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( Chun Chieh Yang ),( Cristobal Garrido Novell ),( Dolores Perez Marin ),( Jose E Guerrero Ginel ),( Garrido Varo ),( Hyun Jeong Cho ),( Moon S. Kim ) 한국농업기계학회 2015 바이오시스템공학 Vol.40 No.2
Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data fromline-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models weredeveloped to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals wereline-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region ofInterest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) wereselected 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 correctlyclassify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showedthat 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 PCAmodels for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.
Differentiation of Beef and Fish Meals in Animal Feeds Using Chemometric Analytic Models
Yang, Chun-Chieh,Garrido-Novell, Cristobal,Perez-Marin, Dolores,Guerrero-Ginel, Jose E.,Garrido-Varo, Ana,Cho, Hyunjeong,Kim, Moon S. Korean Society for Agricultural Machinery 2015 바이오시스템공학 Vol.40 No.2
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.
Fu, X.,Kim, M.S.,Chao, K.,Qin, J.,Lim, J.,Lee, H.,Garrido-Varo, A.,Perez-Marin, D.,Ying, Y. Applied Science Publishers 2014 Journal of food engineering Vol.124 No.-
Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized topic as a result of several food safety scares in the past five years. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely applied for a variety of food quality and safety evaluations. In this study, near-infrared (NIR) hyperspectral imaging technique was investigated to detect low levels (≤1.0%) of melamine particles in milk powders. Following image preprocessing (normalization and background removal), the spectrum of each pixel in the sample images was compared to the pure melamine spectrum by spectral similarity measures including spectral angle measure (SAM), spectral correlation measure (SCM), and Euclidian distance measure (EDM). The three similarity analysis methods provided comparable results for melamine particle detection where imaging allowed visualization of the distribution of melamine particles within images of milk powder mixture samples that were prepared with various melamine concentrations. The classification results were verified by spectral feature comparison between separated mean spectra of melamine pixels and milk powder pixels. The study demonstrated that a combination of NIR hyperspectral imaging technique and spectral similarity analyses was an effective method for melamine adulteration discrimination in milk powders. The method described in this study can also be applied to other chemicals or multi-chemicals adulterant detection in milk powders.