Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches
Civet coffee is the world’s most expensive and rarest coffee bean. Indonesia was the first country to be identified as the origin of civet coffee. First, it is produced spontaneously by collecting civet feces from coffee plantations near the forest. Due to limited stock, farmers began cultivating ci...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/9/7/778 |
| _version_ | 1797436090975518720 |
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| author | Deyla Prajna María Álvarez Marta Barea-Sepúlveda José Luis P. Calle Diding Suhandy Widiastuti Setyaningsih Miguel Palma |
| author_facet | Deyla Prajna María Álvarez Marta Barea-Sepúlveda José Luis P. Calle Diding Suhandy Widiastuti Setyaningsih Miguel Palma |
| author_sort | Deyla Prajna |
| collection | DOAJ |
| description | Civet coffee is the world’s most expensive and rarest coffee bean. Indonesia was the first country to be identified as the origin of civet coffee. First, it is produced spontaneously by collecting civet feces from coffee plantations near the forest. Due to limited stock, farmers began cultivating civets to obtain safe supplies of civet coffee. Based on this, civet coffee can be divided into two types: wild and fed. A combination of spectroscopy and chemometrics can be used to evaluate authenticity with high speed and precision. In this study, seven samples from different regions were analyzed using NIR Spectroscopy with various preparations: unroasted, roasted, unground, and ground. The spectroscopic data were combined with unsupervised exploratory methods (hierarchical cluster analysis (HCA) and principal component analysis (PCA)) and supervised classification methods (support vector machine (SVM) and random forest (RF)). The HCA results showed a trend between roasted and unroasted beans; meanwhile, the PCA showed a trend based on coffee bean regions. Combining the SVM with leave-one-out-cross-validation (LOOCV) successfully differentiated 57.14% in all sample groups (unground, ground, unroasted, unroasted–unground, and roasted–unground), 78.57% in roasted, 92.86% in roasted–ground, and 100% in unroasted–ground. However, using the Boruta filter, the accuracy increased to 89.29% for all samples, to 85.71% for unground and unroasted–unground, and 100% for roasted, unroasted–ground, and roasted–ground. Ultimately, RF successfully differentiated 100% of all grouped samples. In general, roasting and grinding the samples before analysis improved the accuracy of differentiating between wild and feeding civet coffee using NIR Spectroscopy. |
| first_indexed | 2024-03-09T10:57:41Z |
| format | Article |
| id | doaj.art-d9ee36bebf884737b1011f15388314d3 |
| institution | Directory Open Access Journal |
| issn | 2311-7524 |
| language | English |
| last_indexed | 2024-03-09T10:57:41Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj.art-d9ee36bebf884737b1011f15388314d32023-12-01T01:34:43ZengMDPI AGHorticulturae2311-75242023-07-019777810.3390/horticulturae9070778Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric ApproachesDeyla Prajna0María Álvarez1Marta Barea-Sepúlveda2José Luis P. Calle3Diding Suhandy4Widiastuti Setyaningsih5Miguel Palma6Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, IndonesiaDepartment of Analytical Chemistry, Faculty of Sciences, Agrifood Campus of International Excellence (CeiA3), Instituto de Investigación Vitivinícola y Agroalimentaria (IVAGRO), University of Cadiz, 11510 Puerto Real, SpainDepartment of Analytical Chemistry, Faculty of Sciences, Agrifood Campus of International Excellence (CeiA3), Instituto de Investigación Vitivinícola y Agroalimentaria (IVAGRO), University of Cadiz, 11510 Puerto Real, SpainDepartment of Analytical Chemistry, Faculty of Sciences, Agrifood Campus of International Excellence (CeiA3), Instituto de Investigación Vitivinícola y Agroalimentaria (IVAGRO), University of Cadiz, 11510 Puerto Real, SpainDepartment of Agricultural Engineering, Faculty of Agriculture, University of Lampung, Bandar Lampung 35145, IndonesiaDepartment of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, IndonesiaDepartment of Analytical Chemistry, Faculty of Sciences, Agrifood Campus of International Excellence (CeiA3), Instituto de Investigación Vitivinícola y Agroalimentaria (IVAGRO), University of Cadiz, 11510 Puerto Real, SpainCivet coffee is the world’s most expensive and rarest coffee bean. Indonesia was the first country to be identified as the origin of civet coffee. First, it is produced spontaneously by collecting civet feces from coffee plantations near the forest. Due to limited stock, farmers began cultivating civets to obtain safe supplies of civet coffee. Based on this, civet coffee can be divided into two types: wild and fed. A combination of spectroscopy and chemometrics can be used to evaluate authenticity with high speed and precision. In this study, seven samples from different regions were analyzed using NIR Spectroscopy with various preparations: unroasted, roasted, unground, and ground. The spectroscopic data were combined with unsupervised exploratory methods (hierarchical cluster analysis (HCA) and principal component analysis (PCA)) and supervised classification methods (support vector machine (SVM) and random forest (RF)). The HCA results showed a trend between roasted and unroasted beans; meanwhile, the PCA showed a trend based on coffee bean regions. Combining the SVM with leave-one-out-cross-validation (LOOCV) successfully differentiated 57.14% in all sample groups (unground, ground, unroasted, unroasted–unground, and roasted–unground), 78.57% in roasted, 92.86% in roasted–ground, and 100% in unroasted–ground. However, using the Boruta filter, the accuracy increased to 89.29% for all samples, to 85.71% for unground and unroasted–unground, and 100% for roasted, unroasted–ground, and roasted–ground. Ultimately, RF successfully differentiated 100% of all grouped samples. In general, roasting and grinding the samples before analysis improved the accuracy of differentiating between wild and feeding civet coffee using NIR Spectroscopy.https://www.mdpi.com/2311-7524/9/7/778Boruta algorithmcivet coffeeground coffeehierarchical cluster analysisprincipal component analysisrandom forest |
| spellingShingle | Deyla Prajna María Álvarez Marta Barea-Sepúlveda José Luis P. Calle Diding Suhandy Widiastuti Setyaningsih Miguel Palma Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches Horticulturae Boruta algorithm civet coffee ground coffee hierarchical cluster analysis principal component analysis random forest |
| title | Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches |
| title_full | Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches |
| title_fullStr | Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches |
| title_full_unstemmed | Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches |
| title_short | Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches |
| title_sort | enhanced differentiation of wild and feeding civet coffee using near infrared spectroscopy with various sample pretreatments and chemometric approaches |
| topic | Boruta algorithm civet coffee ground coffee hierarchical cluster analysis principal component analysis random forest |
| url | https://www.mdpi.com/2311-7524/9/7/778 |
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