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International Journal of Food Science
Volume 2017 (2017), Article ID 6274178, 7 pages
https://doi.org/10.1155/2017/6274178
Research Article

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

1Laboratory of Bioprocess and Postharvest Engineering, Department of Agricultural Engineering, The University of Lampung, Jl. Soemantri Brojonegoro No. 1, Gedong Meneng, Bandar Lampung, Lampung 35145, Indonesia
2Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10, Rajabasa, Bandar Lampung, Lampung, Indonesia

Correspondence should be addressed to Diding Suhandy

Received 18 January 2017; Revised 12 June 2017; Accepted 18 July 2017; Published 20 August 2017

Academic Editor: Thierry Thomas-Danguin

Copyright © 2017 Diding Suhandy and Meinilwita Yulia. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world’s priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both and RER values.