Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers
The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challengin...
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MDPI AG
2025-07-01
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| Series: | Molecules |
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| author | Krzysztof Przybył Daria Cicha-Wojciechowicz Natalia Drabińska Małgorzata Anna Majcher |
| author_facet | Krzysztof Przybył Daria Cicha-Wojciechowicz Natalia Drabińska Małgorzata Anna Majcher |
| author_sort | Krzysztof Przybył |
| collection | DOAJ |
| description | The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification. |
| format | Article |
| id | doaj-art-69f05ca702d449228a703b58a3d4f51e |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Molecules |
| spelling | doaj-art-69f05ca702d449228a703b58a3d4f51e2025-08-20T03:36:27ZengMDPI AGMolecules1420-30492025-07-013015319910.3390/molecules30153199Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of ClassifiersKrzysztof Przybył0Daria Cicha-Wojciechowicz1Natalia Drabińska2Małgorzata Anna Majcher3Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, PolandFaculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, PolandFaculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, PolandFaculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, PolandThe aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification.https://www.mdpi.com/1420-3049/30/15/3199machine learningensembles of classifiersmead aromasensory analysisodor-active compounds |
| spellingShingle | Krzysztof Przybył Daria Cicha-Wojciechowicz Natalia Drabińska Małgorzata Anna Majcher Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers Molecules machine learning ensembles of classifiers mead aroma sensory analysis odor-active compounds |
| title | Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers |
| title_full | Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers |
| title_fullStr | Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers |
| title_full_unstemmed | Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers |
| title_short | Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers |
| title_sort | machine learning in sensory analysis of mead a case study ensembles of classifiers |
| topic | machine learning ensembles of classifiers mead aroma sensory analysis odor-active compounds |
| url | https://www.mdpi.com/1420-3049/30/15/3199 |
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