Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity

Hadi Parastar, Geert van Kollenburg*, Yannick Weesepoel, André van den Doel, Lutgarde Buydens, Jeroen Jansen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)

Abstract

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 NIR 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.

Original languageEnglish
Article number107149
JournalFood Control
Volume112
DOIs
Publication statusPublished - 1 Jun 2020

Keywords

  • Chemometrics
  • Ensemble learning
  • Handheld NIR
  • Meat authenticity

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