SHS-GC-MS applied in and blend assessment

• Published in: Analytical methods 2023-07, Vol.15 (29), p.3499-359
• Main Authors: Vieira Lyrio, Marcos Valério, Pereira da Cunha, Pedro Henrique, Debona, Danieli Grancieri, Agnoletti, Bárbara Zani, Araújo, Bruno Quirino, Frinhani, Roberta Quintino, Filgueiras, Paulo Roberto, Pereira, Lucas Louzada, Ribeiro de Castro, Eustáquio Vinicius
• Format: Article
• ISSN: 1759-9660; 1759-9679
• DOI: 10.1039/d3ay00510k

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. The present study proposes a new approach for blend assessment based on the volatile composition extracted and analyzed by SHS-GC-MS associated with multivariate and univariate methods for a qualitative (identification) and quantitative evaluation.