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The world market for food supplements is large and is driven by the claims of these products to, for example, treat obesity, increase focus and alertness, decrease appetite, decrease the need for sleep or reduce impulsivity. The use of illegal compounds in food supplements is a continuous threat, certainly because these compounds and products have not been tested for safety by competent authorities. It is therefore of the utmost importance for the competent authorities to know when new products are being marketed and to warn users against potential health risks. In this study, an approach is presented to detect new and unknown stimulants in food supplements using machine learning. More than 20 new stimulants were identified from two different data sources, namely scientific literature applying word embedding on > 2 million abstracts and articles from formal and social media on the world wide web using text mining. The results show that the developed approach may be suitable to detect “unknowns” in the emerging risk identification activities performed by the competent authorities, which is currently a major hurdle.

A fast screening approach for simultaneous detection of twelve pesticides and seven mycotoxins in food and feed samples using flow injection (FI) mass spectrometry (MS) was developed and evaluated. Ten selected pesticides represented five pairs of structural isomers to challenge their MS differentiation without chromatographic separation. Samples of cereals/grains and animal feed were prepared using QuEChERS extraction, diluted and fortified with the selected contaminants to yield their representative maximum levels (MLs) or maximum residue levels (MRLs). Sensitivity of the developed approach was assessed for qualitative screening of the selected contaminants using triple quadrupole MS/MS and ion mobility (IM) HR-TOFMS. Despite the high complexity of the selected matrices and the absence of chromatography, limits of detection in fortified extracts were below established regulatory levels for 14 and 15 selected contaminants by FI-MS/MS and FI-IM-TOFMS analysis, respectively, in 36–100% samples. Experiments with ion mobility TOFMS did not result in successful separation of pesticide structural isomers. However, for three out of five pairs of pesticide structural isomers: cyproconazole and uniconazole, methiocarb and ethiofencarb, and vernolate and pebulate, unique fragment ions were found and confirmed with HR-QTOFMS for their MS differentiation. The developed approach is sensitive enough for rapid multi-residue screening of some pesticides and mycotoxins in complex samples of food and feed, providing high speed and throughput, simplicity and effective use of high-end instrumentation.

A survey on Alternaria toxins in the food categories dried figs (n = 14), sunflower products (n = 24) and tomato products (n = 43) was carried out in the Netherlands on samples collected in retail stores in autumn 2014. The occurrence data from this survey confirmed the previously reported data of a pilot survey in 2013, with tenuazonic acid being an ubiquitous Alternaria toxin in dried figs (100% of the samples), sunflower seeds (80% of the samples) and tomato products (60% of samples) at relatively high concentrations, up to 1728 μg/kg in dried figs and 1350 μg/kg in sunflower seeds. Despite the occurring of high concentrations of tenuazonic acid in sunflower seeds and figs, it is unlikely that the population in the Netherlands is exposed to levels of concern. Alternariol was detected in 27% of the tomato products ranging from LOQ (2 μg/kg) up to 26 μg/kg, while alternariol monomethyl ether was present in 7% of the tomato products. In the worst case situation, consumption of these products may result in exposure at levels above the threshold of toxicological concern of 2.5 ng/kg body weight/day for alternariol. None of seven conjugates of Alternaria toxins was detected above the limit of quantification (LOQ) of 2.5 μg/kg.