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Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton.
Feng, Yingjie; Lv, Yu; Dong, Fujia; Chen, Yue; Li, Hui; Rodas-González, Argenis; Wang, Songlei.
Afiliación
  • Feng Y; College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
  • Lv Y; College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
  • Dong F; College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
  • Chen Y; College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
  • Li H; College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
  • Rodas-González A; Animal Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
  • Wang S; College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China. Electronic address: wangsonglei163@126.com.
Spectrochim Acta A Mol Biomol Spectrosc ; 322: 124844, 2024 Dec 05.
Article en En | MEDLINE | ID: mdl-39053116
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Norfloxacino / Espectroscopía Infrarroja Corta Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Norfloxacino / Espectroscopía Infrarroja Corta Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido