSc Analytical Chemistry - N. Blakiston 52
12. Experimental Analysis Karl Fischer
12.1. Chemical Safety (CoSHH, Risk and MSDS)
Before any analytical work commenced a thorough review of the associated
health/hazard information was conducted.
This included;
Material Safety Data Sheets (MSDS) for each reagent, API and Excipients
used (Effective date: 30-June-2005).
Control of Substances Hazardous to Health (CoSHH) assessments for
Moisture determination by Karl Fisher - Raw Materials (Ref: 000123).
CoSHH assessments for Moisture determination by Turbo Karl Fisher (Ref:
000285).
For all chemical reagents used, the Health Hazard Evaluation reviews state;
“Based on an assessment, as required by the corporate document Chemical Safety in
Laboratories the above mentioned chemicals / quantities do not exceed the quantity of
each chemical handled inside a ventilated enclosure that would exceed the
Occupational Exposure Limit (OEL).
This statement is based on the minimum requirements that suitable Personal
Protective Equipment (PPE) is worn at all times when conducting analysis (Lab coat,
safety glasses and nitrile gloves), and that all work is conducted in a fume cupboard”.
MSc Analytical Chemistry - N. Blakiston 53
12.2. Sample Preparation
Several methods of sample preparation were discussed in order to construct a
calibration set. It was known from previous stability analysis that Prozac routinely
contains 8% H2O rising to approximately 11% H2O after 36 months on stability. This
additional uptake of water is from the humid storage condition or dehydrating of the
gelatine capsule into the matrix.
The initial experiments were going to be focused on systematic drying of samples to
produce a range of moisture contents down to the limit of detection for the KF
apparatus (approximately 1%) followed with incremental ‘wetting’ of samples to
produce water contents above the nominal content.
An alternative approach to sample preparation, which is more broadly recognised, is
to use live manufactured batches and collect NIR data at the same time as normal KF
analysis slowly building a spectral library with batch to batch variation. The limits of
using this method are in constructing a good linear plot which accurately brackets the
expected theoretical values. Unfortunately Prozac is of relatively low manufacturing
volume and is run in periodic campaigns of approximately 10 batches over several
weeks. This would not have been enough to produce the multiple samples required
and would have taken to long in the time window required for this project.
For this reason a single batch was chosen for the analysis as this would give a good
indication of the feasibility of using NIR spectroscopy on production batches.
MSc Analytical Chemistry - N. Blakiston 54
Further discussion on the process of wetting included mixing a known quantity of
water with the blend or using a high humidity environment and allow the matrix to
absorb water. Neither method, it was thought, would deliver a suitable homogenous
blend, although the latter method appeared to be the better option. The process would
have included the use of a salt solution to deliver a known and constant % Relative
Humidity in a closed vessel (Desiccator) or stability chamber. Due to the
unpredictable nature of the absorption this was not pursued as part of this project.
12.3. Loss on Drying Determination
Stage one of the project was to establish the water content of the powder blend and to
identify the drying times that would be required to produce a linear calibration
between approximately 2% H2O and the un-dried value. Once this was identified
portions of the powder blend were dried for various periods before conducting Karl
Fischer analysis.
12.4. Good Manufacturing Practice (GMP) Requirements
Eli Lilly and Company Limited is regulated by the Medicines and Healthcare products
Regulatory Agency (MHRA) and all activities are under taken to current Good
Manufacturing Practice (cGMP). To this end any non-routine activity needs to be
documented and approved before any work is conducted. As stated previously the
material was chosen as it did not represent the finished pharmaceutical item and the
data generated would not impact any regulatory test requirement.
MSc Analytical Chemistry - N. Blakiston 55
All analytical work was documented prior to execution in the following technical
reports. This ensured that all work undertaken had prior approval and a set path of
analysis would be conducted.
12.5. Execution of Technical Reports
12.5.1. QCL-TR-014: Loss on Drying of Prozac Powder Blend
Purpose: The purpose of this protocol was to determine a baseline water content for
Prozac powder blend and to construct a time versus weight loss drying curve.
Results: The material was dried to constant weight in a period of 1 hour. The
percentage loss on drying was determined to be 7.74%.
Discussion and Conclusion: The percentage loss is in general agreement with the
predicted water content. It is known that there are no residual solvents in the raw
materials and the manufacturing process is a dry manufacture process, therefore this
value represents the great majority of water that can be liberated by gentle heating.
Due to the unexpected rapid drying of the sample a representative drying curve could
not be established, but this would add as a guide to further drying experiments.
MSc Analytical Chemistry - N. Blakiston 56
12.5.2. QCL-TR-015: Water Determination on Dried Prozac Powder Blend by Karl Fischer Titration and Near-Infrared
Purpose: The purpose of this protocol is to generate a number of data points to
construct a quantitative model for the determination of water by NIR spectroscopy.
Results: The results generated were not reportable due to failed method acceptance
criteria.
Discussion and Conclusion: The execution of this protocol failed due to the poor
performance of the Karl Fischer method. This is discussed further below.
12.6. Investigation of Failed Execution: QCL-TR-015
When the KF titration result for the water standard check appears to be greater than
110% H2O, the test answer is printed as 999.000% H2O and the result is automatically
excluded from the statistics (see later discussion 12.7.1).
Observation of the course of the titration indicated that the peristaltic pump ran at
constant speed, delivering a steady stream of titrant, and then stopped. After a pause
of 5 seconds, as per method the system terminated the titration as it perceived a stable
end point had been achieved.
Once a sample has been administered, the titration should be started immediately and
rapidly. As the end point is approached the titration rate should be reduced, allowing
time between small aliquot additions, for the reactants to equilibrate. If the rate up to
MSc Analytical Chemistry - N. Blakiston 57
the end point is too rapid an excess of un-reacted titrant will be present and if the wait
period is not long enough (End-Point Time) the electrode will detect the excess titrant
and assume the end point is reached. If the wait time is too long then reproducibility
can become a problem. This is due to variations in the release of tightly bound
residual water or atmospheric ingress, resulting in sporadic over titration, or very long
titration times due to ingress of background water.
A balance therefore, needs to be struck between an accurate end point
‘approximation’ and the time taken to reach a stable reading. A stable end-point is a
significant indication of the course of a titration with no complications. A vanishing
end point would indicate that there is a slow release of water from the matrix or that a
side reaction may be interfering.
By setting a longer ‘End-Point Time’ in the Turbo KF method the requirement above
was met. The titration started rapidly until a point near the ‘End-Point’ was
approached. The instrument then went into the ‘End-Point Time’ mode for
approximately 6-8 seconds before adding small increments of titrant, pausing between
additions. This continued for approximately 20-40 seconds before the titration was
terminated. The experimental optimisation results are not documented or discussed
any further here, but a value of 10 seconds for the ‘End-Point Time’ was found to
give ideal results.
The method allows two modes of instrument operation ‘Sample Only’ and
‘Background Sample’. It was decided as an added precaution to use ‘Background
Sample’ mode to account for residual water and carryover. However this added
MSc Analytical Chemistry - N. Blakiston 58
considerable amount of time to each analysis with an overall testing time of
approximately 40 minutes per sample replicate.
Further investigation found two statements in a laboratory report “When using the
Mettler DL18 for moisture analysis in Atomoxetine® capsules the end point takes a
long time to be reached (typically 30-40 minutes per titration). Results can be
variable as residual water may remain within the sample matrix and therefore may not
be detected by the Karl Fischer. This is believed to be due to the presence of Silicone
fluid in the product formulation, restricting the release of water from the sample”.
Atomoxetine has a similar formulation to Prozac.
“It has been suggested that by adding a proportion of chloroform to the methanol in
the titration vessel that the solubility of the sample may be improved. This has been
proven to work well with compounds containing silicone oil17”. This alternative was
considered and a full review of available literature was conducted to understand the
impact of the solvents used.
Methanol does not dissolve long-chained hydrocarbons, such as starch satisfactorily.
The addition of 1-propanol to the working medium can also alleviate this problem.
Another benefit of 1-propanol addition is that it will remove oil deposits, from coating
the indication electrode. A literature reference32 states “Starch products will not
dissolve;…, formamide extracts the water from them extremely effectively. You can
optimise the extraction capacity by increasing the temperature (50 ˚C). The amount
of formamide …should not exceed 30%, or the stoichiometry…will change and the
MSc Analytical Chemistry - N. Blakiston 59
results falsified”. It was found that during operation, due to the long ‘Turbo’ times
and constant stirring that the temperature of the solution rapidly rose to between 45-
52 ˚C and remained relatively constant.
The changes made to the operation parameters of the instrument proved successful in
generating good reproducibility between samples and water standard checks gave
good response. Therefore no changes were made to the solvent composition.
This was an unexpected result from what was assumed a suitable method for the
analysis of water in Prozac. Modification of Global Method B07766 was not an
intended objective of this project. However the additional time taken to fully
understand and experiment with the various parameters of the Turbo2 KF proved
beneficial from a technical knowledge perspective.
As part of the investigation into the failure of Technical Protocol QCL-TR-015 the
site certification report for Global Method B07766 was reviewed. The summary of
this review and a general review of the method are discussed further.
12.7. Review of method AP1043-01
This method was developed for the determination of water in fluoxetine
hydrochloride capsules equivalent to 10 and 20mg fluoxetine. It was used to support
primary stability studies, but early indications proved water did not cause degradation
to the product on storage. There have been no regulatory limits applied to this test
and the method was deleted from use in 2001. The method and product was chosen
for experimentation, as there would be no regulatory impact to producing results from
live powder blend material.
MSc Analytical Chemistry - N. Blakiston 60
The Prozac powder blend under study has a water content of about 8% H2O. This
equates to approximately 30-60 mg of H2O per sample on the basis of a sample size of
375-750 mg respectively, as per method AP-1043-01 (equations (13) and (14)).
Literature values11 state a systematic error of +0.06% and a scatter of 0.24%, which
infers a total error of approximately 0.3% at this level of water determination.
Method acceptance criteria states that if the results calculated from equation (13) do
not lie between 30-60 mg H2O, then the weight of sample taken for analysis should be
adjusted using equation (14).
Weight (mg) H2O Sample = Sample Weight (mg) x % H2O Sample (13)100%
Original Sample Weight (mg) x 45 (14)Weigh (mg) H2O in Original Sample
Due to the nature of the analysis (the result generated for samples was an unknown)
with regards to the sample transfer method (discussed later) and the expected
variation in water contents, it was not possible to meet this method requirement. The
method stated to take approximately 690 mg (contents of three capsules). However
for the purpose of this investigation a weight between approximately 500 - 1000 mg
was deemed acceptable (see calculated values in Table 11.8a).
12.7.1. Performance checks
In order to ascertain the integrity of the instrumentation throughout the analysis, a
water check was performed at the end of a set of sample analysis. A water check is an
MSc Analytical Chemistry - N. Blakiston 61
addition of a defined (weighed) volume of water introduced into the reaction vessel
and titrated as per the samples. The result has confidence limits of 90.0% - 110.0%
associated with it that the water check result must fall between. This is often termed
‘method data acceptance criteria’.
12.7.2. Review of Site Certification Report B07766
Global Method B07766 was transferred to the Basingstoke Quality Control
Laboratory (Receiving Laboratory) on the 14th December 2000. The requirement for
a successful site transfer (certification) was via a collaborative study with the
Indianapolis Development Laboratory (Originating Laboratory). The batch used for
the transfer was the same as that used for the original method validation.
Previous testing of the batch obtained a mean value of 8.9%. It is not known on how
many samples this was conducted or the range/%RSD of the results. Acceptance
criteria were that the receiving laboratory will generate an average result from four
replicates, which falls between 6.9-10.9% H2O. Two replicates were analysed on two
separate days to give the following results;
Table 12.7.2 Site Certification of Method B07766.
Set UpReplicate 1(% water)
Replicate 2(% water)
Average(% water)
1 8.2 8.4 8.32 7.5 8.3 7.9
Mean 8.1
The data met the criterion. However the % RSD between replicates for ‘Set Up 2’
was 7.2 % and the % RSD between all four replicates was 5.0 %. The four highest %
RSD’s for replicate results generated during the experimental stage of this thesis was
5.0, 6.4, 7.0 and 9.3 % (the latter result was investigated at the time of analysis – see
MSc Analytical Chemistry - N. Blakiston 62
Dixon’s Q test discussion). This would suggest that the adjustments made to the
method improved the reproducibility significantly from the original method with an
average %RSD of 2.2% and a median of 1.2% for all results.
It was also noted that a sodium tartrate dihydrate control was run at the start of the
analysis each time to check the operation of the system, but no post control was run.
This may indicate why the issue of carry over was not noted during method transfer or
during routine analysis. The method states that analysis of a control is optional.
