Food Chemistry 127 (2011) 735–742

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Food Chemistry

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Analytical Methods

Visible micro-Raman spectroscopy for determining glucose contentin beverage industry

I. Delfino a, C. Camerlingo b, M. Portaccio c,⇑, B. Della Ventura c, L. Mita c, D.G. Mita c, M. Lepore c

a Biophysics and Nanoscience Centre, CNISM, Università della Tuscia, Viterbo, Italyb Consiglio Nazionale delle Ricerche, Istituto di Cibernetica ‘‘E. Caianiello’’, Pozzuoli, Italyc Dipartimento di Medicina Sperimentale, Seconda Università di Napoli, Naples, Italy

a r t i c l e i n f o

Article history:Received 9 April 2010Received in revised form 7 December 2010Accepted 1 January 2011Available online 8 January 2011

Keywords:Glucose quantificationRaman spectroscopyMultivariate analysisIndustrial drink quality control

0308-8146/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.foodchem.2011.01.007

⇑ Corresponding author.E-mail address: marianna.portaccio@unina2.it (M.

a b s t r a c t

The potential of Raman spectroscopy with excitation in the visible as a tool for quantitative determina-tion of single components in food industry products was investigated by focusing the attention on glu-cose content in commercial sport drinks. At this aim, micro-Raman spectra in the 600–1600 cm�1

wavenumber shift region of four sport drinks were recorded, showing well defined and separated vibra-tional fingerprints of the various contained sugars (glucose, fructose and sucrose). By profiting of thespectral separation of some peculiar peaks, glucose content was quantified by using a multivariate statis-tical analysis based on the interval Partial Least Square (iPLS) approach. The iPLS model needed for dataanalysis procedure was built by using glucose aqueous solutions at known sugar concentrations as cali-bration data. This model was then applied to sport drink spectra and gave predicted glucose concentra-tions in good agreement with the values obtained by using a biochemical assay. These results represent asignificant step towards the development of a fast and simple method for the on-line glucose quantifica-tion in products of food and beverage industry.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Optical methods are nowadays becoming more and moreimportant in food production and quality control (Chao, Kim, &Lawrence, 2008; Kress-Rogers & Brimelow, 2001). This is mainlydue to the enormous efforts of many researchers in applying re-sults coming from basic research to specific applications and alsoto the advances in producing detectors and sources, along withnew devices (nanostructured surfaces, hybrid systems and so on)enabling the management of ‘‘light’’ in samples of industrial inter-est. A key role in this field is played by Raman spectroscopy (Chan,1996; Thygesen, Lokke, Micklander, & Engelsen, 2003), which pro-vides information about composition and molecular structure ofsamples through the inspection of fundamental vibrations of func-tional groups. Very recently, the use of Raman spectroscopy has al-lowed the characterisation of starch and pectin in potato cells(Synytsya, Copíková, Matejka, & Machovic, 2003; Thygesen et al.,2003), of amygdalin in bitter almonds (Micklander, Brimer, &Engelsen, 2002) and the certification of edible oils (Dardenne &Aparicio, 2001; El-Abassy, Donfack, & Materny, 2009). This tech-nique has also resulted to be able to discriminate among differentkinds of sugars (Goral & Zichy, 1990) and therefore has been usedto differentiate honey from various geographical regions (Good-

ll rights reserved.

Portaccio).

acre, Radovic, & Anklam, 2002). Almost all these studies have beencarried out by using near infrared excitation sources to avoid thefluorescence emission following visible and/or ultra-violet (UV)excitation, which is typical of many biological samples. The useof infrared radiation (IR) for investigating Raman spectra of biolog-ical samples has some drawbacks; the principal one is the loweringof Raman signal intensity, which depends on the fourth power oflaser frequency (McCreery, 2000). In addition, these samples usu-ally have a high content of water that shows a high extinction coef-ficient in this spectral region. Hence, IR radiation can induce a morerelevant sample heating, even though the data acquisition timesusually employed nowadays can be very short. It should be alsonoted that powdered samples have been considered in almost allthe above-mentioned cases, even if Raman spectroscopy can besuccessfully adopted to quantify different analytes in aqueoussolution (Berger, Itzkan, & Feld, 1997; Dardenne & Aparicio,2001). In fact, also high concentrations of water don’t constitutea limit for Raman spectroscopy owing to very low water Ramansignal. This situation is strongly different from IR absorption spec-troscopy for which the contribution due to water usually interfereswith the signals from other components.

