Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment
The application of artificial intelligence for the development of recognition models for food and beverages differentiation has benefited from increasing attention in recent years. For this scope, different machine learning (ML) algorithms were used in order to find the most suitable model for a cer...
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10894 |
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| author | Ariana Raluca Hategan Dana Alina Magdas Romulus Puscas Adriana Dehelean Gabriela Cristea Bianca Simionescu |
| author_facet | Ariana Raluca Hategan Dana Alina Magdas Romulus Puscas Adriana Dehelean Gabriela Cristea Bianca Simionescu |
| author_sort | Ariana Raluca Hategan |
| collection | DOAJ |
| description | The application of artificial intelligence for the development of recognition models for food and beverages differentiation has benefited from increasing attention in recent years. For this scope, different machine learning (ML) algorithms were used in order to find the most suitable model for a certain purpose. In the present work, three ML algorithms, namely artificial neural networks (ANN), support vector machines (SVM) and k-nearest neighbors (KNN), were applied for constructing honey geographical classification models, and their performance was assessed and compared. A preprocessing step consisting of either a component reduction method or a supervised feature selection technique was applied prior to model development. The most efficient geographical differentiation models were obtained based on ANN, when a subset of features corresponding to the markers having the highest discrimination potential was used as input data. Therefore, when the samples aimed to be classified at an intercountry level, an accuracy of 95% was achieved; namely, 99% of the Romanian samples and 73% of the ones originating from other countries were correctly predicted. Promising results were also obtained for the intracountry honey discrimination; namely, the model built for classifying the Transylvanian samples from the ones produced in other Romanian regions had an 85% accuracy. |
| first_indexed | 2024-03-09T19:18:11Z |
| format | Article |
| id | doaj.art-7e67fd319e0e4406af0f59f6cf182191 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:18:11Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7e67fd319e0e4406af0f59f6cf1821912023-11-24T03:34:42ZengMDPI AGApplied Sciences2076-34172022-10-0112211089410.3390/app122110894Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin AssessmentAriana Raluca Hategan0Dana Alina Magdas1Romulus Puscas2Adriana Dehelean3Gabriela Cristea4Bianca Simionescu5National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaNational Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, RomaniaDepartment Mother and Child, Iuliu Hatieganu University of Medicine and Pharmacy, 8 Victor Babes Street, 400012 Cluj-Napoca, RomaniaThe application of artificial intelligence for the development of recognition models for food and beverages differentiation has benefited from increasing attention in recent years. For this scope, different machine learning (ML) algorithms were used in order to find the most suitable model for a certain purpose. In the present work, three ML algorithms, namely artificial neural networks (ANN), support vector machines (SVM) and k-nearest neighbors (KNN), were applied for constructing honey geographical classification models, and their performance was assessed and compared. A preprocessing step consisting of either a component reduction method or a supervised feature selection technique was applied prior to model development. The most efficient geographical differentiation models were obtained based on ANN, when a subset of features corresponding to the markers having the highest discrimination potential was used as input data. Therefore, when the samples aimed to be classified at an intercountry level, an accuracy of 95% was achieved; namely, 99% of the Romanian samples and 73% of the ones originating from other countries were correctly predicted. Promising results were also obtained for the intracountry honey discrimination; namely, the model built for classifying the Transylvanian samples from the ones produced in other Romanian regions had an 85% accuracy.https://www.mdpi.com/2076-3417/12/21/10894honey geographical assessmentisotope fingerprintelemental profileartificial intelligence |
| spellingShingle | Ariana Raluca Hategan Dana Alina Magdas Romulus Puscas Adriana Dehelean Gabriela Cristea Bianca Simionescu Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment Applied Sciences honey geographical assessment isotope fingerprint elemental profile artificial intelligence |
| title | Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment |
| title_full | Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment |
| title_fullStr | Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment |
| title_full_unstemmed | Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment |
| title_short | Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment |
| title_sort | machine learning algorithms in corroboration with isotope and elemental profile an efficient tool for honey geographical origin assessment |
| topic | honey geographical assessment isotope fingerprint elemental profile artificial intelligence |
| url | https://www.mdpi.com/2076-3417/12/21/10894 |
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