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Analytica Chimica Acta 595 (2007) 170–175

X-ray spectrometry and chemometrics in sugar classification, correlationwith degree of sweetness and specific rotation of polarized light

Karen Goraieb ∗, Thais L. Alexandre, Maria Izabel M.S. BuenoUniversidade Estadual de Campinas, UNICAMP, Departamento de Quımica Analıtica, Instituto de Quımica,

POB 6154, Campinas 13084-971, SP, Brazil

Received 7 October 2006; received in revised form 9 April 2007; accepted 16 April 2007Available online 21 April 2007

bstract

This work presents correlations of conventional energy dispersive X-ray fluorescence spectra of common sugars with degrees of sweetnessbtained via sensorial tests and specific rotations of polarized light, both data from the literature. Also, classifications of sugars are achieved based

n their specific structures. Principal component analysis and partial least square chemometric tools are used to establish these modelings. Oncegain it is demonstrated that a common bench-top X-ray spectrometer can be used not only for inorganic analysis, but also shows potential intudies of organic constituents.

2007 Elsevier B.V. All rights reserved.

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eywords: X-ray spectrometry; Sugar sweetness degree; Specific rotation; Mu

. Introduction

.1. Sugars

Sugars have been used as food additives for a long time,aving a wide applicability range and importance associatedith their diverse properties and pleasant taste [1,2].The chemical basis between sugar structures and sweetness

as been known for a long time [3–6]. In 1963, Shallenberger7] postulated that the basic unit of sweet taste is a pair of func-ional groups separated by approximately 0.3 nm, one acting as aroton donor and the other as a proton acceptor. During interac-ion, these groups are assumed to combine reversibly by meansf hydrogen bonds with complementary units in the receptorites. This principle is illustrated schematically in Fig. 1a [8].ier [9] added a second postulate, i.e., that although Shallens-erger’s AH-B unit suffices to produce a sweet taste of fairlyow intensity, similar to that of the carbohydrates, a third struc-

ural feature, X, (Fig. 1b) is required for intense sweet taste in apecific configuration relative to the AH-B pair.

∗ Corresponding author. Tel.: +55 19 3521 3039; fax: +55 19 3521 3023.E-mail address: goraieb@iqm.unicamp.br (K. Goraieb).

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ate analysis

Taste in mammals begins in the tongue, where groups ofpproximately 50 to 100 taste cells are organized into tasteuds located in papillae [10,11]. Humans can detect and dis-inguish five basic taste qualities: sweet, salty, acid, bitternd umami [12–14]. Decades of careful work by sensoryhysiologists have shown that salty and sour perception areediated by ion channels, whereas sweet, bitter and umami

astes are mediated by G-protein-coupled-receptors (GPCR)13,15], all of which are expressed in taste cells within the tasteuds.

Two putative mammalian GPCR taste receptors, known as1R2 and T1R3 are able to detect all natural sugars and artificialweeteners [10]. Each natural or artificial sugar has a sweetnessegree (Table 1), whose values are obtained by sensory analy-is, where assessors, behaving as measuring devices, are ableo discriminate differences between samples or to quantify theerceived intensity of a given attribute [16].

Sugars may present optical isomery that occurs when fourifferent substituents bonded to an asymmetric tetrahedral car-on atom (chiral center) are arranged in two different ways inpace, yielding two stereoisomers. The number of stereoisomers,

n, depends on the n numbers of chiral carbons present in eacholecule [17].One optical isomer is not able to superimpose on its

air. Compounds that exist as optical isomers are frequently

K. Goraieb et al. / Analytica Chimica Acta 595 (2007) 170–175 171

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Fig. 1. (a) The principle of stimulant-receptor interaction in the basic un

eferred to as chiral compounds, and each member of a pair ofptical isomers is named an enantiomer [18]. Enantiomers haveearly identical chemical properties but differ in a characteristichysical property, their interaction with plane-polarized light.n separate solutions, each enantiomer of the pair rotates thelane of plane-polarized light in opposite directions [17]. Dis-inguishing between two enantiomers by the direction in whichhey rotate the polarization of light is often convenient. Conven-ionally, counterclockwise (left) rotating enantiomers are labeleds (−) and right-rotating ones as (+).

