Prediction of Inverted Cane Sugar Adulteration of Honey by Fourier Transform Infrared Spectroscopy S. SIVAKESAVA AND J. IRUDAYARAJ

JFS: Prediction of Inverted Cane Sugar Adulteration of Honey by Fourier Transform Infrared Spectroscopy S. SIVAKESAVA AND J. IRUDAYARAJ Fourier transform infrared (FTIR) spectroscopy with an attenuated total reflection (ATR) sampling accessory has been used to determine the cane medium invert sugar in 3 different varieties of honey. Predictive models were developed to classify the cane sugar-adulterated honey samples, using discriminant analysis. Linear discriminant and canonical variate analysis were used to discriminate adulterated honey samples. The optimum classification of 88 to 96.4% was achieved in a validation set, using linear discriminant analysis with the partial least squares (PLS) data compression technique. Calibrations developed to predict the spiked inverted cane sugar concentration in honey with PLS-1 st derivative method gave standard error of prediction (SEP) between 2.8 to 3.6 % w/w. Keywords: Fourier transform infrared spectroscopy, cane sugar medium invert, linear discriminant analysis, canonical variate analysis, multivariate analysis Introduction BECAUSE HONEY IS LEVOROTATORY AND CONTAINS NOT MORE than 25% water, 0.25% ash, and 8% sucrose, it has always been a prime target for adulteration for reasons of economic gain. The most consistent fraud in the honey industry has been the stretching of honey with preparations based on cane invert sugar, which can be tailored to mimic the natural sucrose-glucose-fructose profile of honey, and is usually difficult to detect. Detection of adulteration in honey is difficult because of the large natural variability of honey, such as differences in species, maturity, environment, processing and storage techniques. Authenticity testing is now being done using different techniques, among them spectroscopic, isotopic, chromatographic, and trace element analysis. Conventional sugar profiling of sucrose-glucose-fructose ratios by enzymatic and chromatographic methods, such as high-performance liquid chromatography (HPLC), will detect outliers from the standard range of values, but the addition of invert sugar may also be detected indirectly through the low levels of oligosaccharides it usually contains. These oligosaccharides are impurities formed during sugar hydrolysis by a process known as reversion. A simple, rapid method for spectrophotometric discrimination of monosaccharides from the oligosaccharide fraction of fruit juice, jam, syrup, and honey sample was proposed in 2000 by Caceres and others. The detection limits achieved allow adulteration of fruit juices with high-fructose corn syrup to be detected up to 4%. However, the application of this approach to honey is complicated, because it contains a number of naturally occurring minor oligosaccharides, which are believed to arise from the transglucosylation activity of natural honeybee enzymes on plant sugars. A microscopic procedure was described to detect adulteration of honey with cane sugar, acid-hydrolyzed cane sugar syrup, or with honey obtained from feeding sugar to bees (Kerkvlier and others 1995). The method consists of preparing a microscope slide of the honey sample and using the sediment in glycerin jelly the same way as in classic pollen analysis. Liquid chromatography with pulsed amperometric detection was used to analyze pure orange juice adulterated with a variety of inexpensive sweeteners (Wudrich and others 1993). This method can be used to detect low levels (5 to 0%) of high-fructose corn syrup, cane sugar hydrolysates, and beet sugar hydrolysates. Carbohydrate chromatography is the one of the methods used to assess the authenticity of honey, maple syrup, fruit juice, UHT milk, natural gums, and soluble coffee (Prodolliet and Hischenhuber 1998). Honey samples have also been characterized with respect to the isotope ratio parameters delta C 13, measured by mass spectrometry (Lindner and others 1996). The delta C 13 values of all honeys tested were similar and typical to plants. Results showed that measurements of D/H(CH 3 ) of ethanol derived from honey and of delta C 13 of honey sugars are useful in the detection of adulteration of citrus honey. However, all these methods are time-consuming and require skilled operators. More recently FTIR spectroscopy, an analytical tool widely used in chemistry, has been employed in the qualitative analysis of food and biological systems. FTIR spectroscopy offers a fast and nondestructive alternative to chemical measurement techniques for qualitative characterization. Infrared spectroscopy has been used to provide information on the molecular composition and structure of a diverse range of materials. Suchanek and others (1996) reported the application of FTIR spectroscopy to differentiate coffee samples. Spectral intensity at 9 different peaks in the FTIR spectra were used in this analysis, and coffee was separated on the basis of its geographic