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Journal of Zhejiang University SCIENCE B 2009 Vol.10 No.6 P.465~471

http://doi.org/10.1631/jzus.B0820299


Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice


Author(s):  Li-juan XIE, Yi-bin YING

Affiliation(s):  College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   ybying@zju.edu.cn

Key Words:  Near-infrared (NIR) spectroscopy, Least squares-support vector machine (LS-SVM), Quality change, Tomato juice


Li-juan XIE, Yi-bin YING. Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice[J]. Journal of Zhejiang University Science B, 2009, 10(6): 465~471.

@article{title="Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice",
author="Li-juan XIE, Yi-bin YING",
journal="Journal of Zhejiang University Science B",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B0820299"
}

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%T Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice
%A Li-juan XIE
%A Yi-bin YING
%J Journal of Zhejiang University SCIENCE B
%V 10
%N 6
%P 465~471
%@ 1673-1581
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0820299

TY - JOUR
T1 - Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice
A1 - Li-juan XIE
A1 - Yi-bin YING
J0 - Journal of Zhejiang University Science B
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SP - 465
EP - 471
%@ 1673-1581
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0820299


Abstract: 
Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that NIR spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage.

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Reference

[1] Acevedo, F.J., Jiménez, J., Maldonado, S., Domínguez, E., Narváez, A., 2007. Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines. J. Agric. Food Chem., 55(17):6842-6849.

[2] Borin, A., Ferrão, M.F., Mello, C., Maretto, D.A., Poppi, R.J., 2006. Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Anal. Chim. Acta, 579(1): 25-32.

[3] Casale, M., Sáiz Abajo, M.J., González Sáiz, J.M., Pizarro, C., Forina, M., 2006. Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy. Anal. Chim. Acta, 557(1-2):360-366.

[4] Chen, J., Arnold, M.A., Small, G.W., 2004. Comparison of combination and first overtone spectral regions of near-infrared calibration models for glucose and other biomolecules in aqueous solutions. Anal. Chem., 76(18): 5405-5413.

[5] Cozzolino, D., Kwiatkowski, M.J., Parker, M., Cynkar, W.U., Dambergs, R.G., Gishen, M., Herderich, M.J., 2004. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Anal. Chim. Acta, 513(1):73-80.

[6] Gestal, M., Gómez-Carracedo, M.P., Andrade, J.M., Dorado, J., Fernández, E., Prada, D., Pazos, A., 2004. Classification of apple beverages using artificial neural networks with previous variable selection. Anal. Chim. Acta, 524(1-2):225-234.

[7] Langeron, Y., Doussot, M., Hewson, D.J., Duchêne, J., 2007. Classifying NIR spectra of textile products with kernel methods. Eng. Appl. Artif. Intell., 20(3):415-427.

[8] Li, Y., Shao, X., Cai, W., 2007. A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. Talanta, 72(1): 217-222.

[9] Liu, L., Cozzolino, D., Cynkar, W.U., Gishen, M., Colby, C.B., 2006. Geographic classification of Spanish and Australian Tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J. Agric. Food Chem., 54(18):6754-6759.

[10] Mouazen, A.M., Karoui, R., de Baerdemaeker, J., Ramon, H., 2006. Classification of Soils into Different Moisture Content Levels Based on VIS-NIR Spectra. 2006 ASABE Annual International Meeting Sponsored by ASABE, Oregon Convention Center, Portland, Oregon.

[11] Pochet, N., de Smet, F., Suykens, J.A.K., de Moor, B., 2004. Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinformatics, 20(17):3185-3195.

[12] Schölkopf, B., Burges, C., Smola, A., 1999. Three Remarks on the Support Vector Method of Function Estimation in Advanced in Kernel Methods: Support Vector Learning. MIT Press, Cambridge, Massachusets, p.25-43.

[13] Suykens, J.A.K., Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural Processing Letters, 9(3):293-300.

[14] Suykens, J.A.K., van Gestel, T., de Brabanter, J., de Moor, B., Vandewalle, J., 2002. Least Squares Support Vector Machines. World Scientific Pub. Co., Singapore.

[15] Thissen, U., Ustun, B., Melssen, W.J., Buydens, L.M.C., 2004. Multivariate calibration with least-squares support vector machines. Anal. Chem., 76(11):3099-3105.

[16] Thompson, C.J., Danielson, J.D.S., Callis, J.B., 1997. Quantification of hydrofluoric acid species by chemical-modeling regression of near-infrared spectra. Anal. Chem., 69(1):25-35.

[17] van Gestel, T., Suykens, J.A.K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., de Moor, B., Vandewalle, J., 2004. Benchmarking least squares support vector machine classifiers. Machine Learning, 54(1):5-32.

[18] Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York, USA.

[19] Zhao, J.W., Chen, Q.S., Huang, X.Y., Fang, C.H., 2006. Qualitative identification of tea categories by near infrared spectroscopy and support vector machine. J. Pharm. Biomed. Anal., 41(4):1198-1204.

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