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Proceedings Paper

Rapid detection of soluble content in beer using spectroscopic technique based on LS-SVM algorithm model
Author(s): Li Wang; Yong He; Fei Liu
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Paper Abstract

For rapid detection of soluble solid content (SSC) in beer, visible/near infrared (Vis/NIR) spectra of 360 beer samples were collected by using Vis/NIR spectroradiometer. Principal component analysis (PCA) was applied for reducing the dimensionality in order to decrease the overlapped information of raw spectral data, 6 principal components (PCs) were selected. The samples were randomly separated into calibration set and validation set, and least squares support vector machine (LS-SVM) algorithm was used to build calibration model of SSC in beer, then the model was employed for the prediction of the validation set. Correlation coefficient (r) of prediction and root mean square error prediction (RMSEP) were used as evaluation standards, and the results indicated that r and RMSEP for the prediction of SSC were 0.9829 and 0.1506. The precision of prediction was obviously higher than that of back-propagation artificial neural network (BP-ANN) and partial least squares (PLS) models, hence PCA and LS-SVM algorithm model with high prediction precision could be applied to the determination of SSC in beer.

Paper Details

Date Published: 27 November 2007
PDF: 7 pages
Proc. SPIE 6723, 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 67230S (27 November 2007); doi: 10.1117/12.783005
Show Author Affiliations
Li Wang, Zhejiang Univ. (China)
Yong He, Zhejiang Univ. (China)
Fei Liu, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 6723:
3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment
Junhua Pan; James C. Wyant; Hexin Wang, Editor(s)

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