Hyperspectral imaging in combination with data fusion for rapid evaluation of tilapia fillet freshness

被引:80
|
作者
Yu, Hai-Dong [1 ]
Qing, Li-Wei [1 ]
Yan, Dan-Ting [1 ]
Xia, Guanghua [1 ,2 ]
Zhang, Chenghui [1 ]
Yun, Yong-Huan [1 ,2 ]
Zhang, Weimin [1 ]
机构
[1] Hainan Univ, Coll Food Sci & Engn, 58 Renmin Rd, Haikou 570228, Hainan, Peoples R China
[2] Hainan Univ, Hainan Engn Res Ctr Aquat Resources Efficient Uti, Haikou 570228, Hainan, Peoples R China
基金
海南省自然科学基金;
关键词
Tilapia fillet; Freshness; Hyperspectral imaging; Data fusion; Wavelength selection; Chemical information visualization;
D O I
10.1016/j.foodchem.2021.129129
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
The potential of two different hyperspectral imaging systems (visible near infrared spectroscopy (Vis-NIR) and NIR) was investigated to determine TVB-N contents in tilapia fillets during cold storage. With Vis-NIR and NIR data, calibration models were established between the average spectra of tilapia fillets in the hyperspectral image and their corresponding TVB-N contents and optimized with various variable selection and data fusion methods. Superior models were obtained with variable selection methods based on low-level fusion data when compared with the corresponding methods based on single data blocks. Mid-level fusion data achieved the best model based on CARS, in comparison with all others. Finally, the respective optimized models of single Vis-NIR and NIR data were employed to visualize TVB-N contents distribution in tilapia fillets. In general, the results showed the great feasibility of hyperspectral imaging in combination with data fusion analysis in the nondestructive evaluation of tilapia fillet freshness.
引用
收藏
页数:9
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