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
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 use...
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6335 |
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| author | George Tsirogiannis Anna-Akrivi Thomatou Eleni Psarra Eleni C. Mazarakioti Katerina Katerinopoulou Anastasios Zotos Achilleas Kontogeorgos Angelos Patakas Athanasios Ladavos |
| author_facet | George Tsirogiannis Anna-Akrivi Thomatou Eleni Psarra Eleni C. Mazarakioti Katerina Katerinopoulou Anastasios Zotos Achilleas Kontogeorgos Angelos Patakas Athanasios Ladavos |
| author_sort | George Tsirogiannis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:09:11Z |
| format | Article |
| id | doaj.art-31bec1df6a434dacb96e77ce76f9084d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:09:11Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-31bec1df6a434dacb96e77ce76f9084d2023-11-23T19:34:54ZengMDPI AGApplied Sciences2076-34172022-06-011213633510.3390/app12136335Probabilistic 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 SamplesGeorge Tsirogiannis0Anna-Akrivi Thomatou1Eleni Psarra2Eleni C. Mazarakioti3Katerina Katerinopoulou4Anastasios Zotos5Achilleas Kontogeorgos6Angelos Patakas7Athanasios Ladavos8Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, GreeceDepartment of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, GreeceDepartment of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, GreeceDepartment of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, GreeceDepartment of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, GreeceDepartment of Biosystems Science and Agricultural Engineering, University of Patras, 30200 Messolongi, GreeceDepartment of Agriculture, International Hellenic University, 57001 Thessaloniki, GreeceDepartment of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, GreeceDepartment of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, GreeceConsumers 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.https://www.mdpi.com/2076-3417/12/13/6335stable isotopesfood authenticityprobabilistic machine learningPDO products |
| spellingShingle | George Tsirogiannis Anna-Akrivi Thomatou Eleni Psarra Eleni C. Mazarakioti Katerina Katerinopoulou Anastasios Zotos Achilleas Kontogeorgos Angelos Patakas Athanasios Ladavos 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 Applied Sciences stable isotopes food authenticity probabilistic machine learning PDO products |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | stable isotopes food authenticity probabilistic machine learning PDO products |
| url | https://www.mdpi.com/2076-3417/12/13/6335 |
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