origin. Principal component analysis (PCA) and discriminant analysis of the FTIR spectra were also used to investigate the potential for determining the authenticity of vegetable oils (Lai and others 1994). Principal component analysis applied to a range of seed oils revealed clustering according to plant species while, when extra-virgin and refined olive oils were subjected to discriminant analysis using PC scores, 93% of the calibration sample set and 100% of the test sample set were correctly identified. Kemsley and others (1996) used FTIR-ATR spectroscopy for studying adulteration of raspberry purees. Adulteration with apple and plum could be detected at minimum levels of about 20%, 972 JOURNAL OF FOOD SCIENCE Vol. 66, No. 7, 2001 2001 Institute of Food Technologists

with sucrose at approximately 4% w/w, and comparable classification success rates were obtained. Authenticity of soy sauce by pattern recognition analysis of mid- and near-ir spectra has additionally been reported (Iizuka and Aishima 1999). The majority of FTIR spectroscopic techniques has relied on advanced statistical procedures for data analysis. To enumerate subtle differences between authentic and adulterated samples, analysis of spectral data requires the use of chemometric analysis. Computer programs to perform the analyses are easy to use, and offer a number of exploratory analyses including PCA, linear discriminant analysis (LDA), and canonical variate analysis (CVA) (Kemsley 1998), which present the data either to show clustering, or to identify outliers. Regression techniques such as partial least squares (PLS) (Haaland and Thomas 1988), and principal component regression (PCR) (Martens and Naes 1988), are used for quantitative measurement. A comparative study by Dupuy and others (1992) of different multivariate calibration methods, as applied to determine glucose, fructose, and sucrose in dried fruit juice extracts, showed that PLS analysis provided the most accurate results. The combination of mid-infrared (MIR) spectroscopy and multivariate statistics for determination of glucose, fructose and sucrose in aqueous mixtures was investigated (Sivakesava and Irudayaraj 2000). Spectral wave number range between 700 to 1530 cm -1 was selected for PCR and PLS analysis to develop calibration models for sugar content determination in liquid foods. Calibration methods developed with PLS and PCR gave average standard error of calibration values of 0.18 and 0.21% (w/w), respectively. The calibrations were successfully applied to predict sugar content in complex mixtures and commercial beverages. Most of the past methods for detecting cane invert sugar in honey are cumbersome and time-consuming. Development of an FTIR spectroscopic procedure will help the honey industry for rapid detection of critical adulterants, which otherwise is not possible by existing methods. The main objective of this study, then, is to develop procedures for rapid detection of cane invert sugar in honey. Specific objectives are: (a) to develop a systematic FTIR-ATR procedure for honey analysis, and (b) to develop appropriate multivariate statistical models for prediction of adulterant concentration. Materials and Methods Samples Liquid cane medium invert sugar samples were provided by Imperial Sugar Company (Sugarland, Tex., U.S.A.). Three varieties of honey, Orange Blossom (University Creamery, University Park, Penn., U.S.A.), Clover, and Buckwheat (Rebuck Apiaries, Montoursville, Penn., U.S.A.), were appropriately adulterated with different quantities of cane medium invert sugars. For honey to be considered adulterated, the concentration of the adulterant should be at least 7% (Robmann and others 1992). The adulteration set included 53 samples in the range between 0.5% and 25% (w/w). The ranges were chosen to evaluate the adequacy of the method to determine adulteration in honey. Up to 39 of these were used for calibration; the remaining were used for validation. Prior to spectroscopic analyses, honey samples were incubated in a water bath at about 50 C for 10 min until all the sugar crystals were melted. The samples were then mixed and kept at room temperature to bring back the temperature of the sample to ambient, prior to FTIR analyses which were done in random order. FTIR analysis Used for analysis was a Bio-Rad FTS 6000 Spectrometer ((Biorad) Cambridge, Mass., U.S.A.). It was equipped with a deuterated triglycine sulphate detector, operating at 32 cm -1 resolution, modulation amplitude at 2. The instrument was allowed to purge for 5 min prior to acquisition of spectra, in order to minimize spectral contribution due to atmospheric carbon dioxide and water vapor. The sampling station was equipped with an overhead ATR accessory, comprised of transfer optics within the chamber, through which infrared radiation was directed to a detachable ATR zinc selenide crystal mounted into a shallow trough for sample containment. The crystal geometry was a 45-degree parallelogram with mirrored angled faces, with