prof. aza

Llyod R Snyder, J. Kirkland, J.L., Glajh, Practical HPLC Method Development

prof. aza

Introduction One of the strength of HPLC is that it is an

excellent quantitative analytical technique. HPLC can be used for the quantitation of the

primary or major component of sample, for mixtures of many compounds at intermediate concentration, and for the assessment of trace impurity concentration (parts per billion or lower) in a matrix.

Properly designed, validated, and executed analytical methods should show high levels of both accuracy and precision for a main component analysis (± 1 to 2% precision and accuracies within 2% of actual values.)

prof. aza

Introduction, continued

Trace-level quantitation often is not as good: however, accuracy within 10% of the true value and precision of ±10 to 20% at the lowest levels of quantitation are still achievable.

A critical requirement for a quantitative method is an ability to measure a wide range of sample concentration with linear response for each analyte.

The UV detector is the most widely used for accurate and precise quantitation in HPLC

prof. aza

Accuracy, Precision, and Linearity Accuracy is defined as the closeness of

the measured value to the true value. The “true value” can be determined by a variety of techniques.

Making accurate measurements in a HPLC method routinely also involves the use of proper calibration techniques and minimizing of error.

prof. aza

Precision Precision refers to the reproducibility of

multiple measurements of a homogenous sample.

This can include reproducibility of results using different instruments, analysts, sample preparations, laboratories, and so on, obtained on a single day or over multiple days.

Different levels of precision are often assessed as part of method validation.

prof. aza

The Linearity The linearity of a method is a measure of

how well calibration plot of response vs. Concentration approximates a straight line, or how well the data fit to the linear equation

○Y = mx + b

○ Where y is the response, x the concentration○ m the slope, and b the intercept of aline fit to

the data

prof. aza

Ideally, a linear relationship (with b~0) is preferred because it is more precise, easier for calculations, and can be defined with fewer standards.

Also, UV detector response for a dilute sample is expected to follow Beer’s law and be linear (with b~0). Therefore, a linear calibration gives evidence that the system is performing property throughout the concentration range of interest.

prof. aza

In addition, a method that is linear permits a quick, convenient check wit one (preferably two) points to confirm calibration accuracy.

If the calibration check values show more than a 2σ deviation from values of the original calibration, a full recalibration may be required.

prof. aza

Limits of Detection and Quantitation The minimum detectable amount of

an alyte (often referred to as the limit of detection (LOD) is the smallest concentration that can be detected reliably.

The LOD is related to both the signal and the noise of the system and usually is defined as a peak whose signal-to-noise (S/N’) ratio is at least 3 : 1.

prof. aza

For example, Fig 14.1a shows a typical example where the signal is three times the detector noise. Here, the noise (peak to peak) is 10 units, while the signal is 30 units.

prof. aza

LOQ The minimum quantitatible amount

[often known as the limit of quantitation (LOQ) ] is the concentration that can be quantitated reliably with a specified level of accuracy and precision. The limit of quantitation can be defined in either of three ways.

One method uses a technique similar to that for LOD but requires a S/N’ ratio of at least 10, as shown in Fig. 14.1b.

prof. aza

In this case, the peak-to-peak noise is 10 units, and the signal is 100 units (measured from the midpoint of the noise to the apex of the the signal peak).

The second method is to define a certain level of precision and determine experimentally how large a peak needs to be for that level of precision.

prof. aza

This can be accomplished by injecting sample concentrations that result in various S/N’ values (e.g., 3, 5, 10, 15, and 20) and determining the precision from multiple injections of each sample concentration.

A third technique assumes that the baseline noise is approximated by Gaussian distribution with a width of 4 standard-deviation units (SD) wide (N’=4σ).

prof. aza

as described in Section 3.2.3, measurement imprecision is affected by baseline noise (one measurement on each side of the peak) and signal (peak height measurement), so the effective uncertainty is approximately 31/2 σ K N’/2.

The coefficient of variation (CV) at low values of S/N’ K 50/(S/N’). Therefore, for a S/N’ of 5, if σ ~ N’/2, the estimated maximum precision using peak-height measurements would be 50/5 or ± 10%.

prof. aza

The third important feature in quantitation is the maximum level of quanitation, defined as the highest concentration that can reliably be determined using the conditions of the method.

