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Efficiency of an automatic peak purity control procedure in high performance liquid chromatography—photodiode array coupling based on evolving factor analysis
W
235
Original Research Paper
Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 21 (1993) 235-242
Elsevier Science Publishers B.V., Amsterdam
Efficiency of an automatic peak purity control procedure in high performance liquid chromatography-photodiode array coupling based on evolving factor analysis Jean A. Gilliard, Jean L. Cumps and Bernard L. Tilquin Pharmaceutical
School,
Universite’ Catholique
de Louvain,
Avenue
Mounier
72, B-1200 Brussels
(Belgium)
(Received 26 February 1993; accepted 25 June 1993)
Abstract
Gilliard, J.A., Cumps, J.L. and Tilquin, B.L., 1993. Efficiency of an automatic peak purity control procedure in high performance liquid chromatography-photodiode array coupling based on evolving factor analysis. Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management,
21: 235-242.
A modified version of evolving factor analysis, a fully multivariate technique promising to check peak purity in liquid chromatography with photodiode array detection, has been commercially implemented. The capabilities of the algorithm are tested on actual chromatographic data. The results may be regarded as quite remarkable considering that no prior information about the solutes is necessary. When using the algorithm, one has however to be aware that the maximal absorbance must be kept lower than 0.4 AU and that a data rate of 1 Hz is required for optimal peak purity assignment, as a result of the influence of non-constant variance and scattering of the spectral data on the computation technique.
INTRODUCTION
Chromatography is one of the means commonly used for the determination of the purity of drugs in the pharmaceutical industry [l]. Moreover, checking peak homogeneity is a critical step
Correspondence to: J.A. Gilliard, Pharmaceutical School, Universitd Catholique de Louvain, Avenue Mounier 72, B1200 Brussels (Belgium).
09255281/93/$06.00
in chromatographic method development and validation in order to guarantee reliable qualitative and quantitative results. Since the introduction of diode array technology, many approaches have been proposed in order to check peak purity in liquid chromatography among which only a handful of methods are actually implemented in commercially available diode array detectors. In fact, most of the current commercial software still propose only the spectral overlay and the ratiogram methods as routine means for checking peak purity.
0 1993 - Elsevier Science Publishers B.V. All rights reserved
J.A. Gilliard et al. /Lab.
236
Recently, a new algorithm based on a modified version of evolving factor analysis has been commercially implemented [2]. The technique fully exploits the multivariate capabilities of the diode array detector and consequently the knowledge of the spectra of the analytes and their possible impurities are unnecessary. The algorithm performs purity analysis in real time, as the chromatogram elutes and indicates how many compounds reside under an impure peak and when impurities occur. In the present paper, a critical study of this algorithm is proposed and the conditions for optimal application of the technique are examined as only very little practical information is found in the operation manual [3]. The capabilities of the algorithm are tested on actual chromatographic data. Typically, the main factors affecting the efficiency of a peak purity determination method are the spectral similarity of the coeluting species, their relative concentration, the chromatographic resolution and the signal to noise ratio. Three pairs of drug compounds with various degrees of spectral similarity (spectra judged as closely related, different and totally different) were used as experimental models by varying the absorbance ratio and the chromatographic resolution.
THEORETICAL
BACKGROUND
The algorithm analyses information using a technique based on principal components analysis (PCA) and is derived from the evolving factor analysis (EFA) method proposed by Maeder and Zuberbiihler [4] and Maeder [5]. This relatively new technique has been developed independently in several laboratories and was referred to as fixed size window evolving factor analysis [61 or differential evolving factor analysis [7]. Both the original and the modified EFA techniques have been well reviewed by Keller and Massart [6,8]; therefore, only a summary of their basic concept is given here. The data obtained by high performance liquid chromatography-diode array detection (HPLCDAD) (the spectrochromatogram) can be described in a matrix form in which the rows are
If.
Manage. 21 (1993) 235-242 /Original Research Paper
n
absorption spectra measured at regular time intervals and the columns are chromatograms measured at different wavelengths. Therefore, methods from linear algebra such as PCA can be used in processing the data. PCA computes the rank of the investigated data matrix and there is a connection between the rank examination by PCA and the number of absorbing species in the spectrochromatogram, and hence in the unresolved chromatographic peaks. PCA gives an eigenvector matrix which describes the factor space of the data and simultaneously a diagonal matrix of eigenvalues (EVs) which account for the rank of the data matrix and consequently for the number of independently absorbing components. In the original EFA method, a series of PCAs is performed on the data matrices formed by successively larger time windows across the peak region, starting with a window of only two or three spectra. The idea is to follow the evolution of the rank in the data matrix with progressing elution, the eigenvalues which result from each PCA are plotted as a function of time. The number of eigenvalues which rise significantly above the noise correspond to the number of components. In the modified version tested here, the analysis is performed by moving a window of a fixed odd number of spectra instead of an increasing size window. The main advantages are a lower detection limit of impurities [6] and a real time analysis of the data.