nominal 10 internal reflections. The depth of penetration, which gives a measure of the intensity of the resulting spectrum, was 1.46 microns. The reference spectrum was recorded, using distilled water. Single-beam spectra were obtained for all samples, and corrected against the background spectrum of the water, to present the spectra in absorbance units. The ATR crystal was carefully cleaned between samples with water, and dried using nitrogen gas. The cleaned crystal was examined for spectral authenticity to ensure that no residue from the previous sampling was retained on the crystal surface. Spectra were collected in duplicate and used in calibration and validation studies. Chemometrics Chemometrics are an assortment of statistical techniques to extract useful information by removing noise from the spectral data. High-dimensional data, in which the number of variates is larger than the number of observations, cannot be used directly for discriminant analysis. PCA or PLS data compression method was used to transform the data set comprising of a large number of intercorrelated variates (wave numbers) into a reduced new set of variates. The information extracted from the data is used to make accurate predictions about unknown samples. Proven to be effective for many quantitative applications, multivariate techniques such as PLS and PCR were used in the present analyses. In some cases, the only result that is desired is to know whether the sample falls within a defined range of allowable variability to determine whether the material is of the desired quality. This can be accomplished by discriminant analysis, which is a generic name for a family of methods used for treating multiple-group classification problems. Two approaches to discriminant analysis were used in present analysis: linear discriminant analysis and canonical variate analysis. Discriminant analysis Discriminant analysis was carried out using the Win-DAS software package (Wiley, Chichester, U.K.). Area normalization of the spectroscopic data was done to compensate for gross differences in spectral response due to physical effects, rather than compositional properties of the samples. PCA data compression was done by correlation methods, which enhance the influence of small, but potentially useful, spectral features (Kemsley 1998). PLS data compression was done by non-orthogonalized formulation, based on the algorithm described in the work by Martens and Naes (1989). Two methods of discriminant analysis were used for tackling multiple-group classification, those being linear discriminant analysis and canonical variate analysis. Vol. 66, No. 7, 2001 JOURNAL OF FOOD SCIENCE 973

Table 1 Description of groups in calibration set Amount of inverted cane sugar in honey a Group Sample no. 0-6% 1 27 6-15% 2 35 15-25% 3 42 a All concentrations are expressed as percentage by weight Linear discriminant analysis LDA estimates the distance between each observation from all group centers. LDA was used to develop the discriminative calibration model, which classified the adulterated samples into 3 groups (Table 1), based on the quantity of beet sugars added to honey. Mahalanobis distance was used as the distance metric in this analysis, Mahalanobis distance being one of the most widely used parameters in discriminant analysis. Using squared Mahalanobis distance metrics, LDA was applied to principal component (PC) scores or PLS factors of original data (Kemsley 1998). The derived quantities, such as group centers and covariance matrices, are calculated from the transformed observations and the assignment of class is performed, according to the goodness of fit. Canonical variate analysis Canonical variate analysis (CVA) is another type of analysis used for discriminating between groups of observations. The calculated canonical variate (CV) scores successively maximize between-groups variance/within-groups; the CV loadings are eigenvectors of a matrix given by [W -1 ] [B], where W is the within-groups covariance matrix, and B is the between-groups covariance matrix (Kemsley 1998). Discriminant analysis models were developed on a calibration sample set and evaluated on a separate validation sample set. The correctly classified samples are expressed as a percentage of the total number of samples in the specific groups. Quantitative analysis The GRAMS 32 software (Galactic Industries Corp., Salem, N.H., U.S.A.) used for this quantitative analysis employed the partial least squares (Haaland and Thomas 1988) and principal component regression algorithms, as described in the work of Martens and Naes (1989). Calibration models were developed with spectra in absorbance units by the use of PLS and PCR analysis, with original and 1 st derivative-transformed spectra. Optimum number of factors selected for calibration were selected based on the predicted residual sum of squares (PRESS), which should be minimized, along with the R 2 from regression. The software was used to find the correlation coefficient between