Journal
JOURNAL OF APPLIED SPECTROSCOPY
Volume 84, Issue 3, Pages 535-541Publisher
SPRINGER
DOI: 10.1007/s10812-017-0506-3
Keywords
SPA-MLR classifier; ROC analysis; hyperspectral imaging; chicken carcass; contaminant detection
Categories
Funding
- Fundamental Research Funds for the Central Universities [KYZ201665]
- Science and Technology Support Plan of China for Jiangsu Province [BE2014708]
- Special Project of Public Welfare Industry (Agriculture) of China [201303083]
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During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.
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