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

Abstract

Fish is an essential source of many nutrients necessary for human health. However, the deliberate mislabeling of similar fish fillet types is common in markets to make use of the relatively high price difference. This is a type of explicit food adulteration. In the present work, spectrochemical analysis and chemometric methods are adopted to disclose this type of fish species cheating. Laser-induced breakdown spectroscopy (LIBS) was utilized to differentiate between the fillets of the low-priced tilapia and the expensive Nile perch. Furthermore, the acquired spectroscopic data were analyzed statistically using principal component analysis (PCA) and artificial neural network (ANN) showing good discrimination in the PCA score plot and a 99% classification accuracy rate of the implemented ANN model. The recorded spectra of the two fish indicated that tilapia has a higher fat content than Nile perch, as evidenced by higher CN and C2 bands and an atomic line at 247.8 nm in its spectrum. The obtained results demonstrated the potential of using LIBS as a simple, fast, and cost-effective analytical technique, combined with statistical analysis for the decisive discrimination between fish fillet species.

Discriminating two bacteria via laser-induced breakdown spectroscopy and artificial neural network

“…The present study used the feed-forward network to classify the obtained LIBS spectra (the network’s input) with and without the nanoparticles based on Matlab R2018b software. A typical feed-forward network consists of a series of layers where the first layer connects to the network input, and each subsequent layer has a connection from the previous layer (Hamdy et al 2022 ). At the same time, the final layer produces the network’s output.…”

“…LIBS’s applications in diverse life science fields, e.g. health (Gaudiuso et al 2019 ), veterinary (Abdel-Salam et al 2017 ), biology (Abdel-Salam et al 2019 ), food (Abdel-Salam et al 2018 ; Hamdy et al 2022 ), microbiology (Diedrich et al 2007 ), etc. were highly appealing.…”

Laser induced breakdown spectroscopy (LIBS) combined with adaptive particle swarm optimization-based radial basis neural network (APSO-RBF) for rapid identification of soils from different regions

“…The 10 soil spectra have a high degree of similarity, the main manifestation of this is that they all contain roughly the same class of elements, but there are significant differences in spectral intensity at different local spectral wavelengths. [27] Fortunately, it is these differences in spectral features that allow us to try to distinguish and recognize them. [28] Fig.…”