Anal. Chem. 1997, 69, 1288-1294
Infrared Spectroscopy: Instrumental Factors Affecting the Long-Term Validity of Chemometric Models Marianne Defernez* and Reginald H. Wilson
Institute of Food Research, Norwich Laboratory, Norwich Research Park, Colney, Norwich NR4 7UA, U.K.
Data from instrumental techniques such as mid-infrared spectroscopy are increasingly being analyzed for sample identification and classification by chemometric methods based on principal component analysis (PCA). However, even modern spectrometers are subject to instability. This may affect PCA, because PCA selects the variables with the largest variance. This paper investigates the relative effects of sources of instrumental instability using a model developed for fruit puree classification. Single-beam spectra, potentially useful for on-line analysis, saw their overall intensity decrease as the infrared source output and/or the detector sensitivity declined. Consequently, single-beam spectra were mainly differentiated by their overall intensity and had to be used with caution in the long term because this strongly affected the analyses. Absorbance spectra were not sensitive to source or detector decay but showed, in the long term, subtle band shape changes and frequency shifts. While these changes were not found to influence analyses involving very different samples, they diminished the success of analyses of data sets with small intrinsic variance. Where there was large spectral differences between sample classes, instrument-related factors were insignificant. However, where spectral differences were more subtle (with a single class), instrumental effects became more important. Suggestions are given to reduce the instrumental and experimental interferences on chemometric analyses, both when recording spectra and for managing spectral databases. Spectroscopy, particularly near- and mid-infrared spectroscopy, is increasingly being applied to problems of sample identification.1 Examples of their use include the authentication and classification of food and raw materials. Determination of whether such materials are authentic, i.e., correspond to their description, usually relies on comparing data recorded for the sample under test with a data set of previously characterized samples whose authenticity is established. The way this comparison is made differs according to the type of data involved. If the data needed to characterize the sample consists of a small number of chemical or physical measurements, these are compared to known values. An example of this is the use of “Richtwerte und Schwankungsbreiten bestimmter Kennzahlen” or RSK values2 in the characterization of fruit juices. If the data characterizing the sample are * Corresponding author: e-mail,
[email protected]. (1) Briandet, R.; Kemsley, E. K.; Wilson, R. H. J. Sci. Food Agric. 1996, 71, 359-366. (2) RSK values, The complete manual; Flussiges Obst GmbH; Shonborn, Germany, 1987.
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a complex spectrum (e.g., NMR or infrared), then this must be compared to previously collected spectra of known samples. This may be done visually if spectra contain particularly characteristic features, but may require chemometric techniques if the important spectral characteristics are not easily recognizable. Chemometric methods are likely to be increasingly needed because modern adulteration can take sophisticated forms.3 Whatever the method used, there is a need to ensure the reliability and the stability of the measurements since genuine and adulterated products may be very close in composition. This is particularly so if data reduction techniques such as principal component analysis (PCA)4 are employed, because these methods select the variables whose variance across the data set is the greatest. Consequently it is important to be able to isolate variance due to instrumental and experimental factors from intrinsic variance in sample sets. The recent increase in the use of mid-infrared spectroscopy for the authentication of foodstuffs is due mainly to instrumental and technical developments. Fourier transform instruments, which are now more common, have advantages over previously used dispersive instruments; they have a higher accuracy and better stability in frequency (Connes’ advantage5 ), a higher signalto-noise ratio for the same measurement time (multiplex advantage, also called Fellget’s advantage6 ), and better power delivery to sample accessories7 (Jacquinot’s advantage8 ). The quality of spectra is now such that a number of studies have been successful using chemometrics to characterize and classify samples based on mid-infrared spectra.1,9 In these, reduction methods such as PCA were widely used for data pretreatment. However, many studies have been carried out over