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Journal of Food Engineering 83 (2007) 430–435

Feasibility of the use of visible and near infrared spectroscopyto assess soluble solids content and pH of rice wines

Fei Liu a, Yong He a, Li Wang a, Hongming Pan b,*

a College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, Chinab Medical Oncology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou 310016, China

Received 17 January 2007; received in revised form 21 March 2007; accepted 23 March 2007Available online 10 April 2007

Abstract

Visible and near infrared (Vis/NIR) transmission spectroscopy and a hybrid chemometric method were applied to determine the sol-uble solids content (SSC) and pH of rice wines. Rice wine samples were scanned by a spectroradiometer within a wavelength region of325–1075 nm. The calibration set was composed of 240 samples and 60 samples were used as the validation set. Two pre-processingmethods were applied on the spectra prior to build PLS regression models. The correlation coefficient (r), standard error of prediction(SEP) and root mean square error of prediction (RMSEP) were 0.95, 0.16 and 0.17 for SSC, while 0.94, 0.02 and 0.02 for pH, respec-tively. Adequate wavelengths for the SSC and pH prediction were proposed according to the x-loading weights and regression coeffi-cients. The results indicated that Vis/NIR spectroscopy is a promising approach for predicting the SSC and pH of the rice wine.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Vis/NIR spectroscopy; Rice wine; Soluble solids content (SSC); pH; Partial least squares (PLS)

1. Introduction

Rice wine, also called as yellow wine, is one of the mostpopular alcoholic beverages in China. Originating fromShaoxing (China), rice wine is traditionally brewed fromglutinous rice, wheat and other medicinal plants or herbsand fermented by Chinese koji (a kind of mould, Aspergil-

lus oryzae, used in rice fermentation). Koji containsenzymes breaking starches in rice into sugars that can befermented by the yeast cells. Glutinous rice contains highprotein, low fat and various microelement sources formould and yeast, which are used for the fermentation(Lu et al., 2007; Que, Mao, Zhu, & Xie, 2006). Brewed withthe unique brewing techniques handed down from theancient generations, rice wine has a bright brown colour,subtle sweet flavour, low alcohol content and a good taste(Wang, 2004). In addition, rice wine is well known for its

0260-8774/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jfoodeng.2007.03.035

* Corresponding author. Tel.: +86 13605716662; fax: +86 571 86971143.E-mail addresses: yhe@zju.edu.cn (Y. He), Panhongming@tom.com

(H. Pan).

medical use function. Though there is no clinical evidence,it is used as a therapeutic product and has been claimed toplay a role in the prevention of cancer and cardiovasculardisease (Chen, Yin, & Xu, 2002; Xie, Dai, Zhao, & Chen,2004).

Soluble solids content (SSC) and pH are two impor-tant quality indices in rice winemaking. SSC in rice winesare mainly organic sugars such as glucose, sucrose andfructose, which influence the wine taste, colour and fla-vour. The pH of rice wine is the measure of its acidity,which is due to organic acids including succinic acid, lac-tic acid, citric acid, malic acid and few others (Sun, Wu,& Liao, 2006; Wang, 2004). Furthermore, pH is animportant parameter of the complicated biochemicalchanges during the fermentation. The pH changes at dif-ferent stages of fermentation and has a great influence onthe growth and propagation of microzymes, the activityof enzyme and the decomposition of some nutrients infermented mash or the decomposition of yeast mesostate(Wei, 2005). In addition, pH has other effects in ricewinemaking, such as the formation of pyranoanthocya-

F. Liu et al. / Journal of Food Engineering 83 (2007) 430–435 431

nins during the wine fermentation and the wine matura-tion process accelerated by gamma irradiation (Chang,2003; Morata, Gomez-Cordoves, Calderon, & Suarez,2006). Thus, the measurements of the SSC and pH of ricewine are critical during the making of rice wine. Tradi-tionally, titration is the standard method (GB/T13662-2000) for SSC evaluation for the rice wine in China.Other reference methods for sugar or pH were used inwine, for instance, reduction of Cu2+ in boiling alkalinemedium for reducing sugars, potentiometry for the deter-mination of pH and high pressure liquid chromatographyfor acids, such as lactic acid, malic acid and tartaric acid(Urbano-Cuadrado, de Castro, Perez-Juan, Garcıa-Olmo,& Gomez-Nieto, 2004). However, these methods are timeconsuming, laborious and costly.