predicted and actual values. The predictability of the models was tested by computing the standard error of calibration (SEC) for calibration data set and the standard error of prediction (SEP) for validation data sets. The term actual concentration refers to the cane medium invert sugar concentration added to the specific sample. Predicted concentration refers to a value computed using spectral data, n is the number of samples in the calibration set, and f is the number of factors used in the calibration model. Cross-validation was used in all cases to minimize the risk of over-fitting the calibrations when evaluating calibration accuracy. Results and Discussion SINGLE-BEAM SPECTRA WERE OBTAINED FOR ALL SAMPLES, AND corrected against the background spectrum of water, in order to present the spectra in absorbance units. In the analysis of aqueous solutions, overlap of the vibrational bands of water with those of the solutes is inevitable, resulting in broad bands that usually cannot be deconvoluted into their constituents. Figure 1 presents the ATR spectra of pure honey (Orange Blossom) and adulterated honey with inverted cane sugar (15% w/w). Close examination of the spectrum reveals the presence of several readily identifiable components, such as water, sugars, and protein. A comparison of the spectra of pure and adulterated samples reveals many similarities and dissimilarities (Figure 1). However, the sheer amount of combined data, with no obvious relation between the intensities of certain peaks and authenticity of honey, makes a visual analysis of these sets virtually impossible. The need for multivariate statistical methods such as the discriminant (LDA, CVA) and quantitative (PLS, PCR) analysis is obvious. Determination of medium cane invert sugar level in 3 honey varieties MIR spectra of three varieties of honey (Orange Blossom, Clover, and Buckwheat) adulterated with inverted cane sugar were collected. A spectral region between 800 to 1500 cm -1 due to sugars (Cadet and Offman 1997) was selected for ch- Figure 1 ATR spectra of pure honey and honey adulterated with inverted cane sugar (15% w/w) 974 JOURNAL OF FOOD SCIENCE Vol. 66, No. 7, 2001

emometric analysis. Principal component analysis and PLS data compression methods were applied to the complete collection of spectra of pure and adulterated specimens, after spectral area normalization. Figure 2 shows the first 3 principal component (PC) loadings and original spectrum with PCA data compression method for Orange Blossom variety of honey. The first 3 PC scores collectively account for 93.4% of the total variability in the calibration set, which indicates that the fourth and remaining PC scores account for substantially smaller proportions of the total variance than the first 3. A potential advantage of the correlation method is that larger spectral features are prevented from dominating the PCA transformation. The second PC score that seems to be the most important for distinguishing the groups accounts for 11.3% of the variation (Figure 3a). PCs associated with large percentage variances often represent variability in major constituents. It is always preferable for a discriminant analysis, then, to be based on differences in major (rather than the particular) constitution of the calibration set. Here the primary use of sign of loadings is to determine the relative contributions of the variables to the PCs. Variations in the content of these spectral regions may be the basis for the observed discrimination. It is possible to ascribe these features to individual absorbing species. The second PC loading obtained in the present study shows many of the features that can be attributed to C-O and C-C stretching modes (Hineno 1977) in the 900 to 1153 cm -1 and to the bending modes of O-C-H, C-C-H, and C-O-H angles in the 1199 to 1470 cm -1 region (Figure 2c). The PCA transformation offers evidence that there are differences between the three groups; however, this information does not appear to be compressed quite as efficiently into the first few PC scores. In such an instance, PLS might be a preferred data compres- Figure 2 The first 3 PC loadings of calibration set using PCA-correlation data compression method, along with the original spectrum of Orange Blossom variety of honey: (a) Original spectrum (b) PC loading 1 (c) PC loading 2 (d) PC loading 3 Figure 3 PC score and PLS factor plots for different data compression techniques: (a) PCA data compression (b) PLS data compression Vol. 66, No. 7, 2001 JOURNAL OF FOOD SCIENCE 975