Very recently, we have applied micro-Raman spectroscopy withexcitation in the visible (visible micro-Raman) to food industry li-quid products. By this approach, clarified fruit juice composition,with particular attention to pectin, fructose and b-carotene con-tent, has been successfully characterised (Camerlingo et al., 2007).

736 I. Delfino et al. / Food Chemistry 127 (2011) 735–742

As a further step towards a larger use of visible micro-Ramantechnique in food industry, in this paper we have investigatedthe possibility to quantitatively determine the concentration of asingle component in liquid samples. In particular, we have focusedour attention on the determination of the glucose content becauseit has a key role in food and beverage industry and Raman spec-troscopy has been shown to allow its quantification in variousmatrices (Batsoulis et al., 2005; Berger et al., 1997; Shih & Smith,2009). As a representative test, different sport drinks have beenexamined, since their consumption has reached significant dimen-sions and they are nowadays a constant element in the diet of dif-ferent social classes and ages (Amendola, Iannilli, Restuccia, Santin,& Vinci, 2004). The quantitative determination of glucose concen-tration in commercial untreated samples has been obtained byanalysing their Raman spectra by means of the interval PartialLeast Square (iPLS) procedure, a multivariate statistical analysisapproach that has shown to be very powerful in the analysis of Ra-man spectra (Hanlon et al., 2000; Delfino et al., 2009). The iPLSmodel has been built by using, as calibration data, Raman spectraobtained from glucose aqueous solutions of known composition,and then it has been applied to sport drink spectra giving glucoseconcentrations in good agreement with the values obtained by abiochemical assay. The results have demonstrated that visible mi-cro-Raman spectroscopy is a feasible method for glucose quantifi-cation in industrial products, such as beverages and fruit juices,without using specific substrates and/or sample preparation proce-dures. Henceforth, this approach represents a significant step to-wards the development of a fast, simple, cost-effective Raman-based method for glucose quantification in products of food andbeverage industry, alternative to expensive, time-, sample- andchemicals-consuming biochemical assays currently used in pro-duction and quality control processes.

2. Materials and methods

2.1. Materials

High purity glucose powder (>99%) was purchased from RIEDEL(Germany Haen AG) and used without any further treatment. Prop-er amount of glucose powder was dissolved in distilled water toobtain glucose concentrations [Glu] in the 25–1050 mM range.Molarity (M) units are used for the concentration. These sampleswere employed for building and testing the iPLS model.

Various commercial (details available on request) sport drinks(A, B, C and D samples) were used for the experimental investiga-tion. Their composition is very similar and is shown in Table 1,whose inspection makes clear that the main components are waterand carbohydrates. Small amounts of protein and lipids are alsopresent.

From A sample three different samples were obtained. A frozen-dried sample (A1) was obtained by the lyophilising procedure re-ported in the next section, and two other samples (A2 and A3)were prepared by dilution with distilled water for a final drink con-

Table 1Analytical composition of sport drinks as reported on the labels.

Ingredients (g/l) Sample A Sample B Sample C Sample D

Carbohydrates 65.7 82 58.3 100Protein 2 � 10�3 0 0 0Lipids 0.19 0 0 0Sodium 318 � 10�3 510 � 10�3 520 � 10�3 n.a.Chloride 415 � 10�3 84 � 10�3 460 � 10�3 n.a.Potassium 80 � 10�3 52 � 10�3 120 � 10�3 n.a.Magnesium 30 � 10�3 20 � 10�3 50 � 10�3 n.a.

n.a. stands for not available.

centration equal to 75% and 50% of A sample. A1 sample was usedfor obtaining a high-quality Raman spectrum that allowed us toclearly identify the contributions of sport drink main components.By using A2 and A3 samples, we preliminarily tested our experi-mental technique and data analysis procedure for predicting glu-cose content in samples more complex than glucose aqueoussolutions.