Vis/NIR spectroscopy is widely used for rapid, low costand non-destructive analysis in industry, such as agricul-ture, pharmaceuticals, food, textiles, cosmetics and poly-mer production (Yan, Zhao, Han, & Yang, 2005). Infood industry, some particular chemical constituents havea strong influence on food quality and analysis, such as eth-anol, water, sugars, organic acids, phenolic compoundsand oxidation (Cen & He, 2007). Vis/NIR spectroscopyhas been utilized to determine the SSC and pH of fruitsand fruit products, such as apple, kiwifruit, Satsuma man-darin, peach, orange juice and bayberry juice (Gomez, He,& Pereira, 2006; Liu & Ying, 2005; McGlone, Jordan, See-lye, & Martinsen, 2002; Shao & He, 2007; Zou, Zhao,Huang, & Li, 2006; Zude, Herold, Roger, Bellon-Maurel,& Landahl, 2006). In winemaking industry, Urbano-Cuad-rado et al. (2004) used near infrared reflectance spectros-copy and multivariate analysis to determine 15parameters in different types of wine, such as ethanol, totalacidity and pH. Cozzolino et al. (2004) used Vis/NIR topredict the phenolic compounds in red wine fermentation.Pontes et al. (2006) studied the classification of distilledalcoholic beverages and verification of adulteration by nearinfrared spectroscopy. Liu, Cozzolino, Cynkar, Gishen,and Colby (2006) applied Vis/NIR spectroscopy in the geo-graphic classification of Spanish and Australian tempran-illo red wines. Inon, Garrigues, and de la Guardia (2006)used mid and near infrared spectroscopy for the determina-tion of the quality properties of beers. Yu, Ying, Fu, andLu (2006) used Fourier transform near infrared spectros-copy to qualify five enological parameters in Chinese ricewine. Some other methods were also applied for the mea-surement of adulteration and of wine composition, suchas electronic tongue and sensory analysis (Apetrei et al.,2007; Cliff, King, & Schlosser, 2007; Parra, Arrieta,Fernandez-Escudero, Rodrıguez-Mendez, & de Saja,2006).

The objectives of this study were to evaluate the feasi-bility of using Vis/NIR spectroscopy for the determina-tion of the SSC and pH of rice wine. Moreover,adequate wavelengths for the SSC and pH predictionwere determined based on x-loading weights and regres-sion coefficients.

2. Materials and methods

2.1. Preparations of rice wine samples

In this study, all the rice wines of different brands andages were obtained from local supermarkets and stored inthe laboratory at room temperature (25 ± 1 �C). In total,six most popular types of rice wines were prepared, namelyGuyuelongshan, Huangzhonghuang, Tapai, one-year-oldKuaijishan, three-year-old Kuaijishan and five-year-oldKuaijishan. The aforementioned rice wines are the mostpopular and common ones in China. The number of sam-ples of each kind was 50, and a total of 300 samples wereprepared for analysis. Before the spectra determination,the wines were homogenized by putting the bottles upsidedown for several times to minimize the influence of depositwhich might be caused by the protein precipitate (Xie,Meng, & Zhou, 2002). If the deposit in the rice wine wasvisible by naked eyes, further treatments should be doneto remove the deposit such as filtration. In this experiment,the wine in the cuvette was clear and no deposit was sus-pended in the liquid.

2.2. Spectra acquisition and reference method for SSC and

pH

For each sample, three transmission spectra werecollected with a handheld Vis/NIR Spectroradiometers-FieldSpec� HandHeld and HandHeld Pro FR (325–1075 nm)/A110070 (Analytical Spectral Devices, Boulder,USA). The light source consists of a Lowell pro-laminterior light source assemble/128930 with Lowell pro-lam 14.5 V Bulb/128690 tungsten halogen bulb thatcould be used both in visible and near-infrared region.The field-of-view (FOV) of the spectroradiometer is25�. The light source was placed at a height of approxi-mately 150 mm above the sample, and the space betweenthe light source and the sample was depended on theenergy of light source and what kind of sample wasdetected. Some adjustments had been done before150 mm was settled. The probe was under the sample,and the distance between the sample and probe was50 mm and the value was fixed by the spectroradiometer.Rice wine sample was placed in a cuvette with a 2 mmlight path length. The transmission spectra from 325 to1075 nm were measured at 1.5 nm intervals with an aver-age reading of 30 scans for each spectrum. Three spectrawere obtained for each sample and the average spectrumof these three measurements was used in the later analy-sis. All spectral data were stored in a computer and pro-cessed using the RS3 software for Windows (AnalyticalSpectral Devices, Boulder, USA) designed with a Graph-ical User Interface.