12.7.3. Influence of solvent on the Karl Fischer reaction
The stoichiometry (molar ratio of H2O:I2) depends on the type of solvent used. In an
alcoholic solvent such as methanol the stoichiometry is 1:1. In a non-alcoholic
solvent, such as Formamide the stoichiometry is 2:1. Studies by Eberius31
demonstrated that iodine and water react in the ratio of 1:1 if the percentage of
methanol in the solvent is greater than 20%. The method meets this requirement by
having a solvent composition of 2% Hydranal buffer in methanol, combined 50/50 v/v
with Formamide.
12.7.4. Sample Transfer to the Karl Fischer Instrument
During the familiarisation and method adjustment phase it was noted that the balance
being used was extremely unstable when trying to back weigh material between the
balance and the KF instrument. This was first thought to be due to the static
behaviour of the Prozac powder blend.
To eliminate the problem of static; a fresh glass weigh boat and antistatic ion source
was used in combination with anti static gloves and an earth connecting wrist strap.
MSc Analytical Chemistry - N. Blakiston 63
This modification made no significant difference to the stability of the balance. As
the fluctuation of the balance appeared to be in an upward direction (continually
increasing in weight), an alternative hypothesis was that the powder once weighed out
from the sealed weighing dish, was absorbing atmospheric water. This problem had
not been encountered when conducting familiarisation testing on un-dried bulk
powder, but presented an issue with dried samples. To test this, weighing was
conducted using a sealed vial. This required the following procedure;
Weigh and tare container and sample.
Transfer this weight to the KF instrument.
Remove container, open KF sample port, remove lid of container and transfer
a quantity of powder into the vessel.
Seal sample, close sample port and weigh sample container.
Transfer weight to the KF instrument.
This method gave a stable balance reading and was deemed suitable for the
experiment. This method did increase the sample transfer time to between 20-30
seconds. The slight increase in time the sample port was open and the time the
sample container remained open was not thought to have increased any water ingress
significantly.
The NIR instrument uses 12 ml sample vials, which are sealed with a rubber stopper.
These vials were suitable for conducting analysis as they reduced sample handling
and transfer. Samples were prepared by weighing approximately 4 g of material into
MSc Analytical Chemistry - N. Blakiston 64
each vial. The vials were sealed with the rubber stopper and parafilm was used for
added protection against water ingress.
12.7.5. QCL-TR-016: Water Determination on Dried Prozac Powder Blend by Karl Fischer Titration and Near-Infrared II
Purpose: The purpose of this protocol is to generate a number of data points to
construct a quantitative model for the determination of water by NIR spectroscopy.
Results: The results generated are presented in Table 11.8b. All results generated
met data acceptance criteria.
Discussion and Conclusion: When conducting the analysis for the 50 minute dried
sample 2 (sample 15 Rep 1 and Rep 2 in the protocol) the first result generated was
significantly different to the replicate (5.187% and 4.343% H2O) with a %RSD of
12.5. A third replicate was conducted which gave a result of 4.568% H2O. To decide
whether all data should be accepted and the result averaged or if the first replicate
should be removed a Dixon’s Q test was conducted. The results for all 50 minute
samples were used as the population and the result of 5.187% was reviewed as a
potential outlier.
MSc Analytical Chemistry - N. Blakiston 65
Dixon’s Q-test
The experimental Q-value (Qexp) is the ratio defined as the difference of the suspect
value from its nearest one divided by the range of the values (Q: rejection quotient).
Qexp = Xn – Xn-1 (15)
Xn – X1
The obtained Qexp value is compared to a critical Q-value (Qcrit) found in tables. This
critical value should correspond to the confidence level (CL) which is usually 95%.
If Qexp > Qcrit, then the suspect value can be characterized as an outlier and it can be
rejected.
The critical value of Q for eight data points at a 95% CL is 0.526.
Q = 5.187 – 4.947 = 0.268 5.187 – 4.293
As this value is smaller than Qcrit the three results will be averaged.
12.8. Evaluation of Karl Fischer Data
12.8.1. Data Acceptance Criteria
All Karl Fischer determinations were reported after the initial method development
stage. As indicated in Protocol QCL-TR-016 one sample had three replicates
conducted as there was an apparent high variance between results, as discussed above.
All standard water checks (Control Samples) passed the limit of 90-110% H2O.
MSc Analytical Chemistry - N. Blakiston 66
One replicate was repeated due to an assignable cause, this was agreed acceptable by
the responsible scientist reviewing the work. A higher than expected result was
generated. The instrument continued to titrate, for longer than normal during the end-
point time stage. Investigation indicated that the stopper in the sample port had not
been replaced correctly and air was able to enter the vessel. The result was deleted
and a fresh replicate was analysed (Un-dried Sample 1 result of 9.311% deleted).
A review of method acceptance criteria for AP1043-01 using equation (13) indicates
that 21 samples are outside of the required 30-60 mg H2O (Table 12.8a). This was to
be expected and does not appear to have any implication on the results. Additionally
the standard water checks conducted gave results between 26.63 - 30.54 mg H2O,
with the majority being below 30 mg H2O. The method state to use 25μl of water for
the instrument calibration and this was assumed to be appropriate for the performance
checks. To meet the method requirements it would therefore be more appropriate to
conduct the instrument calibration on 30 μl aliquots of water.
MSc Analytical Chemistry - N. Blakiston 67
Table 12.8a Karl Fischer Results: Method acceptance criteria
%H2O in Sample Sample Weight (g) Weight mg H2O
Sample Rep.
1Rep.
2Rep.
3Rep. 1 Rep. 2 Rep. 3
Rep. 1
Rep. 2
Rep. 3
QC
L-T
R-0
15 S
ampl
es r
un w
ith Q
CL-
TR
-016
Dried Sample 1 3.248 3.313 1.06664 0.71044 35 24
Dried Sample 2 2.720 2.882 0.64332 0.57232 17 16
Un-Dried Sample 1 8.184 8.279 0.60203 0.58962 49 49
15min-1 7.341 7.402 0.69742 0.53078 51 39
15min-2 7.896 7.425 0.52818 0.92134 42 68
15min-3 7.428 7.378 0.52299 0.71828 39 53
15min-4 7.239 7.245 0.61071 0.55829 44 40
20min-1 7.781 7.349 0.53075 0.56326 41 41
20min-2 7.472 7.460 0.67764 0.64445 51 48
20min-3 7.529 7.347 0.57997 0.52877 44 39
20min-4 7.381 6.981 0.60058 0.61636 44 43
QC
L-T
R-0
16 S
ampl
es w
ith a
dditi
onal
un-
drie
d an
d dr
ied
sam
ples
20min-1 6.334 6.802 0.68954 0.83398 44 57
20min-2 6.719 6.647 0.54493 0.71329 37 47
20min-3 6.561 6.570 0.64731 0.52973 42 35
20min-4 6.641 6.928 0.56422 0.68015 37 47
30min_1 6.024 5.881 0.60333 0.65708 36 39
30min_2 5.869 5.928 0.68133 0.83157 40 49
30min_3 5.937 6.121 0.70193 0.74624 42 46
30min_4 5.542 5.583 0.70978 0.84698 39 47
40min_1 6.134 6.029 0.68525 0.97489 42 59
40min_2 6.149 6.192 0.71237 0.68269 44 42
40min_3 6.377 6.281 0.64525 0.81971 41 51
40min_4 5.666 5.702 0.86135 0.60078 49 34
Un-Dried Sample 1 8.586 8.528 0.73939 0.75297 63 64
Dried Sample 3 4.450 4.441 0.74192 1.01197 33 45
50min_1 4.790 4.905 0.76289 0.76250 37 37
50min_2 * 5.187 4.343 4.568 0.90798 0.70370 0.80131 47 31 37
50min_3 4.756 4.947 0.80860 0.81679 38 40
50min_4 4.701 4.293 1.01671 0.79395 48 34
BA
S 1
607
page
34
A051452 Undried 8.635 8.800 0.64054 0.54095 55 48
A051452 Undried 8.683 8.644 0.77375 0.60887 67 53
A347575 Undried 7.870 7.656 0.76582 0.67774 60 52
A347575 Undried 7.493 7.585 0.64183 0.58869 48 45
A347575 70min 2.827 3.029 0.85102 0.80616 24 24
A347575 90min 2.267 2.158 0.84886 0.83842 19 18
A347575 110 2.519 2.567 0.87229 0.70916 22 18
A347575 130 1.896 1.874 0.78739 1.05112 15 20
BA
S 1
607
page
36
Sample A Un-dried 8.290 8.084 1.23037 0.90941 102 74
Sample B 20min 6.777 6.466 0.57779 0.68788 39 44
Sample C 50min 4.346 4.799 0.51722 0.56804 22 27
Sample D 120min 2.414 2.398 0.57691 0.73258 14 18
Sample A Un-dried**
8.055 7.981 7.957 0.55285 0.53843 0.72517 45 43 58
Sample D 120min** 2.451 2.462 2.652 0.85870 0.63378 0.57292 21 16 15
* Three results generated for this sample
* Five replicate results generated for these sample.
MSc Analytical Chemistry - N. Blakiston 68
Table 12.8b Karl Fischer Results: Background Titre
%H2O in SampleBackground Titre mg
H2O
Sample Rep 1 Rep 2 Rep 3 Mean Diff. %RSD Rep 1 Rep 2 Rep 3
QC
L-T
R-0
15 S
ampl
es r
un w
ith Q
CL-
TR
-016
Dried Sample 1 3.248 3.313 3.281 0.065 1.4 1.38 4.10
Dried Sample 2 2.720 2.882 2.801 0.162 4.1 1.25 3.10
Un-Dried Sample 1 8.184 8.279 8.232 0.095 0.8 3.10 2.01
15min-1 7.341 7.402 7.372 0.061 0.6 1.89 0.83
15min-2 7.896 7.425 7.661 0.471 4.3 1.05 2.34
15min-3 7.428 7.378 7.403 0.050 0.5 1.32 4.48
15min-4 7.239 7.245 7.242 0.006 0.1 2.65 4.66
20min-1 7.781 7.349 7.565 0.432 4.0 0.64 1.32
20min-2 7.472 7.460 7.466 0.012 0.1 1.17 1.27
20min-3 7.529 7.347 7.438 0.182 1.7 1.28 1.44
20min-4 7.381 6.981 7.181 0.400 3.9 1.74 1.90
QC
L-T
R-0
16 S
ampl
es w
ith a
dditi
onal
un-
drie
d an
d dr
ied
sam
ples
20min-1 6.334 6.802 6.568 0.468 5.0 1.64 3.17
20min-2 6.719 6.647 6.683 0.072 0.8 2.78 2.84
20min-3 6.561 6.570 6.566 0.009 0.1 3.72 2.12
20min-4 6.641 6.928 6.785 0.287 3.0 3.60 4.05
30min_1 6.024 5.881 5.953 0.143 1.7 2.11 4.23
30min_2 5.869 5.928 5.899 0.059 0.7 4.70 4.32
30min_3 5.937 6.121 6.029 0.184 2.2 5.41 4.44
30min_4 5.542 5.583 5.563 0.041 0.5 4.10 4.48
40min_1 6.134 6.029 6.082 0.105 1.2 0.59 2.22
40min_2 6.149 6.192 6.171 0.043 0.5 2.03 1.66
40min_3 6.377 6.281 6.329 0.096 1.1 1.93 1.47
40min_4 5.666 5.702 5.684 0.036 0.4 1.42 3.23
Un-Dried Sample 1 8.586 8.528 8.557 0.058 0.5 1.52 0.80
Dried Sample 3 4.450 4.441 4.446 0.009 0.1 2.11 3.58
50min_1 4.790 4.905 4.848 0.115 1.7 4.32 3.66
50min_2 * 5.187 4.343 4.568 4.699 0.844 9.3 2.86 3.64 1.51
50min_3 4.756 4.947 4.852 0.191 2.8 4.89 3.26
50min_4 4.701 4.293 4.497 0.408 6.4 4.55 6.25
BA
S 1
607
page
34
A051452 Undried 8.635 8.800 8.718 0.165 1.3
Prin
ter
Jam
med
- r
esul
ts
not a
vaila
ble.