EXPERIMENTAL
Chemicals
Acetonitrile (HPLC grade) and potassium dihydrogenphosphate (highest purity) were obtained from UCB (Leuven, Belgium). Phosphoric acid 85% (analytical grade) and triethylamine (TEA) (for synthesis) were purchased from Merck (Darmstadt, Germany). HPLC grade water was obtained from an in-house water purification system (Milli-Q, Millipore Corp., Milford, MA). The investigated drugs were gifts from various pharmaceutical manufacturers.
W
J.A. Gilliard et al. /Lab.
Inf Manage. 21 (1993) 235-242/Original
Research Paper
237
Equipment
The HPLC system consisted of a Beckman dual pump Model 126 (Beckman Instruments Inc., Fullerton, CA) and a Beckman injection valve Model 210A equipped with a 20 ~1 sample loop. The chromatographic column was 150 X 4.6 mm i.d. 18C packed with 5 pm particles (Ultrasphere ODS, Beckman) and heated at 40°C by means of a home-made water jacket. The DAD system was a Beckman 168 model with was operated in the ‘resolution mode’ and set up to collect spectra every 0.25, 0.5 or 1 s covering the wavelength range of 210 to 350 nm and collecting a data point every nm. The peak detect parameters were set up as follows: wavelength, 280 nm; bandwidth, 140 nm; threshold, 0.001 AU/min (manual mode); peak width, 0.1 min. The reference channel was set on 450 nm with a 100 nm bandwidth. Data were stored on an IBM compatible personal computer. For ‘manual’ computations, the data were converted to ASCII files using the Beckman ArrayView software and modified EFA was performed on three or eleven consecutive spectra using the Statgraphics software (Statistical Graphics Corporation).
-0.06~~ 2,715
Time [mm)
3.838
Fig. 1. Example of result presentation in case of coelution of four components in equal proportion (nitrazepam, clonazepam, clorazepate and diazepam). Mobile phase: acetonitrile-0.005 M phosphate buffer, 0.003 M TEA, pH 3 (70: 30, v/v); A = 230 nm; A max(212nmj = 0.33 AU; for other parameters, see text. The number of thin horizontal bars indicates the number of coeluting species at that time; hvo large horizontal bars indicate an undetermined (more than three) number of components. For details, see text.
Preliminary experiments were carried out to define the exact eluent composition for the desired chromatographic resolution after peak deconvolution by means of multicomponent analysis. The found compositions are given in Table 1. The pH of the phosphate buffer was adjusted by dropwise addition of phosphoric acid to the KI-I,PO,-TEA solution. The flow rate was always 1 ml/min. Stock solutions of the investigated compounds were prepared in methanol and diluted as required in the mobile phase used. For each mobile phase composition the total concentrations of the compounds were ‘adjusted so as to give a maximum absorbance of approximately 0.35 AU.
HPLC procedures
The mobile phase consisted of acetonitrile0.005 M phosphate buffer, 0.003 M TEA, pH 3.
TABLE 1 Mobile phase composition and corresponding elution order
chromatographic
resolution, R,. For each pair the compounds are given in their
Compound pairs Bromazepam-carbamazepine
Nitrazepam-clonazepam
ACN (o/o,v/v)
K
ACN (o/o,v/v)
R,
ACN f%, v/v)
RS
62 58 56 51 48
0.12 0.33 0.53 0.81 1.08
70 65 58 51 48
0.10 0.29 0.50 0.78 1.07
52 54 56 59 61
0.10 0.31 0.55 0.85 1.02
Ethylloflazepate-diazepam
238
J.A. Gilliard et al. /Lab.
Data analysis
The Real Time Purity algorithmTM of System Gold’” (Beckman) performs a real time determination of the purity status of the peaks using the information from the peak detect channel. The result is graphically displayed under the chromatogram (Fig. 1). The vertical bars indicate the peak beginning and end and the number of thin horizontal bars indicates the number (one, two or three) of components under the peak. Two large horizontal bars indicate an undetermined (more than three) number of components. If the number of components within a peak changes over time, a vertical bar indicates that change.
If. Manage. 21 (1993) 235-242 /Original Research Paper
n
a
b 0.380
RESULTS AND DISCUSSION
Optimal use
Nearly all the techniques proposed for checking peak purity are based on spectra and require that they are correctly measured. The fully multivariate methods such as EFA are still more sensitive to spectral distorsions because they make full use of all available data. Several variables must be carefully controlled to insure a correct acquisition of the data. While some of these variables are set by the selection of the parameters of the detector, others can be adjusted by an appropriate choice of the chromatographic conditions. Spectral range. The 168 Beckman DAD system allows the acquisition of spectral data between 190 and 600 nm. However, the data in the low UV range can be masked by the solvent cutoff. On the other hand, most of the drugs have a spectrum limited to the UV region and have only insignificant absorptivities in the longer wavelengths, so that the consideration of the visible region leads to an artificial increase of the spectral similarity between those compounds. Therefore, we have to limit the spectral range to a region where the information is reliable and the spectral difference is the largest. In this work, a range from 210 to 350 nm was used. Obviously the peak threshold and the peak width must be properly selected to insure a correct detection of
Fig. 2. Chromatograms showing a double coelution of nitrazepam/clonazepam (absorbance ratio, 595; R, = 0.5) and ethylloflazepate/diazepam (absorbance ratio, 97: 3; R, = 0.8), consecutively recorded with a data rate of (a) 1 Hz, (b) 2 Hz, (c) 4 Hz, respectively. Mobile phase: acetonitrile-0.005 M phosphate buffer, 0.003 M TEA, pH 3 (.58:42, v/v); A = 212 nm switched to 229 nm after 2.7 min. For other parameters, see text.
