quantitative analysis of individual sugars during starch hydrolysis by ft-ir/atr spectrometry. part...

Quantitative Analysis of Individual Sugars during Starch Hydrolysis by FT-IR/ATR Spectrometry. Part I: Multivariate Calibration Study--Repeatibility and Reproducibility

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, 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.)

This paper discusses the use of Fourier transform infrared (FT-IR) spectroscopy coupled with an attenuated total reflectance (ATR) accessory as applied to the quantification of individual sugar concentrations (glucose, maltose, maltotriose, and maltodextrines) in real mixtures extracted during starch hydrolysis. Solutions studied contained dry matter rang- ing between 250 and 300 g/kg. Glucose and maltose were detected with the required precision, but not maltotriose or maltodextrines. The measurements are fairly repeatible, and predictions are reproducible.

Index Headings: FT-IR; Spectrometry; ATR; Sugar; PLS.

INTRODUCTION

The general objective of this study is to prove the fea- sibility of Fourier transform infrared (FT-IR) spectrometry as an at-line sensor to control the composition of sugar industry products. Earlier experiments have confirmed the suitability of FT-IR attenuated total reflectance (ATR) spectroscopy for monitoring sugar produc- tion from starch hydrolysis, especially when combined with multivariate regression methods [principal component regression (PCR) and partial least-squares (PLS)1.2]. These preliminary experiments on model mixtures give a very encouraging accuracy. However, in order to de- velop an industrial analytical technique, it is still necessary to study several details. These details can be arbi- trarily grouped into two categories:

1. Methodological Factors. Aspects occurring during and/ or after measurement, including the model-making process and the repeatibility and reproducibility of results.

2. External factors. The factors surrounding the "un- known" liquid, namely, the temperature of solution at the time of measurement and the existence of salts and proteins which may affect spectroscopic measurements.

These two categories of problems will be the subject of parts I and II of this publication. In this part, we will deal only with the methodological factors.

To fully characterize the FT-IR/ATR method for the simultaneous prediction of glucose and its polymers (maltose, maltotriose, and maltodextrin), the first step is to "delve into" the regression software and find the mathematical criteria which are used in prediction. In other

Received 16 December 1993; accepted 18 August 1994. * Author to whom correspondence should be sent.

words, spectral analysis must be used to identify differ- entiating peaks for the individual sugars contained in the hydrolysis mixture. This information can then be compared to regression data.

Second, since the ultimate goal of this endeavor is the industrial, on-line prediction of concentration, the method must be relatively consistent and reliable. Therefore, it is necessary to study the repeatibility of mathematical prediction as well as the reproducibility of spectral measurement. An understanding of the basis for mathematical prediction coupled with an assurance of accuracy, repeatibility, and reproducibility of results will simulta- neously verify the utility of this method and increase its viability in an industrial environment.

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 fiat crystal accessory was used, with a glowbar source and a liquid nitrogen-cooled MCT detector. No filter was used.

Six samples were extracted from two hydrolysis tanks, under various hydrolysis conditions. They were analyzed by high-performance liquid chromatography (HPLC) to measure sugar contentrations. In order to provide a larger range of individual sugars, additional amounts of sugars were added to the mixture in various concentrations. Each original sample was partitioned into four subsamples, three of which were augmented with individual sugars. The final concentration ranges of sugars are shown in Table I. For each sample, total dry matter ranges from 200 to 300 g/kg.

Spectroscopic Measurements and Data Treatment. Room temperature was maintained at 23°C. Each solution was injected into the flat crystal chamber 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.

The interferograms were recorded with no optical filter, averaged on 100 scans, and Fourier transformed with the triangle apodization function in the 1300-850 cm-l spectral range. A 4-cm- ] resolution (a total of 112 wavelengths per scan) was used.

Post-Spectroscopic Mathematical Processing. The Un- scrambler (CAMCO Inc., Norway) statistical software package was used to process all spectroscopic data. Partial least-squares regression was used to correlate all data. The

556 Volume 49, Number 5, 1 995 0003-7028/95/4905-055652.00/0 APPLIED SPECTROSCOPY © 1995 Society for Applied Spectroscopy

TABLE I. Concentration ranges of individual sugars in sample set (after addition).

