Coffee is one of the most popular beverages in the world. The high production and health properties of coffee make it one of the best among daily drinks. Coffee is wrongly identified as only a stimulant because of its caffeine content. On the other hand, coffee is one of the best sources of other bioactive compounds, such as flavonoids and phenolic acids. Organic coffee is produced without artificial fertilizers and pesticides. Not only the high quality of beans but also roasting and brewing times guarantee the best taste and quality of coffee beverages. The aim of the present experiment was to determine the best level of roasting and brewing time for organic and conventional coffee. The experiment was carried out with Peru coffee beans from organic and conventional farms. The contents of caffeine and bioactive compounds were measured in different roasted and brewed coffee drinks. The obtained results showed that the conventional coffee contained significantly more caffeine, total flavonoids, and quercetin derivatives than the organic coffee. On the other hand, the organic coffee was characterized by a higher level of almost all bioactive compounds. The best level of roasting was determined to be medium, and the optimal brewing time was 3 minutes.

The rapidly growing demand for organic food requires the availability of analytical tools enabling their authentication. Recently, metabolomic fingerprinting/profiling has been demonstrated as a challenging option for a comprehensive characterisation of small molecules occurring in plants, since their pattern may reflect the impact of various external factors. In a two-year pilot study, concerned with the classification of organic versus conventional crops, ambient mass spectrometry consisting of a direct analysis in real time (DART) ion source and a time-of-flight mass spectrometer (TOFMS) was employed. This novel methodology was tested on 40 tomato and 24 pepper samples grown under specified conditions. To calculate statistical models, the obtained data (mass spectra) were processed by the principal component analysis (PCA) followed by linear discriminant analysis (LDA). The results from the positive ionisation mode enabled better differentiation between organic and conventional samples than the results from the negative mode. In this case, the recognition ability obtained by LDA was 97.5% for tomato and 100% for pepper samples and the prediction abilities were above 80% for both sample sets. The results suggest that the year of production had stronger influence on the metabolomic fingerprints compared with the type of farming (organic versus conventional). In any case, DART-TOFMS is a promising tool for rapid screening of samples. Establishing comprehensive (multi-sample) long-term databases may further help to improve the quality of statistical classification models.