Some chiral compounds rotate light more extensively thanthers, and specific rotation angles can be assigned to specificotation values, often designated in tables by the symbol [�],hich tells to what extent the compound rotates the light [18].enerally, sugars are chiral compounds having two optically

ctive forms for each chiral carbon in the molecule and rotatehe polarized light, some specific rotation values are presentedn Table 1 [19,20].

.2. X-ray scattering and X-ray spectrometry (XRS)

Traditionally, X-rays are explored in respect to their interac-ions with matter by the following general methods: (i) X-rayiffraction, a well defined scattering effect that translates struc-ural properties of organized environments, and so followsragg’s law, and (ii) photoelectric effect, which reveals qual-

tative and quantitative aspects of each atom in the sample, aethod known as X-ray fluorescence (XRF). In the first case,

he crystallographic characteristics of well organized lattices aresed to acquire sample parameters. In the second case, X-rayuorescence peaks are often used for elemental characteriza-

ion and quantification of a sample where the qualitative anduantitative aspects can be measured with confidence when theample atoms have high enough X-ray absorption coefficients,hat is, for high Z elements [21]. Otherwise, it is well known

hat amorphous samples having low Z elements will interactith X-rays producing (i) broad bands in X-ray diffraction and

ii) intense scattering of X-ray irradiation from the source in-ray fluorescence.

able 1weetness degrees and specific rotation values at 20 ◦C, [�]20, at the sodium-D

ine for some common sugars

ugar Sweetnessdegree

[�]20/◦ Sugar Sweetnessdegree

[�]20/◦

ucrose 1.00 66.5 Lactose 0.16 34.9ructose 1.75 −92.4 Mannitol 0.50 23.3lucose 0.75 52.5

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sweet taste; (b) the X structural feature required for intense sweet taste.

In these cases, the data have not been considered as goodources of information until being allied to chemometrics22–24]. This is the basis of the X-ray spectrometry (XRS) tech-ique, which, besides considering characteristic elemental lines,lso makes use of spectrum regions where the sample scattershe source radiation [22,23].

Scattering occurs when X-ray photons interact with electronsf the target element. When this interaction is elastic, that is,hen no energy is lost in the collision process between the beam

nd the sample, it is called Rayleigh (or coherent) scattering andhen the scattered photons give up a small part of their energyuring the collision, the scattering is referred to as Compton (orncoherent) scattering [21,25].

Common X-ray energy dispersive bench top instrumentsre supplied with a Rh X-ray tube as radiation source; in thisase, the scattering portion is observed between 18.00 keV and3.00 keV, corresponding to rhodium Compton and Rayleigh� and K� energies. Raman profiles, another characteristic of

cattering effects, are hidden under the Compton peak [26–28].mall variations in scattering profiles arise when the chem-

cal environment of irradiated samples is slightly modified,hus being perfect for the study of similar samples that pro-uce scattering. Due to the great spectral similarities, the usef chemometric tools is necessary. By processing the entirepectra, important sample information can be extracted, allow-ng obtaining a fingerprint of the sample, when compared totandards.

The present study describes the use of XRS allied to thehemometric tools PCA (principal component analysis) and PLSpartial least square regression) to evaluate correlations betweenugar X-ray spectra and their sweetness degrees and specificotation values at 20 ◦C; [�]20.

.3. Chemometric techniques (PCA and PLS)

The starting point in all multivariate data analysis is a dataatrix (a data table) denoted as X, with n rows, termed objects,

ften corresponding to the samples, and p columns, termed vari-bles, which comprise the measurements or properties relatedo the objects.

PCA is a multivariate tool and when applied to X promotes theompression of the original data for a new axes system, modelingf data, detection of outliers, selection of main variables, clas-ification of samples and prediction of samples characteristics29].

