DETERMINATION OF ACETIC ACID OF FRUIT VINEGARS USING NEAR INFRARED SPECTROSCOPY AND LEAST SQUARES-SUPPORT VECTOR MACHINE
Two chemometric methods were performed for the determination of acetic acid of fruit vinegars using near infrared (NIR) spectroscopy. Three varieties of fruit vinegars were prepared and 135 samples (45 samples for each variety) were selected for the calibration set, whereas 45 samples (15 samples for each variety) for the validation set. Partial least squares (PLS) analysis was the calibration method as well as extraction method for latent variables (LVs). The first eight LVs were employed as the inputs of least squares-support vector machine (LS-SVM) model. Then LS-SVM model with radial basis function (RBF) kernel was applied to build the regression model compared with PLS model. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.994, 0.814 and -0.091 by PLS, whereas 0.997, 0.651 and 0.011 by LS-SVM, respectively. LS-SVM model outperformed PLS model, but both models achieved an excellent prediction precision. The results indicated that MR spectroscopy combined with chentometrics could be utilized as a high precision and fast way for the determination of acetic acid of fruit vinegars.
Near infrared spectroscopy Fruit vinegar Acetic acid Partial least squares analysis Least squares-support vector machine
FEI LIU LI WANG YONG HE
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1232-1237
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）