Often, this is determined by the limit of linearity of the detector (i.e., when the detector no longer shows a linear response with specified increase in concentration

prof. aza

the range of the method.

The maximum and minimum quantitable amounts will define the range of the method.

If quantitation of higher concentrations is needed, dilution of the sample to bring it into a measurable quantitation range often is the easiest and most appropriate way to effectively extend the range of the method.

prof. aza

Figure 14.1: Signal-to-noise (S/N’) ratio for peak at (a) limit of detection (LOD) = 3:1; (b) limit of quantitation (LOQ) = 10 : 1.

prof. aza

Figure 14.1: Signal-to-noise (S/N’) ratio for peak at (a) limit of detection (LOD) = 3:1; (b) limit of quantitation (LOQ) = 10 : 1.

prof. aza

14.2 Measurement of Signals

prof. aza

14.2.1. Noise

The precision of any signal measuremnt (which is related to assay result) is affected by the size of the peak (signal) relative to the noise. Noise refers to uncertainty in the value of the baseline signal in absence analyte. There are three basic categories of noise: short-term, long-term, and baseline drift. Each of these three types is illustrated in Fig. 14.2,

prof. aza

Short-term noise

Short-term noise (also known as high-frequency noise) is of primary interset for most S/N’ measurement.

Short-term noise can be due of a number of factors, including detector noise, pulsations of the pumping system, and electronic noise in the integration system.

This high-frequency noise component (typically with periodicity > 1 Hz) ultimately limits the ability to measure any signal in HPLC

prof. aza

Long-term noise

Long-term noise (variations in the signal with a frequency , 0.1 Hz) often is indicative of some external source or problem with the systems. Figure 14.2b shows an example of long-term noise with a frequency of one cycle every 3 min

prof. aza

Causes of long-term noise include: Poor on-line mixing of solvent

components causing slight variations in the mobile phase over time.

Temperature variations Bleed of stationary phase from he

column (especially during gradient elution)

Late-eluting compounds from prior injections

prof. aza

long-term noise

Typically, long-term noise of this type, once it is identified, can be corrected before implementing an HPLC method, reference 6 provides many good suggestion for correcting problems related to noise. Baseline noise and its effect on assay precision are discussed further in Section 3.2.3.

prof. aza

Baseline drift Baseline drift, which can be considered a

special type of long-term noise, can occur even in well-developed and validated methods.

The most noticeable type of baseline drift is seen in gradient elution, where the composition of the solvent is deliberately changed during the course of the run; therefore, the response of the detector (typically, UV) may change as a function of solvent composition.

prof. aza

This type of baseline drift is illustrated in Fig, 14.2.c. Reproducible baselines can be established, and both peak height and peak area measurements are possible even when baseline drift due to gradient eluion evident.

The use of modern data system can help this process; however, severe baseline drift often requires manually overriding automated peak integration algorithms, complicating the overall analysis.

prof. aza

Short-term noise

prof. aza

Long-term noise

prof. aza

Drift

Figure 14.2 :

prof. aza

Techniques that can be used to eliminate baseline drift during gradient elution are discussed in Section 8.5.3.

Late-eluting peaks or peaks from previous injection also can appear as baseline drift. This “peaks” elute as very broad bands and sometimes are indistinguisable from other types of baseline drift.

This baseline problem often occurs in isocratic seperation when late-eluting compounds are not cleaned off the column after each injection.

prof. aza

This type of baseline drift can be minimized or eliminated by techniques described in Section 5.4.3.2.

Baseline drift also can be due to changed in the detector. Some refractive index detectors are especially sensitive to temperatur fluctuations; any changes in the intensity of the lamp (aging) or the detector diodes or phototubes.

prof. aza

Although such changes are often on a time scala much longer than the chromatographic run, the drift can become significant if the components of the detectors are near the end of their usable life.

prof. aza

14.2.2. Peak Height The simplest way to measure the response of a

detector to a compound is by determining the peak height of the signal. This method of peak measurement is the preffered approach for trace analysis.

For a well-resolved single component, the peak is the distance between the baseline and the apex of the peak, where the baseline value is the average of many data points taken before the start of the peak and after the end of the peak, as illustrated in Fig. 14.3

prof. aza

If the baseline is changing because of long-term or drift, the measurement of the peak height needs to be modified, as for peak 3 in Fig. 14.3.

