Discrimination of civet coffee using visible spectroscopy

Graciella Mae L Adier  -  Department of Computer and Electronics Engineering, Cavite State University, Philippines
Charlene A Reyes  -  Department of Computer and Electronics Engineering, Cavite State University, Philippines
*Edwin R Arboleda orcid scopus  -  Department of Computer and Electronics Engineering, Cavite State University, Philippines
Received: 1 May 2020; Revised: 26 May 2020; Accepted: 28 May 2020; Published: 31 Jul 2020; Available online: 8 Jun 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
License URL: http://creativecommons.org/licenses/by-sa/4.0

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Article Info
Section: Original Research Articles
Language: EN
Statistics: 268 41
Abstract
Civet coffee is considered as highly marketable and rare. This specialty coffee has a special flavor and higher price relative to regular coffee, and it is restricted in supply. Establishing a straightforward and efficient approach to distinguish civet coffee for quality; likewise, consumer protection is fundamental. This study utilized visible spectroscopy as a non-destructive and quick technique to obtain the absorbance, ranging from 450 nm to 650 nm, of the civet coffee and non-civet coffee samples. Overall, 160 samples were analyzed, and the total spectra accumulated was 960. The data gathered from the first 120 samples were fed to the classification learner application and were used as a training data set. The remaining samples were used for testing the classification algorithm. The study shows that civet coffee bean samples have lower absorbance values in visible spectra than non-civet coffee bean samples. The process yields 96.7 % to 100 % classification scores for quadratic discriminant analysis and logistic regression. Among the two classification algorithms, logistic regression generated the fastest training time of 14.050 seconds. The application of visible spectroscopy combined with data mining algorithms is effective in discriminating civet coffee from non-civet coffee.
Keywords: civet coffee; visible spectroscopy; classification algorithm; absorbance; classification learner application

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