a relatively short time scale. FT-IR spectrometers can be subject to instability over long time periods,10 and it is important to determine whether this may influence the practical ability of the methods used to characterize and classify samples. The effect of instrumental instability on chemometric analyses has not yet been reported. Obviously this issue is of real practical importance: Spectral databases are often recorded over a period of time, and for many foodstuffs, the products of a new season need to be characterized against a (3) Patel, T. New Sci. 1994, 1926, 26-29. (4) Krzanowski, W. J. Principles of multivariate analysis: a user’s perspective; Oxford University Press: New York, 1988. (5) Connes, J. Rev. Opt. 1961, 40, 45. (6) Fellget, P. B. J. Phys. Radium 1958, 18, 187. (7) Green, D. W.; Reedy, G. T. In Fourier transform infrared spectroscopy: Application to chemical systems; Ferraro, J. R.; Basile, L. J., Eds.; Academic Press: New York, 1978; Vol. 1, pp 21-27. (8) Jacquinot, P. 17eme Congre` s du GAMS, 1954. (9) Nault, J. R.; Manville, J. F. Wood Fiber Sci. 1992, 24, 424-431. (10) Bartl, F.; Delgadillo, I.; Davies, A. N.; Huvenne, J. P.; Meurens, M.; Volka, K.; Wilson, R. H. Frenesius J. Anal. Chem. 1996, 354, 1-5. S0003-2700(96)01064-5 CCC: $14.00 Published 1997 Am. Chem. Soc.
database produced previously. Furthermore, authentic samples may not be available at all times, and the cost of creating a database may be high so that the procedure cannot be repeated for each batch of samples to be tested. Although some techniques may seem to have good potential with spectra recorded on a shortterm basis, problems may arise when they are used for routine daily analysis over a number of years. For industry, another potential problem is the use of a spectral database for testing samples recorded with different spectrometers; this was recently investigated by Holland et al.11 The objectives of the study reported here were to examine the following: (1) what instrumental factors vary over long time scales (e.g., instrumental throughput, alignment, and attenuated total reflection crystal degradation) and their relative importance; (2) the long-term effect of these factors on databases and methods relying on the use of PCA, and similar chemometric methods, including multivariate calibration; (3) how the detrimental instrumental or experimental factors may be controlled. EXPERIMENTAL SECTION Sample Preparation. The data used were mid-infrared spectra of strawberry, raspberry, and apple purees. Strawberry and raspberry purees were obtained from fruits collected in 1993 and 1994, mostly in local self-pick fields. Most apple purees were obtained from fruits bought in local supermarkets. A number of samples were also obtained from industrial sources. Purees were prepared as described by Defernez et al.12 Instrumentation and Spectral Acquisition. All spectra were recorded with a Spectra-Tech MonitIR system (Spectra-Tech Applied Systems Inc.) FT-IR spectrometer. The instrument was fitted with an air-cooled silicon carbide infrared source,13 and the temperature of the interferometer was controlled by a thermistor and fan arrangement. A sealed and desiccated optical system optimized the cancellation of water vapor and carbon dioxide absorptions. There were two permanently mounted sampling accessories: one for attenuated total reflection (ATR), and the other one for diffuse reflection (DRIFT). Each was linked to its own deuterated triglycine sulfate (DTGS) detector.14 The transfer optics of the overhead ATR accessory were sealed from the atmosphere by two potassium bromide windows. Through these windows, the infrared radiation was directed into the detachable ATR element. The element used was a zinc selenide crystal (“crystal 1”) mounted into a plate with a shallow trough for sample containment. The crystal geometry was a 45° parallelogram with mirrored angle faces. From the second year of study, a second ZnSe crystal (“crystal 2”) with the same characteristics was also used. In this work, crystal 1 was used unless specified. The purees were spread directly onto the ATR crystal. Spectra were recorded from 800 to 4000 cm-1 at a resolution of 8 cm-1. For each spectrum, 256 interferograms were coadded, a phase (11) Holland, J. K.; Kemsley, E. K.; Wilson, R. H. Transfer of spectral data between Fourier transform infrared spectrometers for use in discriminant analysis of fruit purees. J. Sci. Food Agric., in press. (12) Defernez, M.; Kemsley, E. K.; Wilson, R. H. J. Agric. Food Chem. 1995, 43, 109-113. (13) Griffiths, P. R.; De Haseth, J. A. Fourier transform infrared spectrometry; Wiley-Interscience: New York, 1986; pp 208-209. (14) Griffiths, P. R.; De Haseth, J. A. Fourier transform infrared spectrometry; Wiley-Interscience: New York, 1986; pp 212.
