Variety Identification of Rice Vinegars Using Visible and Near Infrared Spectroscopy and Multivariate Calibrations

Visible and near infrared spectroscopy was investigated to identify the varieties of rice vinegars based on back propagation neural network and least squares-support vector machine. Five varieties of rice vinegars were prepared. Partial least squares discriminant analysis was implemented for calibration and extraction of partial least squares factors. The factors were used as inputs of back propagation neural network and least squares-support vector machine. Finally, back propagation neural network and least squares-support vector machine models were achieved. The threshold value of prediction was set as ±0.1. An excellent precision and recognition ratio of 100% was achieved by both methods. Simultaneously, certain effective wavelengths were proposed by x-loading weights and regression coefficients. The performance of effective wavelengths was validated and an acceptable result was achieved. The results indicated that visible and near infrared spectroscopy could be used as a rapid and high precision method for the identification of different varieties of rice vinegars.

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