peaks and the undesirable baseline disturbances and drift can be minimized by an appropriate equilibration of both the column and the detector and by subtraction of a signal from the reference channel. In the particular case of the algorithm tested here, two additional variables were found to be considered. (1) Data rate. Peak purity assignment by the algorithm can be altered by data rate. When the analysis conditions become more complex (lower resolution, extreme absorbance ratio or higher spectral similarity) the lowest data rate (1 Hz) was found to be required for an optimal purity determination (Fig. 2). This can be easily understood assuming that the size of the moving window is maintained constant whatever the sam-
8
J.A. Gilliard et al. /Lab.
Inf Manage. 21 (1993) 235-242 /Original Research Paper
pling rate. In that case, the occurrence of one impurity will always lead to a higher scattering of the fixed number of spectral data in the hyperspace and consequently to a more significant second eigenvalue with the largest sampling interval. Fig. 3 shows the modified EFA plots obtained for a window of three spectra, using the corresponding data of the first impure peak of the three chromatograms measured consecutively with a
239
“. . .
6 200 Retention
2.20 time (min)
0 2.33
Fig. 4. Log EV versus analysis time of the three first EVs obtained for a window of eleven spectra from the corresponding data of the first peak in Fig. 4. Data rate = 4 Hz. (. . . ) Chromatographic profile; ( -1 log EV.
..
J, 1.93 b
, lo
2.20 Retention time (mm)
247 OL
2
s
0
2 - -2
02
E-4 -6
J, 1.98
J. 2.00
‘..
I
. .. .
2.20 Retention time
: ” Retention
235 (mid
”
219 time
u2 x b m 2
. 10
231 (mid
Fig. 3. Log EV versus analysis time obtained for a window of three spectra from the corresponding data of the first peak in Fig. 4. (a) Data rate = 1 Hz; (b) data rate = 2 Hz; (c) data rate = 4 Hz. ( . . . ) Chromatographic profile; ( -1 log EV.
data rate of 1, 2 and 4 Hz respectively and given in Fig. 2. It clearly appears that the increase of the sampling interval improves the significance of the second eigenvalue when the impurity occurs. If one assumes that the threshold of significance for a second eigenvalue is around -2.3 (the level of the first eigenvalue before the beginning of the peak), the impurity will be easily detected with a data rate of 1 Hz, will remain detectable with a data rate of 2 Hz, but will be undetected with a data rate of 4 Hz. This is totally coherent with what is effectively observed in Fig. 2. To obtain the same performance when using a higher data rate, the size of the moving window should increase in the same way as shown in Fig. 4; this would, however, lead to an increase in computation time. The need of the lower data rate is somewhat of a drawback: a data system should generally acquire 15-20 data points over the peak width [9]. A too low sampling rate leads to a distortion of the shape of the narrow peaks which may result in inaccurate retention time and peak area calculation. (2) False positive results (impurity indication for a pure peak) are generated for high and moderately high values of absorbance. We found experimentally that the maximum absorbance of samples must be kept less than 0.4 AU for correct peak purity assignment. Non-linear calibration graph and heteroscedasticity, i.e., non-uniform variance along the calibration graph [lo],
240 cl
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a 2
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Inf Manage. 21 (1993) 235-242 /Original
Research Paper
n
tween reliability and sensitivity for the impurity detection. The possibility of applying an appropriate correction method for heteroscedasticity in the case of multivariate data has been well discussed by Keller et al. [ll]. Such a data pretreatment should
IU
I 0.6
Wavelength
(nml
Fig. 5. eat,%versus A plots (n = 24) for spectra taken at stop flow for two different heights of a peak of clonazepam corresponding to ca. (a) 0.22 and (b) 0.65 AU maximum absorbance. Mobile phase: acetonitrile-0.005 M phosphate buffer; 0.003 M TEA, pH 3 (50:50, v/v).
were suspected to be responsible for the artefacts. According to Keller et al. [ll], heteroscedasticity appears as the most serious problem for EFA and related techniques; these authors have shown that more principal components than expected are effectively obtained in the presence of manifest heteroscedasticity. The a,,, versus A plots for clonazepam given in Fig. 5 prove the occurrence of heteroscedasticity and show that the importance of this phenomenon depends on the magnitude of the peak absorbance. Whereas the spectral precision profile is not significantly affected for the lower absorbance level (A,, = 0.22 AU), the absolute error becomes larger for high signals when the maximum absorbance is higher (0.65 AU) and may account for the artefacts observed near the apex of the chromatographic peak in the latter case. According to Garden et al. [12], many analytical techniques show constant variance only for a part of the calibration graph whereas relative standard deviations remain reasonably constant over a large dynamic range. A maximum absorbance of 0.35 AU was found to offer a good compromise be-
250 290 Wavelength (nm)
b
330
O.Ly
OJ, , 210
210
,
,
,
250 290 Wavelength (nm)
250 290 Wavelength (nm)
,
<
330
330
1
Fig. 6. Normalized absorbance spectra of the three pairs of drugs. (a) Ethylloflazepate and diazepam were subjectively judged as close; (b) nitrazepam and clonazepam as different; and (c) bromazepam and carbamazepine as totally different with respect to the spectral similarity. The spectra were shown to be insensitive to variations in the proportion of the organic component of the eluent.