correction was performed, and a triangular apodization15 was employed before Fourier transformation. The single-beam spectra were converted to absorbance units using a water spectrum, collected under identical conditions, as background. Chemometric Analysis. Data analysis was performed using Win-Discrim, (K. Kemsley, Norwich, U.K.) a specialized package for spectral analysis including PCA,4 discriminant analysis (DA),16 SIMCA,17 and UNEQ (UNEQual dispersed classes).18,19 PCA is an integral part of SIMCA. For mathematical reasons it is also used prior to DA or UNEQ when the number of variables exceeds that of samples. Here PCA was performed with data sets of samples belonging to one class only (i.e., one fruit type), to simulate situations where a single class is characterized by PCA, as is the case when modeling techniques like SIMCA or UNEQ are used. PCA was also applied to data sets of samples belonging to several classes (i.e., several fruit types), to simulate situations where discriminating techniques like DA are used. In most analyses, the correlation matrix was used for PCA, which implies that the loadings have to be multiplied by the data set standard deviation to restore the spectralike appearance20 for interpretation. To avoid this, the covariance matrix method was used in a number of analyses, as indicated in the text. The experiments reported here examined the effects of the long-term use of spectral databases first with single-beam spectra, and second with absorbance spectra. The potential advantages of using single-beam spectra are that a background spectrum need not be recorded, thus simplifying the procedure for the operator, and that readings can be taken in a shorter time period. More importantly, for on-line analysis, the recording of a background spectrum can be technically problematic. Initially a data set of single-beam spectra of only one class was examined in order to determine the magnitude of the influence of recording time on analyses with SIMCA or PCA followed by UNEQ (experiment 1). For this, puree spectra of a single fruit type were used. Strawberry purees were chosen for illustrative purposes because they were the fruit for which the highest number of spectra were available; 143 spectra were recorded over a period of almost two years. The “fingerprint” region of the single-beam spectra, corresponding to 311 data points between 800 and 2000 cm-1, was subjected to a PCA, both with and without prior area normalization of the spectra. The results of experiment 1 were compared with those of an analysis of single-beam spectra of the empty crystal in order to establish the role played by instrumental factors (experiment 2). A total of 58 single-beam spectra of the empty crystal were recorded over a period of time covering that of the acquisition of the strawberry spectra used above. They were divided into groups according to the date of recording, as shown in Table 1. The fingerprint region of the single-beam spectra (800-2000 cm-1) was subjected to a PCA, both with and without prior area normalization of the spectra. (15) Griffiths, P. R.; De Haseth, J. A. Fourier transform infrared spectrometry; Wiley-Interscience: New York, 1986; pp 15-25. (16) Howells, S. L.; Maxwell, R. J.; Peet, A. C.; Griffiths, J. R. Magn. Reson. Med. 1992, 28, 214-236. (17) Wold, S.; Sjo ¨stro ¨m M. In Chemometrics: theory and application; Kowalski, B. R., Ed.; ACS Symposium Series 52; American Chemical Society: Washington, DC, 1977; pp 243-282. (18) Derde, M. P.; Massart, D. L. Anal. Chim. Acta 1986, 191, 1 -16. (19) Derde, M. P.; Massart, D. L. Anal. Chim. Acta 1986, 184, 33-51. (20) Kemsley, E. K. Chemom. Intell. Lab. Syst. 1996, 33, 47-61.