PLS (partial least squares) regression establishes a quantita-ive relation between the multivariate set of predictor variables,uch as instrumental responses (for example, spectral data) andne or more chemical or physical properties of the samples of

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72 K. Goraieb et al. / Analytica C

nterest (e.g. analyte concentration), developing a mathematicalodel that furnishes important properties. Its basic form applies

o only a single Y-variable and it is denoted as PLS1. It has twoteps: modeling and validation [30]. The modeling step estab-ishes a mathematical relation between the matrix of initial data

(n × p) and the known properties vector y (n × 1) (by deter-ination of the regression coefficient vector b) and validation

ptimizes this relation for a better description of the element ofnterest. After concluding the calibration, it is possible to predictalues for unknowns (ypredicted), according to Eq. (1) [29,31–33].

predicted = xunknownT × b (1)

In this work, full cross validation, which is an internal valida-ion method [32,34], was employed for PCA and PLS analysis.

. Experimental

.1. Reagents

Deionized water (Milli-Q) and several kinds of sugar ofnalytical grade were used to prepare the aqueous sugar solu-ions at 10 g L−1. The sugars used in this study are: Fructose.a. (Vetec and Synth, 2 different lots), Glucose p.a. (Vetecnd Synth, 2 different lots), Lactose p.a. (Synth, Nuclear andetec, 2 different lots), Mannitol p.a. (Synth, 2 different lots),ucrose p.a. (Synth, Nuclear, Vetec), totalizing 15 samples ofugar.

.2. Experimental procedures

.2.1. X-ray spectrometry analysisX-ray spectra were obtained using a bench-top energy dis-

ersive X-ray fluorescence (EDXRF) spectrometer, Shimadzuodel EDX 700, with a Rh X-ray tube, with beam collimation

f 10 mm and a Si (Li) semiconductor detector. One hundredicrolitres of each 10 g L−1 sugar solutions were placed into a

2 mm external diameter cylindrical polyethylene cell having a.5 �m thick Mylar (polyester) film to sustain the sample. The

rradiation conditions used were: 100 s (25% dead time), tubeoltage of 50 kV, current applied to the X-ray tube of 100 �A,nd spectra registered from 0.00 to 40.92 keV, with a resolutionf 0.02 keV, in an air atmosphere. Spectrum acquisitions were

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ig. 2. (a) Two- and (b) three-dimensional X-ray spectra of aqueous sugar solutions

ca Acta 595 (2007) 170–175

erformed six times for each sample (a total of 90 spectra), eachne with 2047 energy values.

.2.2. Data treatmentMatrix X (90 × 2047) was constructed from the XRS spectra

here the lines correspond to the spectrum of a sample, whilehe columns correspond to their respective energies. Then thepectral data were mean-centered prior to PCA and PLS analysis.CA was applied for an initial exploration of the data.

As vectors y, the sweetness degree and the [�]20 valuesere considered separately, both taken from the literature (seeable 1).

PCA and PLS were carried out by using The Unscrambler.2 software, from CAMO®. PLS 1 was used to construct theodels for sweetness degrees and specific rotations. Full-cross

alidation was used to validate the model.

. Results and discussion

.1. PCA analysis

X-ray spectra of the sugar solutions are presented in Fig. 2,here the Raman effect is not visualized, but hidden under theompton peak, a characteristic of 90◦ geometry EDXRF equip-ents. Fig. 2a shows how hard it is to even visualize and so to

nalyze variations among the spectra without multivariate tools.ig. 2b shows the three-dimensional variations in the Compton� peaks, but again it is not possible to observe Raman peaks.As mentioned, PCA analysis was done prior to PLS as an ini-

ial exploratory analysis. PCA results for all irradiated samplesFig. 3) show a grouping tendency for the several kinds of carbo-ydrates along the PC1 axis. Fig. 3 shows: (i) a large dispersionn the case of the sucrose samples, (ii) the sucrose group is nearo the fructose group, probably since sucrose is a disaccharideomposed of fructose and glucose units and (iii) the proximityf lactose and glucose, since the structure of lactose presentswo glucose units.