Concentration Sugar range (g/kg)

G1 ucose 16.2-100 Maltose (DP2) 37.1-93.5 Maltotriose (DP3) 16.5-88.1 Maltodextrine (DPn) 18.5-128.3

standard error of prediction (SEP) was used as an index of calibration performance. Data pretreatment consists of centering and scaling values to unit variance. The scaled data must now be analyzed for factor optimization. The optimized number of factors is obtained by the following procedure: in each calibration set, one fourth is kept as a validation group called a "test-set". Each model is val- idated on this subsample. The factors are introduced in the model step by step. The function "standard error of prediction vs. number of factors" decreases initially with increasing number of factors and then increases. The optimized number of factors, which is kept in the final model, is the one that gives the minimum SEP.

Correlation Coefficient Spectra. In order to determine the relative importance of each wavelength in the prediction of concentration of each sugar, we have computed the correlation coeffÉcients between each individual sugar concentration and each wavelength. These correlation co- efficients are then displayed against wavenumber in a graph called a correlellogram. 3 The peaks and valleys of these correlellograms can be explained by the indicative absorption bands of the infrared spectra. The correlellograms have been calculated for each individual sugar except maltotriose, which did not give a good regression (correlation coefficient only equal to 0.6).

Calibration. The total number of samples was 44, 30 of which were kept in the calibration set and 14 in the validation. Because of previous experiments,~ partial least- squares has been chosen in place of principal component regression and multilinear regression as the most suitable mathematical processing technique. PLS is a multivariate method in which the axes of maximum variance are computed by taking into account both the spectroscopic and the chemical values. In contrast to PCR, this approach leads to nonorthogonal axes, but uses more information at the calibration step because it takes the chemical data into account.

The performance of the model was illustrated by the standard error of prediction or SEP:

SEP = \ /Z(C, - Cp) 2 (1) V n

where Ct is the true concentration, Cp is the predicted (or corrected) concentration, and n is the number of samples. SEP values corrected by a linear regression are denoted as SEPc, and SEPnc refers to noncorrected values.

The precision of a calibration is twice the standard error of prediction. Indeed, at the center of the concentration range, 90% of the predicted values are situated within a +2*SEP interval from the mean value. 4 The precision levels required by the industry are 8, 10, 5, and 5 g/kg, respectively, for glucose, maltose, maltotriose, and mal-

todextrines (also called DPn). These precision levels will be compared with the precision obtained with the different calibrations.

Repeatibility. Repeatibility is the tendency of a sensor to give the same response when used several times in the same experiment. Repeatibility errors result from errors occurring during calibration. The error was modeled as follows: a sample concentration was measured and predicted five times, with the repeatibility index measured by both the difference between the maximum and minimum values and the standard deviation (SD):

\ /Z(C~ - M) ~ SD = (2)

V n

where Cp is the predicted concentration, M is the average of the predicted concentrations, and n is the number of samples.

Reproducibility. Reproducibility is the tendency of a sensor to give the same response when used in the same experimental conditions, but not in the same experiment. This condition expresses the stability of the model in real time. It is a very important feature because it is necessary to have a time-stable model in an automated industrial environment. Reproducibility was determined by record- ing the spectra of the same mixtures on different days and then predicting concentration. If the prediction showed no significant offset, the model was considered stable,

I fa model is not stable, a bias and an offset can appear between experiments. In order to get a good prediction, it is then necessary to know these two parameters for each new experiment. These parameters are computed by a regression made on the predicted samples. They are then used to correct the predicted values. The standard error of prediction after correction by bias and offset is called simply SEP.

In an industrial environment, it is not feasible to calculate the bias and offset on each predicted sample. In order to calculate a correction, eight samples were chosen that could be considered standards. These standards were chosen to provide a range as wide as possible for each individual sugar.

At the beginning and end of each prediction iteration, the standard spectra were recorded. A prediction was computed on the two sets of spectra. Three different regression lines were drawn, one for the eight beginning spectra, one for the eight end spectra and one for all 16 spectra.

It was assumed that these regression lines had the same offset and bias as that of the 16-sample prediction. The prediction of the samples was corrected by these values, and the SEP was calculated on these corrected values. These new standard errors of prediction were called SEPb, SEPm, and SEPe, respectively, for "beginning", "main", and "end".

RESULTS AND DISCUSSION

Evolution of Sugar Content. During hydrolysis, the concentrations of the individual sugars [i.e., glucose, maltose (DP2), maltotriose (DP3), and maltodextrines (DPn)] evolve against time (in hours), as shown in Fig. 1. The points show the moments when samples were taken.

