Quantitative Analysis of Individual Sugars during Starch Hydrolysis by FT-IR/ATR Spectrometry. Part II: Influence of External Factors and Wavelength Parameters

Quantitative Analysis of Individual Sugars during Starch Hydrolysis by FT-IR/ATR Spectrometry. Part II: Influence of External Factors and Wavelength Parameters

V E R O N I Q U E B E L L O N - M A U R E L , * C E L I N E VALLAT, and D A R R E L L G O F F I N E T CEMAGREF, GEAA, BP 5095, 34033 Montpellier Cedex 1, France (V.B.-M., C.V.); and School of Agricultural Engineering, Purdue University, West Lafayette, Indiana 47907, U.S.A. (D.G.)

Earlier experiments have confirmed the suitability of Fourier transform infrared attenuated total reflectance (FT-IR/ATR) spectroscopy for mon- itoring sugar production from starch hydrolysis. However, in order to confirm the suitability of this technique for an industrial use, the influence of external factors (proteins, salts, temperature) on the prediction, us well as the influence of wavelength parameters, must be evaluated. This paper shows that proteins and salts, which occur in low concentrations in sugar solution before purification, have virtually no influence on the detection of sugar. However, sample temperature, already a very important factor in the industrial hydrolysis of starch, does have an influence upon spectral measurements. This influence would therefore affect the prediction ability of a model made at a single temperature. A proven solution to the influence of temperature is to establish a model which takes into account the range of temperatures that occur during the hydrolysis process. Variations in wavelength parameters also have been shown to significantly affect prediction ability.

Index Headings: FT-IR; ATR; Spectrometry; Sugar; Starch; External factors.

I N T R O D U C T I O N

Our objective is to set up a new spectroscopic tool to monitor individual sugar concentrations during starch hydrolysis. During hydrolysis, glucose and its polymers (maltose, maltotriose, and maltodextrines) are produced. All have somewhat similar infrared spectra because the vibration modes generally result from the glucose ring, not from the sequential arrangement.~ This factor makes the study more difficult.

Quantitative analysis of sugars in concentrated liquid solution by Fourier transform infrared attenuated total reflectance (FT-IR/ATR) spectroscopy has great potential when used in combination with multivariate regression me thodsY However, several aspects of this method must still be studied in order to turn this technique into an "industrial tool". Methodological factors such as repeatibility and reproducibility have been studied in the first part of this paper? Problems dealing with external factors or choice of wavelength range and resolution will be studied in this paper.

The first purpose of this study was to show the influence of external agents such as temperature, minerals, and proteins. Indeed, proteins and minerals are found in non- controlled concentration in mixture. The temperature in the measurement cell also varies with time; hydrolysis temperature is around 50°C, and in an on-line imple-

Received 16 December 1993; accepted 19 January 1995. * Author to whom correspondence should be sent.

mentation, it is not feasible to control the temperature throughout the pipes from tank to analysis cell. Do these factors influence the prediction? If so, which procedure must be developed to eliminate this influence? The second purpose was to "fine-tune" the wavelength parameters, which were initially selected somewhat arbitrarily, to find the best wavelength range and the minimal resolution needed. More than improving the precision of prediction, these parameters can result in a shorter processing time.

MATERIALS AND M E T H O D S

Material: Spectrometer and Samples. A Bruker IFS 25 FT-IR spectrometer equipped with a Specac ZnSe ATR flat crystal accessory was used. A glowbar source was used as well as a liquid nitrogen-cooled MCT detector.

The samples were extracted from hydrolysis at various times, analyzed by high-performance liquid chromatog- raphy (HPLC), and augmented with different amounts of individual sugars as described in Part I. 4 The individual sugars that were quantified are glucose, maltose, maltotriose, and maltodextrines (also called DPn).

Spectroscopic Measurements and Data Treatment. Room temperature was maintained at 23°C. Each solution to be analyzed was injected into the flat crystal cham- ber of the ATR accessory. The crystal was washed with distilled water and dried between each sampling. The reference spectrum of water was recorded every six samples to avoid time shifts.

Interferograms were averaged on 100 scans recorded without filter and Fourier transformed with a triangle apodization function in the 1300-850 cm l range (2000- 850 cm-1 for the pure protein experiment). The standard resolution was 4 cm- 1, except in the case of the parameter experiment.