All chemicals, including the enzymes for the biochemical assay,were purchased form Sigma (Sigma–Aldrich, Milano, Italy) andused without further purification.

2.2. Methods

2.2.1. Biochemical assay for glucose determinationA biochemical assay was used in order to validate the concen-

trations obtained with micro-Raman spectroscopy. The enzymaticassay uses glucose oxidase (GOD, EC 1.1.3.4) from Aspergillus niger(154 U mg�1). GOD catalyses the oxidation of glucose to gluconicacid according to the scheme:

glucoseþ O2 !GOD

Gluconic acidþH2O2

The resulting hydrogen peroxide is detected by means of a chro-mogenic oxygen acceptor, composed by phenol and 4-aminophen-azone (4-AP), in the presence of horseradish peroxidase (POD, EC1.11.1.7) (1119 U mg�1) according to the reaction (Kaplan, 1987;Trinder, 1969):

H2O2 þ phenolþ 4-AP !PODquinoneþH2O

The solution absorbance, measured with a spectrophotometer (Per-kin Elmer LS 55) at 505 nm, is proportional to the glucoseconcentration.

To use this method we prepared a working reagent solution(WRS) composed of 15U/mL of GOD, 1 U/mL of POD, [phenol] =0.3 mM and [4-AP] = 2.6 mM in buffer TRIS 100 mM, pH 7.4.

Determination of glucose concentration requires two steps:

(a) One milli litre of WRS is mixed with 10 lL of standard glu-cose solution (5 mM). The solution is incubated for 10 minat 37 �C, then its absorbance is read at k = 505 nm, providingthe reference for our measurements,

(b) One milli litre of WRS is mixed with 10 lL of each investi-gated sample properly diluted with distilled water in orderto be sure that its absorbance is linearly proportional tothe glucose concentration, as required by Beer–Lambertlaw. After incubation for 10 min at 37 �C, the absorbance ofthe sample is read at the above-mentioned wavelength(k = 505 nm). The ratio between the absorbance of the sam-ple and of the standard solution allows us to determine theglucose concentration.

2.2.2. Lyophilising procedureTo lyophilise A sample in order to obtain A1 sample, a labora-

tory-scale freeze-drier (Edwards EF4 Module freeze drier), at-tached to an Edwards high vacuum pump (Crawley, Sussex,England) was used to process 20 ml of frozen samples at a constanttemperature of �48 �C and a vacuum pressure of 1.33 � 10�3 mbarfor 24 h.

2.2.3. Raman spectroscopy measurementsRaman spectroscopy provides information about the composi-

tion and the molecular structure of samples by inspecting funda-mental vibrations of some functional groups. The process ofRaman scattering can be viewed as an inelastic scattering processin which the scattered photon is shifted in frequency from the inci-dent photon as it either loses or gains energy from a particular

I. Delfino et al. / Food Chemistry 127 (2011) 735–742 737

vibration mode of the molecule. A detailed description of the phys-ics of Raman effect is out of the scope of this paper and can befound elsewhere (see, for instance, Lewis & Edwards, 2001). Bycombining Raman spectroscopy with microscopy (micro-Ramanspectroscopy), qualitative and quantitative information can be ob-tained in a noninvasive way also from small amount of samples.This method is a very effective tool in food analysis because it isnon-destructive and usually does not require special preparationof the sample.

The experimental micro-Raman set-up employed for the mea-surements is shown in Fig. 1. The visible laser source was a He–Ne laser operating at a wavelength k = 633 nm, with a maximumnominal power of 17 mW. The laser light was focused on the sam-ple by means of a 50X optical objective (Olympus MPLAN 50X/0.75) on a circular area with diameter of 20 lm. The micro-Ramanspectrometer was equipped with an optical confocal microscope(Olympus BX40) connected by a 50 lm optical fiber to a Jobin–Yvon TriAx 180 monochromator equipped with liquid nitrogen-cooled CCD detector. Three gratings with 300, 600 and1800 grooves/mm were selectable, allowing a maximum spectralwavenumber resolution of 4 cm�1. In this case the 600 grooves/mm grating was selected. The spectra were acquired using accu-mulation times ranging from 60 to 600 s by means of a doubleacquisition process which permits the rejection of spurious peaksdue to direct CCD excitations.