Here the baseline must be interpolated from the beginning to the end of the peak, as shown by the dashed line.

For the peaks that are not resolved completely, peak height can be determined using a tangent skimming method, as illustrated by the mayor component and peak 1 in Fig.14.3.

prof. aza

Figure 14.3: Peak-height measurement in HPLC

prof. aza

However, tangent skimming should be used only for small peaks on the tailing edge of a large preceding peak.

Althought measurement of peak height is a simple manual procedure, most modern data systems also will calculate peak height. However, it is important to verify that the proper baseline has been established, especially for situations such as shown in Fig. 14.3.

prof. aza

14.2.3 Peak Area Peak area is the most widely used

technique for quantitating response in HPLC. The area of a well-resolved peak is defined as the integral of the signal response over time from the beginning until the end of the peak.

This definition is relatively straightforward in theory. However, in practice, the accurate and precise measurement of peak area relies on a number of factors.

prof. aza

First is the need to establish the correct baseline, especially in the presence of short- or long-term noise.

Second, it is necessary to define accurately the beginning and end of the peak (i.e., when the signal can be differentiated from noise at the beginning and when the signal has returned to the baseline). This can be difficult for a non-symmetrical or tailing peak, leading to inaccurate quantitation.

prof. aza

Third, the number of data points necessary to collect across the peak should be large enough to assess accurately the actual peak area. It has been shown that nine data points can accurately describe a Gaussian peak, but that up to 32 points are required for a non-Gaussian (tailing) peak. Far most cases, at least 15 points across the peak of interest are recommended;

prof. aza

This typically means that the sampling rate for the data systems must be at least 3 to 5 points per second (even higher smpling rates for early-eluting sharp peaks and especially for methods using columns of 3-µm particles run at higher flow rates).

Peak area typcally is calculated using an integrator or computerized data system. However, peak areas can also be mesured manually, as shown in Fig. 14.4a and b.

prof. aza

With a data siystem, the peak area is the summation of signal/time “slices” across the peak, as shown schematically in Fig. 14.4c. This method of integration can be very precise if executed properly (typically, precisions better than ± 0.2% for peak with large S/N’).

Note, however, that peak detection algorithms for most data systems (hence, peak area calculations) rely on a threshold value to determine when the peak begins and end;

prof. aza

Differentation of real peaks from short-term noise must also be accomplished. The improper setting of a peak threshold can influence the accuracy of quantitation, as shown in Fig 14.5.

Here the integrated peak area is shown as a function of different peak thresholds for both a symmetrical (As=1.00) and an asymmetrical (As=1.58) peak.

prof. aza

Because of the flatter response on the tailed portion of the asymmetrical peak, the peak-detection algoritm identified the end of the peak too early in each case relative to the peak end identified for the same threshold with the symmetrical peak.

The recovered peak areas (expressed as a percentage of the true peak area) ranged from 99.6 to 99.9% for the symmetrical peak, but only from 92.3 97.8% for the asymmetrical peak.

prof. aza

Each data system or integrator is slightly different; therefore, the proper functioning of a particular system must be ensured for the user to rely on the data generated. Manufactures should provide information on optimum setting as part of instrument purchase; however, instrument accuracy should also be checked periodically to make sure that the measurement of peak area is consistent.

prof. aza

prof. aza

prof. aza

prof. aza

Figure 14.4 : Methods of peak area quantitation in HPLC. (a) method of height x widt an half-height; (b) method of triangulation; (c) Electronic (data systems).

prof. aza

Figure 14.5: Integrated peak area as a function of threshold for (a) a symmetrical peak (As=1.00) and (b) a tailing peak (As = 1.58). Recovered peak area □ = 99.9%, ▲ white =99.8%, ◌ = 99.6%, ◘ = 97.8%, ● = 92.3%.

prof. aza

14.2.4. Peak Height vs Peak Area for Quantitation

Either peak height or peak area can be used for quantitation in HPLC, as long as proper calibration is used with either method. While peak-area quantitation is popular in HPLC, this method is not always the best. For well-behaved nearly symmetrical peaks, peak height can be as precise and more accurate than peak area measurements.

prof. aza

Various operating variables affect the response measurement; these effects are different for peak height or peak are quantitation, as summarized in Table 14.1. Here, the effect is given for small change in an operating parameter on the measurement of peak hight or peak area.

For example, a slall change in the flow rate (F) will affect the peak height measurement (≤ F-0.2 less than it will effect the peak area measurement (F-1).

prof. aza

prof. aza

A change in column conditions tat effects plate number N usually will affect peak height but not peak area measurements. Table 14.2 shows a more detailed list experimental conditions and whether area or peak height quantitation is preferred.

Finally, peak height often is the preferred method of quantitation for trace analysis. Since incomplete resolution of the trace analyte often is a problem, peak height quantitation is more accurate because of less potentential interference in determining peak size.

prof. aza

14.3. Quantittation Methods

Peak-height or peak-area measurements only provide a response in terms of detector signal. This response must be related to the concentration of mass of the compound of interest. To accomplish this, some type of calibration must be performed, whether within the same chromatographic run or a different one.

The four primary techniques for quantitation are normalized peak area, and three using calibration: external standard, internal standard, and the method of standard addition.

prof. aza

prof. aza

14.3.1. Normalized Peak Area After completion of a run and the

integration of all significant peaks in the chromatogram, the total peak area can then be calculated. The area percent of any invidual peak is referred to as the normalized peak area.

An exampe of this is shown in Fig 14.6, where one main peak has 96% of the total area, and four other minor components contribute peak areas of from 0.6 to 1.4% of the total.

prof. aza

Figure 14.6 : Normalized peak area for main component and four minor components

prof. aza

the response factor The technique of normalized peak area is actually

not a calibration method per se, since there is no comparison to known ammounts for any peak in the chromatogram. However, this technique is widely used to estimate the relative amounts of small impurities or degradation compounds in purified material.

The proper use of a normalized-peak-area technique assumes that the response factor for each components is identical (i.e., that the responses per unit of concentration of peaks 2 to 5 are the same as the response for the main peak 1 in Fig. 14.6).

prof. aza

inequality of response factors

This is rarely true in UV detection, where even closely related compounds can have different molar absorptivities. However, the technique of normalized peak area is especially useful in early method-development studies, when characterized standards of all components are not available.

Despite the probable inequality of response factors, it is expedient to use normalized peak areas for these analyses.

prof. aza

While different compounds rarely have the same UV absorbance, bulk- property detectors, such as refractive index (RI) or evaporative light scattering (ELS) do exhibit similar responses for many unrelated compounds.

Their use with normalized peak areas is more reliable; further discussion of these detectors is found in Section 3.3.1. unfortunately, bulk property detectors usually are much less sensitive than UV detection of strongly absorbing compounds.

prof. aza

14.3.2 : External Standard Calibration The most general method for

determining the concentration of an unknown sample is to construct a calibration plot using external standards, as shown in Fig. 14.7.

Standard solutions (sometimes called calibrators) are prepared at known concentrations ( 1.0, 2.0, and 3.0 mg/mL in this case).

prof. aza

Figue 14.7 : Calibration plot for external standard method

prof. aza

A fixed volume of each standard solution is injected and analyzed, and the peak responses are plotted vs. Concentration. The calibrators in this method are referred as external standards, since they are prepared and analyzed in separate chromatograms from those of the unknown sample(s).

Unknown samples are then prepared, injected, and analyzed in exactly the same manner, and the concentration is determined graphically from a calibration plot, or numerically using response factors.

prof. aza

should be linear and have a zerro intercept The calibration plot should be linear and have a

zerro intercept, as in Fig 14.7. in this case, unknown samples 1 and 2 have concentration of 1.5 and 2.4 mg/mL, respectively. If the response is liniar with a zero intercept, the calibration plot theoretically can be determined with only one standard.

However, in practice two or more standard concentration are recommended. The concentration of the standards should be similar to the concentration expected for the samples.

prof. aza

In Fig. 14.7, both samples fall within the concentration range of the standards, so an interpolation provides an accurate measurement of sample concentration.

If the sample concentration falls outside the range of standards used, extrapolation of the calibration plot should be used with caution.

prof. aza

dilution of the sample

In unusual cases where the calibration plot is not linear, sample concentration can be determined by interpolation of results between standards and/or fit to a non-linear equation; however, many more standards are required, and this technique should be used only when no other option exits. In many such cases, the chromatography can be improved to provide a linear response as a function of analyte concentration in the range needed for analysis.

