Literature DB >> 22953929

Evaluation of green coffee beans quality using near infrared spectroscopy: a quantitative approach.

João Rodrigo Santos1, Mafalda C Sarraguça, António O S S Rangel, João A Lopes.   

Abstract

Characterisation of coffee quality based on bean quality assessment is associated with the relative amount of defective beans among non-defective beans. It is therefore important to develop a methodology capable of identifying the presence of defective beans that enables a fast assessment of coffee grade and that can become an analytical tool to standardise coffee quality. In this work, a methodology for quality assessment of green coffee based on near infrared spectroscopy (NIRS) is proposed. NIRS is a green chemistry, low cost, fast response technique without the need of sample processing. The applicability of NIRS was evaluated for Arabica and Robusta varieties from different geographical locations. Partial least squares regression was used to relate the NIR spectrum to the mass fraction of defective and non-defective beans. Relative errors around 5% show that NIRS can be a valuable analytical tool to be used by coffee roasters, enabling a simple and quantitative evaluation of green coffee quality in a fast way.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22953929     DOI: 10.1016/j.foodchem.2012.06.059

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  4 in total

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Authors:  Justyna Grabska; Krzysztof B Beć; Yukihiro Ozaki; Christian W Huck
Journal:  Molecules       Date:  2021-08-27       Impact factor: 4.927

2.  The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans.

Authors:  Alina Mihailova; Beatrix Liebisch; Marivil D Islam; Jens M Carstensen; Andrew Cannavan; Simon D Kelly
Journal:  Food Chem X       Date:  2022-05-06

3.  Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods.

Authors:  Si Yang; Chenxi Li; Yang Mei; Wen Liu; Rong Liu; Wenliang Chen; Donghai Han; Kexin Xu
Journal:  Front Nutr       Date:  2021-06-17

4.  High-throughput metabolic profiling of diverse green Coffea arabica beans identified tryptophan as a universal discrimination factor for immature beans.

Authors:  Daiki Setoyama; Keiko Iwasa; Harumichi Seta; Hiroaki Shimizu; Yoshinori Fujimura; Daisuke Miura; Hiroyuki Wariishi; Chifumi Nagai; Koichi Nakahara
Journal:  PLoS One       Date:  2013-08-01       Impact factor: 3.240

  4 in total

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