A drop of the liquid sample was placed on a microscope glassslide with a single well (1 cm large and 0.1 cm depth) suitablefor investigating liquid specimens. A cover glass (170 lm thick)was placed on the top of the concavity to avoid sample and opticalobjective contamination. For each kind of sample, experimentswere performed several times and repeated on different drops ofthe same sample in order to test the reproducibility of the mea-sure. Since our measurements were carried out on liquid samples,they were affected by scattering and optical aberration effects dueto water and other scattering elements, which reduce the quality ofrecorded spectra. Therefore, it was difficult to extract quantitativeinformation from the spectral data (Aarnoutse & Westerhuis,2005). Nevertheless, using high optical aperture objectives (as inthe case of the objective 50X) and appropriately setting the laserfocus, a reproducible Raman response was obtained. Even if the to-tal collected signal was decreased when confocal microscopy was

Fig. 1. Experimental set-up for micro

used, this approach improved the readability of micro-Raman sig-nal, limiting the reflected light component out-of-focus and, conse-quently, reducing the noise in the spectra. In addition, by usingconfocal geometry, extremely small volumes are sampled mini-mizing scattering and fluorescence effects. In our measurements,the confocality pinhole was fixed to values in the range of 200–500 lm, depending on the experimental light reflection conditions.

2.2.4. Spectra analysisSpectra were preliminarily analysed using the application rou-

tines provided by the software package (‘‘SpectraMax™ Software’’User Guide, Jobin–Yvon Inc., USA) controlling the whole dataacquisition system. In details, we preliminarily removed fluores-cence by using a visual baseline correction based on a 3rd orderpolynomial fit through selected fixed points of the spectrum andthen we analysed the complex spectra in terms of convolutedLorentzian shaped vibration modes. Peaks constituting the spec-trum were manually selected in order to define the starting condi-tions for a best-fit procedure. The best-fit procedure was thenperformed to determine convolution peaks with optimised inten-sity, position and width. Its performance was evaluated by meansof the v2 parameter defined, as usual, by the following formula:

v2 ¼Pn

i¼0Actuali�Calculatedi

RMSNoise

� �2

ðn� f Þ ð1Þ

where the Actual and Calculated are the measured and calculateddata, respectively, RMSNoise is the estimated Root Mean Squarenoise in the Actual data over the fitted region, n is to the numberof data points in the fitted region and f refers to the total numberof variables from the peak and baseline functions. Thus, n � f isthe number of degrees of freedom. The Levenberg–Marquardt algo-rithm was employed to adjust every variable for each peak in an at-tempt to minimise the v2 parameter. This procedure was extremelyuseful for correctly determining the peak positions, widths, heightsand areas of a set of overlapping peaks.

2.2.5. Multivariate statistical analysisPartial Least Squares methods are devoted to find a model

describing some predicted variables in terms of other observables,that is, to build a regression model between two blocks (data

-Raman spectroscopy (see text).

1271 14

59

1371

133610

7311

28919

855

789

732

Ram

an s

igna

l (a.

u.)

1401

1401

968

c

b

a

738 I. Delfino et al. / Food Chemistry 127 (2011) 735–742

matrices) X and Y using a latent variable representation of thesematrices. These variables are calculated so that they explain thedirections of maximum covariance (Boardman, Hui, & Wold,1981; de Jong, 1993; Esbensen, 2000; Wold & Martens, 1983 ). Thisenables to use the general equation of all inverse calibration meth-ods as PLS:

Y ¼ XB ð2Þ

where X is the data matrix represented by specific observables, Y isthe predicted data matrix, and B contains the regression coefficientsdetermined in the calibration step.