APPLIED SPECTROSCOPY 557

140

120

100

E ~+----'~ .o_ 80

80

o 40 E 0

20

0

0 10 20 30 40 50

t ime (hours) FIG. l. Evolution of the concentration of individual sugars during hydrolysis: (D) glucose; (+) maltose (DP2); (~) maltotriose (DP3); (A) maltodextrines (DPn).

The most important measurements occur at the end of the experiment, because they are used to determine whether the hydrolysis should be stopped or allowed to continue.

Assignment of the Spectral Bands. It is interesting to try to assign the important peaks of the different spectra in order to foresee the areas useful for both sugar iden- tification and quantification in a mixture. These peak assignments will help to explain the results of multivariate analysis. The spectra of individual sugars are presented in Fig. 2. Since each of the individual sugars is a polymer of glucose (see glucose and maltose in Fig. 3), the spectra are very similar.

As proposed by Hineno, s the bands appearing in the 1474-1199 cm-~ region are due to the bending modes of O-C-H, C-C-H, and C-O-H angles. The bands around the 1153-904 cm -1 region are assigned to C-O and C-C stretching modes.

The peak at 1149 cm -~ has been identified by Cael et al. 6 as characteristic of pyranose sugars. Determination can be made about whether or not the substance is glucose, but distinguishing between complex pyranose struc- tures is not possible because the number of multiple chains

is ignored. This multiple-chain peak is related to the se- quential arrangement of hydroxyls-i.e., to complex chains of glucose, whatever the length--and would have the same relative height whether a 2-unit or a 100-unit complex chain is present. This peak is much smaller for glucose, however, because the single ring is unique. Therefore, determination can be made about whether the substance is glucose or not, but beyond that the 1149-cm -1 peak cannot aid in manual qualitative analysis.

The peak at 1103 cm- ~ has also been observed by Vas- ko et al., 7 who assigned it to combined vibration modes including CC, CO, and COH. In the pure sugar spectra, this peak seemed to decrease with the increase of degree of polymerization. However, it was never considered as a peak of importance in any of the correlellograms (Fig. 4).

The peak at 1076 cm -L shows a decrease as the degree of polymerization increases. Indeed, this peak is typical of the bending vibration mode of C-1-H. 7,8 This peak was not enhanced in the coefficient spectra.

The peak at 1020 cm-~ corresponds to OH vibrations? This observation is also confirmed by the fact that it is not at all affected by degree of polymerization (see Fig. 2).

The 950-850 cm -~ spectral area appears to be very

1.4

1.2 ,~ ,~- o o 1

0.8 DPn

~ °6 L. " ' ~ 1 < o,4 6~H2QH

0.2 5 ~ o S H ~ H , 0 4 1294 1202 11 0 1018 92B H

Wavenumbers (cm-1) FIG. 2. Spectra of individual sugars. Please note that, in fact, all spectra have the same baseline as that of glucose and have been separated for ease of viewing. FiG. 3.

interesting for purposes of discrimination. For glucose, there is a C- 1-H vibration mode at 911 cm- ~ and an O- H vibration mode at 913 cm-1. n This observation ex- plains why the band at 911 cm -1 seems to be shifted toward higher wavenumbers when the degree of poly-

~H2OH ~H2OH

(A) (B)

(A) Glucose and (B) maltose molecules.

558 Volume 49, Number 5, 1995

TABLE II. Standard errors of calibration and prediction on real samples. SEC and SEP: after correction by the bias and offset; SECnc and SEPnc: nuncorrected values, number of samples.

Malto- n Glucose Maltose tfiose DPn

Range (g/kg) n 16-100 40-93 21-88 18-128 SEC (g/kg) 30 SECnc 3.2 3.9 4.8 4.2

30 SEC 3.2 3.9 4.8 4 SEP (g/kg) 14 SEPnc 4.8 3.4 5.7 6

14 SEP 4.1 3.4 4.9 4.4

mefization increases. Indeed, the band is not shifted, but the amount of OH vibration (not affected by polymerization) increases against the amount of chain-end C- I - H. For glucose, a C-1-H deformation band mixed with a CH2 vibration is also visible at 898 cm -l, as stated by Vasko et al. 7 In long chains, a peak appears at 860 cm l, as seen by Vasko et al. 8 This peak is correlated to the length of the chain.

