Journal
FOOD CONTROL
Volume 112, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodcont.2020.107149
Keywords
Handheld NIR; Chemometrics; Ensemble learning; Meat authenticity
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Funding
- Sharif University of Technology (SUT) [G960613]
- Dutch Research Council (NWO)
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By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, as well as to correctly classify chicken fillets according to the growth conditions of the chickens with good accuracy. In all cases, the random subspace discriminant ensemble (RSDE) method significantly outperformed other common classification methods such as partial least squares-discriminant analysis (PLS-DA), artificial neural network (ANN) and support vector machine (SVM) with classification accuracy of > 95%. This study shows that handheld NM coupled with machine learning algorithms is a useful, fast, non-destructive tool to identify the authenticity of chicken meat. By comparing and combining different protocols to measure the NIR spectra (i.e., through packaging and directly on meat), we show the possibilities for both consumers and food inspection authorities to check the authenticity and origin of packaged chicken fillet.
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