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Table 1. Number and Characteristics of Spectra Used for Comparison Purposes with Strawberry Puree Spectral Analysesa subset no.
no. of spectra
spectra recording period
1 2 3 4 5 6
14 8 8 8 7 13
30.06.93-27.08.93 15.09.93-07.10.93 14.03.94-11.04.94 20.06.94-15.07.94 25.10.94-11.11.94 22.01.95-12.02.95
a These data concern both the single-beam spectra of the empty crystal used in experiment 1 and the absorbance spectra of water used in experiment 6.
A comparison of interferograms collected with different crystals was undertaken to examine the greatest cause of instrumental instability (experiment 3). Data on instrumental drift were obtained from three sources. The first was a reference crystal, a horizontal crystal which is laid onto the accessory holder when the spectrometer is not in use. This protects the spectrometer sealing windows and is used for alignment purposes and as a reference. The intensity of the interferogram with an aligned instrument was taken at 39 different dates. The values were expressed as a percentage of the initial value, recorded at the start of the experiment (2 August 1993). The second source of information on instrumental drift was provided by single-beam spectra of the empty crystal (crystal 1). The sum of the intensities of the 311 data points between 800 and 2000 cm-1 was calculated for each single-beam spectrum. This value was then expressed as a percentage of the initial value, taken at the start of the experiment (29 July 1993). This data was recorded on 57 different dates. The third source of information was provided by the singlebeam spectra recorded with the second ZnSe crystal (crystal 2). The initial value was taken on the 24 June 1994, when the crystal was purchased. These data were recorded for 32 different dates. A data set of single-beam spectra of several classes was then subjected to PCA (experiment 4). This was to establish whether the variability induced by instrumental factors would prevail in analyses by PCA and DA or whether this variability would be too small relative to the variance of the data set to have any influence. For this, a model containing data from three fruit types was studied. The infrared spectra of 335 purees (204 strawberry, 85 raspberry, and 46 apple) were recorded over the whole study period. A PCA was performed with the fingerprint region of these spectra (800-2000 cm-1), both with and without prior area normalization of the spectra. Conversion into absorbance units may compensate for the effects of instrumental factors observed with single-beam spectra. We therefore analyzed a data set of absorbance spectra of only one class, as would be the case when using SIMCA, or PCA and UNEQ (experiment 5). The spectra of strawberry purees used in experiment 1 were transformed into absorbance units. PCAs were carried out first with the whole spectral region between 800 and 4000 cm-1 (831 data points) and second with a reduced spectral region between 800 and 2000 cm-1 (311 data points). Both analyses were performed both with and without prior area normalization of the absorbance spectra. For simplicity, only analyses without prior area normalization are presented here, as similar results were obtained with area normalization. 1290 Analytical Chemistry, Vol. 69, No. 7, April 1, 1997
Figure 1. PCA of 143 picked strawberry puree ATR spectra (single beam, 800-2000 cm-1, correlation matrix). Subset 1 (0), subset 2 (2), subset 3 (]), and subset 4 (b) were, respectively, the following: (1) fresh fruit collected in Summer 1993, spectra recorded between 30.06.93 and 29.07.93, (2) freeze-thawed fruit collected in Summer 1993, spectra recorded between 15.09.93 and 23.09.93, (3) fresh fruit collected in Summer 1994, spectra recorded between 24.06.94 and 15.07.94, and (4) freeze-thawed fruit collected in Summer 1994, spectra recorded between 29.01.95 and 12.02.95.