Different algorithms have been proposed and employed to cal-culate PLS models (Berger, Koo, Itzkan, Horowitz, & Feld, 1999;Lambert, Pelletier, & Borchert, 2005; Nørgaard et al., 2000). Amongthe others, interval PLS (iPLS) is a variant of PLS which is particu-larly promising for the Raman spectra analysis. In fact, it developslocal PLS models on (contiguous or non-contiguous) subintervals ofthe full spectrum (Leardi & Nørgaard, 2004) and, thus, significantRaman features located in specific intervals could be properlyexploited for designing the prediction model. The final iPLS modelis the best among all possible combinations of number and widthof spectral subintervals. The use of iPLS model is successful if itovercomes the global PLS model, performed using the full spec-trum, that is, when the Root Mean Square Error of Cross-Validation(RMSECV) (Berger et al., 1999; Lambert et al., 2005), defined as

RMSECV ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1ðMeas½Glu�i � Pred½Glu�iÞ

2

n

sð3Þ

is lower than the RMSECV global (RMSECVg), representing theRMSECV obtained for global PLS model.

For the PLS analysis the samples are usually divided into a cal-ibration set and a validation set. However, if the validation step isperformed using full cross validation the splitting of the data setinto two subsets can be avoided. In fact, in full cross validation pro-cess one sample is left out from the calibration process, followedby the construction of a calibration model using the remainingsamples, which is then tested on the sample left out. This proce-dure is repeated until each sample has been left out once. Thismeans that the validation procedure consists of two looping steps:(i) the model is built with full cross validation; (ii) outlying sam-ples (spike samples) are detected and removed. Then, the modelis rebuilt from scratch without the outliers so that the informationused to produce them in the previous step is removed from the cal-ibration set. This procedure is repeated until there are no moreoutliers, provided that the calibration set still contains enoughsamples to validate the model with good estimates of the rootmean-square error.

The iPLS method, optimised using full cross validation proce-dure, was used in the present case for setting up a model ableto predict glucose content from Raman spectra of liquid samples,relating glucose content to a selected part of the Raman spectrum(using Eq. (2)). The iPLS model was built using the whole data set(for a total of 120 spectra). The intervals for building the modelwere selected according to RMSECV. For each solution, therepresentative spectrum was obtained by averaging variousspectra.

600 800 1000 1200 1400 1600Wavenumber shift (cm-1)

Fig. 2. Raman spectrum (600–1600 cm�1 spectral region) for a 1050 mM glucosesolution sample (line a) and the corresponding deconvolution in Lorentzians (linesb). The main peak centre positions are labelled. Plot c refers to the result of thedifference between the raw experimental data and the convolution of Lorentziancomponents (residual errors from the fit). The acquisition time was 600 s.

2.2.6. SoftwareThe software employed in this work was properly written for

Raman analysis purposes and is based on the PLS toolbox 3.5 forMATLAB, from Eigenvector Research, for PCA, cluster analysis andPLS-DA, and the iPLS toolbox for MATLAB, implemented by LarsNoorgard (http://www.models.kvl.dk/source/ipls/).

3. Results and discussion

3.1. Glucose samples

The Raman spectrum in the 550–1600 cm�1 interval for thehighest concentration glucose solution (1050 mM) is shown inFig. 2 (curve a). The deconvolution procedure described in Section2.2.4 was applied to the spectrum in the wavenumber range of570–1544 cm�1. The iterative fitting algorithm converged for a re-duced v2 = 1.01 value to a convolution (curve b) of Lorentzianpeaks positioned at the wavenumber positions listed in the Table2. Curve c represents the residual errors for the fit procedure.The reported interval represents the fingerprint region and is themost significant one for the investigated samples. The presenceof the characteristic bands of the glucose a anomer at 789, 855,919, 1336 and 1371 cm�1 and those of b anomer at 1073, and1128 cm�1 indicates that glucose in solution is a mixture of thetwo anomers with predominance of the b configuration, as indi-cated by their relative intensities (Arboleda & Loppnow, 2000; Cer-chiaro, Sant’Ana, Arruda Temperini, & da Costa Ferriera, 2005). Alsothe peaks at 1401 and 1430 cm�1 can be attributed to glucose, asreported in Soderholm, Roos, Meinander, & Hotokka (1999). Thecontributions at 968, 1036 and 1524 cm�1 can be related to someimpurities present even in the high purity glucose powder (>99%)employed in the present experiments. The described features obvi-ously appear also in the spectra obtained for the other examinedglucose solutions, as witnessed by some representative Ramanspectra ([Glu] ranging from 25 to 1050 mM) reported in Fig. 3.The above-mentioned assigned peaks have a height increasingwith the increase of the glucose concentration. These results sug-gest that the glucose content of each solution can be extractedby properly analysing the corresponding Raman spectrum. At thisaim, a proper iPLS model has to be built, by means of the above de-scribed iPLS method. In Fig. 4 a representative spectrum is reportedto show the interval selection procedure for the glucose contentevaluation. The wavenumber region was divided into 20 spectralintervals having the same width. Each vertical bar indicates theRMSECV value obtained by the local PLS model for the correspond-ing interval. The number of latent variables for each model is givenat the bottom of the bar. The horizontal dotted line indicates theRMSECV value for the full-spectrum PLS (global) model (RMSECVg).As can be seen, the results for the two intervals spanning the 550–800 and 1150–1550 cm�1 ranges are characterised by a noticeably

