Literature DB >> 19071292

Coffee varietal differentiation based on near infrared spectroscopy.

I Esteban-Díez1, J M González-Sáiz, C Sáenz-González, C Pizarro.   

Abstract

Near infrared spectroscopy (NIRS) was used to discriminate between arabica and robusta pure coffee varieties and blends of varied varietal composition. Direct orthogonal signal correction (DOSC) pre-processing method was applied on a set of 191 roasted coffee NIR spectra from both pure varieties and blends varying the final robusta content from 0 to 60% (w/w) in order to remove information unrelated to the actual varietal composition of samples. The corrected NIR spectra, as well as raw NIR spectra, were used to develop separate classification models using the potential functions method as class-modelling technique, exploring several options more or less restrictive according to the final number of considered categories. All constructed classification models were compared to evaluate their respective qualities and to show the suitability of applying DOSC method as pre-processing step for developing improved classification models for coffee varietal identification purposes.

Year:  2006        PMID: 19071292     DOI: 10.1016/j.talanta.2006.03.052

Source DB:  PubMed          Journal:  Talanta        ISSN: 0039-9140            Impact factor:   6.057


  11 in total

1.  Feasibility of UV-VIS-Fluorescence spectroscopy combined with pattern recognition techniques to authenticate a new category of plant food supplements.

Authors:  Raffaella Boggia; Federica Turrini; Marco Anselmo; Paola Zunin; Dario Donno; Gabriele L Beccaro
Journal:  J Food Sci Technol       Date:  2017-05-23       Impact factor: 2.701

2.  Rare earth elements distribution in grapevine varieties grown on volcanic soils: an example from Mount Etna (Sicily, Italy).

Authors:  Carmelisa D'Antone; Rosalda Punturo; Carmela Vaccaro
Journal:  Environ Monit Assess       Date:  2017-03-13       Impact factor: 2.513

3.  Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics.

Authors:  Chu Zhang; Tingting Shen; Fei Liu; Yong He
Journal:  Sensors (Basel)       Date:  2017-12-31       Impact factor: 3.576

4.  Comparison of Attenuated Total Reflectance Mid-Infrared, Near Infrared, and 1H-Nuclear Magnetic Resonance Spectroscopies for the Determination of Coffee's Geographical Origin.

Authors:  Jessica Medina; Diana Caro Rodríguez; Victoria A Arana; Andrés Bernal; Pierre Esseiva; Julien Wist
Journal:  Int J Anal Chem       Date:  2017-11-01       Impact factor: 1.885

5.  Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee.

Authors:  Cinthia de Carvalho Couto; Otniel Freitas-Silva; Edna Maria Morais Oliveira; Clara Sousa; Susana Casal
Journal:  Foods       Date:  2021-12-28

6.  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

Review 7.  Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.

Authors:  Lei Feng; Baohua Wu; Susu Zhu; Yong He; Chu Zhang
Journal:  Front Nutr       Date:  2021-06-17

8.  Classification of Coffee Beans by GC-C-IRMS, GC-MS, and (1)H-NMR.

Authors:  Victoria Andrea Arana; Jessica Medina; Pierre Esseiva; Diego Pazos; Julien Wist
Journal:  J Anal Methods Chem       Date:  2016-07-18       Impact factor: 2.193

9.  Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging.

Authors:  Nicola Caporaso; Martin B Whitworth; Stephen Grebby; Ian D Fisk
Journal:  J Food Eng       Date:  2018-06       Impact factor: 5.354

10.  Validation of a Quantitative Proton Nuclear Magnetic Resonance Spectroscopic Screening Method for Coffee Quality and Authenticity (NMR Coffee Screener).

Authors:  Alex O Okaru; Andreas Scharinger; Tabata Rajcic de Rezende; Jan Teipel; Thomas Kuballa; Stephan G Walch; Dirk W Lachenmeier
Journal:  Foods       Date:  2020-01-04
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