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Proceedings Paper

Feasibility study on variety identification of rice vinegars using visible and near infrared spectroscopy and multivariate calibration
Author(s): Fei Liu; Yong He; Li Wang
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Paper Abstract

The feasibility of visible and near infrared (Vis/NIR) spectroscopy, in combination with a hybrid multivariate methods of partial least squares (PLS) analysis and BP neural network (BPNN), was investigated to identify the variety of rice vinegars with different internal qualities. Five varieties of rice vinegars were prepared and 225 samples (45 for each variety) were selected randomly for the calibration set, while 75 samples (15 for each variety) for the validation set. After some pretreatments with moving average and standard normal variate (SNV), partial least squares (PLS) analysis was implemented for the extraction of principal components (PCs), which would be used as the inputs of BP neural network (BPNN) according to their accumulative reliabilities. Finally, a PLS-BPNN model with sigmoid transfer function was achieved. The performance was validated by the 75 unknown samples in validation set. The threshold error of prediction was set as ±0.1 and an excellent precision and recognition ratio of 100% was achieved. Simultaneously, certain effective wavelengths for the identification of varieties were proposed by x-loading weights and regression coefficients. The prediction results indicated that Vis/NIR spectroscopy could be used as a rapid and high precision method for the identification of different varieties of rice vinegars.

Paper Details

Date Published: 3 March 2008
PDF: 7 pages
Proc. SPIE 6621, International Symposium on Photoelectronic Detection and Imaging 2007: Photoelectronic Imaging and Detection, 662120 (3 March 2008); doi: 10.1117/12.790948
Show Author Affiliations
Fei Liu, Zhejiang Univ. (China)
Yong He, Zhejiang Univ. (China)
Li Wang, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 6621:
International Symposium on Photoelectronic Detection and Imaging 2007: Photoelectronic Imaging and Detection
Liwei Zhou, Editor(s)

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