Dilution of the sample to bring the concentration into a range for linear response is another possible option.

prof. aza

response factor, RF

A second technique for determining the concentration of unknown samples uses response factors. A response factor, RF (sometimes called a sensitivity factor), can be detrmined for each standard as follows:

RF = standard area (or peak height)/ standard concentration

prof. aza

In the example of Fig 14.7, RF is exactly 10,000 for all three standards and is the slope of the calibration line, since the intercept is exactly zero. This response factor (RF) can be used to calculate the sample concentration as follows:

Sample conc. = sample area (or peak height)/RF If two or more standars are measured (at different

concentrtions), RF can be taken as the average value of response factors for all standards. This use of multiple standards (while requiring additional measurements) has the advantage of minimizing the uncertainty in determining RF.

prof. aza

The external standards approachis preferred for most samples in HPLC that do not require extensive sample preparation. A major source of error fo the external standards approach is the reprodicibility of sample injection.

For automated, loop-filled injectors used on most auto samplers, the precision of injection is typically better than 0.5%, and this is adequate for most analysis.

prof. aza

If manual (syringe) injection is used, method precision is poorer and other calibration techniques (describe below) often employed. Less precise results also can be obtained from automated, loop-filled injectors that employ patial filling of a sample loop.

For good quantitation using external standards, the chromatographic conditions must remain constant during the separation of all standards and samples (some chromatographic conditions, volume injected, etc).

prof. aza

In addition to their customary use for calibration external standards are often used to ensure that the total chromatographic systems (equipment, column, conditions) is performing properly and can provide reliable results.

The use of standards to validate systems performance is referred to as systems suitability, and this is usually performed before sample analyses begin.

prof. aza

Calibrations are best prepared in the sample matrix, to ensure quantitative accuracy. Trace analysis samples often are prepared in matrix, so that the sample preparation step is an integral part of the calibration procedure, as discussed in Section 14.5.5.

prof. aza

14.3.3. Internal Standards Calibration Another technique for calibration involves

the addition of an internal standard to the calibration solution and samples.

The internal standard is a different compound from the analyte, but one that is well resolved in the separation. The internal standard can compesate for changes in sample size or concentration due to instrumental variations.

prof. aza

One of the main reasons for using an internal standard is for samples requiring significant pretreatment or preparation. Often, sample preparation steps that include reaction (i.e. Derivatization), filtration, extraction, and so on, result in sample losses.

When added prior to sample preparation, a properly chosen internal standard can be used to correct for these sample losses. The internal standard should be chosen to mimic the behavior of the sample compound in these pretreatment steps.

prof. aza

With the internal standard method, a calibration plot is produced by preparing and analyzing calibration solutions containing different concentrations of the compound of interest with a fixed concentration of the internal standard added.

An example of this approach is shown in Figs 14.8 and 14.9 for the analysis of methomyl insecticide using benzanilide as an internal standard.

prof. aza

Figure 14.8 shows the chromatogram of a calibration mixture. The ratio of peak area of methomyl to the benzanilide internal standard is determined for each calibration solution prepared, and this ratio is plotted vs, the methomyl concentration in Fig. 14.9 using solutions with known concentration of methomyl.

This plot can be used directly to determine the concentration of methomyl in samples.

prof. aza

Fig. 14.8: Separation of calibration mixture containing lannate methomyl insecticide with internal standard. Column: 100 x 0.21 cm, 1% β.β-oxydipropionitrile on Zipax, < 37μm, mobile phase, 7% chloroform in n-hexane; flow rate 1.3 mL/min; detector, UV 254 nm, sample injection 20 μL.

prof. aza

The concentration can also be calculated by determining the response factor (RF) for the internal standard plot if the latter is linear with a zero intercept:

prof. aza

Where Armb is the area ratio of the methomyl-benzanilide in the calibration standard solutions and M is the methomyl concentration in the calibration standard solutions. In this case, RF is the slope of the line in Fig. 14.9. the concentration of methomyl in a sample (Cs) is then given by

prof. aza

Figure 14.9: Peak-area ratio calibration with internal standard. Conditions: same as in Fig. 14.8

prof. aza

Requirements for proper internal standard include:

Well resolved from the compound of interest and other peaks

Similar retention (k) to analyte Should mimic the analyte in any sample

preparation steps Does not have to be chemically similar to analyte Commercially available in high purity Stable and unreactive with sample or mobile

phase Should have a similar detector response to the

analyte for concentration used

prof. aza

Perhaps the most challenging requirements is that the internal standard must be separated from all compounds(s) of interest in the separation.