Table 2Main peaks in Raman spectra of 1050 mM glucose solution and Sample A1 in therange 700–1500 cm�1 and tentative assignment in agreement with Arboleda &Loppnow, 2000; Cerchiaro et al., 2005; Soderholm et al., 1999 and Engelsen, http://www.models.kvl.dk.

Peak wavenumber (cm�1)for glucosea

Peak wavenumber (cm�1)for sample A1b

Assignments

634 Fructose710 Sucrose742 Other

789 789 a-anomer855 836 a-anomer

867 Fructose919 918 a-anomer968 Other

1036 Other1073 1065 b-anomer1128 1128 b-anomer

1264 Fructose1271 Glucose1336 1332 a-anomer1371 1375 a-anomer1401 Glucose1430 Glucose1459 1456 Glucose; fructose1524 Other

a see Fig. 2.b see Fig. 7.

600 800 1000 1200 1400 1600Wavenumber shift (cm-1)

Ram

an s

igna

l (a.

u.)

1050 mM5253501751301251055025

-1)

Fig. 3. Raman spectra (600–1600 cm�1 spectral region) of water glucose solutionsat different glucose concentrations (range 20–1050 mM). Spectra obtained with anintegration time of 600 s.

600 800 1000 1200 14000

50

100

150

200

250

RM

SEC

V

Wavenumber shift (cm-1)

Fig. 4. Cross-validated prediction performance (RMSECV) for specific intervalmodels (bars) and for the five PLS-component full-spectrum model (dotted line)on the investigated Raman spectra plotted together with the correspondingnormalised mean spectrum. The italic numbers on the bars indicate the optimalnumber of PLS components used in each interval model.

I. Delfino et al. / Food Chemistry 127 (2011) 735–742 739

worse (that is, higher) RMSECV compared to RMSECVg. On the con-trary, some intervals in the 800–1150 cm�1 range have a RMSECVlower than RMSECVg, thus, being more representative of glucosecontent than the full spectrum. According to RMSECV metric, twointervals were selected: 913–961 cm�1 and 1108–1155 cm�1,including one a anomer peak (that located at 919 cm�1) and a banomer peak (the one located at 1128 cm�1), respectively. The out-lined intervals were used for building the iPLS model, validated bythe cross-validation procedure. The results are shown in Fig. 5,where the intervals selected for the model (highlighted in the leftpanels of the figure) are reported, along with the correspondingcalibration curves (right panels). Each calibration curve showsthe relationship between the ‘‘true’’ glucose concentration (Mea-sured [Glu]), that is, the nominal concentration of each solution,and the value of the same parameter predicted by applying the lo-cal iPLS model (Predicted [Glu]). In particular, in the upper panelsresults obtained by considering the 913–961 cm�1 range are

shown and in the lower panels corresponding results for the1108–1155 cm�1 interval are presented. For each calibration curvea good linear dependence is observed. The final iPLS model wasthen built using both the selected intervals and employed for pre-dicting glucose content from Raman spectra of glucose aqueoussolutions used as unknown set. In Fig. 6, the glucose concentrationgiven by the final iPLS model is reported as a function of the mea-sured concentration. The predicted value is in very good agreementwith the measured one for each sample. The goodness of the pro-cedure is witnessed by a linear fitting with an angular coefficientequal to 1.004 ± 0.008 and a correlation coefficient of 0.991.

