Suhandy, Diding (2018) The Detection and Quantification of Adulteration in Ground Roasted Asian Palm Civet Coffee Using Near-Infrared Spectroscopy in Tandem with Chemometrics. In: The 2nd International Conference on Agricultural Engineering for Sustainable Agriculture Production 2017 (AESAP 2017), 23–25 October 2017, Bogor, Indonesia.

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Suhandy_2018_IOP_Conf._Ser.%3A_Earth_Environ._Sci._147_012011 The Detection and quantifiction.pdf

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Official URL: http://iopscience.iop.org/article/10.1088/1755-131...

Abstract

In the present research, an evaluation of using near infrared (NIR) spectroscopy in tandem with full spectrum partial least squares (FS-PLS) regression for quantification of degree of adulteration in civet coffee was conducted. A number of 126 ground roasted coffee samples with degree of adulteration 0-51% were prepared. Spectral data were acquired using a NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement in the range of 1300-2500 nm. The samples were divided into two groups calibration sample set (84 samples) and prediction sample set (42 samples). The calibration model was developed on original spectra using FS-PLS regression with full-cross validation method. The calibration model exhibited the determination coefficient R2=0.96 for calibration and R2=0.92 for validation. The prediction resulted in low root mean square error of prediction (RMSEP) (4.67%) and high ratio prediction to deviation (RPD) (3.75). In conclusion, the degree of adulteration in civet coffee have been quantified successfully by using NIR spectroscopy and FS-PLS regression in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation.

Item Type: Conference or Workshop Item (Paper)
Subjects: S Agriculture > S Agriculture (General)
Divisions: Fakultas Pertanian (FP) > Prodi Teknik Pertanian
Depositing User: Dr. Diding Suhandy
Date Deposited: 15 Nov 2018 08:09
Last Modified: 15 Nov 2018 08:09
URI: http://repository.lppm.unila.ac.id/id/eprint/9979

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