The analysis of the correlellograms (Fig. 4) reveals the discriminative area. The first interesting feature is that the coefficient spectrum of glucose was markedly different from the ones of maltose and maltodextfines. In fact, the DPn coefficient spectrum was very similar to the one of maltose, but in an inverted sense. This result would in- dicate that the features of these correlellograms are typical of the multipyranose chains.

The glucose correlellogram shows four large peaks. The positive peak at 896-898 cm -t (referred to as 1 in Fig. 4A) confirms the assignment cited above (C-1-H defor-

mation coupled to CH2). The large negative peak at 932 cm-l (2 in Fig. 4B) corresponds to a valley in the glucose spectrum and peak shoulders for the glucose chains. The broad peak at 1130 cm-~ is negative because glucose ab- sorbs less than its polymers at this wavelength.

In the comparison of the maltose and DPn correlellograms, the 850-950 cm -~ area is of great importance. The spectra are similar, with a huge peak at 916 cm-l (2 in Fig. 4C), positive in the case of maltose (it corresponds to the top of the peak) but negative for DPn (it is related to a shoulder). At 940 cm -~ the tendency is reversed because we are dealing with a valley for maltose and a peak shoulder for DPn. Another interesting pair of points occur at 1160 and 1266 cm ~. The first one at 1160 cm -t appears as a crossing point of the spectra, i.e., a reference point, whereas at 1266 cm -l, the peak, though small, increases when the polymerization degree decreases (Fig. 2). Therefore, the 1266-cm I coefficient is positive for maltose and negative for DPn, which has a higher polymerization degree.

The only major discriminating differences between the correlation coefficient spectrum of maltose and that of maltodextfines are at 902 and 1138 cm i. The 912-cm -~ coefficient peak is much greater for maltose, explained by the fact that it corresponds to a maltose IR peak. The 1138-cm i peak is more difficult to explain because it is situated on the shoulder at the 1150-cm -t region, which does not discriminate between degrees of polymerization. However, the intensity of this wavelength appears less for maltodextfines because of the fiat neighbor peak at 1103 cm -l, which would explain why it appears with a negative coefficient.

TABLE IlL Real (Y,) and predicted (Yp) values for the calibration set sugar samples (in g/kg).

Glucose Maltose Maltotriose DPn

No. Y~ Yp Y~ Y. Y, Yo Y, Yo

1 45.283 43.832 63.247 64.978 81.879 79.417 23.214 33.419 2 40.814 44.168 61.222 61.243 60.596 57.679 76.367 77.262 3 45.918 48.734 64.79 65.415 58.392 56.308 71.9 73.515 4 51.41 51.563 60.502 65.173 54.283 54.812 75.804 71.196 5 63.605 58.648 77.851 72.04 44.196 51.354 56.347 58.063 6 67.194 71.982 84.702 80.484 37.908 41.749 53.195 49.357 7 48.904 49.061 58.559 63.904 56.892 53.338 74.145 71.98 8 54.76 54.326 64.152 67.571 53.423 50.025 67.165 65.224 9 56.863 58.588 70.742 71.355 49.973 48.975 62.421 61.844

10 62.251 63.912 72.172 71.539 46.335 44.748 59.743 60.387 11 58.772 62.6 72.735 76.804 56.011 50.585 71.484 79.47 12 67.001 65.168 77.705 76.843 50.19 48.024 66.103 68.586 13 73.257 75.573 82.202 84.92 43.608 41.678 61.935 61.605 14 79.897 80.476 85.003 84.436 37.568 37.04 59.531 59.627 15 45.058 38.672 67.907 63.657 81.472 87.874 23.098 32.626 16 50.902 50.857 75.264 75.16 88.06 88.207 18.482 18.562 17 16.237 16.897 40.876 37.602 53.87 59.724 128.32 126.32 18 45.36 43.885 60.616 62.009 64.947 59.601 75.611 77.051 19 50.414 48.502 64.148 64.91 57.814 55.052 76.139 78.073 20 50.901 48.661 64.853 66.303 58.696 63.66 75.053 70.703 21 62.975 58.205 82.031 72.879 43.758 54.498 60.74 65.269 22 71.835 70.631 84.281 80.078 37.719 42.89 52.93 51.785 23 48.661 47.411 58.268 64.23 61.584 58.277 73.776 74.284 24 54.488 53.165 63.833 67.162 53.157 51.565 71.806 71.96 25 61.25 64.089 74.992 76.726 49.478 49.728 61.803 59.056 26 66.585 67.592 71.457 74.922 50.827 44.814 59.151 60.533 27 58.19 62.492 76.965 77.657 55.456 52.706 75.727 72.96 28 66.338 63.624 76.936 76.068 54.644 49.914 70.399 74.471 29 79.499 81.092 89.555 87.082 37.381 41.682 59.235 57.267 30 99.48 94.684 93.031 94.469 21.37 23.058 52.786 53.668


800 600

-"-' 400 E

.~ 200 o E: o

-200 0 o -400

-600

-800

1294

i I 3 ]

f Lt 1 2 j !

