Introduction In the last years, consumers increased the demand for high-quality and healthy beverages, including coffee. To date, among the techniques potentially available to determine the overall quality of coffee beverages, metabolomics is emerging as a valuable tool. Objective In this study, 47 ground coffee samples were selected during the 2018 Edition of the “International coffee tasting” (ICT) in order to provide discrimination based on both chemical and sensory profiles. In particular, 20 samples received a gold medal (“high quality” group), while lower sensory scores characterized 27 samples (without medal). Methods Untargeted metabolomics based on ultra-high pressure liquid chromatography coupled with quadrupole-time-of-flight (UHPLC-QTOF) and head space-gas chromatography coupled with mass spectrometry platforms followed by multivariate statistical approaches (i.e., both supervised and unsupervised) were used to provide new insight into the searching of potential markers of sensory quality. Results Several compounds were identified, including polyphenols, alkaloids, diazines, and Maillard reaction products. Also, the headspace/GC-MS highlighted the most important volatile compounds. Polyphenols were scarcely correlated to the sensory parameters, whilst the OPLS-DA models built using typical coffee metabolites and volatile/Maillard compounds possessed prediction values > 0.7. The “high quality” group showed specific metabolomic signatures, thus corroborating the results from the sensory analysis. Overall, methyl pentanoate (ROC value = 0.78), 2-furfurylthiol (ROC value = 0.75), and L-Homoserine (ROC value = 0.74) established the higher number of significant (p < 0.05) correlations with the sensory parameters. Conclusion Although ad-hoc studies are advisable to further confirm the proposed markers, this study demonstrates the suitability of untargeted metabolomics for evaluating coffee quality and the potential correlations with the sensory attributes. Graphic abstract
Increasingly sophisticated instrumentation for chemical separations and identification has facilitated rapid advancements in our understanding of the metabolome. Since many analyses are performed using either mass spectroscopy (MS) or nuclear magnetic resonance (NMR) spectroscopy, the spin 1/2 stable C-13 isotope is now widely used as a metabolic tracer. There is a strong interest in quantitative analysis of metabolic flux through pathways in vivo, particularly in human patients. Although instrumentation advances and scientific interests in metabolism are increasing in parallel, a practical and rational design of a C-13 tracer study can be challenging. Prior to planning the details of a tracer experiment, is it important to consider whether the analytical results will be sensitive to flux through the pathways of interest. Here, we briefly summarize the various approaches that have been used to design carbon tracer experiments, outline the sources of complexity, and illustrate the use of a software tool, tcaSIM, to aid in the experimental design of both MS and NMR data in complex systems including patients.