sion method. Studying PLS factor plots can identify the critical spectral regions. The first-against-second PLS factor plot showed that there is a fairly noticeable division between the 3 groups of Orange Blossom variety of honey (Figure 3b). In fact, the first PLS factor alone provides quite a good discrimination and is important in classifying the groups that account for 15.7% of the total variability. Figure 4 show the loadings of factors using PLS data compression method. Large (positive or negative) weights in the PLS 1 st -loading spectra indicate important wave numbers, and weights close to zero are less important variates. Both C-O and C-C stretching modes (900 to 1000 cm -1 ) and O-C-H, C-C-H, C-O-H angles (1180 to 1450 cm -1 ), due to bending modes, play a major role in discriminating the groups (Figure 4a) which indicates that discrimination of honey sugars is based mostly due to differences in these critical regions related to sugars in the spectra. LDA using the squared Mahalanobis distance metric was applied to successively larger subsets of PC and PLS scores. The re-assignment success rate obtained for the calibration and validation set is plotted in Figure 5. More PC or PLS scores are required to increase the classification accuracy in the calibration set. Seven PLS scores are required to give 97.4% correct re-assignments for the calibration set, whereas 6 PLS scores are required to give a 96.4% correct re-assignments for validation (Figure 5b). Substantially more PC scores (10 compared to 6 PLS factors) are required to obtain 93% correct re-assignments (Figure 5b). The modeling nature of PLS leads to scores that are tailored for the task at hand (Kemsley 1998). In general, it is a good idea to settle for the simplest, lowest-dimensional model that gives acceptable results; that is, models with fewer variates are less likely to exhibit over-fitting, tend to be more stable, and have better generalization ability. In the present study, PLS is the pre- Figure 4 The first 3 PLS loadings of calibration set using PLS data compression method: (a) PLS loading 1 (b) PLS loading 2 (c) PLS loading 3 Figure 5 The re-assignment success rate as against the number of PLS and PC scores used in LDA of the adulterated honey (Orange Blossom): (a) PCA data compression (b) PLS data compression 976 JOURNAL OF FOOD SCIENCE Vol. 66, No. 7, 2001

Table 2 Results of LDA and CVA for medium cane invert sugar estimation in 3 varieties of honey using PLS data compression % Correct % Correct classification of classification of Factors calibration samples validation samples Orange blossom LDA 6 94.1 96.4 CVA 6 92.3 89.3 Clover LDA 5 91.2 88.2 CVA 5 97.1 94.1 Buckwheat LDA 5 91.2 88.2 CVA 4 97.1 94.1 Combined calibration data for Orange blossom, Clover, and Buckwheat LDA 10 73.5 68.6 CVA 10 83.3 78.4 ferred data compression method, and the LDA model with 6 PLS scores were the optimum methods for the validation set observations. To avoid over-fitting, the number of PLS factors used in the study did not exceed one-sixth the number of independent specimens in the calibration set (Kemsley 1998). Since 39 independent calibration samples were used, the above condition is satisfied and over- fitting of the LDA model is minimized. Table 2 shows the results using LDA models to determine the level of cane invert sugar in 3 varieties of honey, using PLS data compression. LDA models for different varieties of honey successfully classified 91 to 94% of the calibration sample set and the success rate in validation set was between 88 to 96% (Table 2), showing a successful LDA model. PLS data compression method was used for CVA analysis Figure 6 Discriminant canonical variate analysis of cane medium invert sugar in Orange Blossom variety of honey using PLS data compression method upon the subset of PLS scores. Figure 6 shows the CV score plot obtained with the 95% tolerance regions for Orange Blossom variety of honey samples adulterated with inverted cane sugar, using 6 PLS factors. The objects in figure 7 can be divided into 3 clusters. The CV plot displays reasonably well separated groups for all the different cane sugar adulterations of honey examined in this study. The ellipses of clusters show the 95% tolerance regions for each adulterated group. Their centers can be calculated from the means of the group coordinates. The adulterated honey samples show clear differences (depending on the cane invert sugar concentration in honey). The higher adulterant concentration samples are placed on the right, and the lower concentration samples on the left side in Figure 6. The addition of the adulterant causes a gradual shift in the position of the related objects toward the positive end of CV 1. The effectiveness of the CVA model was evaluated by recording the number of correctly and incorrectly assigned members of the different classes. Some of the observations fall within the tolerance regions of multiple, overlapped groups, and is therefore assigned to all of the groups. Assignments were counted as successful when any 1 of the group assignments is successful, for the purpose of calculating the correct assignments. The discriminant CVA models accurately classified 92 to 97% of the calibration sample sets, and 89 to 94% of the validation samples sets (Table 2). Data from the calibration sets of all 3 varieties of honey were merged, and new calibrations were obtained to develop a single calibration model in which to determine cane medium