A051452 Undried 8.683 8.644 8.664 0.039 0.3
A347575 Undried 7.870 7.656 7.763 0.214 1.9
A347575 Undried 7.493 7.585 7.539 0.092 0.9
A347575 70min 2.827 3.029 2.928 0.202 4.9
A347575 90min 2.267 2.158 2.213 0.109 3.5
A347575 110 2.519 2.567 2.543 0.048 1.3
A347575 130 1.896 1.874 1.885 0.022 0.8
BA
S 1
607
page
36 Sample A Un-dried** 8.290 8.084 8.187 0.206 1.8 1.05 4.04
Sample B 20min 6.777 6.466 6.622 0.311 3.3 0.35 2.76
Sample C 50min 4.346 4.799 4.573 0.453 7.0 3.13 4.12
Sample D 120min** 2.414 2.398 2.406 0.016 0.5 1.80 3.25
Sample A Un-dried** 8.055 7.981 7.957 8.073 0.074 1.6 3.73 2.77 2.41
Sample D 120min** 2.451 2.462 2.652 2.475 0.011 4.1 4.32 4.95 4.28
* Three results generated for this sample
** Five replicate results generated for these sample (%RSD Calculated on all 5).
Plot of Average %H2O Vs %RSD
y = -0.2945x + 3.9037
R2 = 0.0774
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5%H2O
%R
SD
MSc Analytical Chemistry - N. Blakiston 69
12.8.2. Review of Variance between Replicates
To understand if there was any correlation between the water content analysed and the
reproducibility between replicates a plot of average % water content versus % RSD
was conducted (Figure 12.8.2).
Figure 12.8.2. Replicate
Variance; Correlation of
values to identify if
measured water content is
independent of the
reproducibility of replicates.
A relationship between the two parameters would be evident if the trend line had an
R2 value approaching 1.0 with data points falling on or close to it. The data points are
relatively random and no positive correlation appears to exist. The ‘Best Fit Line’
constructed suggests that reproducibility is slightly improved with increased water
content, but this is tenuous.
12.8.3. Background Sample Analysis
The result for the background water content varied between 0.35 – 6.25 mg. The
average value was 2.78 mg and the Standard Deviation on all results was 1.37 and the
% RSD was 0.01.
Further investigation could be conducted to identify any relationship between the
background titre and the sample results. However this is beyond the scope of this
MSc Analytical Chemistry - N. Blakiston 70
project. It can be stated that the background content of the vessel measured after 5
minutes of Turbo time is equivalent to between 1 and 15% of the water value in the
sample. This variance might be expected to have a consequence on the sample
replicates, but the reproducibility of sample replicates for this type of water release
profile appears little affected.
Without further analysis it is assumed that the background titre is giving a relatively
accurate result for the water content present between analyses.
12.8.4. Summary
Forty one samples in all were run on the Karl Fischer and the NIR instrument. Two
samples were of an un-dried stability batch A051452, the rest were un-dried and dried
powder blend samples from batch A347575.
13. Experimental Analysis NIR
Before any of the project samples were run initial training on the instrument was
conducted using various ‘doped’ organic solvents. These samples were ‘doped’ with
a range of water contents by micro syringe and produced excellent calibration sets
with R2 values greater than 99.5%. A sample of the dried bulk Prozac powder and un-
dried was also run and a disenable difference could be seen between the two spectra.
This was a critical step in the initiation and feasibility of this project.
The instrument used was a BrukerTM MPA FT-NIR, using Opus Quant version 5.5
software. Procedure: BPD-071708-003-024 (BrukerTM MPA FT-NIR Spectrometer -
Routine Operation and Maintenance) was followed for all operations.
MSc Analytical Chemistry - N. Blakiston 71
The instrument was used in integrating sphere mode as per Attachment: Default
Parameter Settings.
13.1. Performance Calibration check
Prior to performing any analysis with the instrument a daily system suitability test
was performed, for which certain criteria had to be met. An example of a successful
test report can be seen in Attachment 3 - Bruker PQ Test Protocol.
Typical automated, internal checks conducted by modern NIR instruments include;
Wavelength Linearisation
Photometric Instrument Noise
Peak to Peak Noise
Electronic Communication
Bias
Wavelength Precision, Accuracy and Bandwidth
These checks and adjustments are conducted against built in reference materials and
electronic diagnostic checks. The self calibration takes about 20 minutes to run and is
ready for operation immediately the test certificate is printed and verified. As I did
not have an account for the instrument all performance checks and sample analysis
were observed by a trainer. For more information refer to the manufacturer’s
instrument manual67.
MSc Analytical Chemistry - N. Blakiston 72
13.2. Sample Analysis
The sample vials were rotated by hand to evenly distribute the powder. Each sample
was placed on the sampling turntable and analysed once only for the determination of
the calibration set.
For the repeatability determination the sample was analysed then the vial was gently
shaken to redistribute the sample and read again. The samples were also randomly
tapped on the bench to increase compaction and bulk density.
13.3. Calibration Models
The 41 samples analysed were assigned their respective ‘True’ value as obtained from
the average result of the replicate Karl Fischer analysis. The overlain spectra for each
sample are depicted in Figure 13.3a.
Figure 13.3a An overlay of all 41 sample spectra; depicting the greatest change in
peak intensity at approximately 5180cm-1.
MSc Analytical Chemistry - N. Blakiston 73
It can be seen that the area around 5180cm-1 has a spectral peak which shows variation
with water content - Peak height increases with increasing water content. A similar,
but less apparent correlation can be seen in other areas of the spectra. There is clear
benefit to overlaying the spectra as you can immediately see if there are any ‘rogue’
spectra. A closer inspection of the primary spectral peak can be seen in Figure 13.3b.
Figure 13.3b Primary water peak at 5180cm-1 (combination band).
13.4. Mathematical Pre-treatment and Processing
The first step after the initial look at the sample spectra is to distinguish any spectral
peaks that appear to vary with concentration of the analyte. From this stage on it can
be a case of trial and error (“No general recommendations can be given as to which
data processing methods should be used. The best method has to be found
empirically by trial and error” 67) with applying various pre-treatments and
chemometrics, or an individual may have a preferred selection of mathematical
applications they like to use or the system can analyse the data for you and give
various options to the operator.
MSc Analytical Chemistry - N. Blakiston 74
Without any prior experience in NIR spectral analysis options 1 and 3 were attempted.
After much button pushing three acceptable models were chosen to compare and
contrast. Each gave a relatively good linear plot and spread of data points.
The three calibration models; Linear Regression, Vector Normalised + PCA and
System Optimised were saved in the data base as R1, R2 and R3 respectively.
13.5. Calibration of Data Set R1
The first model applied to the data was a simple linear regression;
In the simplest model Beer’s law applies and the constituent of interest has a single
absorption band and is contained in a non-absorbing matrix (Burns et al, 200136). If
the spectrophotometer also has no noise, non-linearity or any other electrical or
mechanical fault, then in this ideal case the absorbance peak is exactly proportional to
the concentration of the constituent. The slope and the intercept can be easily
determined and the usual formula for Beer’s law applies.
A = εcl (16)
Where A is the absorbance, є is the molar absorption coefficient, c is the concentration
of the active and l is the pathlength. Where the pathlength is unity then ε is equal to
the slope of the line.
This relationship proved to exist for the primary water band as depicted above and so
the peak between 5454.4 cm-1 and 4902.3 cm-1 was integrated using the local maxima
MSc Analytical Chemistry - N. Blakiston 75
and minima, representing the start and the end of the peak. The peak was integrated
using a forced baseline model, which dropped the local maxima down as a
perpendicular to define the area of interest. A linear regression was then applied with
no pre-treatment or other mathematical manipulation.
13.6. Calibration of Data Set R2
For the second calibration the same data was used but ‘Vector Normalisation’
mathematical pre-treatment was applied. Normalizing a vector produces a unit
vector. This unit vector is produced by measurement of the vector length, then
dividing the x, y and z values by this value. This then describes a new vector. The
sum of the squared intensities is calculated and the spectrum is divided by the square
root of this sum. A full theoretical view of various chemometric manipulations is
described by Cowe, et al. (1995) 69.
A Principal Component Analysis (PCA) was then applied to ‘clean’ the spectra. The
band of interest was picked at 5473.4 cm-1 to 3795.5 cm-1.
“PCA is mathematically defined as an orthogonal linear transformation that
transforms the data to a new coordinate system such that the greatest variance by any
projection of the data comes to lie on the first coordinate (called the first principal
component), the second greatest variance on the second coordinate, and so on”
(Wikipidea).
PCA reduces the spread of the data set by retaining those characteristics of the data
set that contribute most to its variance (eigenvalue: matrices are broken down into
their eigenvectors, which are called factors or principle components). This reduces
MSc Analytical Chemistry - N. Blakiston 76
the large multivariate data of the original spectra, and is undertaken by keeping lower-
order principal components and ignoring higher-order ones. Such low-order
components often contain the "most important" aspects of the data. With increasing
numbers of factors less and less variance is modelled until additional factors are only
modelling variations due to noise.
A PCA factor of 5 was applied with a rank of 4. This is the number of Partial Least
Squared regression algorithms that are applied to the data, which selects the best
correlation function between spectral and concentration matrix. It also determines the
number of principle components used; too few leads to poor reproduction of the
spectral data, hence spectral changes may not be observed (“underfitting”), too many
leads to the measurement of spectral noise in the regression (“overfitting”)
13.7. Calibration of Data Set R3
For this calibration the Quant software was used to perform an optimisation routine.
This performs multiple chemometric and pre-treatment operations to produce the best
possible solution for the calibration model. This process takes several minutes of
computation and provides several possible solutions, which the operator must then
decide on. The integration of the best model was extremely complex and appeared to
have used multiple and varied manipulations across the whole of the spectral region
chosen. This included 1st and 2nd derivation, straight line subtraction, constant offset
elimination, various normalization techniques (vector, min, and max) and other
corrections.
MSc Analytical Chemistry - N. Blakiston 77
The region of analysis was 7502.3 cm-1 to 5446.4 cm-1. This area does not look at the
primary combination water band, so it is assumed that other spectral information is
available, which relates to water concentration. This may also be due to a flatter
baseline across this region.
13.8. Comparison of R1, R2 and R3
All three calibration models demonstrated a visibly good linear correlation (Figures
13.8a, 13.8b and 13.8c).
Figure 13.8a Calibration plot for Model R1.
MSc Analytical Chemistry - N. Blakiston 78
Figure 13.8b Calibration and Validation plot for Model R2.
Figure 13.8c Calibration and Validation plot for Model R3.
MSc Analytical Chemistry - N. Blakiston 79
Table 13.8 provides a comparison of the calibration models R2 and R3.
Table 13.8 Comparison of calibration models R2 and R3.
R2 Calibration
R2 Cross Validation
R3 Calibration
R3 Cross Validation
R2 96.31 94.42 99.82 95.74RMSECVRMSEE 0.386
0.4510.0952
0.394
The determination coefficient (R2) gives the percentage of variance present in the true
component values, which is reproduced in the regression. R2 approaches 100% as the
fitted concentration values approach the true values. This is different to the
conventional use of R2, which approaches 1.0 with the true value (17):
(17)
Where; the residual (Res) is the difference between the true and the fitted value. Thus
the sum of squared errors (SSE) is the quadratic summation of these values (18).
(18)
The root mean square error of estimation RMSEE is calculated from this sum, with M
being the number of standards and R the rank. The true value is approached as the
value approaches zero:
MSc Analytical Chemistry - N. Blakiston 80
(19)
A comparison of the resulting analysis values, from cross validation, with the original
raw data allows the calculation of the predictive error of the complete data system, the
RMSECV (Root Mean Square Error of Cross Validation). This is a quantitative
measure for the mean accuracy of the predictive capability of the chemometric model.
The smaller the RMSECV, the better the quality of the model.
It can be concluded that model R3 gives a better linear fit than R2 for both the
calibration and cross calibration models. The RMSEE value for the calibration model
is significantly improved for the R3 model compared to R2. However the R3 model
is not significantly better than R2 for the cross validation model. This is confirmed by
visual comparison of the plots, which show an extremely tight population, close to the
line for the calibration plot of R3 compared to R2, but greater variability in the cross
validation plot. This is also seen by comparison of the R3 calibration and validation
R2 values of 99.82 and 95.74. This would indicate that all of the chemometrics
applied produce a good calibration fit, but is less effective at predicting the true value.
For R2 both calibration and validation plots demonstrate similar variation.
It is not so easy to compare model R1 to the other two models as the statistics applied
by the software are different. R1 produced a linear calibration model with a
correlation coefficient of 0.9736, which translates to an R2 value of 94.79. This is
lower than the other two models, which is apparent by visual comparison.
MSc Analytical Chemistry - N. Blakiston 81
When constructing the data models initially the data population resembled a non-
linear fit. This improved as more data points were modelled. It was first thought that
the powder blend had a maximum water saturation limit and a minimum quantity due
to bound water. This produced a correlation similar to a Boltzman sigmoid
distribution. This is still slightly evident in the linear regression model with a
flattening of the data points at the top and bottom of the chart.
The ‘Best-Fit’ line for each model can be seen to intersect very closely with the origin
and would suggest that measurement outside of the calibrated range is possible.
