Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy

Haiyan Yu, Hongjian Lin, Huirong Xu, Yibin Ying, Bobin Li, Xingxiang Pan

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

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 γ and the kernel parameter σ2, 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 (R val2) 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.

Original languageEnglish (US)
Pages (from-to)307-313
Number of pages7
JournalJournal of agricultural and food chemistry
Volume56
Issue number2
DOIs
StatePublished - Jan 23 2008

Keywords

  • Enological parameter
  • Least-squares support vector machines
  • Near-infrared spectroscopy
  • Rice wine age

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