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Evaluation of Arabica Coffee Fermentation Using Machine Learning.
Rocha, Renata A R; Cruz, Marcelo A D da; Silva, Lívia C F; Costa, Gisele X R; Amaral, Laurence R; Bertarini, Pedro L L; Gomes, Matheus S; Santos, Líbia D.
Afiliación
  • Rocha RAR; Biotechnology Institute, University Federal of Uberlândia, Patos de Minas 38700-002, MG, Brazil.
  • Cruz MADD; Biotechnology Institute, University Federal of Uberlândia, Patos de Minas 38700-002, MG, Brazil.
  • Silva LCF; Biotechnology Institute, University Federal of Uberlândia, Patos de Minas 38700-002, MG, Brazil.
  • Costa GXR; Faculty of Chemical Engineering, Federal University of Uberlândia, Patos de Minas 38702-178, MG, Brazil.
  • Amaral LR; Laboratory of Bioinformatics and Molecular Analysis (LBAM), Federal University of Uberlândia, Patos de Minas 38702-178, MG, Brazil.
  • Bertarini PLL; Faculty of Electrical Engineering, Federal University of Uberlândia, Patos de Minas 38702-178, MG, Brazil.
  • Gomes MS; Laboratory of Bioinformatics and Molecular Analysis (LBAM), Federal University of Uberlândia, Patos de Minas 38702-178, MG, Brazil.
  • Santos LD; Faculty of Chemical Engineering, Federal University of Uberlândia, Patos de Minas 38702-178, MG, Brazil.
Foods ; 13(3)2024 Feb 01.
Article en En | MEDLINE | ID: mdl-38338590
ABSTRACT
This study explores the variances in the organic, chemical, and sensory attributes of fermented coffee beans, specifically examining how post-harvest processes influence cup quality. Coffee fruits from the Catuaí IAC-144 variety were processed using both natural coffee (NC) and pulped coffee (PC) methods. The fruits were then subjected to self-induced anaerobic fermentation (SIAF) using one of the following fermentation

methods:

solid-state fermentation (SSF) or submerged fermentation (SMF). Within these methods, either spontaneous fermentation (SPF) or starter culture fermentation (SCF) was applied. Each method was conducted over periods of 24, 48, and 72 h. For this purpose, two-hundred-liter bioreactors were used, along with two control treatments. Numerous parameters were monitored throughout the fermentation process. A comprehensive chemical profiling and sensory analysis, adhering to the guidelines of the Specialty Coffee Association, were conducted to evaluate the influence of these fermentation processes on the flavor, aroma, and body characteristics of the coffee beverage across multiple dimensions. Data analysis and predictive modeling were performed using machine learning techniques. This study found that NC exhibited a higher production of acids (citric, malic, succinic, and lactic) compared to PC, resulting in distinct chemical and sensory profiles. The decision tree showed that fructose and malic and succinic acids were identified as the main factors enhancing sensory notes during cupping. SMF promoted higher concentrations of lactic acid, while SSF led to increased ethanol content. Consequently, the SIAF process enhances the sensory quality of coffee, adding value to the product by generating diverse sensory profiles.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Foods Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Foods Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Suiza