The reference value of SSC was measured by an Abbe-benchtop refractometer (Model: WAY-2S, Shanghai Preci-sion & Scientific Instrument Co. Ltd., Shanghai, China).The refractive index accuracy is ±0.0002 and the �Brix

Fig. 1. Part of the rice wine absorbance spectra before (a) and after (b)pre-treatment, and the average spectra of each brand (c).

432 F. Liu et al. / Journal of Food Engineering 83 (2007) 430–435

(%) range is 0–95% with temperature correction. The pHwas measured using a pH meter (Model: PHS-4CT, Shang-hai Dapu Instrument Co. Ltd., Shanghai, China), withaccuracy of 0.001.

2.3. Pre-treatments of the raw spectra

The transmission spectra were firstly transformed intoabsorbance spectra using log(1/T). Three absorbance spec-tra for each sample were averaged into one spectrum andtransformed into ASCII format by using the ASD View-SpecPro software. The pre-treatment was performed usingthe Unscrambler V 9.6 (CAMO PROCESS AS, Oslo, Nor-way). Savitzky-Golay with the segment size of five, whichwas determined to be the optimal number after some trials,was applied to smooth the spectra. Standard normal vari-ate (SNV) was applied for light scatter correction andreduction of changes of light path length (Barnes, Dhanoa,& Lister, 1989). To avoid a low signal-to-noise ratio, onlythe spectral data between 350 and 1050 nm were used forlater analysis.

Two hundred and forty rice wine samples (40 samples ofeach kind of rice wine) were selected randomly for the cal-ibration set; the remaining 60 samples (10 of each kind ofrice wine) were used for the validation set.

2.4. Partial least squares (PLS) analysis

A multianalysis method of partial least squares analy-sis is widely employed in Vis/NIR spectroscopy analysis.PLS analysis can be performed to establish the regressionmodel leading to the content prediction of chemical com-ponents. PLS considers simultaneously the variablematrix Y and the variable matrix X. In the developmentof PLS model, full cross-validation was used to validatethe quality and to prevent overfitting of calibrationmodel. The predictive capability of model was evaluatedby the following standards: standard error of calibration(SEC), standard error of prediction (SEP) and correlationcoefficient (r) between the predicted value and referencevalue. A good model should have a low SEC and SEP,high correlation coefficient and small difference betweenSEC and SEP. Some other standards such as slope andbias should be taken into consideration in particular sit-uations. An example of such situation was the use of val-ues of slope and bias for distinguishing systematic errorsand studying the correlation between the reference andVis/NIR models.

The adequate wavelengths describing the features ofspectra for SSC and pH were determined by x-loadingweights and regression coefficients. PLS loading weightswere specific to PLS and expressed by the information ateach wavelength (X-variable) related to the variation inSSC and pH (Y) summarized by the u-scores. The loadingweights were normalized, so that their lengths could beinterpreted as well as their directions. Wavelengths (vari-ables) with large loading weight values were important

for the prediction of SSC and pH (Y). With similar func-tion, regression coefficients were primarily used to checkthe effects of different wavelengths (X-variables) in predict-ing SSC and pH (Y). Large absolute values indicate theimportance and the significance of the effect.

Fig. 2. Reference versus predicted values for SSC (a) and pH (b) of ricewines.

F. Liu et al. / Journal of Food Engineering 83 (2007) 430–435 433

3. Results and discussion

3.1. Overview of the spectra

Fig. 1a shows part of original absorbance spectra. Thetrends of spectra were quite similar, however, large varia-tions can be observed among different samples, whichcould result from the different colour and chemical compo-nents content of samples. After applying the pre-processingmethod as described in Section 2.3, the baseline shifts areeliminated (as shown in Fig. 1b). In order to observe thegeneral spectral features, the average spectra of each brandwere shown in Fig. 1c.

