Literature DB >> 32560064

Reliable Discrimination of Green Coffee Beans Species: A Comparison of UV-Vis-Based Determination of Caffeine and Chlorogenic Acid with Non-Targeted Near-Infrared Spectroscopy.

Adnan Adnan1, Marcel Naumann1, Daniel Mörlein2, Elke Pawelzik1.   

Abstract

Species adulteration is a common problem in the coffee trade. Several attempts have been made to differentiate among species. However, finding an applicable methodology that would consider the various aspects of adulteration remains a challenge. This study investigated an ultraviolet-visible (UV-Vis) spectroscopy-based determination of caffeine and chlorogenic acid contents, as well as the applicability of non-targeted near-infrared (NIR) spectroscopy, to discriminate between green coffee beans of the Coffea arabica (Arabica) and Coffea canephora (Robusta) species from Java Island, Indonesia. The discrimination was conducted by measuring the caffeine and chlorogenic acid content in the beans using UV-Vis spectroscopy. The data related to both compounds was processed using linear discriminant analysis (LDA). Information about the diffuse reflectance (log 1/R) spectra of intact beans was determined by NIR spectroscopy and analyzed using multivariate analysis. UV-Vis spectroscopy attained an accuracy of 97% in comparison to NIR spectroscopy's accuracy by selected wavelengths of LDA (95%). The study suggests that both methods are applicable to discriminate reliably among species.

Entities:  

Keywords:  Arabica; Robusta; caffeine; chlorogenic acid; food fraud; linear discriminant analysis

Year:  2020        PMID: 32560064     DOI: 10.3390/foods9060788

Source DB:  PubMed          Journal:  Foods        ISSN: 2304-8158


  4 in total

1.  Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.

Authors:  Weverton Gomes da Costa; Ivan de Paiva Barbosa; Jacqueline Enequio de Souza; Cosme Damião Cruz; Moysés Nascimento; Antonio Carlos Baião de Oliveira
Journal:  PLoS One       Date:  2021-01-12       Impact factor: 3.240

2.  Effective screen-printed potentiometric devices modified with carbon nanotubes for the detection of chlorogenic acid: application to food quality monitoring.

Authors:  Hisham S M Abd-Rabboh; Abd El-Galil E Amr; Ahmed M Naglah; Abdulrahman A Almehizia; Ayman H Kamel
Journal:  RSC Adv       Date:  2021-12-02       Impact factor: 4.036

Review 3.  Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review.

Authors:  Emmanuel Ekene Okere; Ebrahiema Arendse; Helene Nieuwoudt; Olaniyi Amos Fawole; Willem Jacobus Perold; Umezuruike Linus Opara
Journal:  Foods       Date:  2021-12-09

4.  NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends.

Authors:  Balkis Aouadi; Flora Vitalis; Zsanett Bodor; John-Lewis Zinia Zaukuu; Istvan Kertesz; Zoltan Kovacs
Journal:  Molecules       Date:  2022-01-08       Impact factor: 4.411

  4 in total

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