Work

Fishy forensics: FT-NIR and machine learning based authentication of Mediterranean anchovies (Engraulis encrasicolus)

Year: 2024

Type: article

Abstract: Seafood authentication and traceability is a challenging issue owing to its complex supply chain and fishing in international waters.NIR spectroscopy has been successfully used to authenticate food of animal and plant origin.In this study, FT-NIR was used to discriminate between Mediterranean anchovies (Engraulis encrasicolus) fished from Adriatic, Balearic, and Tyrrhenian Sea.The spectra were prepared using the standard normal variate (SNV) and the Savitzky-Golay 1st derivative, 2nd order (SG), as well as both together.The model was built, after outlier removal, with linear-support vector machine (L-SVM), polynomial-SVM (P-SVM), k-nearest neighbor (k-NN) and Random Forest (RF).Spectral preprocessing improved model classification accuracy for all algorithms.The data could not be put into groups by linear algorithms like L-SVM and k-NN because the NIR spectra were not linear and had many columns.Non-linear algorithms, P-SVM and RF when coupled with SG+SNV, successfully produced models with maximum robustness.P-SVM and RF models had 100 % accuracy in training set and 95.7 % and 95.5 % accuracy in testing set, respectively and 95.2 and 95.1 accuracy in cross-validation set.

Source: Journal of Food Composition and Analysis

DOI: 10.1016/j.jfca.2024.106847

Authors: Nidhi Dalal, María José Sáiz, Antonio Giandonato Caporale, Francesco Baldini, Simon Armen Babayan, Paola Adamo

Institutions: University of Naples Federico II, National Centre for Technology and Food Safety, University of Glasgow

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDGs: Life below water, Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,190