13.9. Repeatability Five Replicates - Between Methods
13.9.1. Repeatability between methods
Two samples of powder blend were analysed five times by KF analysis. The 120
minute dried sample was also analysed five times by NIR analysis to compare the
repeatability between the two methods. The results are shown in Table 13.9.1.
Table 13.9.1 Repeatability data for KF and NIR comparison.
ReplicateA347575 Undried
A347575 120 min.
R1 R2 R3
1 8.290 2.414 2.626 2.666 2.428
2 8.084 2.398 2.513 2.651 2.377
3 8.055 2.451 2.576 2.676 2.66
4 7.981 2.462 2.536 2.755 1.839
5 7.957 2.652 2.522 2.785 2.707
Mean 8.073 2.475 2.555 2.707 2.402
sd 0.132 0.102 0.047 0.060 0.346
%RSD 1.632 4.126 1.825 2.199 14.388
Min 7.957 2.398 2.513 2.651 1.839
Max 8.290 2.652 2.626 2.785 2.707
MSc Analytical Chemistry - N. Blakiston 82
13.9.2. Review of water replicates by KF
For the Karl Fischer analysis there appears to be less variability on results at the
higher water concentration. This may be due to poor homogeneity within the dried
sample (binding, clumping etc.) or due to instrument precision at this lower level.
Further work would be needed to substantiate this. To check variability of the
sampling process the un-dried KF analysis was performed on variable sample
weights; 1.23037, 1.23037, 0.72517, 0.55285 and 0.53843 g respectively. The results
indicated that a lower reading was attained for the lower weights taken.
The alternative conclusion might be that a high result is attained by using large
weights. In this instance the first two samples had values outside the method
acceptance criteria of 30-60 mg of water (equation 6 and 7). The values were 102 and
74 mg of water respectively.
The range for both batches was approximately 0.3% absolute water content.
13.9.3. Review of water replicates by NIR
The five readings were taken by the same method as stated above, then compared
using the three models; R1, R2 and R3.
The mean value for models R1 and R3 fell between the minimum and maximum
value obtained by KF analysis. This would suggest that at this level they offer a
better predicted value than model R2.
MSc Analytical Chemistry - N. Blakiston 83
The repeatability for models R1 and R2 were better than those obtained by KF
analysis 1.8 and 2.2 respectively compared to 4.1 %RSD. Model R3 demonstrated a
high variance between replicates of 14.4 %RSD. On this basis, with this limited data,
the linear regression model offers good reproducibility with a mean predicted value
close to the true value.
13.9.4. Comparison of variance between methods
13.9.4.1. F-test
An F-test was conducted on each calibration model comparing the five replicate
readings on the NIR to the five replicate titrations on the reference method. This
compares the standard deviations of two data sets to establish whether they represent
normal populations and that the population variances are equal.
The calculation follows;
F = S12/S2
2 (20)
F is always calculated to have a value greater than 1. This value is then compare to
table values (Fcrit), which take into account both sample populations (in this case 5)
and the significance level (in this case 95%). The test used is a two-tailed test; as
results can be higher or lower than the theoretical values (for tests on the mean) or in
this case if the two standard deviation values differ significantly. It is useful to note
that Microsoft Excel has built in functionality to conduct this analysis;
MSc Analytical Chemistry - N. Blakiston 84
Table 13.9a F-test between KF and R1 for five replicate results
F-Test Two-Sample for Variances R1
Variable
1Variable
2Mean 2.4754 2.5546Variance 0.01043 0.00217Observations 5 5df 4 4F 4.798P(F<=f) 0.079F Critical 9.604
Table 13.9b F-test between KF and R2 for five replicate results
F-Test Two-Sample for Variances R2
Variable
1Variable
2Mean 2.4754 2.7066Variance 0.01043 0.00354Observations 5 5df 4 4F 2.945P(F<=f) 0.160F Critical 9.604
Table 13.9c F-test between KF and R3 for five replicate results
F-Test Two-Sample for Variances R3
Variable
1Variable
2Mean 2.4022 2.4754Variance 0.11946 0.01043Observations 5 5df 4 4F 11.453P(F<=f) 0.018F Critical 9.604
It can be seen that F calculated, for R1 and R2 is less than F critical (table value) and
therefore any difference between the NIR data and the reference method is not
significant. The value of P (the probability) is also greater than the 95% confidence
level entered for alpha (0.025) in excel, for a two tailed test, which also confirms this
MSc Analytical Chemistry - N. Blakiston 85
result. For model R3 the opposite is true and therefore a significant difference is
present between the reference method and the NIR data.
13.9.4.2. t-test
This confirmation allows us now to conduct further analysis to compare the average
results from each data set to understand if there is any significance between them. If
no significant difference is found then this will suggest that the two sets of data, in
each case, could come from the same population.
The comparison of two means is undertaken using one of two types of t-test depicted
below;
There are two methods to compare the mean (average) values depending on the
results generated from the F-test these are;
t = (mean1 - mean2)/√( S12/n1 + S2
2 /n2) (21)
and
t = (mean1 - mean2)/S√( 1/n1 + 1/n2) (22)
Where S is the pooled standard deviation and the value t has two degrees of freedom
(2 × n-1). The latter equation will be used for comparison of the reference method to
R1 and R2 as this is applied for a successful F-test i.e. a‘t-test assuming equal
variance’. The value for alpha (α) was set at 0.05 (equivalent to 95% significance).
MSc Analytical Chemistry - N. Blakiston 86
Table 13.9d t-test between KF and R1 for five replicate results
t-Test: Two-Sample Assuming Equal Variances R1 Vs KF
Variable R1 Variable KFMean 2.555 2.475Variance 0.0022 0.0104Observations 5 5Pooled Variance 0.0063Hypothesized Mean Difference 0df 8t Stat 1.5774P(T<=t) two-tail 0.1534t Critical two-tail 2.3060
Table 13.9e t-test between KF and R2 for five replicate results
t-Test: Two-Sample Assuming Equal VariancesR2 Vs KF
Variable R2 Variable KFMean 2.707 2.475Variance 0.0035 0.0104Observations 5 5Pooled Variance 0.0070Hypothesized Mean Difference 0df 8t Stat 4.3736P(T<=t) two-tail 0.0024t Critical two-tail 2.3060
For the R1 model; As t Stat is less than tcrit (table value) and the value for P is greater
than the value for α, then any difference between the means is not significant. This
indicates that the two sets of data have come from the same population.
For the R2 model; As t Stat is greater than tcrit and the value for P is less than the
value for α, then any difference between the means is significant.
MSc Analytical Chemistry - N. Blakiston 87
13.9.4.3. ANOVA on 5 replicates
The results of the F-test and t-test appear to prove that model R1 (linear regression
with no mathematical manipulation) has produced a sample mean and standard
deviation within the same population as the reference method for five replicate
analysis.
To confirm this, an Analysis of Variance (ANOVA) will be conducted to compare all
four data sets;
Table 13.9f ANOVA: Single Factor.
SUMMARYGroups Count Sum Average Variance
Reference Method 5 12.377 2.475 0.0104R1 5 12.773 2.555 0.0022R2 5 13.533 2.707 0.0035R3 5 12.011 2.402 0.1195
ANOVASource of Variation SS df MS F P-value F crit
Between Groups 0.25509 3 0.08503 2.50808 0.09582 3.23887Within Groups 0.54244 16 0.03390Total 0.797534 19
If Fcalc < Fcrit and Pvalue is > than α (0.05), then there is no significant difference between
the data sets.
This proves that although the ANOVA indicates that all four data series are from the
same population the F-test and t-test prove that only model R1 is actually
representative to the reference method.
MSc Analytical Chemistry - N. Blakiston 88
13.9.4.4. Confidence Limits
To assess how representative the mean data and standard deviation of each data set
are, with respect to the reference method we can look at the confidence limit for each
mean value. This will indicate any systematic error associated with the data.
The calculation used for this analysis is;
μ = mean ± t(s/√n) (23)
where;
μ (greek letter mu) = the theoretical value 2.475 % H2O
t = critical table value for n-1 degree of freedom at the 95%
confidence interval.
n = number of samples.
Model R1
μ = 2.555 ± 2.57(0.047/√5)
μ = 2.555 ± 0.054 % H2O
Model R2
μ = 2.707 ± 2.57(0.06/√5)
μ = 2.707 ± 0.068 % H2O
Model R3
μ = 2.402 ± 2.57(0.346/√5)
μ = 2.402 ± 0.398 % H2O
As μ = 2.475 % H2O it can be seen that the theoretical value μ does not fall within the
confidence limit of Model R1 or R2, but does for R3. This demonstrates that an
MSc Analytical Chemistry - N. Blakiston 89
inherent, systematic error is involved in the models for R1 and R2 using five replicate
readings. However this is not unexpected as model R3 has a mean close to the true
value and a high variance, so this does not necessarily indicate this is the better
method.
13.9.4.5. Random Error
Further determination was conducted to identify if any difference between mean
model values and the reference method were solely as a result of random error. The
lower the probability of such a difference occurring by chance, the less likely it is that
the null hypothesis is true. The null hypothesis is generally rejected if the probability
of such a difference occurring by chance is less that 1 in 20 (i.e. 0.05 or 5%) and in
such a case the difference is said to be significant at the 0.05 level. The following
equation is used for this analysis;
t = (mean - μ)√n/s) (24)
Where t is the critical table value for n-1 (tcrit = 2.78) samples and μ is the expected or
theoretical quantity, with s being the standard deviation.
For Model R1
t = (2.555 - 2.475) √5/0.047t = 3.81
For Model R2
t = (2.707 - 2.475) √5/0.06t = 8.65
For Model R3
t = (2.402 - 2.475) √5/0.346t = 0.472
MSc Analytical Chemistry - N. Blakiston 90
This would indicate that systematic error is involved and model R1 and R2 fails and
that there is significant bias above the calculated mean at the lower end of the
determination (circa 2.5 % H2O). This also indicates that model R3 is a more
acceptable solution at low water content levels.
13.10. Repeatability 16 Replicates - Within Method
13.10.1. Repeatability NIR measurements
Sixteen readings were taken for Batch A051452 using the above strategy to present
the sample in different orientations. The sample vials were sealed throughout the
analysis. The results of the 16 readings indicate any variance (absolute) between the
instrument performance or the bulk powder composition/orientation. The readings
taken were then calculated as predicted values from each of the three calibration
models and compared to the ‘True’ value, as produced for the reference KF method.
The results are depicted in Table 13.10a.
MSc Analytical Chemistry - N. Blakiston 91
Table 13.10a Predicted NIR values Vs KF - Repeatability
Predicted Values Taken From the Calibration Plots %H2OLinear Regression
R1Vector Normalization + PCA
R2System Self Optimisation
R3
Result Residual Result Residual Result Residual
8.229 0.435 8.625 0.039 8.613 0.051
8.263 0.401 8.766 0.102 8.556 0.108
8.269 0.395 8.876 0.212 9.056 0.392
8.400 0.264 8.884 0.220 8.911 0.247
8.287 0.377 8.834 0.170 8.995 0.331
8.365 0.299 8.834 0.170 8.772 0.108
8.356 0.308 8.869 0.205 8.687 0.023
8.138 0.526 8.913 0.249 9.382 0.718
8.241 0.423 8.974 0.310 9.213 0.549
8.275 0.389 9.026 0.362 9.563 0.899
8.235 0.429 8.959 0.295 9.095 0.431
8.192 0.472 8.863 0.199 8.444 0.220
8.057 0.607 8.994 0.330 8.722 0.058
8.163 0.501 8.938 0.274 8.387 0.277
8.088 0.576 9.046 0.382 9.859 1.195
8.057 0.607 9.155 0.491 8.902 0.238
Max 8.400 0.607 9.155 0.491 9.859 1.195
Min 8.057 0.264 8.625 0.039 8.387 0.023
Range 0.343 0.343 0.530 0.452 1.472 1.172
Residual 0.438 8.226 0.246 8.413 0.283 8.299
Mean 8.226 0.438 8.910 0.251 8.947 0.365
Median 8.238 0.426 8.899 0.235 8.907 0.262
SD 0.105 0.105 0.123 0.112 0.407 0.331
%RSD 1.276 23.956 1.376 44.531 4.554 90.499
True Value from KF = 8.664 %H2O Average of two replicates; 8.683 and 8.644 %H2O
No mean result for the calibration models falls within the upper and lower replicate
values for the reference method. This was not unexpected as the %RSD for the KF
replicates was 0.3. The %RSD and range for model R1 was better than R2 and both
were significantly better than R3. The mean result for R1 showed a low bias while
R2 and R3 showed a similar mean (%RSD between them of 0.3) with high bias. This
data was too limited to reach any further conclusions in comparison to the true mean
due to the tight nature of the values around the mean. Repeat analysis of this
experiment would elucidate the best performing model. Further statistical analysis
would established how many replicate NIR readings are required to give the required
confidence level that the average falls within a suitable value of the true result.
