4.6 Article

Time dependent berry maturation for planting density levels in Coffea arabica L. beans: Mixture design-fingerprinting using near-infrared transmittance spectroscopy

期刊

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jfca.2020.103795

关键词

Arabica coffee; Metabolic quantification; Mixture design-fingerprint; NIA; Planting density; Principal component analysis

资金

  1. Consorcio Brasileiro de Pesquisa e Desenvolvimento de Cafes [02.09.20.008.00]
  2. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [001]
  3. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [302204/2018-0, 151843/2019-8, 312959/2019-2]

向作者/读者索取更多资源

This study employed a three-step metabolomics strategy to determine the metabolite abundances in coffee beans from the same Coffea arabica plants planted at different densities and during different berry maturation periods. By using a combination of spectral analysis, chemometrics, and classical analytical assays, the study successfully identified the chemical changes in the coffee beans and their patterns related to planting density and harvest period. The analysis revealed gradual changes in the chemical profiles of coffee beans from the first to the third harvest, confirming the significant impact of planting density and harvest period on the chemical composition of coffee beans.
A three-step metabolomics strategy, employing near-infrared transmittance spectroscopy coupled with mixture design-fingerprints, chemometrics, and classical analytical assays, was used to determine metabolite abundances in coffee beans originating from three periods of berry maturation at the same of Coffea arabica plants cultivated at two planting densities. A first step screening showed that ethanol/dichlommethane and ethanol/dichlommethane/acetone extracts contained chemical information permitting classifications of beans cultivated at different planting densities whereas the acetone extract permitted the discrimination of time dependent berry maturation. In the second phase, selected extracts originating from different agronomical management conditions indicated that the 1680, 1660, and 1090 nm bands of the bean fingerprints were associated with these chemical changes. Finally, the third step consisted of classical analytical quantifications of caffeine, carbohydrates, lipids, and chlorogenic acids. This validated most of the conclusions obtained from the PCA score and loading exploratory analysis. The planting density and the harvest period systematic PCA-score patterns indicated gradual changes in the chemical profiles of coffee beans from the first to the third harvest. Partial least squares-discriminant analysis and ANOVA-simultaneous component analysis confirmed the classification and statistical significance of the agronomical management main effects and their interactions.

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