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Issue 26, 2018
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Accuracy and stability improvement in detecting Wuchang rice adulteration by piece-wise multiplicative scatter correction in the hyperspectral imaging system

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Abstract

The adulteration of rice in the food industry is a very serious problem nowadays. To realize the rapid and stable identification of adulterated Wuchang rice, a hyperspectral imaging system (380–1000 nm) has been introduced in this study. Piece-wise multiplicative scatter correction (PMSC) was first used to correct the non-linear additive and multiplicative scatter effects. Then, the adulterated rice samples were identified via support vector machines (SVM). The PMSC-SVM model was attained over the whole spectral range, with the correct classification rate (CCR) increased from 95.47% to 99.20%, the kappa coefficient increased from 0.95 to 0.99, and the prediction CV (coefficient of variation) decreased from 3.04% to 1.56%. Furthermore, a simplified PMSC-SVM model was established, where 13 principal components were selected using 5-fold cross-validation. The CCR was increased from 95.40% to 99.08%, the kappa coefficient was increased from 0.94 to 0.99, and the prediction CV was decreased from 3.02% to 1.72%. The results demonstrated that the accuracy and stability for identifying adulterated rice has been improved by PMSC in the hyperspectral imaging system.

Graphical abstract: Accuracy and stability improvement in detecting Wuchang rice adulteration by piece-wise multiplicative scatter correction in the hyperspectral imaging system

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Publication details

The article was received on 29 Mar 2018, accepted on 30 May 2018 and first published on 30 May 2018


Article type: Paper
DOI: 10.1039/C8AY00701B
Citation: Anal. Methods, 2018,10, 3224-3231
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    Accuracy and stability improvement in detecting Wuchang rice adulteration by piece-wise multiplicative scatter correction in the hyperspectral imaging system

    Y. Yu, H. Yu, L. Guo, J. Li, Y. Chu, Y. Tang, S. Tang and F. Wang, Anal. Methods, 2018, 10, 3224
    DOI: 10.1039/C8AY00701B

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