For a simple mixture, this may not be difficult; however, a complex mixture often makes this requirements more difficult to achieve, and use of an internal standard may not be practical.

prof. aza

Althought an internal standard does have advantages incertain situations, it does not always produce improved results.

For example, the precision of measurements using an internal standard can be poorer than an external standard calibration method due to the uncertainty in measuring two peaks ratio rather than just the analyte.

prof. aza

For this reasonm and the additional complexity of selecting another compound without interference from other peaks, the use of internal standards usually is reserved for methods that require extensive sample preparation. The external-standard calibration method is preferred for most analyses.

prof. aza

14.3.4. Method of Standard Addition A calibration standard ideally should be prepared

in a blank matric to provide the best calibration for actual samples. Thus, a blank matrix of drug formulation components without the drug substance or an animal feed without added coumpound usually can be used for standard calibration solutions.

In some cases, however, it is not possible to prepare a representative standard solution that does not already contain the analyte of interst.

prof. aza

For example, a serum sample without endogenous insulin is difficult to prepare as a blank matrix. In these cases, the method of standard addition can be used to provide a calibration plot for quantitative analysis.

The method of standard addition is most often used in trace analysis. In this approach, different weight of analyte(s) are added to the sample matrix, which initially contains an unknown concentration of the analyte.

prof. aza

Extrapolation of a plot of response found for the standard-addition calibration concentrations to zero concentration defines the original concentration in the unspiked sample. An example of this is shown in Fig. 14.10. for the analysis of 5-hydroxyindoleacetate acid (HIAA) in human cerebrospinal fluid.

The slope of this calibration plot is equal to the response factor RF for this assay, which can then be used to calculate the analyte oncentration in the original sample (with no added analyte).

prof. aza

As shown in Fig. 14.10, the original concentration of HIAA in this example was 60ng/mL. This method of calibration does not eliminate the need to obtain proper separation of interfering compounds, or other actors, such as stable baselines in the chromatogram.

An important aspect of the method of standard additions is that the response prior to spiking additional analyte should be high enough to provide a reasonable S/N’ ratio (> 10); otherwise,the result will have poor precision.

prof. aza

prof. aza

14.4. Sources of Error in QuantitationError in any part of the HPLC method can have an

effect on accuracy and/or precision. Good accuracy in HPLC relies on:

A representative sample Minimum overlap of bands or interferences Good peak shape Accurate calibration with purified standards Proper data handling, including integration.

prof. aza

Good precision depends on

Sample preparation technique Instrument reproducibility, including

injection technique. Acceptable S/N’ ratio for the peak of

interest Good peak shape Proper data handling, including

integration Method of quantitation or calibration.

prof. aza

coefficient of variation (CV)

Overall, the imprescision of quantitative result can be expressed as thesum of all precission errors expressed as

σ2tot = σ2 a + σ2

b + σ2c +...........

Whereas σ2tot is the overall precision error

[sometimes called the coefficient of variation (CV)] and σ2 a’ σ2

b, and σ2c refer to the precision error

from various sources (a, b, c), such as injection, S/N’ of detector, or sample pretreatment.

prof. aza

The consequence of Eq. 14.6 is that only those factors that are major will contribute significantly to precision error of the overall analysis. For example, if four sources of imprecision are:

prof. aza

In this particular example, the contribution from the sample pretreatment dominates the overall imprecision of the method. Elimination of all other sources of imprecision would not improve the method precision significantly.

prof. aza

Therefore, to improve method precision significantly, it is usually necessary to reduce the major contribution to imprecision (sample pretreatment, in this case).

Primary sources of error in HPLC result from sampling and sample preparation, chromatographic effects, and signal processing or data handling. Each of these is discussed in term of the effect on accuracy and/or precision.

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza

prof. aza