Comparison of mutton freshness grade discrimination based on hyperspectral imaging, near infrared spectroscopy and their fusion information. (4th January 2021)

• Record Type: Journal Article
• Authors: Zhu, Rongguang; Bai, Zongxiu; Qiu, Yuanyuan; Zheng, Minchong; Gu, Jianfeng; Yao, Xuedong
• Is Part Of: Journal of food process engineering. Volume 44:Number 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-04
• Subjects: Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; 664.005
• DOI: 10.1111/jfpe.13642
• Languages: English
• ISSNs: 0145-8876

Abstract

Abstract: The discrimination models of mutton freshness grades based on hyperspectral imaging (HSI, 400–1, 000 nm), near infrared spectroscopy (NIRS, 900–2, 500 nm) and their fusion information were established and compared in this study. Mutton freshness was divided into three grades by the comprehensive evaluation criterion. Then the characteristic variables were screened from preprocessed full‐band variables, and discriminant models were established and compared based on the full‐band variables, the characteristic variables and fusion information. The results showed that both full‐band models of HSI and NIRS showed good performance, and the accuracies of all datasets were higher than 92.6%. The model accuracy decreased gradually with the decrease of the variables number when modeling with characteristic variables. When modeling with fusion information, the extreme learning machine models had better performance, and the accuracies of all datasets were over 92.7%. Comprehensively considering the accuracy, the cost, the efficiency of data acquisition and analysis, the fusion of HSI and NIRS had no significant effect compared to the single sensor with full‐band variables, and NIRS was possibly more economical and practical than HSI. This study provides reference and theoretical basis for selection of sensors in future studies and development of simplified, low‐cost detection devices for mutton freshness grade. Practical Applications: Based on the full‐band variables, the characteristic variables, and fusion information of HSI (400–1, 000 nm) and NIRS (900–2, 500 nm), the discrimination models of mutton freshness grades were established and compared. The characteristic variables were screened by the methods of CARS, GA, SPA and their synergic methods. We could come to a conclusion that both full‐band models of HSI and NIRS showed good results. Comprehensively considering the accuracy, the cost, the efficiency of data acquisition and analysis, the fusion of HSI and NIRS had no significant effect compared to the single sensor based on the full‐band variables, and NIRS was possibly more economical and practical than HSI. This study provides reference and theoretical basis for selection of sensors in future studies and development of simplified, low‐cost detection devices for mutton freshness grade. Abstract :