Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton

被引:3
|
作者
Feng, Yingjie [1 ]
Lv, Yu [1 ]
Dong, Fujia [1 ,2 ]
Chen, Yue [1 ]
Li, Hui [1 ]
Rodas-Gonzalez, Argenis [3 ]
Wang, Songlei [1 ]
机构
[1] Ningxia Univ, Coll Food Sci & Engn, Yinchuan 750021, Peoples R China
[2] Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832003, Peoples R China
[3] Univ Manitoba, Anim Sci, Winnipeg, MB R3T 2N2, Canada
基金
中国国家自然科学基金;
关键词
Hyperspectral imaging; Stochastic configuration networks; Data fusion; Residues detection; VARIABLE SELECTION; SPECTROSCOPY;
D O I
10.1016/j.saa.2024.124844
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R2p) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat.
引用
收藏
页数:12
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