Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Differentiation of Beef and Fish Meals in Animal Feeds Using Chemometric Analytic Models

  • Yang, Chun-Chieh (Environmental Microbial and Food Safety Laboratory) ;
  • Garrido-Novell, Cristobal (Faculty of Agriculture and Forestry Engineering, Non-Destructive Spectral Sensors Unit, University of Cordoba) ;
  • Perez-Marin, Dolores (Faculty of Agriculture and Forestry Engineering, Non-Destructive Spectral Sensors Unit, University of Cordoba) ;
  • Guerrero-Ginel, Jose E. (Faculty of Agriculture and Forestry Engineering, Non-Destructive Spectral Sensors Unit, University of Cordoba) ;
  • Garrido-Varo, Ana (Faculty of Agriculture and Forestry Engineering, Non-Destructive Spectral Sensors Unit, University of Cordoba) ;
  • Cho, Hyunjeong (Experiment & Research Institute, National Agricultural Products Quality Management Service) ;
  • Kim, Moon S. (Environmental Microbial and Food Safety Laboratory)
  • Received : 2015.05.13
  • Accepted : 2015.05.30
  • Published : 2015.06.01
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Abstract

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data from line-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models were developed to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals were line-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region of Interest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) were selected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA) methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctly classify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showed that the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1% for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCA models for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.

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