SHS-GC-MS applied in Coffea arabica and Coffea canephora blend assessment.

Autor: Vieira Lyrio MV; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com., Pereira da Cunha PH; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com., Debona DG; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com., Agnoletti BZ; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com., Araújo BQ; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com., Frinhani RQ; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com., Filgueiras PR; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com., Pereira LL; 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., Ribeiro de Castro EV; Federal University of Espírito Santo (UFES), Department of Chemistry, Campus Goiabeiras, Avenida Fernando Ferrari, 514, CEP 29075-910 Vitoria, Espírito Santo, Brazil. filgueiras.pr@gmail.com.
Jazyk: angličtina
Zdroj: Analytical methods : advancing methods and applications [Anal Methods] 2023 Jul 27; Vol. 15 (29), pp. 3499-3509. Date of Electronic Publication: 2023 Jul 27.
DOI: 10.1039/d3ay00510k
Abstrakt: Considering the great economic significance of Coffea arabica (arabica) associated with the lower production cost of C. canephora (conilon), blends of these coffees are commercially available to reduce costs and combine sensory attributes. Thus, analytical tools are required to ensure consistency between real and labeled compositions. In this sense, chromatographic methods based on volatile analysis using static headspace-gas chromatography-mass spectrometry (SHS-GC-MS) and Fourier transform infrared (FTIR) spectroscopy associated with chemometric tools were proposed for the identification and quantification of arabica and conilon blends. The peak integration from the total ion chromatogram (TIC) and extracted ion chromatogram (EIC) was compared in multivariate and univariate scenarios. The optimized partial least squares (PLS) models with uninformative variable elimination (UVE) and chromatographic data (TIC and EIC) have similar accuracy according to a randomized test, with prediction errors between 3.3% and 4.7% and R p 2 > 0.98. There was no difference between the univariate models for the TIC and EIC, but the FTIR model presented a lower performance than GC-MS. The multivariate and univariate models based on chromatographic data had similar accuracy. For the classification models, the FTIR, TIC, and EIC data presented accuracies from 96% to 100% and error rates from 0% to 5%. Multivariate and univariate analyses combined with chromatographic and spectroscopic data allow the investigation of coffee blends.
Databáze: MEDLINE
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