Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment
Coffee quality, which ultimately is reflected in the beverage aroma, relies on several aspects requiring multiple approaches to check it, which can be expensive and/or time-consuming. Therefore, this study aimed to develop and calibrate an electronic nose (e-nose) coupled with chemometrics to approa...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/13/1/23 |
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| author | Yhan S. Mutz Samara Mafra Maroum Leticia L. G. Tessaro Natália de Oliveira Souza Mikaela Martins de Bem Loyane Silvestre Alves Luisa Pereira Figueiredo Denes K. A. do Rosario Patricia C. Bernardes Cleiton Antônio Nunes |
| author_facet | Yhan S. Mutz Samara Mafra Maroum Leticia L. G. Tessaro Natália de Oliveira Souza Mikaela Martins de Bem Loyane Silvestre Alves Luisa Pereira Figueiredo Denes K. A. do Rosario Patricia C. Bernardes Cleiton Antônio Nunes |
| author_sort | Yhan S. Mutz |
| collection | DOAJ |
| description | Coffee quality, which ultimately is reflected in the beverage aroma, relies on several aspects requiring multiple approaches to check it, which can be expensive and/or time-consuming. Therefore, this study aimed to develop and calibrate an electronic nose (e-nose) coupled with chemometrics to approach coffee-related quality tasks. Twelve different metal oxide sensors were employed in the e-nose construction. The tasks were (i) the separation of <i>Coffea arabica</i> and <i>Coffea canephora</i> species, (ii) the distinction between roasting profiles (light, medium, and dark), and (iii) the separation of expired and non-expired coffees. Exploratory analysis with principal component analysis (PCA) pointed to a fair grouping of the tested samples according to their specification, indicating the potential of the volatiles in grouping the samples. Moreover, a supervised classification employing soft independent modeling of class analogies (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares support vector machine (LS-SVM) led to great results with accuracy above 90% for every task. The performance of each model varies with the specific task, except for the LS-SVM models, which presented a perfect classification for all tasks. Therefore, combining the e-nose with distinct classification models could be used for multiple-purpose classification tasks for producers as a low-cost, rapid, and effective alternative for quality assurance. |
| format | Article |
| id | doaj-art-039f860e074b4833a72b806973c0465f |
| institution | Kabale University |
| issn | 2227-9040 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj-art-039f860e074b4833a72b806973c0465f2025-01-24T13:26:56ZengMDPI AGChemosensors2227-90402025-01-011312310.3390/chemosensors13010023Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality AssessmentYhan S. Mutz0Samara Mafra Maroum1Leticia L. G. Tessaro2Natália de Oliveira Souza3Mikaela Martins de Bem4Loyane Silvestre Alves5Luisa Pereira Figueiredo6Denes K. A. do Rosario7Patricia C. Bernardes8Cleiton Antônio Nunes9Department of Food Science, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilDepartment of Food Science, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilDepartment of Chemistry, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilDepartment of Food Science, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilDepartment of Chemistry, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilDepartment of Food Science, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilDepartment of Food Science, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilDepartment of Food Engineering, Federal University of Espírito Santo, Alegre 29500-000, ES, BrazilDepartment of Food Engineering, Federal University of Espírito Santo, Alegre 29500-000, ES, BrazilDepartment of Food Science, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, MG, BrazilCoffee quality, which ultimately is reflected in the beverage aroma, relies on several aspects requiring multiple approaches to check it, which can be expensive and/or time-consuming. Therefore, this study aimed to develop and calibrate an electronic nose (e-nose) coupled with chemometrics to approach coffee-related quality tasks. Twelve different metal oxide sensors were employed in the e-nose construction. The tasks were (i) the separation of <i>Coffea arabica</i> and <i>Coffea canephora</i> species, (ii) the distinction between roasting profiles (light, medium, and dark), and (iii) the separation of expired and non-expired coffees. Exploratory analysis with principal component analysis (PCA) pointed to a fair grouping of the tested samples according to their specification, indicating the potential of the volatiles in grouping the samples. Moreover, a supervised classification employing soft independent modeling of class analogies (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares support vector machine (LS-SVM) led to great results with accuracy above 90% for every task. The performance of each model varies with the specific task, except for the LS-SVM models, which presented a perfect classification for all tasks. Therefore, combining the e-nose with distinct classification models could be used for multiple-purpose classification tasks for producers as a low-cost, rapid, and effective alternative for quality assurance.https://www.mdpi.com/2227-9040/13/1/23MOSspecialty coffeechemometrics<i>Coffea arabica</i><i>Coffea canephora</i>food quality |
| spellingShingle | Yhan S. Mutz Samara Mafra Maroum Leticia L. G. Tessaro Natália de Oliveira Souza Mikaela Martins de Bem Loyane Silvestre Alves Luisa Pereira Figueiredo Denes K. A. do Rosario Patricia C. Bernardes Cleiton Antônio Nunes Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment Chemosensors MOS specialty coffee chemometrics <i>Coffea arabica</i> <i>Coffea canephora</i> food quality |
| title | Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment |
| title_full | Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment |
| title_fullStr | Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment |
| title_full_unstemmed | Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment |
| title_short | Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment |
| title_sort | effectiveness of an e nose based on metal oxide semiconductor sensors for coffee quality assessment |
| topic | MOS specialty coffee chemometrics <i>Coffea arabica</i> <i>Coffea canephora</i> food quality |
| url | https://www.mdpi.com/2227-9040/13/1/23 |
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