W
J.A. Gilliard et al. /Lab.
improve allowing peaks. It real time
a
In& Manage. 21 (1993) 235-242 /Original Research Paper
the performance of the algorithm by a higher maximum absorbance to the should of course be compatible with the analysis.
10 .\_____2
,
J
055 Chromatographlc
01 b
3’
Nitrazepam-
“I
01
c
3
resolution
Clonazepam
050 Chromatographic
Bromazepam
102
107 resolutlon
-Carbamazeplne
241
Efficiency
Fig. 6 shows the UV spectra of the three chosen drug pairs. As in most applications we can expect that the impurities can be chemically related, ethylloflazepate-diazepam are subjectively judged as close, nitrazepam-clonazepam as different and bromazepam-carbamazepine as totally different with respect to spectral similarity. Since the molar absorptivities of the two components in each overlapping pair are different, the results are expressed by the ratio of the corresponding maximum absorbances. The results obtained with the different techniques are given in Fig. 7 where the lines indicate the limits of detection of the impurity. The experimental data give a detection limit in the range of 0.3-9% of the total absorbance depending on spectral similarity and chromatographic resolution. These results may be regarded as quite remarkable considering that no prior information about the solutes is necessary and the low information content of UV spectra. Nevertheless they are somewhat disappointing in comparison with those obtained by Keller and coworkers [6,13,14] with a highly similar technique referred to as fixed size window EFA (FSW). However, these authors use subjective visual criteria to decide whether an eigenvalue may be considered as significant [6], something which of course cannot be applied in a fully automated method.
CONCLUSION
O-
Chromatographic
1.08 resolution
Fig. 7. Detection limits of an impurity as function of maximum absorbance ratio and chromatographic resolution for (a) ethylloflazepate-diazepam (high spectral similarity): ( W) ethylloflazepate as impurity, (0) diazepam as impurity; (b) nitrazepam-clonazepam (intermediate spectral similarity): ( n ) nitrazepam as impurity, ( q ) clonazepam as impurity; (c) bromazepam-carbamazepine (low spectral similarity): ( n ) bromazepam as impurity, (0) carbamazepine as impurity. For details, see text.
The most important parameter influencing the choice of a method for peak purity control in HPLC-DAD is whether the spectra of the analytes and possible impurities are known. When such information is not available, only a few methods remain applicable among which the tested algorithm appears to be a first choice method in view of the results obtained. The technique allows real time analysis and results can therefore be obtained without saving the data. The ability to give purity information instantaneously needs however intensive calculations so that only a small moving window is used for the
242
J.A. Gilliard et al. /Lab.
PCA computations and no treatment for heteroscedasticity is applied to the data. Consequently, the lowest data rate is required and the maximum absorbance allowed is restricted to 0.4 AU for an optimal peak purity assignment. Finally, the algorithm indicates how many compounds reside under the peaks and when impurities occurred. The number of horizontal lines must be understood as the number of components statistically separable and therefore must rather be considered indicative.
ACKNOWLEDGEMENT
We wish to thank Analis (Namur-Gent, Belgium) for the loan of the Gold chromatographic system.
REFERENCES 1 J. Van Rompay, Purity determination and evaluation of new drug substances, Journal of Pharmaceutical and Biomedical Analysis, 4 (1986) 725-732. 2 J. Schaefer, An HPLC system with next generation diode array technology, International Chromatography Laboratory, 1 (1990) 6-15. 3 System Gold’” Operation Manual, Beckman Instruments,
Fullerton, CA, 1992. 4 M. Maeder and A.D. Zuberbfhler, The resolution of overlapping chromatographic peaks by evolving factor analysis, Analytica Chimica Acta, 181 (1986) 287-291.
If.
Manage. 21 (1993) 235-242 /Original Research Paper
n
5 M. Maeder, Evolving factor analysis for the resolution of overlapping chromatographic peaks, Analytical Chemistry, 59 (1987) 527-530. 6 H.R. Keller and D.L. Massart,
Peak purity control in liquid chromatography with photodiode array detection by a fiied size moving window evolving factor analysis, Ana-
lytica Chimica Acta, 246 (1991) 379-390. 7 J. Kankare, J. Lukkari, T. Pajunen, J. Ahonen
and C. Visby, Evolutionary spectral factor analysis of doping-undoping processes of thin conductive polymer films, Journal
of Electroanaytical Chemistry and Interfacial Electrochemistry, 294 (1990) 59-72. 8 H.R. Keller and D.L. Massart, Evolving factor analysis, Chemometrics and Intelligent Laboratory Systems, 12 (1992) 209-224. 9 G.I. Ouchi, Chromatographic data acquisition: analog-todigital conversion and raw data storage, LC-GC Infernational, 10 (1991) 18-22.
