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Characterisation of coffees according to their origins is of utmost importance for commercial qualification. In this study, the aroma profiles of different batches of three monoorigin roasted Coffea arabica coffees (Brazil, Ethiopia and Guatemala) were analysed by Proton-Transfer-Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS). The measurements were performed with the aid of a multipurpose autosampler. Unsupervised and supervised multivariate data analysis techniques were applied in order to visualise data and classify the coffees according to origin. Significant differences were found in volatile profiles of coffees. Principal component analysis allowed visualising a separation of the three coffees according to geographic origin and further partial least square regression-discriminant analysis classification showed completely correct predictions. Remarkably, the samples of one batch could be used as training set to predict geographic origin of the samples of the other batch, suggesting the possibility to predict further batches in coffee production by means of the same approach. Tentative identification of mass peaks aided characterisation of aroma fractions. Classification pinpointed some volatile compounds important for discrimination of coffees.

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Proton Transfer Reaction (PTR), combined with a Time-of-Flight (ToF) Mass Spectrometer (MS) is an analytical approach based on chemical ionization that belongs to the Direct-Injection Mass Spectrometric (DIMS) technologies. These techniques allow the rapid determination of volatile organic compounds (VOCs), assuring high sensitivity and accuracy. In general, PTR-MS requires neither sample preparation nor sample destruction, allowing real time and non-invasive analysis of samples. PTR-MS are exploited in many fields, from environmental and atmospheric chemistry to medical and biological sciences. More recently, we developed a methodology based on coupling PTR-ToF-MS with an automated sampler and tailored data analysis tools, to increase the degree of automation and, consequently, to enhance the potential of the technique. This approach allowed us to monitor bioprocesses (e.g. enzymatic oxidation, alcoholic fermentation), to screen large sample sets (e.g. different origins, entire germoplasms) and to analyze several experimental modes (e.g. different concentrations of a given ingredient, different intensities of a specific technological parameter) in terms of VOC content. Here, we report the experimental protocols exemplifying different possible applications of our methodology: i.e. the detection of VOCs released during lactic acid fermentation of yogurt (on-line bioprocess monitoring), the monitoring of VOCs associated with different apple cultivars (large-scale screening), and the in vivo study of retronasal VOC release during coffee drinking (nosespace analysis).

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The aim of this study was to assess the effects of seasonal variation on the changes of the fatty acid (FA) and triacylglycerol (TAG) composition of bovine milk fat (MF) in a nonseasonal milking system. Weekly milk samples were collected from 14 dairy factories and pooled per week as representative samples of the average Dutch bovine milk. The sample collection started in May 2017 and finished in April 2018, resulting in a total of 52 samples, corresponding to each week of the year. The samples were analyzed for MF content (%) and FA and TAG composition using gas chromatography with flame-ionization detection. The increased intake of C18:3 cis-9,12,15 through grass feeding in spring and summer was associated with major changes in MF FA composition, including reduced proportions of de novo synthesized FA and presence of several rumen biohydrogenation products and conjugated linoleic acid isomers in MF. These changes in seasonal FA composition had an effect on TAG seasonal variation. The TAG seasonal variation showed that all TAG groups were significantly different between months. The low molecular weight and the medium molecular weight TAG groups increased in winter and decreased in summer, whereas the high molecular weight TAG groups increased in summer and decreased in winter. Based on pooled monthly samples, MALDI-TOF-mass spectrometry allowed the analysis of even-and oddchain TAG species in MF based on their total carbon number and number of double bonds. These analyses indicated saturated TAG species to be greatest in winter, whereas monounsaturated, polyunsaturated, and odd-chain TAG species were greatest in summer. Our study showed that TAG seasonal variation in a nonsea-sonal milking system is influenced by the variation in FA composition throughout the seasons.