As some temporal effects were observed in experiment 5, the relative importance of instrumental factors here was established by comparison of experiment 5 with an analysis of absorbance spectra of water, a relatively stable and consistent compound (experiment 6). A single-beam spectrum of an empty crystal and a single-beam spectrum of a crystal covered with water were recorded prior to the acquisition of puree spectra. Consequently, it was possible to produce a water spectrum in absorbance units by taking the empty crystal as a background. This gave a total of 58 water spectra (see Table 1), which were submitted to PCA to determine the existence of any clustering or trend related to time of recording. The analysis was performed with the whole spectral region and repeated with smaller sections of the spectra: first between 1060 and 2500 cm-1, then between 1060 and 1470 cm-1, and finally between 1110 and 1470 cm-1. This was so as to include increasingly fewer peaks. The PCAs presented here were performed with the covariance matrix so that the loadings were directly interpretable, but similar results were obtained with correlation matrices. A data set of absorbance spectra of several classes was then analyzed to examine whether the effects observed in experiment 5 were of sufficient magnitude to influence PCA and DA (experiment 7). The spectra used in experiment 4 were analyzed in the same way after transformation into absorbance units. RESULTS AND DISCUSSION Experiment 1. As shown in Figure 1, in the analysis without area normalization, the first PC discriminated the single-beam spectra into four groups corresponding to the four periods during which they were recorded. This PC accounted for 93.8% of the total variance of the data set. Its loading showed high contributions from the whole spectral region, although slightly less in the carbohydrate region. This indicated that the main source of variation between these spectra arose from a general difference
Figure 3. Time (number of days from 30.06.93) of recording vs first PC scores, for a set of 143 spectra of picked strawberry purees (single beam, 800-2000 cm-1) (O) and for a set of 58 spectra of empty ATR crystals (single beam, 800-2000 cm-1) (4).
Figure 2. Example of spectra used in this study: strawberry recorded in July 1993 (A), raspberry recorded in July 1993 (B), apple recorded in September 1993 (C), and strawberry recorded in February 1995 (D); (a) as single-beam spectra and (b) as absorbance spectra.
of intensity over the whole spectral range. As shown in Figure 2a, spectra recorded at the beginning of the period had overall higher intensities than those recorded at the end, which, when multiplied by a high positive loading value, gave positive scores. However, spectra recorded at the end of the period of study had overall lower values, yielding negative scores. In Figure 3a the first PC scores as a function of the time of recording, along with the regression line between these two variables, are shown. This illustrates a gradual loss in output of the spectrometer. This could be due to either a loss of intensity of the light produced by the source or of sensitivity of the detector or to a progressive degradation of the crystal surface or the transparency of the sealing windows. The variance of observations around the regression line may be explained by a number of factors. There might be some other influences on the performance of the spectrometer, such as the alignment or the laboratory temperature, which are not necessarily constant with time. Furthermore, the fruit composition varies according to ripeness, variety, and seasonal variation and possibly also according to whether it is fresh or freeze-thawed; this may influence the first PC score. When the single-beam spectra were area normalized prior to the analysis, the first PC was not sufficient to enable the four
periods of time during which the spectra were recorded to be distinguished. Instead, the first two PCs were necessary; the first accounted for 58.6% of the variance, and the second for 31.6%. A comparison of all spectra showed that the difference between them was greatly reduced by the area normalization, and no timedependent trend was apparent. This finding was similar to that reported by Defernez et al.12 but now on a longer time scale. The results suggest that the apparent differentiation between fresh and freeze-thawed fruits reported in that paper may, in fact, be due to a temporal effects on spectrometer performance. Experiment 2. Figure 4 depicts the scores obtained by the spectra on the first two PC axes when no area normalization was performed. The first PC separates the spectra into clusters corresponding to the periods during which they were recorded. As the delineation of time periods may influence the results, Figure 3b shows the relationship between the first PC score and time. The strong similarity of this relationship to that observed in Figure 3a suggests that the variance in the strawberry puree analysis was dominated by the loss of overall spectral intensity rather than fruit characteristics. Here, the spread of the data points away from the regression line is less important, due to the absence of variance arising from the fruit characteristics. The spectra were also grouped quite tightly on the second PC axis within each period of time, and each cluster tended to yield quite different values. This suggests that there was also some contribution related to short time scale, nonlinear changes in the second PC axis, possibly caused by the alignment of the spectrometer, the laboratory temperature, or some similar variable. When the spectra were area normalized prior to analysis, the first two PCs Analytical Chemistry, Vol. 69, No. 7, April 1, 1997
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Figure 4. PCA of 58 empty crystal spectra (single beam, 8002000 cm-1, correlation matrix): period 1 (b), period 2 (4), period 3 (9), period 4 (×), period 5 ([), and period 6 (0).