The dispersion in the sucrose group is justified by their higheverage and residual variance values (mainly for SN samples),

hich can be considered as outliers (Fig. 4). The SN scattering

ffects (Fig. 5) furnish more intense K� Compton (∼19.2 keV)nd K� Rayleigh peaks, justifying this adverse effect. So theseamples were removed prior to the PLS analysis.

with their Compton (K� and K� peaks) and Rayleigh (K� and K� peaks).

K. Goraieb et al. / Analytica Chimica Acta 595 (2007) 170–175 173

Fig. 3. PCA scores graph (PC 01 × PC 02) for the sugars studied using the entirespectra.

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Table 2PLS parameters correlating sugar X-ray spectra and sweetness degrees

Parameter Region

Whole spectrum Scattering region

Calibration Validation Calibration Validation

Slope 0.996512 0.987568 0.992094 0.926316OCR

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ig. 4. Residual graph from the PCA analysis using the whole spectra. The firstetter indicates the sugar, the second one indicates the manufacturer and therabic number indicates the lot (in the case of differents lots).

As described in Section 1, these results show that XRS alliedo chemometric tools is able to extract informations about thehemical environment that can be considered a fingerprint forach sample.

Fig. 5. XRS spectra of sucrose samples.

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ffset 0.002780 0.026886 0.006301 0.042147orrelation 0.998255 0.986300 0.996039 0.967942MSE 0.032884 0.093782 0.049509 0.140971

.2. PLS analysis

.2.1. Sweetness degreeSweetness degree models were obtained using PLS1, not

onsidering the SN outlier samples. Calibration and full crossalidation models were obtained, considering: (a) the wholepectrum and (b) only the scattering region of the spectrumaround 18 to 23 keV). Using the whole spectrum, 12 latentariables (LV) are necessary to explain the data variance whilesing the scatter portion of the spectra, 16 LVs are needed. PLSpredicted versus ymeasured plots for this parameter are shown inig. 6.

From the PLS ypredicted versus ymeasured plots, it is possible tobtain important parameters, such as slope, offset, correlationndex and root mean square error (RMSE1), all presented inable 2.

Good correlations are seen for both calibration and full crossalidation models either for the whole spectrum or for the scatter-ng region. In the case of calibration models, very similar resultsf RMSEC and slope are obtained for both spectrum regions.his indicates that scattering region is enough to construct thealibration model for calculating sweetness degree. In full crossalidation models, the performance of the whole spectra is betterhan the one for the scattering portion for correlation, slope andMSECV, but even so the scattering is still enough to produceood values for sweetness degree.

A prediction evaluation was also applied for some externalugar samples, whose results are listed in Table 3.

Table 3 shows good prediction values for all the studied sug-rs, although some sugars present high deviations (e.g. sucroserom Nuclear and Vetec). For most of the samples the devia-ions are acceptable. Thus, XRS with multivariate data treatmentan be useful to predict the sweetness degree of sugar as anlternative to sensory tests.

.2.2. Specific rotationSpecific rotation models (calibration and full cross vali-

ation) using PLS 1 were processed with the same regionsvaluated in item 3.2.1. The necessary number of LV processinghe whole spectra to construct calibration and validation models

ithout over fitting is 12 while the scattering portion requires1 LV. The resulting specific rotation at 20 ◦C, [�]20, ypredictedersus ymeasured plots are shown in Fig. 7.

1 The RMSE for calibration is RMSEC and the RMSE for full cross validations RMSECV.

174 K. Goraieb et al. / Analytica Chimica Acta 595 (2007) 170–175

Fig. 6. PLS ypredicted vs. ymeasured plots for determining sweetness degree using the whole spectra and (b) the scattering region.