3.2. Sport drink samples

In Fig. 7, the Raman spectrum (curve a) of the A1 sample (thealiquot of A sample that underwent the lyophilising procedure) isshown together with the results of the deconvolution procedure.In this case, the iterative fitting algorithm converged for a reducedv2 = 1.94 to the convolution of Lorentzian peaks reported in Fig. 7(curve b). Curve c represents the residual errors for the fit proce-dure. By inspecting Fig. 7, it comes clear that the glucose peaks al-ready evidenced in Raman spectra of aqueous glucose solutions areobserved in A1 sample spectrum along with other peaks typical ofthe sample (see Table 2). According to literature, they can be as-signed to fructose and sucrose, thus indicating the presence ofthese sugars in the sample. In particular, peaks at 634, 867, 1264and 1456 cm�1 can be assigned to fructose according to Cerchiaroet al., 2005. The peak at 710 cm�1 can be ascribed to sucrose(Engelsen, www.models.kvl.dk) while the peak at 742 cm�1 hasto be attributed to some other unknown components. Raman spec-tra of A, A2 and A3 samples are shown in Fig. 8 and the main peaksare indicated and labelled. Even though the signal intensities andsignal-to-noise ratio of the spectra are worse than those of thespectrum reported in Fig. 7, the main peaks are clearly evident,as, for instance, those assigned to a and b anomers of glucose. Ithas to be underlined that the 918 and 1128 cm�1 peaks, that fellin the intervals selected for building iPLS model for glucose quan-tification, are clearly detectable in sport drinks Raman spectra,resulting to be well distinguished by the specific features of otherliquid sample components. This suggests that the built iPLS modelcan be used for the quantitative evaluation of glucose content inthe investigated sport drinks. When iPLS analysis, using the previ-ously selected intervals (913–961 and 1108–1155 cm�1), is per-

600 800 1000 1200 1400

Wavenumber shift (cm-1)

600 800 1000 1200 1400

Wavenumber shift (cm-1)

Ram

an s

igna

l (a.

u.)

Fig. 5. Selected intervals used in the iPLS model (left panels) for Raman spectrum and the corresponding predictions obtained using single intervals (right panels). Numbersrepresent the different spectra.

0 100 200 300 400 500 600 700 800 900 1000 11000

100

200

300

400

500

600

700

800

900

1000

1100

Pred

icte

d [G

lu]

(mM

)

Measured [Glu] (mM)

r=0.991

Fig. 6. Predicted concentration of glucose (Pred [Glu]) as a function of the nominalones (Measured [Glu]) as obtained by using the iPLS model.

600 800 1000 1200 1400 1600

742

1332

867

710

918

836

634

1456

1375

1264

1128

1065

c

b

a

Wavenumber shift (cm-1)

Ram

an s

igna

l (a.

u.)

Fig. 7. Raman spectrum (600–1600 cm�1 spectral region) of sample A1, that is, ofthe aliquot of sample A that underwent the liophylisation procedure (line a) and thecorresponding deconvolution in Lorentzians (lines b). The main peak centrepositions are labelled. Plot c refers to the result of the difference between the rawexperimental data and the convolution of Lorentzian components (residual errorsfrom the fit). The acquisition time was 600 s.

