Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches
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Published:2023-07-07
Issue:7
Volume:9
Page:778
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ISSN:2311-7524
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Container-title:Horticulturae
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language:en
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Short-container-title:Horticulturae
Author:
Prajna Deyla1, Álvarez María2, Barea-Sepúlveda Marta2, Calle José Luis P.2, Suhandy Diding3ORCID, Setyaningsih Widiastuti1ORCID, Palma Miguel2ORCID
Affiliation:
1. Department of Food and Agricultural Product Technology, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, Indonesia 2. Department 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, Spain 3. Department of Agricultural Engineering, Faculty of Agriculture, University of Lampung, Bandar Lampung 35145, Indonesia
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.
Funder
Universitas Gadjah Mada
Subject
Horticulture,Plant Science
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