1202 1110 1018 926

Wavenumbers(cm-1 )

200 150 100

~ 50 ~ o ~_o -50

-100 -150

o -200 -250 , -300 -350

1294

2

1202 1110 1018

Waven um bers(cm- 1 )

1 I

926

(g)

E:

O ~E

O O

500/ 400 300 200 100

0 -100 -200 -300 -400

i

I ! 2

(A)

TABLE IV. Predicted values for the same sample and measurement of repeatibility.

Glucose Maltose Maltotriose DPn

Real 99.48 93.03 21.37 52.78 Predicted 1 96.99 93.55 20.32 52.16 Predicted 2 97.86 94.02 19.95 51.79 Predicted 3 97.09 93.60 20.43 51.35 Predicted 4 96.94 94.39 20.93 50.43 Predicted 5 96.69 94.22 21.31 50.73

Max. - Min." 1.17 0.85 1.36 1.73 SD b 0.39 0.33 0.48 0.64

1294 1202 1110 1018 926

Wav en u m bers(cm- 1 ) (c)

FIG. 4. Correlellograms for individual sugars (* 1000): (A) glucose; (B) maltose; (C) DPn.

" Max. - Min. = difference between the maximum and minimum values.

b SD = standard deviation.

than ten times the prediction error (especially in the cases of glucose and maltose). This small standard error shows the good repeatibility of the measurements.

Reproducibility. To evaluate reproducibility, we made predictions on the same samples over a period of five days. As can be seen in Table V, the SEP, SEPnc, and SEPm are very similar. In most cases, a correction by standards does not improve results; the calculation of the square root of the sum of SEP squared [R(S2)] allows a global estimation of the best result to be made, here cor- responding to the lower value. In other words, the model is stable and the lack of bias permits the calculation of SEP without a correction of the predicted values.

As shown in Fig. 6, the linear regression values of the calibration, standardization, and validation sets are very

TABLE V. Comparison of standard error of prediction (g/kg) obtained from the same model and the same samples on different days?

Malto- Glucose Maltose triose DPn R(S 2)

Day 1 SEC 2.612 3.282 5.499 3.543 7.772 SECnc 2.617 3.287 5.529 3.548 7.799

Precision of the Calibration Model. The PLS2 model SECm 2.793 3.426 7.008 3.723 9.083

has selected seven factors which explain 91.7% of the Day 2 variance in the chemical data. The standard errors o f SEC 2.393 2.987 5.311 3.861 7.600 calibration and prediction are given in Table II. SECnc 2.419 3.034 5.346 3.655 7.550

SECm 2.857 3.358 5.319 3.643 7.810 The corrected values o f standard error are generally

better than the noncorrected ones, but the difference is Day 3 especially visible with the maltotfiose and DPn predic- SEC 2.897 3.066 5.467 3.975 7.968

SECnc 3.596 3.301 5.903 3.978 8.631 tions. In both cases, at least one point is an outlier, as SECm 2.915 3.306 5.608 4.111 8.233 shown in Table III.

Day 4 The predicted (Yp) vs. real (Yr) values o f sugar content

SEC 3.248 3.281 4.938 3.651 7.683 are given in Fig. 5. The predicted values are computed SECnc 3.844 3.782 5.026 3.709 8.252 f rom the equation o f calibration. The real values are mea- SECm 3.516 3.618 4.947 3.705 7.978 sured by HPLC. Glucose and maltose, which require a Day 5 less severe precision than maltotriose and maltodextrines SEE 2.760 3.509 4.811 4.152 7.767 do, meet the industry precision requirements. SECnc 2.765 3.519 4.829 4.179 7.798

R e p e a t i b i l i t y . The results for the five predictions on the SECm 3.493 3.738 4.970 4.345 8.353 same sample and the two repeatibility indices are given in Table IV. The standard errors are small, in general less

"Results which are outside the proscribed limits of precision are in italics.

