Determination of acetic acid of fruit vinegars using near infrared spectroscopy and least squares-support vector machine

Abstract

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 NIR spectroscopy combined with chemometrics could be utilized as a high precision and fast way for the determination of acetic acid of fruit vinegars.