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Proceedings Paper

Chemometric analysis for near-infrared spectral detection of beef in fish meal
Author(s): Chun-Chieh Yang; Cristóbal Garrido-Novell; Dolores Pérez-Marín; José E. Guerrero-Ginel; Ana Garrido-Varo; Moon S. Kim
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Paper Abstract

This paper reports the chemometric analysis of near-infrared spectra drawn from hyperspectral images to develop, evaluate, and compare statistical models for the detection of beef in fish meal. There were 40 pure-fish meal samples, 15 pure-beef meal samples, and 127 fish/beef mixture meal samples prepared for hyperspectral line-scan imaging by a machine vision system. Spectral data for 3600 pixels per sample, in which individual spectra was obtain, were retrieved from the region of interest (ROI) in every sample image. The spectral data spanning 969 nm to 1551 nm (across 176 spectral bands) were analyzed. Statistical models were built using the principal component analysis (PCA) and the partial least squares regression (PLSR) methods. The models were created and developed using the spectral data from the purefish meal and pure-beef meal samples, and were tested and evaluated using the data from the ROI in the mixture meal samples. The results showed that, with a ROI as large as 3600 pixels to cover sufficient area of a mixture meal sample, the success detection rate of beef in fish meal could be satisfactory 99.2% by PCA and 98.4% by PLSR.

Paper Details

Date Published: 13 May 2015
PDF: 7 pages
Proc. SPIE 9488, Sensing for Agriculture and Food Quality and Safety VII, 94880I (13 May 2015); doi: 10.1117/12.2178331
Show Author Affiliations
Chun-Chieh Yang, Agricultural Research Service (United States)
Cristóbal Garrido-Novell, Univ. de Córdoba (Spain)
Dolores Pérez-Marín, Univ. de Córdoba (Spain)
José E. Guerrero-Ginel, Univ. de Córdoba (Spain)
Ana Garrido-Varo, Univ. de Córdoba (Spain)
Moon S. Kim, Agricultural Research Service (United States)

Published in SPIE Proceedings Vol. 9488:
Sensing for Agriculture and Food Quality and Safety VII
Moon S. Kim; Kuanglin Chao; Bryan A. Chin, Editor(s)

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