Figure 5. Signal obtained using different crystals, expressed as a percentage of its initial value: reference crystal (b), crystal 1 (0), and crystal 2 (4).
were needed to clearly distinguish the different periods of time. This was similar to the results obtained in experiment 1. The results described here showed that a decrease in spectral intensity due to experimental factors masked the fruit characteristics of interest in PCA in experiment 1. Possible causes included a loss of either intensity of the light produced by the source or of sensitivity of the detector, or a degradation of the crystal surface or the transparency of the sealing windows. However, the windows were polished in August 1994, which did not alter the linear trend of the changes observed and was therefore unlikely to be the main explanation. The following experiment examines the possibility of the crystal surface degradation being the main factor affecting the spectral intensity. Experiment 3. Figure 5 shows the loss of output intensity for three ATR crystals (reference crystal, crystal 1, crystal 2). The similarity between the behavior of the reference crystal and crystals regularly in contact with samples is striking. Crystal 1 was polished around day 450. However, after polishing, the trend in the signal did not strongly differ from the trends observed with 1292
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the other crystals. Consequently, it is unlikely that the general drop in intensity observed is associated with a degradation of the crystal surface. During November 1994 (days 489-518) the temperature of the spectrometer was not as well regulated as normal. Therefore, the internal temperature was not always constant, which may explain the slightly inconsistent position of the points on the graph. ATR crystals may undergo some physical degradation due to the repeated exposure to various samples and cleaning procedures. Although the reference crystal might simply degrade through aging, it is not exposed to any of the conditions above and is unlikely to degrade at the same speed and in the same way. The similarity of the time-dependent changes between the crystals used here suggests that these changes are not due to crystal surface degradation. The loss of intensity of the spectra is most likely due to a loss in output of the source or in sensitivity of the detector, and surface degradation may play only a minor role in the observed drop in intensity. Experiment 4. In experiments 1-3 the influence of the instrument performance on PCA of single-beam spectra was examined. This was done by considering only one type of fruit, because it was a simpler system introducing a limited number of variables into the analysis. It also allowed the reasons that might limit the success of an analysis by a single-class model (SIMCA, UNEQ) to be identified. However, the influence of these factors may be different when a system comprising several types of fruit (DA) is analyzed, because the variability between spectra due to the fruit composition itself is much larger. This problem was investigated with the three fruit types model studied by Defernez et al.12 The score plots for the non-area-normalized set of data on the first two principal components were very similar to those obtained with only one fruit type. The first PC axis was clearly correlated with time, as the overall intensity was the main spectral characteristic (Figure 2a). Some clustering according to fruit type appeared on the second PC, but this was weak. When the spectra were normalized, as for single fruit type analysis, the first two PC axes were needed to visualize the four periods of time. The strong influence of time ruled out the possibility of using single-beam spectra for fruit identification. Experiment 5. When the full spectral region was used, PCA revealed some grouping of the data according to the time of recording. The plot of the third against the fourth PC scores, shown in Figure 6, best highlights this; all four periods are almost separated. On the fourth PC axis, data from the first two periods of recording obtained lower scores than that from the last two. This could be due to seasonal variation of the fruits, as the first two periods corresponded to spectra recorded from purees prepared with fruits collected in 1993, and the last two with fruits collected in 1994. However, it could also reflect some change in the spectrometer behavior not totally compensated for by the conversion into absorbance units. The main features of the third loading were very strong, negative values in the water OH stretch region between 2900 and 3700 cm-1, and relatively strong, positive values in the carbohydrate region between 950 and 1150 cm-1. Plots of spectra recorded during each of the four periods confirmed that data from the different periods tended to follow a distinctive pattern in the water region. In the sugar region, this pattern was not clear. The fourth loading also showed a large feature in the water OH stretch
Figure 6. PCA of 143 strawberry puree ATR spectra (absorbance, 800-4000 cm-1, correlation matrix): period of recording 1 (30.06.9329.07.93) (0), period 2 (15.09.93-23.09.93) (2), period 3 (24.06.9415.07.94) (]), and period 4 (29.01.95-12.2.95) (×).
