Yu Haiyan, Lin Hongjian, Xu Huirong, Ying Yibin, Li Bobin, Pan Xingxiang
College of Biosystems Engineering and Food Science, Zhejiang University, 268 Kaixuan St., Hangzhou 310029, China.
J Agric Food Chem. 2008 Jan 23;56(2):307-13. doi: 10.1021/jf0725575. Epub 2008 Jan 1.
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.
提出了使用最小二乘支持向量机（LS-SVM）结合近红外（NIR）光谱来预测酿酒参数和鉴别米酒年份。分别将主成分分析（PCA）得到的前十个主成分（PC）的得分和径向基函数（RBF）用作LS-SVM模型的输入特征子集和核函数。通过网格搜索和留一法交叉验证找到了最优参数，即回归误差γ的相对权重和核参数σ2。与偏最小二乘（PLS）回归相比，LS-SVM的性能略优，酒精含量、可滴定酸度和pH值的验证决定系数（Rval2）更高，验证均方根误差（RMSEP）更低。当用于鉴别米酒年份时，LS-SVM比判别分析（DA）给出了更好的结果。基于这些结果，可以得出结论，LS-SVM结合近红外光谱是一种可靠且准确的米酒质量评估方法。
