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Proceedings Paper

Rapid discrimination of main red meat species based on near-infrared hyperspectral imaging technology
Author(s): Lu Qiao; Yankun Peng; Kuanglin Chao; Jianwei Qin
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Paper Abstract

Meat is the necessary source of essential nutrients for people including protein, fat, and so on. The discrimination of meat species and the determination of meat authenticity have been an important issue in the meat industry. The objective of this study is to realize the fast and accurate identification of three main red meats containing beef, lamb and pork by using near-infrared hyperspectral imaging (HSI) technology. After acquiring the hyperspectral images of meat samples, the calibration of acquired images and selection of the region of interest (ROI) were carried out. Then spectral preprocessing method of standard normal variate correction (SNV) was used to reduce the light scattering and random noise before the spectral analysis. Finally, characteristic wavelengths were extracted by principal component analysis (PCA), and the Fisher linear discriminant method was applied to establish Fisher discriminant functions to identify the meat species. All the samples were collected from different batches in order to improve the coverage of the models. In addition to the validation of sample itself in train set and cross validation, three different meat samples were sliced at the size of 2cm×2cm×2 cm approximately and were spliced together in one interface to be scanned by HSI system. The acquired hyperspectral data was applied to further validate the discriminant model. The results demonstrated that the near-infrared hyperspectral imaging technology could be applied as an effective, rapid and non-destructive discrimination method for main red meats.

Paper Details

Date Published: 17 May 2016
PDF: 9 pages
Proc. SPIE 9864, Sensing for Agriculture and Food Quality and Safety VIII, 98640U (17 May 2016); doi: 10.1117/12.2223296
Show Author Affiliations
Lu Qiao, China Agricultural Univ. (China)
Yankun Peng, China Agricultural Univ. (China)
Kuanglin Chao, USDA-ARS (United States)
Jianwei Qin, USDA-ARS (United States)

Published in SPIE Proceedings Vol. 9864:
Sensing for Agriculture and Food Quality and Safety VIII
Moon S. Kim; Kuanglin Chao; Bryan A. Chin, Editor(s)

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