region, but with one part positive and one part negative. This suggests a more complex feature of the water was responsible, such as the band shape or a slight frequency shift of this band. When the limited spectral region was examined, the first two PCs again showed no clustering by time of recording. Clustering was apparent on the higher PC axes, with the scores on the third PC being higher for data recorded during the last two periods than data recorded during the first two. The fourth PC tended to isolate higher scores among data recorded during the last period. The third loading showed high negative contributions from the carbohydrate region and high positive ones in the pectin area, as well as toward the region of 1230 cm-1. This suggests an influence of seasonal variation, with a variable carbohydrate content relative to pectin and other components. Experiment 6. Experiment 5 suggested that some experimental parameters may modify band shapes or frequencies and contribute to spectra being clustered on higher PC axes. To further examine this, water absorbance spectra were analyzed. Whatever the spectral region used, the first or second PC always discriminated between spectra recorded before October 1994 and those recorded afterward; spectra recorded between the 25 October 1994 and 11 November 1994 formed one cluster, and those recorded between the 22 January 1995 and 12 February 1995 formed another cluster. Both clusters were separated from the rest of the data (Figure 7a). The first PC accounted for between 75 and 95% of the variance. Higher PCs also isolated a group of points from one particular period in some analyses. However, this only corresponded to a very small percentage of the variance, typically less than 1%. The last analysis involved only the region between 1110 and 1470 cm-1, where there was no actual absorbance peak. Despite this, the data corresponding to the last two periods were also distinctive, mainly on the first PC axis. The first loading was high for the whole frequency range, with slightly higher values for higher wavenumbers. The spectra were virtually identical, but detailed inspection of the region between 1110 and 1470 cm-1
Figure 7. (a) PCA of 58 water spectra (absorbance, 1110-1470 cm-1): period 1(b), period 2 (4), period 3 (9), period 4 (×), period 5 ([), and period 6 (0). (b) Selection of spectra used for the above analysis. Characteristic samples, labeled A-E, are cross-referenced in the figure.
showed some small variation in the intensities. This corresponded to the score on the first PC axis, as is shown in Figure 7. This slight difference was apparent for the whole spectral range from 800 to 4000 cm-1. However, it was neither a simple offset nor a multiplicative factor. It could not therefore be solely explained by a drift of the machine between the recordings of the background and the water spectra or by a phenomenon resulting in a path length change. Several factors may have contributed to these slight differences between spectra. The fact that the last two periods tended to be isolated from the others leads to several possible interpretations. It is possible that the path length distribution was altered slightly by the crystal being polished. A second possibility concerns the temperature inside the spectrometer. At the beginning of the fifth period, it was noticed that the temperature inside the device was not well regulated. Therefore, during that period, the inside of the spectrometer was at a higher temperature than previously. By the beginning of the sixth period the temperature was regulated, but at a level possibly slightly higher than before the incident. This change may have affected the band shape and frequencies in the spectra. Analytical Chemistry, Vol. 69, No. 7, April 1, 1997
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Experiment 7. Very similar results were obtained whether or not spectra were area normalized. An example of spectra of different fruit types is shown in Figure 2b. Clustering according to fruit species was obvious in the plots of the two first PC axes, and no major influence of time could be seen in the scores obtained by the spectra on the different PC axes. Consequently, analyses using the PC scores are unlikely to be significantly affected by a delay between the training set and the test samples being recorded. CONCLUSIONS Infrared spectra are affected by a number of instrumental and experimental factors. In this work, the relative magnitude of these different factors was examined. The effect of spectral variability on data analysis involving PCA was studied, and the relative importance of the sources of instability was established. This gives some insight into both the short-term and the long-term suitability of chemometric methods for sample identification and classification, and for the broader use of multivariate methods for the analysis of