740 I. Delfino et al. / Food Chemistry 127 (2011) 735–742

formed on the spectra of Fig. 8, a glucose concentration of 136 ± 15,115 ± 14 and 72 ± 6 mM is obtained for A, A2 and A3 samples,respectively. The highest value is in good agreement with the re-sult of enzyme assay that gives a value of 144 ± 8 mM (see A sam-ple, Table 3) for the undiluted sample. Also for the other samples,the use of iPLS procedure allowed us to obtain an estimation of glu-cose concentration that is in good agreement with results of bio-chemical assays (see Table 3). Accordingly, a glucose content of84 ± 14% and 53 ± 6% with respect to that of the A sample is ob-

tained for A2 and A3 samples, respectively, comparing well withthe nominal dilution values (75% and 50%, respectively). Represen-tative Raman spectra of other commercial sport drinks, that is, of B,C, and D samples are shown in Fig. 9. The main peaks already seenfor A sample are still evident. In particular, the 918 and 1128 cm�1

peaks, that were selected for building the iPLS model for glucosequantification are clearly observed. When the previously described

600 800 1000 1200 1400 1600

14561370

13321264

11281065920

836634

A3

A2

A

Ram

an I

nten

sity

(a.

u.)

Raman Shift (cm-1)

Fig. 8. Representative Raman spectra (600–1600 cm�1 spectral region) of sample A,A2 and A3 as obtained using an integration time 600 s. Main peaks are indicatedand labelled.

Table 3Glucose concentration values for different sport drink samples as evaluated fromRaman spectra and enzymatic assay.

Samples Glucose content fromRaman spectra (mM)

Glucose content fromenzymatic assay (mM)

A 136 ± 15 144 ± 8A2 115 ± 14 108 ± 7A3 72 ± 6 72 ± 5B 280 ± 50 240 ± 9C 123 ± 8 125 ± 5D 290 ± 40 250 ± 10

600 800 1000 1200 1400 1600

7421456

13701332

126411281065

918

836

D

C

B

Ram

an I

nten

sity

(a.

u.)

Raman Shift (cm-1)

Fig. 9. Representative Raman spectra (600–1600 cm�1 spectral region) of sample B,C and D as obtained using an integration time 600 s. Main peaks are indicated andlabelled.

A A2 A3 B C D0

50

100

150

200

250

300

350

Mea

sure

d [G

lu]

(mM

)

Sample

Raman spectroscopy Biochemical assay

Fig. 10. Measured glucose concentration for commercial beverage samples asobtained by iPLS analysis of Raman spectra and by biochemical assay.

I. Delfino et al. / Food Chemistry 127 (2011) 735–742 741

procedure is performed on spectra of Fig. 9, a glucose concentra-tion of 280 ± 50, 123 ± 8 and 290 ± 40 mM is obtained for B, Cand D samples, respectively. Also for these samples a satisfactoryagreement with the results of the biochemical assay is obtained,as shown in Fig. 10, where all the results on sport drink glucoseconcentration are summarised. The small differences between bio-chemical and iPLS predictions outlined for all the samples ensuresthat iPLS analysis is able to feasibly evaluate the content of a singlecomponent (glucose, in the present case) by the analysis of Ramanspectra including also fingerprints of various components. As far as

the error affecting the iPLS prediction, it is worth noting that forfour over the six examined cases the error bar is comparable withthe one affecting results of biochemical assay.

4. Conclusions

Glucose content in aqueous solutions and in commercial bev-erages was determined by applying the iPLS method for the anal-ysis of their Raman spectra, obtained by employing a visible lasersource and a micro-Raman apparatus. To obtain useful Ramanspectra from liquid heterogeneous samples is often difficult andmany samples are therefore dried before being measured. Fur-thermore, it is necessary to employ more expensive laser sourcesand detectors in the infrared region in order to reduce fluores-cence effects. In the present case, the use of confocal geometry en-abled us to obtain informative Raman spectra by means of a lessexpensive visible light apparatus. In addition, by using confocalgeometry extremely small volumes are sampled minimizing scat-tering and fluorescence effects. These results demonstrate thatvisible light micro-Raman spectroscopy in combination withappropriate multivariate methods can be successfully adopted toquantitatively determine glucose contents in liquid samples with-out any treatment. In particular, the iPLS method allowed us theuse of very simple samples for calibration since we were inter-ested in the concentration determination of a single component.Obviously micro-Raman spectroscopy together with the i-PLSmethod can be employed for analysing more than one substanceat a time in a heterogeneous compound using appropriate calibra-tion samples avoiding the use of different expensive biochemicalassays.

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