MSc Analytical Chemistry - N. Blakiston 92
To accomplish this, a frequency distribution can be conducted and the confidence
limits can be established using;
mean ± tn-1 (s/√n) (24)
The value for t can be taken from confidence tables for a certain number of degrees of
freedom at a defined confidence interval.
13.10.2. ANOVA on 16 Replicates
This was conducted as previously on the five replicates;
Table 13.10b ANOVA: Single Factor - R1:R2:R3
SUMMARYGroups Count Sum Average Variance
R1 16 131.615 8.226 0.0110R2 16 142.556 8.910 0.0150R3 16 143.157 8.947 0.1660
ANOVASource of Variation SS df MS F P-value F critBetween Groups 5.277 2 2.638 41.207 6.76E-11 3.20432Within Groups 2.881 45 0.06403
Total 8.158 47
If Fcalc < Fcrit and Pvalue is > than α (0.05), then there is no significant difference between
the data sets. There is significant difference between the three data sets. To test if this
failure is due to model R1, the data set is removed and ANOVA conducted again.
There is no significant difference between model R2 and R3 once R1 is removed;
MSc Analytical Chemistry - N. Blakiston 93
Table 13.10c ANOVA: Single Factor - R2:R3
SUMMARYGroups Count Sum Average Variance
R2 16 142.556 8.910 0.0150R3 16 143.157 8.947 0.1660
ANOVASource of Variation SS df MS F P-value F critBetween Groups 0.011 1 0.0113 0.1247 0.7265 4.1709Within Groups 2.716 30 0.0905
Total 2.727 31
13.10.3. Outlier Check for Repeatability
A Dixon’s Q-test was applied to each of the 16 samples results for each of the
calibration models. No outliers existed in the data set predictions for either models
(Table 13.10c).
Table 13.10c Dixon’s Q-test applied to NIR repeatability study.
Dixon's Q testR1 R2 R3
8.057 8.625 8.387
8.057 8.766 8.444
8.088 8.834 8.556
8.138 8.834 8.613
8.163 8.863 8.687
8.192 8.869 8.722
8.229 8.876 8.772
8.235 8.884 8.902
8.241 8.913 8.911
8.263 8.938 8.995
8.269 8.959 9.056
8.275 8.974 9.095
8.287 8.994 9.213
8.356 9.026 9.382
8.365 9.046 9.563
8.400 9.155 9.859
Qexp
Upper0.000 0.266 0.039
Qexp
Lower0.102 0.206 0.201
Qcrit for 15 data points = 0.384Qcrit for 20 data points = 0.342
0
0.1
0.2
0.3
0.4
0.5
0
0.25
0.5
0.75
1
1.25
MSc Analytical Chemistry - N. Blakiston 94
13.10.4. Residual Analysis - Frequency
The residuals have been plotted below for each of the models to identify if they
follow an expected distribution with the highest frequency closest to the true value;
R1 follows an expected distribution with the higher percentage of residuals for the 16
replicates falling near the true value. R2 appears on this data set to have a bias around
0.2 to 0.3 % away from the true value and R3 appears random. This would indicate
that for replicate analysis R1 performs better.
Figure 13.10.4 Frequency plots for calibration model R1, R2 and R3.
Quantiles R1 100.0% maximum 1.195099.5% 1.195097.5% 1.195090.0% 0.987875.0% quartile 0.519550.0% median 0.262025.0% quartile 0.108010.0% 0.04262.5% 0.02300.5% 0.02300.0% minimum 0.0230
Moments Mean 0.3653125Std Dev 0.3306044Std Err Mean 0.0826511upper 95% Mean 0.5414792lower 95% Mean 0.1891458N 16
Quantiles 100.0% maximum 0.4910099.5% 0.4910097.5% 0.4910090.0% 0.4147075.0% quartile 0.3250050.0% median 0.2345025.0% quartile 0.1772510.0% 0.083102.5% 0.039000.5% 0.039000.0% minimum 0.03900
Moments Mean 0.250625Std Dev 0.1116064Std Err Mean 0.0279016upper 95% Mean 0.3100958lower 95% Mean 0.1911542N 16
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
MSc Analytical Chemistry - N. Blakiston 95
Quantiles 100.0% maximum 0.6070099.5% 0.6070097.5% 0.6070090.0% 0.6070075.0% quartile 0.5197550.0% median 0.4260025.0% quartile 0.3800010.0% 0.288502.5% 0.264000.5% 0.264000.0% minimum 0.26400
Moments Mean 0.4380625Std Dev 0.1049403Std Err Mean 0.0262351upper 95% Mean 0.4939812lower 95% Mean 0.3821438N 16
13.10.5. Review of Residuals for Repeatability
The residuals for each model were calculated by comparing the predicted values to
the true, reference value. The results are shown below.
MSc Analytical Chemistry - N. Blakiston 96
Table 13.10d Residual analysis for predicted versus true for NIR.R1 R2 R3
Sample TRUE Predicted Residual Predicted Residual Predicted Residual
Prozac2blend_1.0 6.568 6.788 -0.220 6.611 -0.043 7.043 -0.475
Prozac2blend_2.0 6.683 6.684 -0.001 6.474 0.209 6.362 0.321
Prozac2blend_3.0 6.566 7.142 -0.577 7.141 -0.576 6.457 0.109
Prozac2blend_4.0 6.785 6.881 -0.097 7.059 -0.275 6.601 0.184
Prozac2blend_5.0 5.953 6.189 -0.237 5.982 -0.030 6.337 -0.385
Prozac2blend_6.0 5.899 6.395 -0.496 6.261 -0.363 6.261 -0.363
Prozac2blend_7.0 6.029 6.274 -0.245 6.145 -0.116 6.094 -0.065
Prozac2blend_8.0 5.563 6.136 -0.574 5.971 -0.409 6.166 -0.604
Prozac2blend_9.0 6.082 5.533 0.549 5.261 0.821 5.444 0.638
Prozac2blend_10.0 6.171 5.649 0.522 5.516 0.655 5.578 0.593
Prozac2blend_11.0 6.329 5.701 0.628 5.548 0.781 6.258 0.071
Prozac2blend_12.0 5.684 5.706 -0.022 5.450 0.234 5.346 0.338
Prozac2blend_13.0 4.848 7.787 -2.940 4.549 0.299 4.586 0.262
Prozac2blend_14.0 4.699 5.087 -0.388 4.854 -0.155 5.077 -0.378
Prozac2blend_15.0 4.852 5.162 -0.311 5.011 -0.160 4.933 -0.082
Prozac2blend_16.0 4.497 4.801 -0.304 4.762 -0.265 4.570 -0.073
Prozac2blend_Dried.0 4.446 4.234 0.212 4.147 0.299 3.711 0.735
Prozac2blend_UnDried.0 8.557 7.868 0.689 8.021 0.536 8.001 0.556
Prozacblend_10.0 7.661 7.514 0.147 7.595 0.066 7.765 -0.105
Prozacblend_11.0 7.403 7.537 -0.134 7.706 -0.303 7.526 -0.123
Prozacblend_12.0 7.242 7.556 -0.314 7.651 -0.409 7.854 -0.612
Prozacblend_13.0 7.565 7.126 0.439 7.183 0.382 7.338 0.227
Prozacblend_14.0 7.466 6.964 0.502 6.811 0.655 6.988 0.478
Prozacblend_16.0 7.181 7.019 0.162 7.038 0.143 7.197 -0.016
Prozacblend_A051452_1.0 8.718 8.236 0.481 8.663 0.054 8.987 -0.270
Prozacblend_A051452_2.0 8.664 8.229 0.435 8.595 0.068 8.333 0.330
Prozacblend_A347575_1.0 7.763 8.031 -0.268 8.095 -0.332 7.694 0.069
Prozacblend_A347575_110minutes.0 2.213 2.419 -0.207 2.534 -0.322 2.393 -0.181
Prozacblend_A347575_130minutes.0 1.885 1.885 0.000 2.054 -0.169 1.376 0.509
Prozacblend_A347575_2.0 7.539 7.870 -0.331 7.973 -0.434 7.703 -0.164
Prozacblend_A347575_90minutes.0 2.543 2.628 -0.085 2.840 -0.297 2.746 -0.203
Prozacblend_Dry_1.1 3.281 2.421 0.860 2.703 0.578 3.104 0.177
Prozacblend_Dry_2.0 2.801 2.056 0.745 2.293 0.508 2.382 0.419
Prozacblend_Original.0 8.292 7.849 0.443 7.761 0.531 7.983 0.309
Prozacblend_9.0 7.372 7.431 -0.059 7.682 -0.311 7.871 -0.500
Prozacblend_15.0 7.438 7.047 0.391 7.080 0.358 7.281 0.157
Prozacblend_A347575_120.1 2.406 2.626 -0.220 2.741 -0.335 2.826 -0.420
Prozacblend_A347575_20min.0 6.622 7.301 -0.680 7.367 -0.746 7.070 -0.449
Prozacblend_A347575_50min.0 4.573 5.505 -0.933 5.809 -1.237 3.945 0.628
Prozacblend_A347575_70min.0 2.928 3.671 -0.743 3.667 -0.739 3.837 -0.909
Prozacblend_A347575_Undried_A.0 8.187 8.014 0.173 8.230 -0.043 8.170 0.017
Mean 0.000 -0.022 0.018
Min 0.000 0.030 0.016
Max 0.933 1.237 0.909
It can be seen from the mean residual values that each model on average produces a result within 0.02% of the true water content. In particular the mean result for model R1 reports a value of 0.000%. The greatest error from the true value for each model was 0.933, 1.237 and 0.909 % water respectively. For model R1 and R2 this is the same data point.
MSc Analytical Chemistry - N. Blakiston 97
Matched Pair AnalysisThe matched pair analysis is a comparison of the two sets of results; predicted and
actual (true – column 1). The analysis shows that there is reasonable correlation
between the results. However there is also significant spread, most likely due to the
error associated with the reference method and that of the NIR method.
Figure 13.10.5 Matched pair plots for calibration model R1, R2 and R3.
Difference: Column 2-Column 1 - R1
-1.0
-0.5
0.0
0.5
1.0
Diff
eren
ce: C
olum
n 2-
Col
umn
1
Column 1
Column 2
1 2 3 4 5 6 7 8 9
Mean: (Column 2+Column 1)/2
Column 2 5.95005 t-Ratio -0.00071Column 1 5.9501 DF 40Mean Difference -4.9e-5 Prob > |t| 0.9994Std Error 0.06887 Prob > t 0.5003Upper95% 0.13915 Prob < t 0.4997Lower95% -0.1392N 41Correlation 0.97363
MSc Analytical Chemistry - N. Blakiston 98
Difference: Column 4-Column 1 - R2
-1.0
-0.5
0.0
0.5
1.0
Diff
eren
ce: C
olum
n 4-
Col
umn
1
Column 1
Column 4
1 2 3 4 5 6 7 8 9
Mean: (Column 4+Column 1)/2
Column 4 5.97156 t-Ratio 0.301272Column 1 5.9501 DF 40Mean Difference 0.02146 Prob > |t| 0.7648Std Error 0.07124 Prob > t 0.3824Upper95% 0.16545 Prob < t 0.6176Lower95% -0.1225N 41Correlation 0.97179
MSc Analytical Chemistry - N. Blakiston 99
Difference: Column 6-Column 1 - R3
-1.0
-0.5
0.0
0.5
1.0D
iffer
ence
: Col
umn
6-C
olum
n 1
Column 1
Column 6
1 2 3 4 5 6 7 8 9
Mean: (Column 6+Column 1)/2
Column 6 5.93156 t-Ratio -0.2977Column 1 5.9501 DF 40Mean Difference -0.0185 Prob > |t| 0.7675Std Error 0.06227 Prob > t 0.6163Upper95% 0.10731 Prob < t 0.3837Lower95% -0.1444N 41Correlation 0.97891
MSc Analytical Chemistry - N. Blakiston 100
14. Discussion of Results
The reference method (Karl Fischer Titration) has been discussed at length to have
not been entirely reliable. With this in mind the NIR models can only be as good as
the data used to produce them. However the more data that can be used in the
calibration model the greater the accuracy of the method. This is especially true if the
data is adjusted by removal of outliers.
The problems associated with the reference method are nicely surmised by
MacDonald et. al;
“Karl Fischer methods are commonly used to determine water contents, but can be
prone to errors in all but experienced hands, and risk compromising the sample when
the vial is opened to atmosphere in order to dissolve and titrate the contents” 51.
He further stated that for quantitative analysis;
“The calibration set must comprehensively cover all variations in analyte and matrix
components likely to be encountered in real samples, in order that the calibration
equation will be robust and reliable” 51.
