Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural network in revealing cheating of similarly looking fish fillets

Résumé

Abstract Fish is an essential source of protein and many nutrients necessary for the welfare of human health. However, the deliberate mislabeling in fish fillet types of high similarity is common in popular markets to make use of the relatively high price difference. This, of course, is a type of explicit food adulteration. Therefore, in the present work, spectrochemical analysis and chemometric methods have been adopted for disclosing this type of fish species cheating for the benefit of the customers and the market reliability. As a spectrochemical analytical technique, Laser-Induced Breakdown Spectroscopy (LIBS) has been utilized to differentiate between the fillets of the low-priced Tilapia and the expensive Nile Perch. Currently, LIBS is available for in situ measurements, namely in the markets and fish distribution centers, making it a distinguished method for such tasks compared to other conventional analytical methods. Furthermore, the acquired spectroscopic data have been analyzed statistically using principal component analysis (PCA) and artificial neural network (ANN). The obtained results demonstrated the potential of using LIBS as a simple, fast, accurate, and cost-effective analytical technique combined with a proper statistical analysis method for the decisive discrimination between fish fillets species.

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