10 D.L. Massart, B.G.M. Vandeginste, S.N. Deming, Y. Michotte and L. Kaufman (Editors), Chemometrics: a textbook, Elsevier, Amsterdam, 1988, pp. 84-86. 11 H.R. Keller, D.L. Massart, T.Z. Liang and O.M. Kvalheim, Evolving factor analysis in the presence of heteroscedastic noise, Analytica Chimica Acta, 263 (1992) 29-36.
12 J.S. Garden, D.G. Mitchell and W.N. Mills, Nonconstant variance regression techniques for calibration-curve-based analysis, Analytical Chemistry, 52 (1980) 2310-2315. 13 H.R. Keller, D.L. Massart, T.Z. Liang and O.M. Kvalheim, A comparison of the heuristic evolving latent projections and evolving factor analysis methods for peak purity control in HPLC-DAD, Analytica Chimica Acta, 267 (1992) 63-71.
14 H.R. Keller, D.L. Massart and J.O. De Beer, Window evolving factor analysis for assessment of peak homogeneity in liquid chromatography, Analytical Chemistry, 65 (1993) 471-475.
235
Original Research Paper
Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 21 (1993) 235-242
Elsevier Science Publishers B.V., Amsterdam
Efficiency of an automatic peak purity control procedure in high performance liquid chromatography-photodiode array coupling based on evolving factor analysis Jean A. Gilliard, Jean L. Cumps and Bernard L. Tilquin Pharmaceutical
School,
Universite’ Catholique
de Louvain,
Avenue
Mounier
72, B-1200 Brussels
(Belgium)
(Received 26 February 1993; accepted 25 June 1993)
Abstract
Gilliard, J.A., Cumps, J.L. and Tilquin, B.L., 1993. Efficiency of an automatic peak purity control procedure in high performance liquid chromatography-photodiode array coupling based on evolving factor analysis. Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management,
21: 235-242.
A modified version of evolving factor analysis, a fully multivariate technique promising to check peak purity in liquid chromatography with photodiode array detection, has been commercially implemented. The capabilities of the algorithm are tested on actual chromatographic data. The results may be regarded as quite remarkable considering that no prior information about the solutes is necessary. When using the algorithm, one has however to be aware that the maximal absorbance must be kept lower than 0.4 AU and that a data rate of 1 Hz is required for optimal peak purity assignment, as a result of the influence of non-constant variance and scattering of the spectral data on the computation technique.
INTRODUCTION
Chromatography is one of the means commonly used for the determination of the purity of drugs in the pharmaceutical industry [l]. Moreover, checking peak homogeneity is a critical step
Correspondence to: J.A. Gilliard, Pharmaceutical School, Universitd Catholique de Louvain, Avenue Mounier 72, B1200 Brussels (Belgium).
09255281/93/$06.00
in chromatographic method development and validation in order to guarantee reliable qualitative and quantitative results. Since the introduction of diode array technology, many approaches have been proposed in order to check peak purity in liquid chromatography among which only a handful of methods are actually implemented in commercially available diode array detectors. In fact, most of the current commercial software still propose only the spectral overlay and the ratiogram methods as routine means for checking peak purity.
0 1993 - Elsevier Science Publishers B.V. All rights reserved
J.A. Gilliard et al. /Lab.
236
Recently, a new algorithm based on a modified version of evolving factor analysis has been commercially implemented [2]. The technique fully exploits the multivariate capabilities of the diode array detector and consequently the knowledge of the spectra of the analytes and their possible impurities are unnecessary. The algorithm performs purity analysis in real time, as the chromatogram elutes and indicates how many compounds reside under an impure peak and when impurities occur. In the present paper, a critical study of this algorithm is proposed and the conditions for optimal application of the technique are examined as only very little practical information is found in the operation manual [3]. The capabilities of the algorithm are tested on actual chromatographic data. Typically, the main factors affecting the efficiency of a peak purity determination method are the spectral similarity of the coeluting species, their relative concentration, the chromatographic resolution and the signal to noise ratio. Three pairs of drug compounds with various degrees of spectral similarity (spectra judged as closely related, different and totally different) were used as experimental models by varying the absorbance ratio and the chromatographic resolution.
THEORETICAL
BACKGROUND
The algorithm analyses information using a technique based on principal components analysis (PCA) and is derived from the evolving factor analysis (EFA) method proposed by Maeder and Zuberbiihler [4] and Maeder [5]. This relatively new technique has been developed independently in several laboratories and was referred to as fixed size window evolving factor analysis [61 or differential evolving factor analysis [7]. Both the original and the modified EFA techniques have been well reviewed by Keller and Massart [6,8]; therefore, only a summary of their basic concept is given here. The data obtained by high performance liquid chromatography-diode array detection (HPLCDAD) (the spectrochromatogram) can be described in a matrix form in which the rows are
If.
