In their recently published paper on the Special Issue Applications of Machine Learning in Food Industry, the authors of this MED Food TTHubs-based publication studies the application of stable isotope analysis to discriminate the geographical origin of bottarga samples from Messolongi and samples from different regions and explains its use as a decision mechanism for the certification of individual samples.
Consumers are increasingly interested in the geographical origin of foodstuff, as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used for products like wine, oil, and meat by using large datasets and analysis. On the other hand, only few studies have been conducted for the discrimination of seafood origin and especially for mullet roes or bottarga products, and even fewer investigate a small number of samples and datasets. Stable isotopes of Carbon (C), Nitrogen (N), and Sulfur (S) analysis of bottarga samples from four different origins were carried out. The first results show that the stable isotopes ratios of C, N, and S could be used to discriminate the Greek PDO Bottarga (Messolongi) from other similar products by using a probabilistic machine learning methodology. That could use limited sample data to fit/estimate their parameters, while, at the same time, being capable of describing accurately the population and discriminate individual samples regarding their origin.
Reference: Tsirogiannis, G.; Thomatou, A.-A.; Psarra, E.; Mazarakioti, E.C.; Katerinopoulou, K.; Zotos, A.; Kontogeorgos, A.; Patakas, A.; Ladavos, A. Probabilistic Machine Learning for the Authentication of the Protected Designation of Origin of Greek Bottarga from Messolongi: A Generic Methodology to Cope with Very Small Number of Samples. Appl. Sci. 2022, 12, 6335. https://doi.org/10.3390/app12136335
You can find the full paper here.