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Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy.
J Agric Food Chem. 2008 Jan 23; 56(2):307-13.JA

Abstract

The use of least-squares support vector machines (LS-SVM) combined with near-infrared (NIR) spectra for prediction of enological parameters and discrimination of rice wine age is proposed. The scores of the first ten principal components (PCs) derived from PC analysis (PCA) and radial basis function (RBF) were used as input feature subset and kernel function of LS-SVM models, respectively. The optimal parameters, the relative weight of the regression error gamma and the kernel parameter sigma 2, were found from grid search and leave-one-out cross-validation. As compared to partial least-squares (PLS) regression, the performance of LS-SVM was slightly better, with higher determination coefficients for validation (Rval2) and lower root-mean-square error of validation (RMSEP) for alcohol content, titratable acidity, and pH, respectively. When used to discriminate rice wine age, LS-SVM gave better results than discriminant analysis (DA). On the basis of the results, it was concluded that LS-SVM together with NIR spectroscopy was a reliable and accurate method for rice wine quality estimation.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Yu H
College of Biosystems Engineering and Food Science, Zhejiang University, 268 Kaixuan St., Hangzhou 310029, China.
Lin H
No affiliation info available
Xu H
No affiliation info available
Ying Y
No affiliation info available
Li B
No affiliation info available
Pan X
No affiliation info available

MeSH

Least-Squares AnalysisOryzaQuality ControlSeedsSpectroscopy, Near-InfraredTime FactorsWine

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18167072

Citation

Yu, Haiyan, et al. "Prediction of Enological Parameters and Discrimination of Rice Wine Age Using Least-squares Support Vector Machines and Near Infrared Spectroscopy." Journal of Agricultural and Food Chemistry, vol. 56, no. 2, 2008, pp. 307-13.
Yu H, Lin H, Xu H, et al. Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy. J Agric Food Chem. 2008;56(2):307-13.
Yu, H., Lin, H., Xu, H., Ying, Y., Li, B., & Pan, X. (2008). Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy. Journal of Agricultural and Food Chemistry, 56(2), 307-13. https://doi.org/10.1021/jf0725575
Yu H, et al. Prediction of Enological Parameters and Discrimination of Rice Wine Age Using Least-squares Support Vector Machines and Near Infrared Spectroscopy. J Agric Food Chem. 2008 Jan 23;56(2):307-13. PubMed PMID: 18167072.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy. AU - Yu,Haiyan, AU - Lin,Hongjian, AU - Xu,Huirong, AU - Ying,Yibin, AU - Li,Bobin, AU - Pan,Xingxiang, Y1 - 2008/01/01/ PY - 2008/1/3/pubmed PY - 2008/4/9/medline PY - 2008/1/3/entrez SP - 307 EP - 13 JF - Journal of agricultural and food chemistry JO - J Agric Food Chem VL - 56 IS - 2 N2 - The use of least-squares support vector machines (LS-SVM) combined with near-infrared (NIR) spectra for prediction of enological parameters and discrimination of rice wine age is proposed. The scores of the first ten principal components (PCs) derived from PC analysis (PCA) and radial basis function (RBF) were used as input feature subset and kernel function of LS-SVM models, respectively. The optimal parameters, the relative weight of the regression error gamma and the kernel parameter sigma 2, were found from grid search and leave-one-out cross-validation. As compared to partial least-squares (PLS) regression, the performance of LS-SVM was slightly better, with higher determination coefficients for validation (Rval2) and lower root-mean-square error of validation (RMSEP) for alcohol content, titratable acidity, and pH, respectively. When used to discriminate rice wine age, LS-SVM gave better results than discriminant analysis (DA). On the basis of the results, it was concluded that LS-SVM together with NIR spectroscopy was a reliable and accurate method for rice wine quality estimation. SN - 0021-8561 UR - https://www.unboundmedicine.com/medline/citation/18167072/Prediction_of_enological_parameters_and_discrimination_of_rice_wine_age_using_least_squares_support_vector_machines_and_near_infrared_spectroscopy_ DB - PRIME DP - Unbound Medicine ER -