Post-Spectroscopic Mathematical Processing. The Un- scrambler statistical software package (CAMCO Inc., Norway) was used to process all spectroscopic data. Par- tial least-squares (PLS) was used to establish all models. The standard error of prediction (SEP) was used as an index of calibration performance. Statistical details are described in Part I. 4

Determining Influence of Temperature. The temperature was controlled by the thermostatic ATR crystal and a regulated hot water bath. Three temperatures were studied, corresponding to classical temperatures of hydrolysis and the others at temperatures below that of hydrolysis in 5°C increments. Spectra were taken at 40, 45, and 50°C

Volume 49, Number 5, 1995 0003-7028/95/4905-056352.00/0 APPLIED SPECTROSCOPY 563 © 1995 Society for Applied Spectroscopy

for the same samples extracted from hydrolysis. The total number of samples was 40, and the sugar ranges are presented in Table I.

Two different methods have been attempted to establish the calibration:

1. Calibration at 40°C on the 40 samples; predictions at 40, 45, and 50°C on the same samples. These standard errors of prediction are called SEP 1.

2. Calibration including all temperatures; that is to say, the same 14 samples were taken from each of the first three experiments (40, 45, and 50°C) and a calibration was made with them (42 samples in all), called the "universal calibration". So, predictions were made at 40, 45, and 50°C on the remaining samples. These SEP are called SEP2.

If temperature has an influence, it is necessary either to determine how it alters the calibration line (by a model) or to integrate this influence into the calibration. The first case requires finding the offset and bias of the predictions to correct the predicted values, and the second case requires a "universal" calibration model, which integrates the temperature influence. In the first case, eight samples, were chosen as standards. 4 They were chosen to provide a range as wide as possible for each individual sugar. These standards were used at each new prediction set to compute bias and offset.

The standard error of calibration is labeled SEC when the predicted values have been corrected by the coefficients of the linear regression of the whole calibration set. SECnc denotes predicted values which have not been corrected. Standard error of calibration including a correction using the coefficients of the linear regression of the set of the eight standards is labeled SECm. The same terminology applies for the standard error of prediction.

The precision of a calibration was defined as twice the standard error of prediction. Indeed, at the center of the concentration range, 90% of the predicted values are sit- uated within a _+2.SEP interval from the mean value? The precision levels required by the industry are, respectively, 8, 10, 5, and 5 g/kg for glucose, maltose, maltotriose, and maltodextrines (DPn). They will be compared to the precision obtained with the different calibrations.

Determining Influence of Proteins. Proteins are present during hydrolysis of starch. At the end of the process they are extracted during sugar purification. The maximum protein concentration in hydrolysis tanks is 4 g/kg.

In order to simulate the observed protein concentration variations, proteins were added to a fixed sugar mixture (in which glucose, maltose, maltotriose, and DPn concentrations were, respectively, 62, 72, 46, and 59 g/kg) in different concentrations: 1, 2, 3 and 4 g/kg. Measure- ments were taken at 45°C, and changes were looked for in the prediction using the multitemperature calibration. The influence of proteins was estimated by calculation of the standard deviation of the prediction values of the five samples (having 0, 1, 2, 3, and 4 g/kg protein concentrations). If M is the mean of the five predicted values (Pi), the standard deviation (SD 1) is given by:

SD1 = 5 (1)

TABLE I. Concentration ranges of individual sugars during hydrolysis (after addition).

Concentration Sugar range (g/kg)

Glucose 3.1-100 Maltose 18.7-81.1 Maltotriose 27.3-68.7 DPn 49.1-110.5

In addition, a single sample was measured six times and the prediction values (Si) calculated. The subsequent standard deviation (SD2) calculations allowed the determination of the repeatibility of measurement.

SD2 = 6 (2)

Determining Influence of Minerals. The presence of mineral salts is due to the addition of sulfuric acid to stop the hydrolysis. They are present at very low concentration, with a maximum concentration in hydrolysis tanks of 1.1 g/kg of dry matter. Their influence was determined in the same manner as for proteins; minerals were added to the same fixed-sugar mixture in different concentrations (0.3, 0.6, 0.9, and 1.2 g/kg).

Evaluation at Wavelength Parameters. In order to de- velop the most efficient prediction algorithm, a choice must be made with regard to the degree of resolution as well as the exact wavelength range which will provide the best prediction. Previous experiments of this type were performed with the use of 4-cm-~ resolution. A calibration set of 126 samples was measured at 4-cm ~ and then again at 2-cm- ' resolution to ascertain whether or not a higher resolution model would significantly affect the accuracy of prediction. An additional 54-sample set was kept for validation and SEP calculation.

