Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches

Prajna, Deyla and Álvarez, María and Barea-Sepúlveda, Marta and Calle, José Luis P. and Suhandy, Diding and Setyaningsih, Widiastuti and Palma, Miguel (2023) Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches. Horticulturae, 9 (7). pp. 1-13. ISSN 23117524

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Abstract

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.

Item Type: Article
Additional Information: Cited by: 2; All Open Access, Gold Open Access
Uncontrolled Keywords: Boruta algorithm; civet coffee; ground coffee; hierarchical cluster analysis; principal component analysis; random forest; support vector machine
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Agricultural Technology > Food and Agricultural Product Technology
Depositing User: Siti Marfungah Marfungah
Date Deposited: 16 Aug 2024 08:01
Last Modified: 16 Aug 2024 08:01
URI: https://ir.lib.ugm.ac.id/id/eprint/2996

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