infrared spectra. Previous work12 had shown that very similar results may be obtained by PCA and DA with both single-beam and absorbance spectra. This indicated that single-beam spectra may be used to simplify the acquisition of spectra, especially for on-line applications. However, choice of spectral format may have certain implications if an analysis is to be carried out with spectra recorded over a long time period. This work has shown that single-beam spectra may be subject to a considerable temporal drift, due to the decay of the infrared source or of the detector. This temporal drift here constituted a large effect (loss of almost one-third of the output intensity in two years) and influenced the results of a PCA regardless of the variability present from the samples. Consequently, the use of single-beam spectra for long-term analysis was precluded. The use of absorbance spectra was expected to compensate for the effects outlined above. However, temporal clustering was apparent in the PCA scores. The magnitude of this was much smaller than that observed for single-beam spectra; the clustering was not detectable visually when the PCA involved samples of very varied composition (spectra from different fruit types), but it was apparent when the variability between the samples was lower (spectra from only one fruit type). The comparison of the PCA results from fruit samples, and from water samples, revealed that several factors contributed to this temporal instability. One was the seasonal variability in the fruit composition. Another was slight changes in band shape and frequencies of absorption, corresponding to the internal temperature of the spectrometer being slightly increased. This change is thought to have been the major instrumental contribution to the disparity observed between absorbance spectra; there was no major effect detected from polishing either the KBr sealing windows or the ATR crystal or from changing the ATR crystal itself. This study suggests that care should be taken in long-term studies involving multivariate analysis of infrared spectra. The
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source decay or the loss of detector sensitivity can have a considerable effect on an analysis. This should be kept in mind when performing analyses with single-beam spectra. Second, although the effect of crystal degradation or replacement observed here was minimal, it is important to remember that if the number of reflections changes, area normalization should be used to compensate for the change in global intensity. Third, other experimental conditions may vary according to the spectrometer and caution should be taken. Although the changes may not be immediately visible on the spectra, even small alterations can affect multivariate analyses, especially those involving data sets with little variance. Precautions should be taken to limit the likely extent of any problems at the stage of spectral acquisition. Working in a controlled, temperature-stable atmosphere, is preferable. Additionally, it is advisable to update the models used for classification by including spectra recorded near the date of sample testing and by regularly discarding old spectra. This should minimize the effect of different experimental conditions and limit the influence of seasonal variation in food materials. Problems may also appear in the short-term use of multivariate methods with infrared spectroscopy. Effects were observed here related to a lack of temperature control due to a fault on the spectrometer. However, other units may be affected by similar problems on a short-term basis. For these instruments, it is particularly advisable to record a background spectrum immediately before each sample spectrum. It is also important to be aware of the possibility of nongenuine group structure being detected. In conclusion, various sources of instrumental instability may affect infrared spectra, particularly in the long term. This instability has the potential of severely affecting their chemometric analysis. Here, we have examined the different sources of problems and their relative importance. Whilst some effects can be compensated by simple data treatment, others require careful monitoring of the spectral database. However, we have shown that adequate care should ensure that the multivariate analyses performed with mid-infrared spectra are valid in the long-term. ACKNOWLEDGMENT The authors thank the European Union (Human Capital and Mobility grant under the AIR programme) and the BBSRC for funding this work, and the U.K. Jam Manufacturers’ Association for the provision of some of the samples used in this study. They also thank James Holland, Kate Kemsley, and Klaus Wellner for helpful suggestions and advice.
Received for review October 15, 1996. Accepted January 23, 1997.X AC961064O X
Abstract published in Advance ACS Abstracts, March 1, 1997.