The wavelength range previously used was 1300 to 850 cm-~. 4 A study of the same samples used in the resolution experiment above will show the effects of narrowing the range of wavelengths. This approach would cut out wavelengths which are not useful in prediction. This procedure may also serve to reduce matrix size, thereby requiring less computer processing time. After an examination of the criteria used by the software, 4 a reduced range of 1188 to 910 cm-~ was selected for comparison.

For the purpose of discussion, 4 cm-~ is termed "low resolution"; 2 cm -~, "high resolution"; the range 1300- 850 cm -~, "full" range; and the reduced range 1188-910 cm- l, "cut" range.

Comparisons were made by predicting the concentrations of the same group of samples with models derived from each of the following categories: high resolution/full range (225 wavelengths), high resolution/cut range (140 wavelengths), low resolution/full range (112 wavelengths), and low resolution/cut range (70 wavelengths). Evaluation of significant differences was made with the use of an F-test.

The sum of square errors (SSE) of each calibration is calculated for each individual sugar:

SSE = ~ (Yp - y,)2 (3) i - l

564 Volume 49, Number 5, 1995

TABLE II. Standard errors of calibration and prediction with calibration at 400C. °

Malto- Glucose Maltose triose DPn R(S 2)

Day 1/Calibration 400C SEC 2.791 3.400 3.802 3.656 6.868 SECnc 2.844 3.499 3.828 3.660 6.956 SECm 3.239 4.079 4.411 4.559 8.208

Day 2/Prediction 45°C SEP1 2.735 2.949 5.161 3.402 7.375 SEPlnc 5.130 3.541 6.192 5.177 10.198 SEPlm 4.060 3.440 5.805 3.764 8.729

Day 3/Prediction 50°C SEP1 3.403 3.313 4.444 4.360 7.831 SEPlnc 6.632 4.292 8.276 4.392 12.255 SEPlm 4.353 4.093 5.024 5.518 9.560

Day 4/Prediction 400C SEP1 3.074 3.382 3.934 4.070 7.275 SEPlnc 3.804 3.528 4.359 4.213 7.979 SEPlm 4.341 4.448 5.032 5.979 9.985

"SEP: SEP corrected by the regression coefficients on the prediction samples; SEPm: SEP corrected by the regression coefficients obtained on the eight standard samples; SEPnc: noncorrected SEP. In italics are the figures which do not give a sufficient precision.

where n is the number of validation samples, Yo is the predicted concentration, and Yt is the true concentration. F is calculated with the use of the equation:

n - 2p SSE1 - SSE2 F = -

p SSE2

where n is the number of validation samples, p is the number of variables in the predicted vs. real values regression, SSE1 is the sum of square error of the first model, and SSE2 is the sum of square error of the second model.

In the F-chart at 0.975% of probability, the significant limit for the experiment (P = 1, n = 54) is 5.37. If the calculated F value is equal to or exceeds the indicated limit, the difference between models is considered significant. The best model in this case is the one with the lower standard error of prediction.

TABLE IlL Standard errors of calibration and prediction with universal calibration."

Malto- Malto- Glucose Maltose triose dextrine R(S 2)

Day 1/Calibration 40 + 45 + 50°C SEC 2.943 2.478 4.103 3.584 6.669 SECnc 2.951 2.501 4.109 3.434 6.606

Day 2/Prediction 45°C SEP2 2.286 3.803 4.267 3.840 7.255 SEP2nc 2.949 4.583 5.297 4.404 8.784 SEP2m 2.551 4.454 4.748 4.086 8.099



" SEP: SEP corrected by the regression coefficients on the prediction samples; SEPm: SEP corrected by the regression coefficients obtained on the eight standard samples; SEPnc: noncorrected SEP. In italics are the figures which do not give a sufficient precision.

RESULTS AND DISCUSSION

Influence of Temperature. The standard errors of calibration and prediction are given in Table II for the calibration at 40°C, and in Table III for the universal calibration. SEP 1 and SEP2 refer to the calibration at 40°C and the universal calibration, respectively.

The comparisons are made by the calculation of the square root of the sum of the SEPs squared JR(S2)] of the different sugars, which allows a global estimation to find the best result, here corresponding to the lower value.