In this instance the calibration models were mainly determined on dried samples of
one bulk batch and it is not known what impact this has had on the matrix. The one
sample run that was of a different batch (A051452) gave results from the true value of
0.07% to 0.48% H2O for the three models used. This is within acceptable limits of the
reference method, which had variability between replicates of up to 0.884% H2O, with
MSc Analytical Chemistry - N. Blakiston 101
an average of 0.164% H2O between replicates. To put this into perspective the site to
site transfer validation of the KF method had a variance of 0.8 % H2O between
replicates.
A disadvantage with multivariate analysis is that the prediction model often requires
an extensive calibration set to account for all of the possible sample variances in order
to achieve optimal performance. The calibration samples also need to resemble those
of real analytical samples and this is not always possible.
Brutsche (1996) 75 states; “For simple one component preparations, 40-60 spectra are
enough for calibration”, and “A basic requirement is that different batches of
excipients and active ingredients, as many as possible…”
Therefore this initial feasibility will need further development to incorporate a greater
range of batches to include the required variation in matrix. This was also borne out
by Plugge et al. whom used 474 results obtained by the spectroscopic method that
were compared to 4952 batch samples run by KF analyses72. This gives some idea of
the large amount of data required to produce a good calibration model. For
pharmaceutical use there may a limited number of batches made per year. For Eli
Lilly at Basingstoke there are several products that are only made a few times per year
and less than one hundred for even the highest volume product.
The analysis of 16 repeat readings by NIR demonstrated that there was a significant
variability. As the calibration models were produced by only one reading each time it
is assumed that improvement would be made with the averaging of multiple readings.
MSc Analytical Chemistry - N. Blakiston 102
For instance Webster et al. states “Each spectrum from the FT-NIR was an average of
20 readings”70. Windham et al. measured the sample 10 times and produced a 0.39 %
RSD for forage samples76 and achieved R2 values between 0.84 - 0.97 for the
calibration model, which gave values of 7.43-9.11% water for 20 forage samples.
The use of complicated chemometric techniques and mathematical manipulations lend
the technique of NIR analysis to statisticians rather than the humble analytical chemist
and this may be another reason for the slow uptake of the technique in an industry
dominated by chromatography. The use of NIR spectroscopy is a fantastic
opportunity for the future development of quality control in the pharmaceutical
industry and acceptance by the regulating bodies (discussed later) is an encouraging
sign.
The forty one samples used for the calibration model demonstrated that good linear
response can be achieved. This is true no matter whether a simple linear regression is
used, or a complicated multivariate manipulation is conducted. The R2 values for the
limited data were more than acceptable and demonstrated a good visual fit. The
predicted values showed a variation of up to 1.237%, which could be improved I am
sure with further work. However even at this level of error the method may be
suitable for certain applications. For example in a drying process for bulk material
this level of accuracy may be acceptable to validate a total blend time. For finished
product this may be acceptable if the expected results are significantly below
regulatory limits. It may be possible to use this as a check method and only
implement the reference method if a result above a certain control limit is obtained.
MSc Analytical Chemistry - N. Blakiston 103
The statistical analysis used to review the three NIR models did not highlight any one
model being superior to another. For pure simplicity and relatively good performance
in the analysis model R1 (linear regression) appears to have performed slightly better
than the other two models. As more data is collected this may be confirmed or
negated.
14.1 Further Discussion on Results
The NIR procedure had several advantages compared to the reference technique;
Cost
Speed
Safety
Ease of Use
Non-Destructive
Investigation of Suspect Results
14.1.1 Cost
The Bruker NIR Instrument was purchased at a cost of £45K this is compared to the
combined cost of £20K for the Turbo2 titrator and associated balance.
Servicing costs for the two instruments are comparable. The Karl Fischer requires
additional routine maintenance to replace titrant tubing and molecular sieve. This
reduces the resources available for testing (see speed).
MSc Analytical Chemistry - N. Blakiston 104
Consumables for the NIR include sample vials and stoppers. This is a relatively
cheap commodity. The Karl Fischer reagents and consumables cost, on average £200
per month, assuming that approximately 60 batches per month are analysed.
Within the Eli Lilly laboratories at Basingstoke there are four Karl Fischer
instruments, which include three different models. These are required to give flexible
use of resources, but also because different methods require different instrumentation
and set-up conditions. The KF instrumentation has only one purpose and that is to
test the concentration of water in a sample. The ability of NIR spectroscopy to
replace many different pieces of test equipment makes this technique an attractive
proposition. It is already being used for excipients and API identification and could,
potentially make the Karl Fischer equipment redundant.
14.1.2 Speed
The NIR calibration process required lengthy analysis by the KF reference technique
to produce the required number of data points for the NIR model. Once the
calibration had been established and is stored in the NIR instrument it is available for
use. A daily NIR performance check is conducted, which requires approximately 2-5
minutes of analyst time to run. Quantitative determinations can then be performed in
less than five minutes per sample, depending on the number of readings required. The
run time to analyse 12 samples by NIR spectroscopy for the determination of water
took approximately 30 minutes. The same analysis conducted by the KF method took
approximately 12 hours.
As Analyst time is costed to approximately £40 p/hr this can be directly related into a
saving of nearly £500.
MSc Analytical Chemistry - N. Blakiston 105
The simplicity of the NIR analysis passes on another advantage in that there is a huge
saving in training time and possibly validation time in the transfer of methods from
one laboratory to another.
This would need to be balanced against the volume of batches that require to be tested
and the time involved in validating a suitable method.
14.1.3 Non-destructive
NIR is a non destructive technique and any suspect analytical result can be fully
investigated. This may be by repeat analysis or confirmation by a reference method.
Karl Fischer is a destructive technique, which makes any investigation potentially
very difficult. Karl Fischer analysis has a high potential to produce failures within the
laboratory and is one of the techniques that produce the most investigations at Eli
Lilly. There are several reasons for this;
Most methods have acceptance criteria, which may be a range or %RSD
between replicates. Due to the nature of the analysis this will occasionally
fail.
There is a number of sample transfer steps required and any mistake by the
analyst or link between the balance and KF instrument can cause failure.
Some methods require a final method suitability check to be conducted. If this
is not conducted the test needs to be repeated.
Routine performance checks between samples may fail. The reasons for
failure are usually difficult to investigate.
MSc Analytical Chemistry - N. Blakiston 106
The sample may be spilt when transferred.
The balance may not be stable when a reading is taken or environmental
conditions may effect the balance operation.
The wrong reagents may be used.
Reagents may be exhausted during analysis.
Poor judgement in sample preparation by the analyst.
Tablets have been known to stick to the vessel and do not disintegrate causing
failure.
All of these issues have occurred in the last three years within the Basingstoke
laboratory.
Brandenberger et al. stated; “This comparison shows considerable deviation of the
results from the different methods… The KF method has been used by untrained
personnel, which could explain at least partially the difference between the results of
the KF and the new method” (instant coffee powders 0.5% absolute difference on a
water value of approximately 1.5%)73.
14.1.4 Containment and Safety
Karl Fischer analysis involves unavoidable exposure of the sample to the atmosphere
and possible change in moisture content during weighing and measurement. This was
a problem experienced with the dried samples. This means that the analyst is also
exposed to the product.
MSc Analytical Chemistry - N. Blakiston 107
The risk assessment for KF operation requires the use of a ventilated area; usually
work is conducted within a fume cupboard. The analyst must wear personal
protective equipment including nitrile gloves during any material or reagent
operation. The reagents used are particularly toxic and are flammable.
NIR analysis offers an extremely safe alternative, which requires no reagents and the
sample can remain sealed throughout.
MSc Analytical Chemistry - N. Blakiston 108
15 Conclusion
Quantitative measurement by Fourier Transform Near-Infrared (FT-NIR)
spectroscopy is reliant on data from a reference method. The Karl Fischer (KF)
volumetric titration method proved initially to give poor performance and results that
could net be relied upon.
During the feasibility study this method was further developed to produce results,
which met current method acceptance criteria in line with other KF methods. This
was not the original aim of the objectives stated and required significant investment of
investigative time.
Forty one samples were analysed in duplicate by the reference method and by FT-NIR
spectroscopy. The results from the reference method were used to develop three
calibration models using the NIR software package (Opus-Quant). The three models
included a linear regression model (R1), a Vector Normalized + Principle Component
Analysis (R2) model and a fully system optimised model (R3).
The calibration models produced values of 0.9736 (correlation coefficient), 96.31 (R2)
and 99.82 (R2) respectively. Models R2 and R3 had Root Mean Square Error of
Estimation values of 0.386 and 0.0952 respectively.
The validation models for R2 and R3 produced R2 of 94.42 and 95.74 with Root Mean
Square Error of Cross Validation values of 0.451 and 0.394.
MSc Analytical Chemistry - N. Blakiston 109
The predicted cross validation values gave a mean residual value of 0.000, -0.022 and
0.018 for each model with a maximum variance of 0.933, 1.237 and 0.909 % H2O
respectively. Models R2 and R3 identified one outlier each, this was not unexpected
and full use of the software package clearly show these data points lie significantly
outside of the normal population. However this was not fully investigated or
discussed as part of this feasibility study.
The NIR models gave acceptable prediction based on only 41 data points and were
comparable to the reference method with respect to both precision and accuracy.
There was no clear distinction in superiority between the three models as statistical
analysis demonstrated, but the linear regression model appeared to perform slightly
better than the other two models. Many NIR analysis methods reviewed were based
on complex multivariate chemometric manipulation. However this investigation has
proven that simple is often better and should certainly be the first approach to
establishing a linear relationship.
The NIR method gave rapid and non-destructive measurement with no sample pre-
treatment and eliminated all hazards associated with the reference method. The
savings of materials, equipment utilisation and resource are considerable over the
reference method once a fully validated NIR method is available.
MSc Analytical Chemistry - N. Blakiston 110
16 A Review of Method Validation
Analytical method validation has been defined as a procedure used to prove,
unequivocally that a test method should consistently yield what it is expected to do,
with adequate accuracy and precision. This is borne out by the statement in the
United States Pharmacopeia20: “The accuracy of an analytical method is the closeness
of test results obtained by that method to the true value”.
Regulatory authorities require that data provided for applications for marketing have
been acquired using validated methods. The Food and Drugs Administration
Guideline21 requires that data be provided”… over the range of interest (ca. 80-120%
of label claim).
In the highly regulated pharmaceutical industry there has been a history of increasing
method validation requirements. Prior to 1979 there was no mention of this
requirement in the guidance documents for Good Manufacturing Practice (GMP).
There are now numerous guidance notes21, 23 and regulated requirements to ensure a
method is ‘Fit for Purpose’.
The International Conference on Harmonisation of Technical Requiremements for
Registration of Pharmaceuticals for Human Use (ICH) has helped to rationalise some
of the key requirements across the industry. The guidelines introduced have helped
define the general validation characteristics required for analytical test methods;
MSc Analytical Chemistry - N. Blakiston 111
16.1 Specificity
Specificity is the ability to assess unequivocally the analyte in the presence of
components that may be expected to be present. Typically, these might include
impurities, degradants, matrix, etc. Lack of specificity of an individual analytical
procedure may be compensated by other supporting analytical procedure(s).
16.2 Linearity
The linearity of an analytical procedure is its ability (within a given range) to obtain
test results that are directly proportional to the concentration (amount) of analyte in
the sample.
16.3 Range
The range of an analytical procedure is the interval between the upper and lower
concentration (amounts) of analyte in the sample (including these concentrations) for
which it has been demonstrated that the analytical procedure has a suitable level of
precision, accuracy, and linearity.
16.4 Accuracy
The accuracy of an analytical procedure expresses the closeness of agreement
between the value which is accepted either as a conventional true value or an
accepted reference value and the value found. This is sometimes termed trueness.
16.5 Precision
The precision of an analytical procedure expresses the closeness of agreement
(degree of scatter) between a series of measurements obtained from multiple sampling
of the same homogeneous sample under the prescribed conditions. Precision may be
MSc Analytical Chemistry - N. Blakiston 112
considered at three levels: Repeatability, intermediate precision and reproducibility.
Precision should be investigated using homogeneous, authentic samples. However, if
it is not possible to obtain a homogeneous sample it may be investigated using
artificially prepared samples or a sample solution. The precision of an analytical
procedure is usually expressed as the variance, standard deviation, or coefficient of
variation of a series of measurements.
16.6 Repeatability
Repeatability expresses the precision under the same operating conditions over a
short interval of time. Repeatability is also termed intra-assay precision.
16.7 Intermediate Precision
Intermediate precision expresses within laboratories’ variations: Different days,
different analysts, different equipment, etc.
16.8 Reproducibility
Reproducibility expresses the precision between laboratories (collaborative studies,
usually applied to standardisation of methodology).
