Literature DB >> 32213986

Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples.

Nerea Núñez1, Xavi Collado1, Clara Martínez1, Javier Saurina1,2, Oscar Núñez1,2,3.   

Abstract

In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.

Entities:  

Keywords:  HPLC-UV fingerprinting; coffee; food adulteration; food authentication; non-targeted analysis; partial least squares regression (PLSR); principal component analysis (PCA)

Year:  2020        PMID: 32213986     DOI: 10.3390/foods9030378

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


  4 in total

1.  Profiling, monitoring and conserving caterpillar fungus in the Himalayan region using anchored hybrid enrichment markers.

Authors:  Zhengyang Wang; Wa Da; Chandra Singh Negi; Puspa Lal Ghimire; Karma Wangdi; Pramod K Yadav; Zhuoma Pubu; Laiku Lama; Kuenga Yarpel; Sarah C Maunsell; Yong Liu; Krushnamegh Kunte; Kamaljit S Bawa; Darong Yang; Naomi E Pierce
Journal:  Proc Biol Sci       Date:  2022-04-27       Impact factor: 5.530

2.  Recent Developments in the Applications of Fingerprinting Technology in the Food Field.

Authors:  José S Câmara; Sonia Medina; Rosa Perestrelo
Journal:  Foods       Date:  2022-07-07

3.  Development of a fast and simple method to identify pure Arabica coffee and blended coffee by Infrared Spectroscopy.

Authors:  Alexandre Cestari
Journal:  J Food Sci Technol       Date:  2021-06-16       Impact factor: 3.117

Review 4.  Metabolomics-Based Approach for Coffee Beverage Improvement in the Context of Processing, Brewing Methods, and Quality Attributes.

Authors:  Mohamed A Farag; Ahmed Zayed; Ibrahim E Sallam; Amr Abdelwareth; Ludger A Wessjohann
Journal:  Foods       Date:  2022-03-18
  4 in total

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