R(S 2) = [(SEPg, ..... )2 --I- (SEPma,tose) 2 + (SEPma,tot,ose) 2 + (SEesp.)2] '/2. (5)

The precisions obtained with the different calibrations will be compared to those specified by the industry. Val- ues which are not in accord with industry standards ap- pear in italics in Tables II and III.

120 0

100

= 80 O C o 60

O 40

20

0

Reg r./Pred. ~ .... ~ ~_~..~,.,~

Regr./Stan. - J S ~ ~ ;~,,,ff.n

[ _ j J 'Regr./Calib.

0 20 40 60 80 100

Real Concent rations Fro. 1. Predicted vs. true sugar content of glucose in real mixture for calibration at 40°C, prediction and standardization at 45°C: (Q) prediction; (t2) calibration; ('~r) standard.

APPLIED SPECTROSCOPY ,565

100 o 90

80 c 70 o o 60 C o 50 0 40 -o • 30

2o • 10

0

Re0r.,S£ Regr./Calib. ~ . . . ~ S ~

~ f ~ Regr./Pred.

I - - - t . . . . I I

0 20 40 60 80 100 Real Concent rations

FIG. 2. Predicted vs. true sugar content o f glucose in real mix ture for universal cal ibrat ion (40, 45, and 50°C), predict ion and standardizat ion at 45°C: (~) prediction; (12]) calibration; (-~-) standard.

In the first case (calibration at 40°C, Table II), a correction by standards is necessary in prediction except on day 4, where SEPnc is better than SEPm (predicted values corrected by standards). On the contrary, in the second case (universal calibration, Table III), the SEP, SEPnc, and SEPm are very similar; consequently a correction is unnecessary. These SEPs are better than those of the first case.

These results show the influence of temperature. The way to bypass this influence is to establish a calibration including the temperature parameter, in which case a correction by standards is unnecessary and the results are improved; in this manner we detect glucose and maltose within the desired limits of precision. The temperature is not introduced as another variable of the model (in addition to intensities), but its variability is taken into account by building a calibration on spectra recorded at different temperatures.

The graphs "predicted vs. true sugar content" for glucose show that the linear regression on standards is nearer the calibration line than the prediction line (Fig. 1) when the calibration is made at 40°C. The inverse is true with the universal calibration (Fig. 2), where the standardization does not improve predicted values.

Moreover, the spectra of the same sample measured at different temperatures (Fig. 3) can be compared. The slight

difference visible between the two spectra explains the different predicted values. As illustrated by Fig. 4 in the case of glucose, the correlellograms 4 are very different for the 40°C and universal calibrations. However, it appears difficult to explain the source of these differences.

Influence of Proteins. Figure 5 shows the spectra of pure proteins (extracted from sugar mixtures at the end of the process) taken at high concentration (4 g/kg) and low concentration (1 g/kg). A small absorbance only was detected between 1680 and 1510 cm -t (outside the work range). The proteins do not show large band within the experimental range (1300-850 cm-1), and therefore have no influence on measurement.

Table IV gives the results of standard deviation ob-

800 600 400

m 200

E o -200 -400 -600 -800

1294

,/ v

1230 1166 1102 1038 974 910

Wavenumbers(cm-1 ) (A)

0.7

0.6 / ' ~

o c8°6 0 , T° ,5°o x~ O 0.3 ....... T= 55°C

< 0.2

0.1 ...................... / \ . . . . . . . . . . . . . . . . .

1294 1230 1t66 1102 1038 974 910

Wavenumber (cm-1)

FiG. 3. Compar ison o f spectra o f the same sample measured at different temperatures.

800 600 400 200

'D 0 -200 -400 -600 -800

-1000 1294 1230 1166 1102 1038 974 910

Wavenumbers(cm-1 ) (B)

FiG. 4. Correlellogram ofglucose from (A) the 40° and (B) the universal (40, 45, and 50°C) calibrations (* 1000).


TABLE IV. Measurement of the influence of proteins; predicted values 0.05 for the samples with fixed sugar and varying proteins.

Proteins added (g/kg) Glucose Maltose Maltotr iose

0 92.83 69.28 48.31 1 90.57 67.91 49.72 2 91.35 67.56 50.65 3 93.22 72.32 44.01 4 93.97 72.09 43.86

Max. - Min ° 3.40 4.75 6.80 SD b 1.25 2.02 2.85

" Max. - Min. = the difference between the m a x i m u m and m i n i m u m values.

h SD = s tandard deviat ion.