FIG. 5. Regression curves of predicted vs. real concentrations of individual sugars in a real mixture: (A) glucose; (B) maltose; (C) maltotriose; (D) DPn. ([2) Calibration points; (+) validation points.


120

100 "O O ~ 80

0_~ 0 0

60

40

20

0 10

+ I J] [']3

+ +N

. . . . . . . . . ~ . . . . . . . . . . . . . . . -t . . . . . . . i - }

30 50 70 90

Real Concentrat ions

110

c 7o 0

~ 8 ~ c

o 0

1

90 T

80 ~

70 •

60 T

50 ~

40 ! i

30 ~ -

30

0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . "

, . , J

50 70 90


(/3

8

: 5 E ¢8

o o

90 80 70 60 50 40 30 20 10

10 30 50 70

Real Concentrations

90

1 o,)

-o~1 ~e "o c

13. c o O

40

20

00

80

60

40

20

0 10 30 50 70 90 110 130


(t)

(B)

(C)

(D)


110 100 =

.o 90 80 E

• 70 o o 60 O 50

~ 40

~ 30 a_ 20

10

Regr./Pred. ~ . Regr . /Stan. _ ~ c~

j Regr./Cal.

I I I I

10 30 50 70 90 110 Real Concentrations

FIG. 6. Regression (Regr.) curves of predicted vs. real concentrations of glucose in 60DE solution for calibration (Cal.), standardization (Stan.) and validation (Pred.) sets: (0) prediction; ([2) calibration; (.~-) standard.

similar. The linear regression on standards is further from the calibration line than the prediction line. Therefore, a correction by standards is unnecessary, and the reproducibility is satisfactory.

CONCLUSION

Acceptable results were obtained for the detection of glucose and maltose in real solution extracted during hydrolysis; the standard errors of prediction, respectively, are 4.1, 3.4, 4.9, and 4.4 g/kg for glucose, maltose, maltotriose, and maltodextrines, whereas the required pre- cisions are, respectively, 8, 10, 5, and 5 g/kg. Moreover, the rcpeatibility is satisfactory; the repeatibility error is generally not larger than 0.6 g/kg and on average equal to 0.45 g/kg, which gives a repeatibility precision better than 1 g/kg. The reproducibility of the method is also satisfactory with real samples. As the standard errors of prediction of measurement made on different days are equal to the standard errors of calibration, the time stability of the model is shown. Moreover, no bias and offset adjustments are needed for each day, which is an important feature in automated industrial use. Therefore, the potential of this method for sugar analysis in industry is confirmed.

Further experiments must be run in order to test the

influence of external factors (proteins, salts, and temperature) to simulate industrial conditions.

ACKNOWLEDGMENTS

We would like to acknowledge the contribution of Dr. Feinberg from CIQUAL (Pads), who allowed us to use the Unscrambler software; Dr. Trystram from ENSIA (Massy) for his cooperation; Dr. Mattali, from ARD (Pads), for the analysis of the samples; and Professor Pourcin from the Universit6 de Provence (Marseille) for his valuable advice.

1. V. Bellon and C. Vallat, "Individual Sugar Content Control by the use of FI'-IR Spectroscopy Coupled with an ATR Accessory", un- published data.

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

3. K. H. Norris, "Extracting Information from Spectrophotometnc Curves: Predicting Chemical Information from Visible and Near- Infrared Spectra", in Proceedings of the IUFOST Symposium, H. Martens and H. Russwurrn, Jr., Eds. (Applied Science Publishers Ltd., Essex, U.K., 1982), p. 95.

4. R" G" Miller' Jr" Simultane°us Statistical lnference (Spdnger'Verlag' New York, 1981), 2nd ed.

5. M. Hineno, Carbohydrate Res. 56, 219 (1977). 6. J. Cael, J. Koenig, andJ. Blackwell, Carbohydrate Res. 32, 79 (1974). 7. P. D. Vasko, J. Blackwell, and J. L. Koenig, Carbohydrate Res. 23,

407 (1972). 8. P. D. Vasko, J. Blackwell, and J. L. Koenig, Carbohydrate Res. 19,

297 (1971).


quantitative analysis of individual sugars during starch hydrolysis by ft-ir/atr spectrometry. part...

Documents