SHS-GC-MS applied in <i>Coffea arabica</i> and <i>Coffea canephora</i> blend assessment
Marcos Valério Vieira Lyrio, Pedro Henrique Pereira da Cunha, Danieli Grancieri Debona, Bárbara Zani Agnoletti, Bruno Q. Araújo, Roberta Quintino Frinhani, Paulo R. Filgueiras, Lucas Louzada Pereira, Eustáquio Vinícius Ribeiro de Castro
Abstract
> 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.
Topics & Concepts
Coffea canephoraCoffea arabicaUnivariateGas chromatography–mass spectrometryCoffeaIdentification (biology)Multivariate statisticsChemistryChromatographyMathematicsHorticultureBotanyMass spectrometryBiologyStatisticsMeat and Animal Product QualityCoffee research and impactsSpectroscopy and Chemometric Analyses