Developing a multispectral imaging for simultaneous prediction of freshness indicators during chemical spoilage of grass carp fish fillet

文献类型： 外文期刊

第一作者： Cheng, Jun-Hu

作者： Cheng, Jun-Hu;Sun, Da-Wen;Qu, Jia-Huan;Pu, Hong-Bin;Cheng, Jun-Hu;Sun, Da-Wen;Qu, Jia-Huan;Pu, Hong-Bin;Sun, Da-Wen;Zhang, Xiao-Chao;Song, Zhongxiang;Chen, Xinghai;Zhang, Hong

作者机构：

关键词： Multispectral imaging;Grass carp;Chemical spoilage;LS-SVM;GA;Freshness

期刊名称：JOURNAL OF FOOD ENGINEERING （ 影响因子：5.354； 五年影响因子：5.144 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： This study investigated the feasibility of developing a multispectral imaging method using key wavelengths from hyperspectral images for modeling and simultaneously predicting total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and K value in grass carp fillet during chemical spoilage. The established least-squares support vector machine (LS-SVM) and multiple linear regression (MLR) models using five successive projection algorithm (SPA)-selected and six genetic algorithm (GA)-selected wavelengths showed excellent performances for predicting TVB-N and K value with R(2)p > 0.900 and RPD > 3.000, and poor results for TBARS value prediction. The LS-SVM model using six GA-selected wavelengths showed good reliability and was considered the best for simultaneous determination of TVB-N, TBARS and K value. The distribution maps of chemical spoilage changes were generated using image processing algorithms. The results demonstrated the feasibility of developing a rapid and on-line multispectral imaging system using the feature wavelengths and chemometrics analysis. (C) 2016 Elsevier Ltd. All rights reserved.

分类号： TS2

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