Manage. 21 (1993) 235-242 /Original Research Paper
n
absorption spectra measured at regular time intervals and the columns are chromatograms measured at different wavelengths. Therefore, methods from linear algebra such as PCA can be used in processing the data. PCA computes the rank of the investigated data matrix and there is a connection between the rank examination by PCA and the number of absorbing species in the spectrochromatogram, and hence in the unresolved chromatographic peaks. PCA gives an eigenvector matrix which describes the factor space of the data and simultaneously a diagonal matrix of eigenvalues (EVs) which account for the rank of the data matrix and consequently for the number of independently absorbing components. In the original EFA method, a series of PCAs is performed on the data matrices formed by successively larger time windows across the peak region, starting with a window of only two or three spectra. The idea is to follow the evolution of the rank in the data matrix with progressing elution, the eigenvalues which result from each PCA are plotted as a function of time. The number of eigenvalues which rise significantly above the noise correspond to the number of components. In the modified version tested here, the analysis is performed by moving a window of a fixed odd number of spectra instead of an increasing size window. The main advantages are a lower detection limit of impurities [6] and a real time analysis of the data.
EXPERIMENTAL
Chemicals
Acetonitrile (HPLC grade) and potassium dihydrogenphosphate (highest purity) were obtained from UCB (Leuven, Belgium). Phosphoric acid 85% (analytical grade) and triethylamine (TEA) (for synthesis) were purchased from Merck (Darmstadt, Germany). HPLC grade water was obtained from an in-house water purification system (Milli-Q, Millipore Corp., Milford, MA). The investigated drugs were gifts from various pharmaceutical manufacturers.
W
J.A. Gilliard et al. /Lab.
Inf Manage. 21 (1993) 235-242/Original
Research Paper
237
Equipment
The HPLC system consisted of a Beckman dual pump Model 126 (Beckman Instruments Inc., Fullerton, CA) and a Beckman injection valve Model 210A equipped with a 20 ~1 sample loop. The chromatographic column was 150 X 4.6 mm i.d. 18C packed with 5 pm particles (Ultrasphere ODS, Beckman) and heated at 40°C by means of a home-made water jacket. The DAD system was a Beckman 168 model with was operated in the ‘resolution mode’ and set up to collect spectra every 0.25, 0.5 or 1 s covering the wavelength range of 210 to 350 nm and collecting a data point every nm. The peak detect parameters were set up as follows: wavelength, 280 nm; bandwidth, 140 nm; threshold, 0.001 AU/min (manual mode); peak width, 0.1 min. The reference channel was set on 450 nm with a 100 nm bandwidth. Data were stored on an IBM compatible personal computer. For ‘manual’ computations, the data were converted to ASCII files using the Beckman ArrayView software and modified EFA was performed on three or eleven consecutive spectra using the Statgraphics software (Statistical Graphics Corporation).
-0.06~~ 2,715
Time [mm)
3.838
Fig. 1. Example of result presentation in case of coelution of four components in equal proportion (nitrazepam, clonazepam, clorazepate and diazepam). Mobile phase: acetonitrile-0.005 M phosphate buffer, 0.003 M TEA, pH 3 (70: 30, v/v); A = 230 nm; A max(212nmj = 0.33 AU; for other parameters, see text. The number of thin horizontal bars indicates the number of coeluting species at that time; hvo large horizontal bars indicate an undetermined (more than three) number of components. For details, see text.
Preliminary experiments were carried out to define the exact eluent composition for the desired chromatographic resolution after peak deconvolution by means of multicomponent analysis. The found compositions are given in Table 1. The pH of the phosphate buffer was adjusted by dropwise addition of phosphoric acid to the KI-I,PO,-TEA solution. The flow rate was always 1 ml/min. Stock solutions of the investigated compounds were prepared in methanol and diluted as required in the mobile phase used. For each mobile phase composition the total concentrations of the compounds were ‘adjusted so as to give a maximum absorbance of approximately 0.35 AU.
HPLC procedures
The mobile phase consisted of acetonitrile0.005 M phosphate buffer, 0.003 M TEA, pH 3.
TABLE 1 Mobile phase composition and corresponding elution order
chromatographic
resolution, R,. For each pair the compounds are given in their
Compound pairs Bromazepam-carbamazepine
Nitrazepam-clonazepam
ACN (o/o,v/v)
K
ACN (o/o,v/v)
R,
ACN f%, v/v)
RS
62 58 56 51 48
0.12 0.33 0.53 0.81 1.08
70 65 58 51 48
0.10 0.29 0.50 0.78 1.07
52 54 56 59 61
0.10 0.31 0.55 0.85 1.02
Ethylloflazepate-diazepam
238
J.A. Gilliard et al. /Lab.
Data analysis
The Real Time Purity algorithmTM of System Gold’” (Beckman) performs a real time determination of the purity status of the peaks using the information from the peak detect channel. The result is graphically displayed under the chromatogram (Fig. 1). The vertical bars indicate the peak beginning and end and the number of thin horizontal bars indicates the number (one, two or three) of components under the peak. Two large horizontal bars indicate an undetermined (more than three) number of components. If the number of components within a peak changes over time, a vertical bar indicates that change.
