Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning
The application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data fo...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2624-7402/6/4/255 |
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| author | Dthenifer Cordeiro Santana Rafael Felipe Ratke Fabio Luiz Zanatta Cid Naudi Silva Campos Ana Carina da Silva Cândido Seron Larissa Pereira Ribeiro Teodoro Natielly Pereira da Silva Gabriela Souza Oliveira Regimar Garcia dos Santos Rita de Cássia Félix Alvarez Carlos Antonio da Silva Junior Matildes Blanco Paulo Eduardo Teodoro |
| author_facet | Dthenifer Cordeiro Santana Rafael Felipe Ratke Fabio Luiz Zanatta Cid Naudi Silva Campos Ana Carina da Silva Cândido Seron Larissa Pereira Ribeiro Teodoro Natielly Pereira da Silva Gabriela Souza Oliveira Regimar Garcia dos Santos Rita de Cássia Félix Alvarez Carlos Antonio da Silva Junior Matildes Blanco Paulo Eduardo Teodoro |
| author_sort | Dthenifer Cordeiro Santana |
| collection | DOAJ |
| description | The application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of these algorithms. The coffee beans were harvested one year after the seedlings were planted. The fresh beans were taken to the spectroscopy laboratory (Laspec) at the Federal University of Mato Grosso do Sul, Chapadão do Sul campus, for spectral evaluation using a spectroradiometer. For the analysis, the dried coffee beans were ground and sieved for the quantification of caffeine, which was carried out using a liquid chromatograph on the Waters Acquity 1100 series UPLC system, with an automatic sample injector. The spectral data of the beans, as well as the spectral data of the roasted and ground coffee, were analyzed using machine learning (ML) algorithms to predict caffeine content. Four databases were used as input: the spectral information of the bean (CG), the spectral information of the bean with additional clone information (CG+C), the spectral information of the bean after roasting and grinding (CGRG) and the spectral information of the bean after roasting and grinding with additional clone information (CGRG+C). The caffeine content was used as an output to be predicted. Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. The support vector machine algorithm showed the best accuracy in predicting caffeine content when using hyperspectral data from roasted and ground coffee beans. This performance was significantly improved when clone information was included, allowing for an even more accurate analysis. |
| format | Article |
| id | doaj-art-9232f5b9ef3d4b0cb61f024a6e83b2af |
| institution | OA Journals |
| issn | 2624-7402 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | AgriEngineering |
| spelling | doaj-art-9232f5b9ef3d4b0cb61f024a6e83b2af2025-08-20T02:01:01ZengMDPI AGAgriEngineering2624-74022024-11-01644480449210.3390/agriengineering6040255Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine LearningDthenifer Cordeiro Santana0Rafael Felipe Ratke1Fabio Luiz Zanatta2Cid Naudi Silva Campos3Ana Carina da Silva Cândido Seron4Larissa Pereira Ribeiro Teodoro5Natielly Pereira da Silva6Gabriela Souza Oliveira7Regimar Garcia dos Santos8Rita de Cássia Félix Alvarez9Carlos Antonio da Silva Junior10Matildes Blanco11Paulo Eduardo Teodoro12Agronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilAgronomy Department, Professor Cinobelina Elvas Campus, Federal University of Piauí, Bom Jesus 58930-000, PI, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilPlant Sciences Building, Department of Horticulture, The University of Georgia, Athens, GA 30602, USAAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilDepartment of Geography, State University of Mato Grosso (UNEMAT), Sinop 78555-000, MT, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilAgronomy Department, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MS, BrazilThe application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of these algorithms. The coffee beans were harvested one year after the seedlings were planted. The fresh beans were taken to the spectroscopy laboratory (Laspec) at the Federal University of Mato Grosso do Sul, Chapadão do Sul campus, for spectral evaluation using a spectroradiometer. For the analysis, the dried coffee beans were ground and sieved for the quantification of caffeine, which was carried out using a liquid chromatograph on the Waters Acquity 1100 series UPLC system, with an automatic sample injector. The spectral data of the beans, as well as the spectral data of the roasted and ground coffee, were analyzed using machine learning (ML) algorithms to predict caffeine content. Four databases were used as input: the spectral information of the bean (CG), the spectral information of the bean with additional clone information (CG+C), the spectral information of the bean after roasting and grinding (CGRG) and the spectral information of the bean after roasting and grinding with additional clone information (CGRG+C). The caffeine content was used as an output to be predicted. Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. The support vector machine algorithm showed the best accuracy in predicting caffeine content when using hyperspectral data from roasted and ground coffee beans. This performance was significantly improved when clone information was included, allowing for an even more accurate analysis.https://www.mdpi.com/2624-7402/6/4/255support vector machinespectroscopysecondary metabolites |
| spellingShingle | Dthenifer Cordeiro Santana Rafael Felipe Ratke Fabio Luiz Zanatta Cid Naudi Silva Campos Ana Carina da Silva Cândido Seron Larissa Pereira Ribeiro Teodoro Natielly Pereira da Silva Gabriela Souza Oliveira Regimar Garcia dos Santos Rita de Cássia Félix Alvarez Carlos Antonio da Silva Junior Matildes Blanco Paulo Eduardo Teodoro Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning AgriEngineering support vector machine spectroscopy secondary metabolites |
| title | Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning |
| title_full | Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning |
| title_fullStr | Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning |
| title_full_unstemmed | Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning |
| title_short | Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning |
| title_sort | caffeine content prediction in coffee beans using hyperspectral reflectance and machine learning |
| topic | support vector machine spectroscopy secondary metabolites |
| url | https://www.mdpi.com/2624-7402/6/4/255 |
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