Benchtop Volatilomics and Machine Learning for the Discrimination of Coffee Species

The main characteristics of the large number of coffee species are differences in aroma and caffeine content. Labeled blends of <i>Coffea arabica</i> (<i>C. arabica</i>) and <i>Coffea canephora</i> (<i>C. canephora</i>) are common to broaden the flavor profile or enhance the stimulating effect of the beverage. New emerging species such as <i>Coffea liberica</i> (<i>C. liberica</i>) further increase the variability in blends. However, significant price differences between coffee species increase the risk of unlabeled blends and thus influence food quality and safety for consumers. In this study, a prototypic hyphenation of trapped headspace-gas chromatography-ion mobility spectrometry-quadrupole mass spectrometry (THS-GC-IMS-QMS) was used for the detection of characteristic compounds of <i>C. arabica</i>, <i>C. canephora</i>, and <i>C. liberica</i> in green and roasted coffee samples. For the discrimination of coffee species with IMS data, multivariate resolution with multivariate curve resolution–alternating least squares (MCR-ALS) prior to partial least squares–discriminant analysis (PLS-DA) was evaluated. With this approach, the classification accuracy, as well as sensitivity and specificity, of the PLS-DA model was significantly improved from an overall accuracy of 87% without prior feature selection to 92%. As MCR-ALS preserves the physical and chemical properties of the original data, characteristic features were determined for subsequent substance identification. The simultaneously generated QMS data allowed for partial annotation of the characteristic volatile organic compounds (VOC) of roasted coffee.