16.9 Detection Limit
ICH Definition: The detection limit of an individual analytical procedure is the lowest
amount of analyte in a sample which can be detected but not necessarily quantitated
as an exact value. This is only required for limit testing for impurities.
MSc Analytical Chemistry - N. Blakiston 113
16.10 Quantitation Limit
ICH Definition: The quantitation limit of an individual analytical procedure is the
lowest amount of analyte in a sample which can be quantitatively determined with
suitable precision and accuracy. It is used particularly for the determination of
impurities and/or degradation products.
16.11 Robustness/Ruggedness
The robustness of an analytical procedure is a measure of its capacity to remain
unaffected by small, but deliberate, variations in method parameters and provides an
indication of its reliability during normal usage.
16.12 System Suitability Testing
System suitability testing is an integral part of many analytical procedures. The tests
are based on the concept that the equipment, electronics, analytical operations and
samples to be analysed constitute an integral system that can be evaluated as such.
System suitability test parameters to be established for a particular procedure depend
on the type of procedure being validated.
The ICH guidelines were developed mainly for the validation of analytical procedures
primarily based on the analyte being in solution and using univariate mathematical
treatment of resultant data. This proved specific and suitable for the validation of
many routinely used chromatographic methods, but is more difficult in application to
NIR spectroscopic methods. To this end by 1990 there were a series of requirements
and guidelines published24 by various authorities that included:
MSc Analytical Chemistry - N. Blakiston 114
Guideline for Submitting Samples and Analytical Data for Methods Validation,
1987
Guidance Notes on Applications of Product Licences, 1987
Committee for Proprietary Medicinal Products Guidance Note on Analytical
Validation, 1989
Guidelines on the Quality, Safety and Efficacy of Medicinal Products for Human
Use, 1990
These guidelines attempted to define terms, but were vague as to what was exactly
required for a validation exercise. To help clarify the position several papers were
published on the practical approaches to method validation in pharmaceutical
analysis25 and this work was presented at the “Second International Symposium26 on
Pharmaceutical and Biomedical Analysis”. The Pharmaceutical Analytical Sciences
Group (PASG), which is an association of analytical chemists within the research
based UK pharmaceutical industry, agreed to determine current practice employed by
its membership. The results of a conducted survey, published in 199427 proved useful
for assessing the approaches to method validation and recommended a harmonised
guideline for validation within the pharmaceutical industry. This finally led to
revision of Pharmacopoeial text to allow for the use of new techniques of analysis;
The BP 1993 contains the following statement in the General Notices:
“this does not imply that performance of all tests in a monograph is necessarily a
prerequisite for a manufacturer in assessing compliance with the Pharmacopoeia
before release of a product. The manufacturer may assure himself that a product is of
Pharmacopoeial quality, from in-process controls or from a combination of both”.
MSc Analytical Chemistry - N. Blakiston 115
Further, the USP 23 stated in its General Notices that:
“ data derived from manufacturing process validation and from in process controls
may provide greater assurance that a batch meets a particular monograph
requirement than analytical data......On the basis of such assurances, the analytical
procedures in the monograph may be omitted.......Compliance may be determined also
by the use of alternative methods, chosen for advantages in accuracy, sensitivity,
precision, selectivity or adaptability to automation or computerised data reduction or
other special circumstances. Such alternative methods shall be validated”.
Official recognition of NIR spectroscopy was not achieved until 1990. The edition of
AOAC Official Methods of Analysis discussed a method titled “Piperazine in drugs”
(Official Methods of Analysis, 1990) was the first official NIR method in
pharmaceutical analysis. More recently a reflectance NIR assay analysis of intact
bulk tablets was conducted (Sterwin 500 mg paracetamol tablets), which
demonstrated successful application in meeting the regulatory submission
requirements for the pharmaceutical industry39, 40.
In the latter paper each validation characteristic was discussed in turn and proven to
be met through successful validation of the NIR method. It also discussed additional
routes to the analysis for validating more problematic spectral analysis.
Specificity can be achieved through selectively labelling all the components of
particular spectra, showing zero-order interference, or mathematical pre-treatment and
chemometrics could be applied to deconvolute the spectra. Demonstrating that spiked
interfering compounds or increases in others components do not interfere would be
MSc Analytical Chemistry - N. Blakiston 116
another suitable method. Proving a qualitative match to a reference library would also
be acceptable.
Linearity is demonstrated by the construction of calibration and sample sets. NIR
calibration plots use the predicted assay value Vs reference values and applies
statistical evaluation to demonstrate that the method is linear.
The Range of the linear calibration is dependant on the ease of using a suitable
calibration set. This is limited to the variation of manufacturing performance (tablet
weight, thickness or quantity of active present) unless representative samples can be
made. An ideal situation is one where the drug product is manufacture in the same
physical parameters with only a change in the API content (e.g. 25, 50, 100 mg).
Accuracy is conducted as part of the calibration and can be easily determined with
comparison to other known analysis methods. The Precision (degree of scatter) is the
variation between different readings of the same homogenous sample. Most NIR
methods suggest the averaging of multiple readings to increase the precision of the
true value as this only takes a few minutes per sample. The Intermediate Precision or
Repeatability can be achieved easily by measurements with different analysts on
different equipment (if available) on different days.
Inter-laboratory Precision or Reproducibility is not a general requirement unless the
method is for broad use, such as a Pharmacopoeial reference method. The final
requirement for validation is normally applied to solution stability, tolerance in
weighing practice, volumetric process and extraction times. This is the Robustness
MSc Analytical Chemistry - N. Blakiston 117
(the ability to remain unaffected by deliberate method variation) of the method and
due to the nature of sample preparation with NIR analysis, has little implication.
However other variances need to be reviewed such as; environmental conditions,
sample preparation (especially for ground or powdered samples, due to particle size
and bulk density variance), sample orientation and variation in the method of analysis
(probe parameters, vial diameters, cell thickness).
The daily operation of any analytical instrument is checked using a set calibration
process. This may be conducted as part on the analytical run using internal standards,
or using external standards such as in balance calibration. The System Suitability
check for a modern NIR spectrometer is an automated integral part of the system.
The instrument will conduct internal diagnostics and performances checks of the
filters using set parameters. This will take approximately twenty minutes at the start
of each day and will generate a report detailing the tests performed and the criteria
met.
As more companies invest and develop the use of the versatility and analysis speed of
this technique, greater guidance will become available from the regulators as to the
requirements for method validation. This may generate a snowball effect, but as yet
movement forward in this area has been slow.
MSc Analytical Chemistry - N. Blakiston 118
17 Reference Review
17.1 Applications of NIR Spectroscopy
The first examples of the application of NIR spectroscopy was in the examination of
cereal and forage products, specifically looking at moisture content and protein
analysis. This is still a significant area of application for the technique, but has now
spread into many other material handling sectors including industries such as;
petrochemicals, fine chemicals, polymers, food, packaging, medical and
pharmaceuticals. A good review of the development of NIR analysis has been
documented by Drennen et al, (1993) 74.
Some of these applications are discussed in more detail below, with the emphasis
mainly on applications in the pharmaceutical industry;
17.1.1 Qualitative Determinations
Examples of NIR spectroscopy used for qualification purposes in the pharmaceutical
industry include; identification of raw materials (API and Excipients), finished
product, assessing homogeneity and in-process monitoring of key stages of
pharmaceutical production such as blending and coating.
17.1.1.1 IdentificationNIR reflectance spectroscopy is used in Basingstoke QCL to identify raw materials
and there have been well documented examples including; the identification and
discrimination of penicillin-type drugs (1982) 53 and more recently rapid, non-
destructive methods of analysis for the identification of pure powdered drugs and
actives in tablets33.
MSc Analytical Chemistry - N. Blakiston 119
A published application by Dempster (1993)34, 35, described the development of non-
invasive methods to confirm the identity of blister-packed tablets for clinical trial
supplies. The approach described the examination of the tablets through unopened
opaque blister packs and presentation of single exposed tablets. Four strengths of the
experimental drug were examined (2, 5, 10 and 20% w/w of the active), a placebo and
a marketed comparator containing 80% w/w of the active. The method was also able
to distinguish film-coated and non-film coated tablets.
These papers illustrate the diverse applications of NIR spectroscopy for identification
purposes.
17.1.1.2 In-Process Monitoring
This is a great strength for NIR spectroscopy analysis due to its non-destructive nature
and rapid analysis time. Investigation by Rantanen (1998)50 to monitor the moisture
content in a fluidized bed granulator with a multi-channel NIR moisture sensor proved
useful for validation purposes. The Standard Error of Performance (SEP) was found
to be 0.2%. There is a movement within the industry, supported by the FDA, to
incorporate Process Analytical Technologies (PAT). This on-line sampling and
testing builds additional quality into the validation of a process, which ensures greater
confidence in the product and can lead to reduced end item monitoring66.
MSc Analytical Chemistry - N. Blakiston 120
17.1.1.3 Homogeneity
The following are examples of the use of NIR spectroscopy to determine
homogeneity. The homogeneity of a typical direct compression pharmaceutical
powder blend was monitored by reflectance NIR spectroscopy by Wargo (1996) 54.
They concluded that NIR spectroscopy had great potential as an analytical tool in
powder blend analysis.
Another paper published on the on-line monitoring of powder blend homogeneity by
Sekulic (1996) 57 described the use of a diffuse reflectance fibre-optic probe during the
blending process. Spectral changes occurred which eventually converged to a point
of constant variance when a blend of sodium benzoate, microcrystalline cellulose,
lactose and magnesium stearate were suitably blended. Their results demonstrated
that a blend was suitably homogeneous before a typical blending period was
complete.
These papers illustrate how, by the use of NIR spectroscopy, the blending process
need not be a time limiting factor in the pharmaceutical industry due to, for example,
waiting for laboratory analysis to confirm batch homogeneity. Furthermore, NIR
spectroscopy can more accurately predict, through greater sampling, the optimum
blending time for homogeneity.
MSc Analytical Chemistry - N. Blakiston 121
17.1.2 Quantitative Determinations
17.1.2.1 Particle Size
The particle size distribution of powdered pharmaceutics is important as it affects
powder flow, dissolution rate and compressibility. Typical particle size analysis is by
forward angle light scattering or sieve analysis. However this is a time consuming
and sample destructive technique.
A study was published on the use of reflectance NIR spectroscopy to the
measurement of granule particle size growth of microcrystalline cellulose during
granulation by Rantanen (1998) 50. The results demonstrated the use of NIR
spectroscopy for on-line determination of the particle size.
This was followed by a publication on the determination of cumulative particle size
distribution of microcrystalline cellulose using MLR and PLSR by O’Neil (1999) 55.
This study demonstrated a rapid determination of the cumulative frequency which
represented a development over previously published studies by Ciurczac (1986) 56
which proved a linear relationship between the ratio of two wavelengths versus mean
particle size.
NIR spectroscopy therefore has the potential to be used in the pharmaceutical industry
for determining both the mean particle size and cumulative particle size of powders
and granules.
MSc Analytical Chemistry - N. Blakiston 122
17.1.2.2 Powders and Tablets
There are numerous examples of NIR spectroscopy being used to non-destructively
quantify the active in powders and tablets (crushed or whole).
As early as 1987, reflectance NIR spectroscopy was used to determine the
nicotinamide content in pre-mixes (Osborne 198758). This enabled 36 samples to be
quantified in around 30 minutes.
Later Corti (199059) published a paper on reflectance NIR spectroscopy being used to
quantify the following solid binary mixtures:
diazepam (1.5% w/w,.SEP:.0.11%.w/w)
otillonium bromide (14.8%.w/w,.SEP:.0.37%.w/w)
dicloxacillin (34.0%.w/w,.SEP:.0.52%.w/w)
sodium ampicillin (66.0%.w/w,.SEP:.0.67%.w/w)
The authors concluded that the results were satisfactory if the percentage active
content was not below 1% w/w.
Corti et al, continued work in this field and described how streptomycin sulphate
(70.2% w/w) and cloxacillin benzathine (96.0% w/w) powders were quantified using
reflectance NIR spectroscopy. The SEP for these quantitative determinations was 1.03
and 0.95% w/w respectively. Several antibiotic compounds were also reviewed,
sodium ampicillin (85.0% w/w, SEP:.1.08%.w/w) and gentamicin sulphate
(66.0%.w/w,.SEP:.0.78%.w/w).
MSc Analytical Chemistry - N. Blakiston 123
Zappala (1977) 60, measured meprobamate in ground tablets by measuring the
transmittance of NIR radiation. The results compared favourably with the reference
United States Pharmacopeia method with a coefficient of variation (CV) of 0.7%.