TABLE V. Measurement of the repeatability on a sample of proteins (1 g/kg).

Repeatabi l i ty Glucose Maltose Maltotr iose

Max. - Min." 2.86 6.61 6.75 SD" 1.02 2.21 2.19

. Max. - Min. = the difference between the m a x i m u m and m i n i m u m values.

b SD = s tandard deviat ion.

TABLE VI. Measurement of the influence of salts; predicted values for the samples with fixed sugar and varying salts.

Salts added (g/kg) Glucose Maltose Maltotf iose

0 73.78 58.23 37.32 0.3 73.57 58.34 38.33 0.6 73.04 58.25 40.06 0.9 72.88 58.24 41.29 1.2 73.25 59.88 41.16

Max. - Min." 0.90 1.65 3.97 SD b 0.33 0.65 1.57

" Max. - Min. = the difference between the m a x i m u m and m i n i m u m values. SD = s tandard deviat ion.

TABLE Vl l . Measurement of the repeatability on a sample of fixed salt concentration (1.2 g/kg).

Glucose Mal tose Maltotr iose

Max. - Min." 0.94 1.05 1.58 SD ~ 0.37 0.44 0.54

" Max. - Min. = the difference between the m a x i m u m and m i n i m u m values.

b SD = s tandard deviat ion.

TABLE VIII. Standard errors of prediction (SEP) for parameter experiment.

Malto- Model type Glucose Maltose triose DPn

High resolut ion/ful l range 2.13406 2.24733 3.08401 2.34948 High resolu t ion/cut range 1.88116 2.66575 3.07098 2.17937 Low reso lu t ion / fu l l range 2.46396 3.20346 3.10777 3.30328 Low reso lu t ion /cu t r ange 2.17706 2.37577 3.13845 2.14724

o 0.04 =o o~ 0.03

m ° 0.02 .o < 0.01

1998 1870 1742 1614 1486 1358 1230 1102 974

Wavenumber (cm-1) L

" ' " 4g/kg 1 g/kg i

FIG. 5. Spectra o f proteins (extracted from sugar mixtures) at high (4 g/kg) and low (1 g/kg) concentrat ions .

tained on measurements of samples with fixed sugar and varying protein content. Table V shows the results of the repeatibility experiment on a single sample of proteins. The standard deviations for samples with varying proteins (Table IV) are comparable to those for the measurement of repeatibility (Table V). Therefore, the SD is not a result of the effect of proteins, but instead comes from the error of repeatibility.

Because of this consideration, as well as the fact that proteins do not absorb within the standard work range, it can be concluded that proteins do not influence the determination of sugars by FT-IR/ATR spectrometry.

Influence of Minerals. As shown in Fig. 6, salts present a very small absorbance within the experimental range.

Table VI provides the results obtained from measurements of samples with fixed sugar and varying salt concentrations. A repeatibility experiment was also performed on a single sample of salt solution. The results are presented in Table VII.

As in the case of proteins, the SDs of repeatibility for fixed-salt solutions (Table VII) are very similar to those of the varying-salt samples (Table VI), with the exception of maltotfiose. This observation again implies that the deviation in value is due solely to the error of repeatibility, and not the presence of salts. Moreover, the lack of influence can be attributed to the fact that salts (and proteins) occur in such small concentrations when compared to glucose, maltose, maltotdose, and maltodex- trins. Therefore, variations in these low concentrations have no visible effect.

Wavelength Parameters. Table VIII shows the regression-corrected SEP values (in g/kg) for each of the four sugars in each of the four experimental cases. Table IX

0.05 El

i 0.04 " ~ - - . - - - . ~ ' , ..... . .....

0.03

0.02

< 0.01

0 1294 1230 1166 1102 1038 974 910

Wavenumber (cm-1) !

" 0.3 g/kg - - 1.2 g/kg ! i

F[o. 6. Spectra o f salts (extracted f rom sugar process) at high ( 1.2 g/kg) and low (0.3 g/kg) concentrat ions .

APPLIED SPECTROSCOPY 567

TABLE IX. F-test values and comparison results for parameter experiment.