invert sugar concentration in the honey. Results show that the overall success rate for discrimination is lower than that obtained for the individual varieties (Table 2). Combining the calibration data for 3 varieties of honey increased the number of optimum factors (Table 2). Although the combined model had lower success rates yet, the potential for a unified model exists perhaps for selective groups. Determination of medium cane invert concentration in 3 honey varieties Statistical models were developed to predict inverted cane sugar concentration in 3 different varieties of honey. FTIR spectra of honey adulterated with cane medium invert sugar were collected and used to develop calibration models using PLS, PLS-1 st derivative, PCR, and PCR-1 st derivative. Correlation coefficients (R 2 ), the factors included in the calibration methods, and the SEP values for calibrations models are given in Table 3. The calibration and validation results showed that PLS performed better, compared to PCR. PCR does not consider the reference values when selecting or constructing spectral components, whereas the PLS regression model uses the information content of the reference values when constructing spectral components (Osborne and others 1993). The lowest SEP value of 2.76% (w/w) and highest R 2 of 0.964 was achieved with the PLS-1 st derivative calibration method in the validation set for clover variety of honey. Combining the calibration data for 3 varieties of honey increased the number of optimum factors, SEC, and SEP in calibration and validation data sets (Table 3). The results demonstrate the potential of FTIR spectroscopy to distinguish different levels of inverted cane sugar in honey, quantitatively and qualitatively. The testing and analysis procedure could be made robust, if honey from more sources and regions were included. Vol. 66, No. 7, 2001 JOURNAL OF FOOD SCIENCE 977

Table 3 Calibration and validation results using different calibration methods for cane medium invert sugar estimation in 3 varieties of honey (number of samples in calibration set: 39; validation set: 14) Calibration Validation Calibration method Factors a R 2b SEC c SEP d R 2b Orange blossom PLS 6 0.954 2.55 3.52 0.883 PCR-1 st derivative 6 0.928 2.40 3.08 0.927 PCR 10 0.953 2.75 4.50 0.836 PCR-1 st derivative 24 0.944 3.89 2.71 0.959 Clover PLS 7 0.960 2.75 3.05 0.948 PLS-1 st derivative 6 0.972 2.45 2.76 0.964 PCR 10 0.922 3.47 4.03 0.938 PCR-1 st derivative 10 0.910 3.26 4.01 0.944 Buckwheat PLS 7 0.955 3.17 4.22 0.936 PLS-1 st derivative 6 0.950 2.84 3.51 0.956 PCR 10 0.928 3.29 5.19 0.873 PCR-1 st derivative 9 0.936 2.92 4.84 0.878 Combined calibration data for Orange blossom, Clover and Buckwheat PLS 15 0.809 5.83 6.11 0.704 PLS-1 st derivative 12 0.833 5.37 5.21 0.766 PCR 17 0.743 6.97 6.87 0.685 PCR-1 st derivative 17 0.767 6.28 5.98 0.694 a Optimum number of factors b Correlation coefficient c Standard error of calibration (%, w/w) d Standard error of prediction (%, w/w) Conclusions THE RESULTS OF THIS STUDY SHOW THAT FTIR SPECTROSCOpy, in conjunction with multivariate statistical analysis, can be used to detect cane sugar adulteration in honey samples. The method described is rapid, simple, and suitable for large-scale monitoring of honey samples by industries. LDA successfully classified 88 to 96% of validation set using PLS data compression method, and the concentration of added cane sugar in honey was successfully determined using the PLS-1 st derivative (R 2 = 0.93 to 0.96) method. References Caceres A, Cardenas S, Gallego M, Valcarcel M. 2000. A continuous spectrophotometric system for the discrimination/determination of monosaccharides and logisaccharides in foods. Anal Chim Acta 404(1):121-129. Cadet F, Offmann B. 1996. Extraction of characteristic bands of sugars by multidimensional analysis of their infrared spectra. Spectrosc Lett 29(3):523-536. Dupuy N, Meurens M, Sombret B, Legrand P, Huvenne JP. 1992. Determination PLS-1 st derivative of sugars and organic acids in fruit juices by FT MID-IR investigation of dry extract. J Appl Spectrosc 46(5):860-863. Haaland DM, Thomas EV. 1988. 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Z Lebensm Unters Forsch 195(3):307-311. Sivakesava S, Irudayaraj J. 2000. Determination of sugars in aqueous mixtures using mid-infrared Spectroscopy. Appl Eng Agric 16(5):543-550. Suchánek M, Filopova H, Volka K, Delgadillo I. 1996. Qualitative analysis of green coffee by infrared spectroscopy. Fresenius J Anal Chem 354(3):327-332. Wudrich GG, McSheffrey S, Low NH. 1993. Liquid-chromatographic detection of a variety of inexpensive sweeteners added to pure orange juice. J AOAC Intern 76(2):342-354. MS 20000532 Authors Sivakesava and Irudayaraj are with Pennsylavnia State University s Dept. of Agricultural and Biological Engineering in the Agricultural Engineering Building, Room 249, University Park, PA 16802. Address inquiries to author Irudayaraj (E-mail: josephi@psu.edu). 978 JOURNAL OF FOOD SCIENCE Vol. 66, No. 7, 2001