Multivariate calibration applied to study of volatile predictors of arabica coffee quality

Bárbara Zani Agnoletti; Gabriely Silveira Folli; Lucas Louzada Pereira; Patrícia Fontes Pinheiro; Rogério Carvalho Guarçoni; Emanuele Catarina da Silva Oliveira; Paulo Roberto Filgueiras

doi:10.1016/j.foodchem.2021.130679

Multivariate calibration applied to study of volatile predictors of arabica coffee quality

Food Chem. 2022 Jan 15:367:130679. doi: 10.1016/j.foodchem.2021.130679. Epub 2021 Jul 23.

Authors

Bárbara Zani Agnoletti¹, Gabriely Silveira Folli², Lucas Louzada Pereira³, Patrícia Fontes Pinheiro⁴, Rogério Carvalho Guarçoni⁵, Emanuele Catarina da Silva Oliveira³, Paulo Roberto Filgueiras²

Affiliations

¹ Federal University of Espirito Santo/UFES, Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, Brazil. Electronic address: barbara.agnoletti@edu.ufes.br.
² Federal University of Espirito Santo/UFES, Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, Brazil.
³ Federal Institute of Espírito Santo, Department of Food Science and Technology, Avenida Elizabeth Minete Perim, S/N, Bairro São Rafael, CEP 29375-000 Venda Nova do Imigrante, Espírito Santo, Brazil.
⁴ Federal University of Viçosa, Department of Chemistry, Avenida Peter Henry Rolfs, S/N, CEP 36570-900 Viçosa, Minas Gerais, Brazil.
⁵ Capixaba Institute of Technical Assistance, Research and Extension - INCAPER, Department of Statistics, Rua Afonso Sarlo, 160, Bento Ferreira, CEP 29052-010 Vitória, Espírito Santo, Brazil.

PMID: 34352695
DOI: 10.1016/j.foodchem.2021.130679

Abstract

The chemical complexity of coffee influences the sensory evaluation of the beverage, the main method used to define the quality of the coffee. In view of the subjectivity that method offers, we propose the association of an instrumental method with multivariate calibration (PLS and GA-SVR) to predict the quality of arabica coffee as support for sensory analysis. Arabica coffee samples were submitted to sensory evaluation using the Specialty Coffee Association (SCA) protocol and HS-SPME-GC/MS analysis. The models presented RMSEp results from 0.20 to 0.25, within the evaluation range the quality levels of sensory attributes (0.25). For the fragrance/aroma attribute, a value of R²p equal to 0.8503 was reached. 15 volatile compounds were identified as responsible for predicting the quality of arabica coffee, among which, 1-nonadecene was first reported as an impact compound in the prediction of important sensory attributes.

Keywords: Chemical markers; Coffee quality; Partial least squares; Sensory analysis; Support vector machines.

MeSH terms

Calibration
Coffea*
Coffee*
Gas Chromatography-Mass Spectrometry
Odorants / analysis

Substances

Coffee