Broad (2000) 61, published a paper on the application of transmittance NIR
spectroscopy to the uniformity of content of intact steroid tablets. Tablets containing
5, 10, 15, 20 and 30 mg (2.94, 5.88, 8.82, 11.76 and 17.64% w/w steroid) were
quantified with a measurement of error for a single tablet having a relative standard
error of less than 2.5% for a given active level.
A guideline has also been published, Smith et al., (2002)61, for the transfer of a
reflectance NIR reflectance assay for paracetamol in intact tablets between two
instruments of the same type. The authors successfully demonstrated the transfer of
PLS regression and MLR model assays along with the criteria deemed necessary to
conclusively prove transfer and justify any correction method utilised.
These papers illustrate that NIR spectroscopy can be used to quantify powders and
tablets, whether pharmaceutical premixes, antibiotics or binary mixtures and therefore
can be used as a replacement for the reference techniques such as HPLC.
MSc Analytical Chemistry - N. Blakiston 124
18 Brief History of Water Analysis by NIRS
Typical pharmaceutical methods for water determination are based on weight loss by
drying or by Karl Fischer titration techniques16.
NIR absorption bands in the region of 1900 to 2000 nm are indicative of the presence
of water as these are due to the combination of fundamental bending and stretching
vibrations of the OH bond. The intensity of this absorption band and the shape
depends on the degree of hydrogen bonding in environment in which the water is
located. The stronger the hydrogen bonding, the longer the wavelength of the NIR
absorption observed.
The determination of water is one of the more extensively published applications of
NIR spectroscopy and quantitative water determinations in the order published and
the major findings are detailed below:
Keyworth52 (1961), detailed the use of transmittance NIR spectroscopy to determine
the water content of methanol and ethylene glycol by single wavelength linear
regression. The content of water assayed was 0.03 to 4.00% v/v for methanol and
2.2 to 5.0% v/v for ethylene glycol. He concluded that the NIR method compared
favourably with the reference method and had the advantage of being non-destructive.
Hollenberg45 (1982), utilised transmittance NIR spectroscopy to determine hydration
numbers in 16 soluble anhydrous amino acids by single wavelength linear regression.
MSc Analytical Chemistry - N. Blakiston 125
The results demonstrated that the method was precise and comparable to available
literature values for both monomer and homopolypeptide hydration numbers.
Williams46 (1983), used reflectance NIR spectroscopy to determine the moisture
content of wheat by Multiple Linear Regression (MLR) using up to six wavelengths.
The NIR instruments were found to be superior to the reference oven method for the
determination of moisture in most cases.
Last47 (1993), used reflectance NIR spectroscopy for the determination of the
moisture in an experimental freeze-dried injection product by single wavelength, dual
wavelength linear regression and Partial Least Squares Regression (PLSR). This
study emphasised the need to include all possible manufacturing process variables in
order to obtain robust calibrations.
Blanco48 (1997), used reflectance NIR spectroscopy and a fibre-optic probe to
determine the water content in ferrous lactate. The results departed from the reference
Karl Fischer titration method by less than 1.5%.
Zhou49 (1998), determined the moisture levels in hygroscopic drug substances by
diffuse reflectance NIR spectroscopy. Samples were prepared with moisture levels
from 0.5 to 11.4% w/w and calibration models were built using PLSR giving a
Standard Error of Prediction (SEP) of 0.11% w/w.
Webster (2003) 70, developed and qualified a quantitative method for the
determination of moisture in topical formulations. This was developed for in process
MSc Analytical Chemistry - N. Blakiston 126
monitoring and as a check on finished goods. This method was suitable for routine
release of moisture, but further development was required to support active purity and
stability indication.
Brűlls (2007) 70, looked at OH combination bands and overtones (OH-stretch) using
spectral peak area analysis accurate predictions were made for moisture content of
lyophilized PVP in the range of 0-22%.
The above publications illustrate the wide spread application of NIR spectroscopy to
the determination of water content in both solid and liquid samples with examples
dating as far back as 1961.
MSc Analytical Chemistry - N. Blakiston 127
19 Future Work
Further development of an NIR method for Prozac determination would not be a
viable project, as this investigation was only a feasibility study and has no real benefit
to Eli Lilly. However as a student project this work could be continued to prove that
full validation of a NIR method for water determination is possible.
NIR analysis as a replacement technique for moisture determination has demonstrated
that a great opportunity exists to reduce lead times, costs, increase analyst safety, be
non-destructive and reduce potential errors.
With this in mind a suitable product candidate for development would be one that is
of high volume, has water content well below regulatory limits and has a high toxicity
level. NIR spectroscopy has been proven successful in the analysis of whole coated
tablets and thus testing could easily be conducted on-line in the manufacturing area.
One product manufactured at Basingstoke has a small variability in water content
between manufactured sections. If tested as a composite sample, the replicate KF
determinations would fail the method reproducibility limit. As batches are produced
as a four or even eight section size the time savings that NIR analysis offer are vast.
MSc Analytical Chemistry - N. Blakiston 128
20 Reference to Raw Data
20.1 Bench Books
BAS 1318 page 27, 28BAS 1420 page 116, 117, 118BAS 1441 page 41, 43, 44BAS 1447 page 64, 65BAS 1448 page 132BAS 1607 page 16 – 37BAS 1609 page 17BAS 1613 page 13, 15, 16, 17
20.2 Analytical Data Wallet
ADW: 32352ADW: 32365
MSc Analytical Chemistry - N. Blakiston 129
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35. Dempster, M.A., MacDonald, B.F., Gemperline,P.J., Boyer, N.R., “A NIR reflectance analysis method for the non-invasive identification of film-coated and non-film-coated, blister-packed tablets”, Analytica Chimica Acta, 1995, 310, 43-51.
36. Burns D., A., Cuirczak, E., W., “Handbook of Near-Infrared Analysis”, Marcel Dekker, New York, 2001.
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38. D.A.Skoog, T.A. Nieman, F.J. Holler, “Principles of Instrumental Analysis”, 5th ed., W Brookes/Cole, ISBN 0-03-002078-6.
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51. MacDonald, B.F., Prebble, K.A., “Some applications of NIR reflectance analysis in the pharmaceutical industry”, Journal of Pharmaceutical & Biomedical Analysis, 1993, 11(11/12), 1077-1085.
52. Keyworth, D. A., “Talanta”, 1961, 8, 461.53. Rose, J. R., “Qualitative and Quantitative Aanalysis of Pharmaceuticals with
NIRA”, Proceedings of the 2nd Annual Symposium on NIRA, Technicaon, Tarrytown, NY, 1982.
54. Wargo, D. J., Drennen, J. K., J. Pharm. Biomed. Anal., 1996, 14, 1415.55. O’Neil, A. J., Jee, R. D., Moffat, A. C., Analyst, 1999, 124, 33.56. Ciurczak, E., Torlini, P., Demkowicz, P., Spectroscopy, 1986, 1, 36.57. Sekulic, S. S., Ward, H. W., Brannegan, D. R., Stanley, E. D., Evans, C. L.,
Sciavolino, S. T., Hailey, P. A., Aldridge, P. K., Anal. Chem., 1996, 68, 509.58. Osborne, B. G., Analyst, 1987, 112, 313.59. Corti, P., Dreassi, E., Corbini, G., Ballerini, R., Gravina, S., Pharm. Acta.
Helv., 1990, 65, 189.60. Zappala, A. F., Post, A., J. Pharm. Sci., 1977, 66, 292.61. Broad, N. W., Jee, R. D., Moffat, A. C., Smith, M. R., Analyst, 2000, 125,
2054.62. Smith, M. R., Jee, R. D., Moffat, A. C., Analyst, 2002, 127, 1682.63. Franklin, E. Barton II, “Theory and Principles of NIRS”, Spectroscopy
Europe, 14/1, (2002), p12.64. Miller, C. E., “Sources of non-linearity in NIR methods”, NIR News, 1993,
4(6), 3-5.65. Morisseau, K.M., Rhodes, C.T., “Pharmaceutical uses of NIRS”, Drug
Development and Industrial Pharmacy, 1995, 21(9), 1071-1090.66. Forcinio, H., “Pharmaceutical industry embraces NIR technology”, 2003,
18(9), 17-19.67. Bruker Opus Version 5.5 Quant Spectroscopic Software, “Handbook”, 2004.68. Fearn, T., “A look at some standard pre-treatments for spectra”, NIR news,
1999, 10(3), 10-11.69. Cowe, I. A., McNicol, J. W., Appl. Spectrosc., 1985, 39, 257.70. Webster, G. K., Farrand, D. A., Johnson, E., Litchman, M. A., Broad, N.,
Maris, S., “Use of NIRS for quantitative determination of elamectin and moisture in topical formulations”, J. Pharmaceuticals and Biomedical Anal., 33(1), 2003, 21-32.
71. Brűlls, M., Folestad, S., Sparén, A., Rasmuson, A., Salomonsson, J., “Applying spectral peak area analysis in NIR spectroscopy moisture assays”, Journal of Pharmaceutical and Biomedical Anal., 44(1), 2007, 127-136.
72. Plugge, W., Van Der Vlies, C., “The use of NIR spectroscopy in the quality control laboratory of the pharmaceutical industry”, Journal of Pharmaceutical & Biomedical Analysis, 10(10-12), 797-803, 1992.
73. Brandenberger, H., Bader, H., “Determination of. Powder Moisture by Azeotropic Distillation and Near-Infrared Spectrophotometry”, anal. Chem., 33(13) 1961, 1947-1949.
74. Drennen, J. K., Lodder, R. A., “Pharmaceutical Applications of NIRS”, Advanves in NIR measurement, 1, 93-112, (1993).
75. Brutsche, A., “Advances in NIR Spectroscopy”, European Pharmaceutical Review, 1996, Sep, 45-51.
76. Windham, W. R., Barton, F.E., “Moisture in Forage NIR Reflectance Spectroscopy”, J. ASSOC. OFF. ANAL. CHEM., 74(2), 1991, 325-331.
MSc Analytical Chemistry - N. Blakiston 132
Attachment 1: Tabulated Overview moisture determination methods 30.
MSc Analytical Chemistry - N. Blakiston 133
Attachment 2: Orion Turbo2TM Volumetric Karl Fisher Titrator.
1. Blender drive cover2. Pump observation window3. Tube platen4. Drain pump switch5. Platen release/engage lever6. Pump rotor7. Glass sealing plate8. Pivot release catch9. Vessel clamp screws10. Protective guard11. Spillage tray12. Vessel13. Electrode14. Sample port stopper15. Cam arm16. Protective guard retaining screw17. Blender motor inter-lock position (magnet)18. ‘P’ clip jet tube retainer19. Blender drive belt20. Shaft seal retainer
Orion Turbo2 TM Volumetric Karl Fisher Titrator - Rear View
8. Waste bottle vacuum tube10. Desiccant guard tubes, vessel11. Desiccant guard tube, reagent12. Interconnecting power cable sockets13. Fuse, power cable14. Reagent tube connector15. Socket for console interconnecting signal cable
(Turbo2TM Instruction Manual)
MSc Analytical Chemistry - N. Blakiston 134
Attachment 3: Bruker PQ Test Protocol
MSc Analytical Chemistry - N. Blakiston 135
Attachment 4: Pay-Off Matrix for Water Determination A
Speed of analysis
Easeofuse
Halogen
TGA
Oven
KF
DE/RE
Speed of Analysis versus Ease of Use
NIR
MSc Analytical Chemistry - N. Blakiston 136
Attachment 5: Pay-Off Matrix for Water Determination B
Wide Sample Range
MeasuringRangeHalogen
TGAOven
KF
DE/RE
Wide Sample Range versus Measuring Range
NIR
MSc Analytical Chemistry - N. Blakiston 137
Attachment 6: Pay-Off Matrix for Water Determination C
Accuracy
Halogen
TGA
Oven
KF
DE/RE
Accuracy versus Selectivity
NIR
Selectivity
MSc Analytical Chemistry - N. Blakiston 138
Attachment 7: Bruker MPA FT-NIR Default Parameter Settings.
MSc Analytical Chemistry - N. Blakiston 139
Attachment 8: Equipment
Prozac Powder blend Sample containing capsule fragments.
Turbo 2 Karl Fisher: Peristaltic pump, delivery tubes, motor drive, sample aperture and Turbo blade.
Turbo Karl Fischer set up with;Balance, Reagents, Printers, Calibration weights and syringe.
Bruker MPA FT-NIR
This can measure in a multiple of orientations including fibre optic probe.
MSc Analytical Chemistry - N. Blakiston 140
Attachment 9: Method AP-1043-01
Turbo2 Karl Fischer Assay for WaterIn Fluoxetine Hydrochloride Capsules
Equivalent to 10 and 20 mg Fluoxetine.
MSc Analytical Chemistry - N. Blakiston 141
Attachment 10: Technical Protocols
QCL-TR-014
QCL-TR-015
QCL-TR-016