| Title: | "Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GCxGC-TOFMS, Chemometrics, and Machine Learning" |
| Author(s): | Zou Y; Gaida M; Franchina FA; Stefanuto PH; Focant JF; |
| Address: | "Organic and Biological Analytical Chemistry Group, MolSys Research Unit, University of Liege, 4000 Liege, Belgium. Department of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, 44121 Ferrara, Italy" |
| DOI: | 10.3390/molecules27061806 |
| ISSN/ISBN: | 1420-3049 (Electronic) 1420-3049 (Linking) |
| Abstract: | "Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GCxGC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples" |
| Keywords: | Caffeine Chemometrics *Coffee Machine Learning *Solid Phase Microextraction Pca Pls-da aroma profile coffee decaffeination random forest solid-phase microexaction t-test time-of-flight mass spectrometry two-dimensional gas chromatography; |
| Notes: | "MedlineZou, Yun Gaida, Meriem Franchina, Flavio A Stefanuto, Pierre-Hugues Focant, Jean-Francois eng 30897864/FWO/FNRS Belgium EOS Grant/ Switzerland 2022/03/27 Molecules. 2022 Mar 10; 27(6):1806. doi: 10.3390/molecules27061806" |