Glucose Maltose Maltotfiose DPn Total

High Res. Full vs. cut 14.92 21.16 0.442 8.43 0.400 Best model Cut Full Neither Cut Neither

Low Res. Full vs. cut 14.61 42.54 1.03 71.06 25.24 Best model Cut Cut Neither Cut Cut

Full range High vs. Low res. 17.32 53.66 0.804 50.79 25.89 Best model High res. High res. High res. High res. High res.

Cut range High vs. Low res. 17.64 13.47 2.31 1.57 0.039 Best model High res. Low res. Neither Neither Neither

gives the resulting F-test values. From Table IX, it can be concluded that:

1. At high resolution, cut range prediction is generally better than that of full range.

2. At low resolution, cut range prediction is generally better than that of full range.

3. At cut range, high resolution and low resolution predictions are roughly equivalent.

4. At full range, high resolution prediction is better than that of low resolution.

From these observations, it is possible to classify the different models (comparatively, from best to worst):

Cut Range/Low Res. = Cut Range/High Res. > Full Range/High Res. > Full Range/Low Res.

In this way it can be seen that a model using the reduced wavelength range (1180-910 cm-l), whatever the resolution, allows a significantly higher accuracy of prediction than the other models, regardless of the fact that maltotriose, when considered alone, is virtually unaffected by such alterations.

CONCLUSION

Among the external factors studied in this paper, temperature was found the most important. Whereas reproducibility was good 4 when the temperature was kept con- stant, a large shift was detected when the predictions were done at temperatures different from the calibration; whereas the calibration done at 40°C gives a global SEC equal to 7 g/kg, the predictions done at 40, 45, and 50°C give SEPs respectively equal to 8, 10.1, and 12.2 g/kg. To avoid the necessity of strictly regulating the temperature from hydrolysis tank to analysis cell (a costly and difficult alternative), we have established a calibration which takes into account the possible fluctuations in temperature. In this case, whereas the calibration (including 40, 45, and 50°C) gives an SEP equal to 6.6 g/kg, the predictions at 40, 45, and 50°C give SEPs respectively equal to 8.7, 8, and 7.6 g/kg, which do not show any shift. Neither proteins nor salts, occurring in uncontrolled yet

very small concentrations, have an appreciable effect upon the prediction of individual sugar concentrations. The standard deviation after addition of up to 4 and 1.2 g/kg of, respectively, salts and proteins was not significantly higher than the standard deviation of repetitions on the same sample. Therefore, they do not need to be taken into account.

The models obtained with diverse wavelength config- urations (i.e., high/low-resolution or normal/cut-range spectra) were compared with an F-test. The use of a reduced wavelength range, 1180-910 cm -~, substantially improves the prediction ability of a given model. The use of 2-cm-~ resolution, which dramatically increases both the scanning time by increasing the mirror path and total number of wavelengths recorded and used in prediction (thereby increasing computer processing time), does not significantly increase prediction ability to warrant its pref- erential use over 4-cm-~ resolution.

The absence of noncorrectible influences due to external factors facilitates the utility of the FT-IR/ATR method for the on-line analysis of individual sugars in concentrated liquid solution. Subsequent work will consist of developing a program whose purpose is to coordinate and control all of the elements necessary to a continuous, real-time, on-line application, including the ability to detect malfunctions.

ACKNOWLEDGMENTS

We acknowledge Dr. Feinberg from CIQUAL (Paris), who allowed us to use the Unscrambler software there; M. Pauwels from ARD, who provided us with analyzed samples; and Dr. Trystram from ENSIA and Dr. Bertrand from INRA (Nantes) for their valuable advice.

1. J. Caei, J. Koenig, andJ. Blackwell, Carbohydrate Res. 32, 79 (1974). 2. V. Bellon and C. Vallat, "Individual Sugar Content Control by the

use of VF-IR Spectroscopy Coupled with an ATR Accessory", un- published data.

3. F. Cadet, D. Bertrand, P. Robert, J. Maillot, J. Dieudonn6, and C. Rouch, Appl. Spectrosc. 45, 166 (1991).

4. V. Bellon-Maurel, C. Vallat, and D. Goflinet, Appl. Spectrosc. 49, 556 (1995).

5. R.G. Miller, Jr., Simultaneous Statistical Inference (Spfinger-Verlag, New York, 1981), 2nd ed.


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Quantitative Analysis of Individual Sugars during Starch Hydrolysis by FT-IR/ATR Spectrometry. Part II: Influence of External Factors and Wavelength Parameters