Table 3Prediction results for sugar sweetness degrees

Sugar Manufacturer Sweetness degree Deviation

Real Predicted

Fructose Vetec 1.75 1.75 0.09Fructose Synth (lot 1) 1.75 1.74 0.11Fructose Synth (lot 2) 1.75 1.74 0.07Glucose Nuclear 0.75 0.72 0.12Glucose Synth (lot 1) 0.75 0.83 0.08Glucose Synth (lot 2) 0.75 0.69 0.04Lactose Nuclear 0.16 0.23 0.03Lactose Synth 0.16 0.17 0.03Lactose Vetec (lot 1) 0.16 0.14 0.04Lactose Vetec (lot 2) 0.16 0.26 0.03Mannitol Synth (lot 1) 0.50 0.50 0.03Mannitol Synth (lot 2) 0.50 0.34 0.03Sucrose Nuclear 1.00 1.03 0.18SS

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Table 4PLS parameters correlating sugar X-ray spectra and specific rotation

Parameter Region

Whole spectrum Scattering region

Calibration Validation Calibration Validation

Slope 0.998960 0.946508 0.994999 0.960237OCR

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ucrose Synth 1.00 0.92 0.04ucrose Vetec 1.00 0.92 0.16

As shown in Table 4, the whole spectrum furnishes similaresults when compared to the scattering portion for calibrationf [�]20. Nevertheless the best results are found for offset and

MSEC. In the case of full cross validation, both regions pro-uce close slopes, offsets and correlations, but high RMSECV.n general, a better performance for PLS analysis of [�]20 isbtained using the whole spectrum.

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ffset 0.018766 −3.334905 0.060554 −2.779776orrelation 0.999480 0.980245 0.997496 0.966184MSE 1.811920 11.20726 4.354853 16.29919

Multivariate data treatment has furnished a way to study whatas formerly considered non-informative data. Specifically for

cattering produced by low-Z samples in energy dispersive X-raypectrometers, the situation is a high energy source (most com-only from Rh X-ray tubes) being scattered by each atom. Thenal result is that the photons reach positions outside the atomsabove their Fermi levels), interacting with other scattered pho-ons in constructive and destructive ways. This multiplicity ofpecific interactions is captured in the spectra and the multivari-te calibration is able to reveal the total chemical environmenteing irradiated. It can be considered a fingerprint for each sam-

le. In the specific case of measuring sweetness degrees andpecific rotation angle of polarized light, this value is a directonsequence of the sugar structure, the chemical environmenteing responsible for the total variance in the data set.

lues using the whole spectra and (b) the scattering region.

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. Conclusion

This work shows that PCA is able to verify discriminationsf several kinds of sugars, according to their structures. It isvident that dissimilar disaccharides (sucrose and lactose) formroups around their monosaccharide units, fructose and glucose.lso, it is possible to indicate that the variables positioned in

he spectrum scattering region are the most influential ones iniscriminating the samples and so in the constructions of PLSodels.In the case of PLS, the use of the scattering portion

s enough to construct calibration and full cross valida-ion models with good RMSE, correlation and slope forweetness degree, because this part of spectrum directly trans-ates the chemical environments of the samples, with thedvantages of speed, not generating residuals, minimal sam-le preparation and the ease of data treatment. The lastoint is of utmost importance compared to sensory analysisn determining sweetness degree, which is a very labo-ious and time-consuming task, requiring trained analysts16].

For specific rotation ([�]20) analysis, a good calibrationodel is obtained for both regions studied, but better perfor-ance is achieved by using the whole spectrum. In the case of

ull cross-validation for [�]20, both studied regions have similarerformance, except for the RMSECV parameter that is smallersing the whole spectra. It has been demonstrated in this workhat chemometric tools, such as PCA and PLS, allied to XRS,re useful to discriminate sugars, distinguishing their structures,nd to evaluate parameters like specific rotation of polarizedight and sweetness degree, being an attractive alternative forensory analysis.

Additionally, chemometrics allied to XRS is also able tovaluate organic (as sugars) parameters in a single sample. Theultivariate data treatment once again furnishes a way to explorehat was formerly considered a drawback in X-ray spectrometry

the scattering region).

cknowledgments

The authors wish to acknowledge CNPq and CENP fornancial support and Prof. Carol H. Collins for Englisheview.

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