If. Manage. 21 (1993) 235-242 /Original Research Paper
n
a
b 0.380
RESULTS AND DISCUSSION
Optimal use
Nearly all the techniques proposed for checking peak purity are based on spectra and require that they are correctly measured. The fully multivariate methods such as EFA are still more sensitive to spectral distorsions because they make full use of all available data. Several variables must be carefully controlled to insure a correct acquisition of the data. While some of these variables are set by the selection of the parameters of the detector, others can be adjusted by an appropriate choice of the chromatographic conditions. Spectral range. The 168 Beckman DAD system allows the acquisition of spectral data between 190 and 600 nm. However, the data in the low UV range can be masked by the solvent cutoff. On the other hand, most of the drugs have a spectrum limited to the UV region and have only insignificant absorptivities in the longer wavelengths, so that the consideration of the visible region leads to an artificial increase of the spectral similarity between those compounds. Therefore, we have to limit the spectral range to a region where the information is reliable and the spectral difference is the largest. In this work, a range from 210 to 350 nm was used. Obviously the peak threshold and the peak width must be properly selected to insure a correct detection of
Fig. 2. Chromatograms showing a double coelution of nitrazepam/clonazepam (absorbance ratio, 595; R, = 0.5) and ethylloflazepate/diazepam (absorbance ratio, 97: 3; R, = 0.8), consecutively recorded with a data rate of (a) 1 Hz, (b) 2 Hz, (c) 4 Hz, respectively. Mobile phase: acetonitrile-0.005 M phosphate buffer, 0.003 M TEA, pH 3 (.58:42, v/v); A = 212 nm switched to 229 nm after 2.7 min. For other parameters, see text.
peaks and the undesirable baseline disturbances and drift can be minimized by an appropriate equilibration of both the column and the detector and by subtraction of a signal from the reference channel. In the particular case of the algorithm tested here, two additional variables were found to be considered. (1) Data rate. Peak purity assignment by the algorithm can be altered by data rate. When the analysis conditions become more complex (lower resolution, extreme absorbance ratio or higher spectral similarity) the lowest data rate (1 Hz) was found to be required for an optimal purity determination (Fig. 2). This can be easily understood assuming that the size of the moving window is maintained constant whatever the sam-
8
J.A. Gilliard et al. /Lab.
Inf Manage. 21 (1993) 235-242 /Original Research Paper
pling rate. In that case, the occurrence of one impurity will always lead to a higher scattering of the fixed number of spectral data in the hyperspace and consequently to a more significant second eigenvalue with the largest sampling interval. Fig. 3 shows the modified EFA plots obtained for a window of three spectra, using the corresponding data of the first impure peak of the three chromatograms measured consecutively with a
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data rate of 1, 2 and 4 Hz respectively and given in Fig. 2. It clearly appears that the increase of the sampling interval improves the significance of the second eigenvalue when the impurity occurs. If one assumes that the threshold of significance for a second eigenvalue is around -2.3 (the level of the first eigenvalue before the beginning of the peak), the impurity will be easily detected with a data rate of 1 Hz, will remain detectable with a data rate of 2 Hz, but will be undetected with a data rate of 4 Hz. This is totally coherent with what is effectively observed in Fig. 2. To obtain the same performance when using a higher data rate, the size of the moving window should increase in the same way as shown in Fig. 4; this would, however, lead to an increase in computation time. The need of the lower data rate is somewhat of a drawback: a data system should generally acquire 15-20 data points over the peak width [9]. A too low sampling rate leads to a distortion of the shape of the narrow peaks which may result in inaccurate retention time and peak area calculation. (2) False positive results (impurity indication for a pure peak) are generated for high and moderately high values of absorbance. We found experimentally that the maximum absorbance of samples must be kept less than 0.4 AU for correct peak purity assignment. Non-linear calibration graph and heteroscedasticity, i.e., non-uniform variance along the calibration graph [lo],
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tween reliability and sensitivity for the impurity detection. The possibility of applying an appropriate correction method for heteroscedasticity in the case of multivariate data has been well discussed by Keller et al. [ll]. Such a data pretreatment should
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were suspected to be responsible for the artefacts. According to Keller et al. [ll], heteroscedasticity appears as the most serious problem for EFA and related techniques; these authors have shown that more principal components than expected are effectively obtained in the presence of manifest heteroscedasticity. The a,,, versus A plots for clonazepam given in Fig. 5 prove the occurrence of heteroscedasticity and show that the importance of this phenomenon depends on the magnitude of the peak absorbance. Whereas the spectral precision profile is not significantly affected for the lower absorbance level (A,, = 0.22 AU), the absolute error becomes larger for high signals when the maximum absorbance is higher (0.65 AU) and may account for the artefacts observed near the apex of the chromatographic peak in the latter case. According to Garden et al. [12], many analytical techniques show constant variance only for a part of the calibration graph whereas relative standard deviations remain reasonably constant over a large dynamic range. A maximum absorbance of 0.35 AU was found to offer a good compromise be-
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Fig. 6. Normalized absorbance spectra of the three pairs of drugs. (a) Ethylloflazepate and diazepam were subjectively judged as close; (b) nitrazepam and clonazepam as different; and (c) bromazepam and carbamazepine as totally different with respect to the spectral similarity. The spectra were shown to be insensitive to variations in the proportion of the organic component of the eluent.
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In& Manage. 21 (1993) 235-242 /Original Research Paper
the performance of the algorithm by a higher maximum absorbance to the should of course be compatible with the analysis.
