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

• Published in: Journal of food engineering 2016-08, Vol.182, p.9-17
• Main Authors: Cheng, Jun-Hu, Sun, Da-Wen, Qu, Jia-Huan, Pu, Hong-Bin, Zhang, Xiao-Chao, Song, Zhongxiang, Chen, Xinghai, Zhang, Hong
• Format: Article
• Language: English
• Subjects: Algorithms; Carp; Chemical spoilage; Ctenopharyngodon idella; Fillets; Freshness; Freshwater; Grass carp; Grasses; Imaging; LS-SVM; Mathematical models; Multispectral imaging; Spoilage; Wavelengths
• ISSN: 0260-8774; 1873-5770
• DOI: 10.1016/j.jfoodeng.2016.02.004

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 R2P > 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. •The spectral information was extracted from hyperspectral images in fish fillet.•Five and six key wavelengths were selected using SPA and GA.•LS-SVM and MLR models showed good performances for predicting TVB-N and K value.•The established models failed to predict the TBARS value.•The distribution maps of chemical spoilage were generated.