3.2. Prediction of SSC and pH using Vis/NIR spectra

According to the aforementioned evaluation standardsof PLS model, a good model with nine principal compo-nents (latent variables) was built for the prediction ofSSC and pH of rice wines. The results of calibrationand cross-validation are shown in Table 1. Sixty rice winesamples were used for the validation of the PLS model.The predicted results for SSC and pH are shown inFig. 2. The correlation coefficient, SEP and RMSEP were0.95, 0.16 and 0.17 for SSC, while 0.94, 0.02 and 0.02 forpH, respectively. The results indicated a satisfying perfor-mance of using Vis/NIR spectroscopy and PLS for thedetermination of SSC and pH of rice wines. The predic-tion accuracy is better than that described the studies.Urbano-Cuadrado et al. (2004) predicted reducing sugars,pH and other parameters in different types of wine byusing NIR reflectance spectroscopy (the r values forreducing sugar and pH were 0.844 and 0.905, respec-tively). Apetrei et al. (2007) applied electronic tongue topredict the reducing sugars (r = 0.843) in red wines. Shaoand He (2007) applied Vis/NIR spectroscopy to deter-mine the SSC (r = 0.85) and pH (r = 0.92) in bayberryjuice. Yu et al. (2006) used Fourier transform near infra-red spectroscopy to qualify the pH value (r = 0.906) inChinese rice wine.

According to Fig. 2b, the prediction plots for pH wereseparated into two parts. This was due to a little bithigher pH value of Huangzhonghuang rice wine in com-parison with the other five. The mean value of pH refer-ence values of Huangzhonghuang was 4.259, while themean value of the other five brands was 4.109, so theabsolute difference was very small. Considering that all

Table 1Calibration and cross-validation results for SSC and pH of 240 rice wine sam

Parameters Sample number n = 240 Calibrat

Mean SDa Range rb

SSC(�Brix) 16.8 0.501 15.4–17.7 0.942pH 4.134 0.061 4.068–4.321 0.938

a SD: Standard Deviation.b r: correlation coefficient.

six kinds of rice wines are common in China, and themodel should have the ability to predict the SSC andpH of all six different rice wines, the Huangzhonghuangrice wine was kept in the model.

3.3. Effective wavelength for predicting SSC and pH

The x-loading weights and regression coefficients, asdescribed in Section 2.4, are shown in Figs. 3 and 4. Thefirst nine principal components were used for x-loadingweights and regression coefficients, which were suggested

ples

ion Cross-validation

RMSEC SEC rb RMSEV SEV

0.167 0.168 0.931 0.182 0.1830.021 0.021 0.924 0.023 0.023

Fig. 3. X-loading weights (a) and regression coefficients (b) for SSC in ricewines.

Fig. 4. X-loading weights (a) and regression coefficients (b) for pH of ricewines.

434 F. Liu et al. / Journal of Food Engineering 83 (2007) 430–435

by the Unscrambler V 9.6 software after initial trials withdifferent numbers of principal components.

Considering both x-loading weights and regression coef-ficients, Fig. 3 shows some strong peaks and valleys at cer-tain wavelengths, which were thought to be moresignificant for SSC in rice wines, such as 450–455, 540,640 and 990–1000 nm. Fig. 4 shows that the wavelengthsof 450–455, 640, 920, and 990–995 nm are the most ade-quate for determination of pH of rice wines. In the visibleregion, 450–455 nm and 640 nm may represent the winecolour differences because 540 nm was related to the winepigments (total anthocyanins) as reported by Somers(1998). Moreover, caramel pigments are added into the ricewine to adjust the colour and pH to get a good taste beforethe rice wine becomes market products. Wavelengtharound 920 nm was relatively more important for pH pre-diction, as reported by Shao and He (2007). The sharedwaveband of 990–1000 nm for SSC and pH was producedby the O–H stretch with second overtone from sugars,organic acids and flavonoids (Sasic & Ozaki, 2001Sasic,S., & Ozaki, Y. 2001). Thus, the 990–1000 nm and920 nm and 990–995 nm might be the sensitive wavelengthsfor SSC and pH of rice wines, respectively.

4. Conclusions

The determination of SSC and pH of rice wines wassuccessfully carried out through Vis/NIR spectroscopywith the combination of chemometric method of PLS.An excellent precision was achieved since thecorrelation coefficient, SEP and RMSEP were 0.95,0.16 and 0.17 for SSC, while 0.94, 0.02 and 0.02 forpH, respectively. In the mean time, adequate wavelengthswere determined based on x-loading weights and regres-sion coefficients. Thus, Vis/NIR spectroscopy has thepotential ability to determine the SSC and pH of ricewines. Further studies are needed in order to improvethe calibration accuracy, stability and robustness, andto further interpret and develop applications in rice winefermentation.

Acknowledgements

This study was supported by the National Science andTechnology Support Program (2006BAD10A04), Teachingand Research Award Program for Outstanding Young

F. Liu et al. / Journal of Food Engineering 83 (2007) 430–435 435

Teachers in Higher Education Institutions of MOE, PRC,Natural Science Foundation of China (Project No.30671213).

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