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Fig. 6 shows the UV spectra of the three chosen drug pairs. As in most applications we can expect that the impurities can be chemically related, ethylloflazepate-diazepam are subjectively judged as close, nitrazepam-clonazepam as different and bromazepam-carbamazepine as totally different with respect to spectral similarity. Since the molar absorptivities of the two components in each overlapping pair are different, the results are expressed by the ratio of the corresponding maximum absorbances. The results obtained with the different techniques are given in Fig. 7 where the lines indicate the limits of detection of the impurity. The experimental data give a detection limit in the range of 0.3-9% of the total absorbance depending on spectral similarity and chromatographic resolution. These results may be regarded as quite remarkable considering that no prior information about the solutes is necessary and the low information content of UV spectra. Nevertheless they are somewhat disappointing in comparison with those obtained by Keller and coworkers [6,13,14] with a highly similar technique referred to as fixed size window EFA (FSW). However, these authors use subjective visual criteria to decide whether an eigenvalue may be considered as significant [6], something which of course cannot be applied in a fully automated method.
CONCLUSION
O-
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Fig. 7. Detection limits of an impurity as function of maximum absorbance ratio and chromatographic resolution for (a) ethylloflazepate-diazepam (high spectral similarity): ( W) ethylloflazepate as impurity, (0) diazepam as impurity; (b) nitrazepam-clonazepam (intermediate spectral similarity): ( n ) nitrazepam as impurity, ( q ) clonazepam as impurity; (c) bromazepam-carbamazepine (low spectral similarity): ( n ) bromazepam as impurity, (0) carbamazepine as impurity. For details, see text.
The most important parameter influencing the choice of a method for peak purity control in HPLC-DAD is whether the spectra of the analytes and possible impurities are known. When such information is not available, only a few methods remain applicable among which the tested algorithm appears to be a first choice method in view of the results obtained. The technique allows real time analysis and results can therefore be obtained without saving the data. The ability to give purity information instantaneously needs however intensive calculations so that only a small moving window is used for the
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PCA computations and no treatment for heteroscedasticity is applied to the data. Consequently, the lowest data rate is required and the maximum absorbance allowed is restricted to 0.4 AU for an optimal peak purity assignment. Finally, the algorithm indicates how many compounds reside under the peaks and when impurities occurred. The number of horizontal lines must be understood as the number of components statistically separable and therefore must rather be considered indicative.
ACKNOWLEDGEMENT
We wish to thank Analis (Namur-Gent, Belgium) for the loan of the Gold chromatographic system.
REFERENCES 1 J. Van Rompay, Purity determination and evaluation of new drug substances, Journal of Pharmaceutical and Biomedical Analysis, 4 (1986) 725-732. 2 J. Schaefer, An HPLC system with next generation diode array technology, International Chromatography Laboratory, 1 (1990) 6-15. 3 System Gold’” Operation Manual, Beckman Instruments,
Fullerton, CA, 1992. 4 M. Maeder and A.D. Zuberbfhler, The resolution of overlapping chromatographic peaks by evolving factor analysis, Analytica Chimica Acta, 181 (1986) 287-291.
If.
Manage. 21 (1993) 235-242 /Original Research Paper
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5 M. Maeder, Evolving factor analysis for the resolution of overlapping chromatographic peaks, Analytical Chemistry, 59 (1987) 527-530. 6 H.R. Keller and D.L. Massart,
Peak purity control in liquid chromatography with photodiode array detection by a fiied size moving window evolving factor analysis, Ana-
lytica Chimica Acta, 246 (1991) 379-390. 7 J. Kankare, J. Lukkari, T. Pajunen, J. Ahonen
and C. Visby, Evolutionary spectral factor analysis of doping-undoping processes of thin conductive polymer films, Journal
of Electroanaytical Chemistry and Interfacial Electrochemistry, 294 (1990) 59-72. 8 H.R. Keller and D.L. Massart, Evolving factor analysis, Chemometrics and Intelligent Laboratory Systems, 12 (1992) 209-224. 9 G.I. Ouchi, Chromatographic data acquisition: analog-todigital conversion and raw data storage, LC-GC Infernational, 10 (1991) 18-22.
10 D.L. Massart, B.G.M. Vandeginste, S.N. Deming, Y. Michotte and L. Kaufman (Editors), Chemometrics: a textbook, Elsevier, Amsterdam, 1988, pp. 84-86. 11 H.R. Keller, D.L. Massart, T.Z. Liang and O.M. Kvalheim, Evolving factor analysis in the presence of heteroscedastic noise, Analytica Chimica Acta, 263 (1992) 29-36.
12 J.S. Garden, D.G. Mitchell and W.N. Mills, Nonconstant variance regression techniques for calibration-curve-based analysis, Analytical Chemistry, 52 (1980) 2310-2315. 13 H.R. Keller, D.L. Massart, T.Z. Liang and O.M. Kvalheim, A comparison of the heuristic evolving latent projections and evolving factor analysis methods for peak purity control in HPLC-DAD, Analytica Chimica Acta, 267 (1992) 63-71.
14 H.R. Keller, D.L. Massart and J.O. De Beer, Window evolving factor analysis for assessment of peak homogeneity in liquid chromatography, Analytical Chemistry, 65 (1993) 471-475.