While the relevance of Bacillus (B.) cereus as a major cause of gastroenteritis is undisputed, the role of the closely related B. thuringiensis in foodborne disease is unclear. B. thuringiensis strains frequently harbor enterotoxin genes. However, the organism has only very rarely been associated with foodborne outbreaks, possibly due to the fact that during outbreak investigations, B. cereus is routinely not differentiated from B. thuringiensis. A recent EFSA scientific opinion stresses the urgent need for further data allowing for improved risk assessment, in particular as B. thuringiensis is a commonly used biopesticide. Therefore, the aim of this study was to gain further insights into the hazardous potential of B. thuringiensis. To this end, 39 B. thuringiensis isolates obtained from commercially used biopesticides, various food sources, as well as from foodborne outbreaks were characterized by panC typing, panC-based SplitsTree analysis, toxin gene profiling, FTIR spectroscopic analysis, a cytotoxicity assay screening for enterotoxic activity, and a sphingomyelinase assay. The majority of the tested B. thuringiensis isolates exhibited low (23%, n = 9) or mid level enterotoxicity (74%, n = 29), and produced either no (59%, n = 23) or low levels (33%, n = 13) of sphingomyelinase, which is reported to act synergistically with enterotoxins Nhe and Hbl. One strain isolated from rosemary was however classified as highly enterotoxic surpassing the cytotoxic activity of the high-level reference strain by a factor of 1.5. This strain also produced vast amounts of sphingomyelinase. Combining all results obtained in this study into a fingerprint pattern, several enterotoxic biopesticide strains were indistinguishable from those of isolates from foods or collected in association with outbreaks. Our study shows that many B. thuringiensis biopesticide strains exhibit mid-level cytotoxicity in a Vero cell assay and that some of these strains cannot be differentiated from isolates collected from foods or in association with outbreaks. Thus, we demonstrate that the use of B. thuringiensis strains as biopesticides can represent a food safety risk, underpinning the importance of assessing the hazardous potential of each strain and formulation used.

The Bacillus cereus group comprises genetical closely related species with variable toxigenic characteristics. However, detection and differentiation of the B. cereus group species in routine diagnostics can be difficult, expensive and laborious since current species designation is linked to specific phenotypic characteristic or the presence of species-specific genes. Especially the differentiation of Bacillus cereus and Bacillus thuringiensis, the identification of psychrotolerant Bacillus mycoides and Bacillus weihenstephanensis, as well as the identification of emetic B. cereus and Bacillus cytotoxicus, which are both producing highly potent toxins, is of high importance in food microbiology. Thus, we investigated the use of a machine learning approach, based on artificial neural network (ANN) assisted Fourier transform infrared (FTIR) spectroscopy, for discrimination of B. cereus group members. The deep learning tool box of Matlab was employed to construct a one-level ANN, allowing the discrimination of the aforementioned B. cereus group members. This model resulted in 100% correct identification for the training set and 99.5% correct identification overall. The established ANN was applied to investigate the composition of B. cereus group members in soil, as a natural habitat of B. cereus, and in food samples originating from foodborne outbreaks. These analyses revealed a high complexity of B. cereus group populations, not only in soil samples but also in the samples from the foodborne outbreaks, highlighting the importance of taking multiple isolates from samples implicated in food poisonings. Notable, in contrast to the soil samples, no bacteria belonging to the psychrotolerant B. cereus group members were detected in the food samples linked to foodborne outbreaks, while the overall abundancy of B. thuringiensis did not significantly differ between the sample categories. None of the isolates was classified as B. cytotoxicus, fostering the hypothesis that the latter species is linked to very specific ecological niches. Overall, our work shows that machine learning assisted (FTIR) spectroscopy is suitable for identification of B. cereus group members in routine diagnostics and outbreak investigations. In addition, it is a promising tool to explore the natural habitats of B. cereus group, such as soil.

Analysis of pollen grains reveals valuable information on biology, ecology, forensics, climate change, insect migration, food sources and aeroallergens. Vibrational (infrared and Raman) spectroscopies offer chemical characterization of pollen via identifiable spectral features without any sample pretreatment. We have compared the level of chemical information that can be obtained by different multiscale vibrational spectroscopic techniques. Pollen from 15 different species of Pinales (conifers) were measured by seven infrared and Raman methodologies. In order to obtain infrared spectra, both reflectance and transmission measurements were performed on ground and intact pollen grains (bulk measurements), in addition, infrared spectra were obtained by microspectroscopy of multigrain and single pollen grain measurements. For Raman microspectroscopy measurements, spectra were obtained from the same pollen grains by focusing two different substructures of pollen grain. The spectral data from the seven methodologies were integrated into one data model by the Consensus Principal Component Analysis, in order to obtain the relations between the molecular signatures traced by different techniques. The vibrational spectroscopy enabled biochemical characterization of pollen and detection of phylogenetic variation. The spectral differences were clearly connected to specific chemical constituents, such as lipids, carbohydrates, carotenoids and sporopollenins. The extensive differences between pollen of Cedrus and the rest of Pinaceae family were unambiguously connected with molecular composition of sporopollenins in pollen grain wall, while pollen of Picea has apparently higher concentration of carotenoids than the rest of the family. It is shown that vibrational methodologies have great potential for systematic collection of data on ecosystems and that the obtained phylogenetic variation can be well explained by the biochemical composition of pollen. Out of the seven tested methodologies, the best taxonomical differentiation of pollen was obtained by infrared measurements on bulk samples, as well as by Raman microspectroscopy measurements of the corpus region of the pollen grain. Raman microspectroscopy measurements indicate that measurement area, as well as the depth of focus, can have crucial influence on the obtained data.

Main conclusion Chemical imaging of pollen by vibrational microspectroscopy enables characterization of pollen ultrastructure, in particular phenylpropanoid components in grain wall for comparative study of extant and extinct plant species. A detailed characterization of conifer (Pinales) pollen by vibrational microspectroscopy is presented. The main problems that arise during vibrational measurements were scatter and saturation issues in Fourier transform infrared (FTIR), and fluorescence and penetration depth issues in Raman. Single pollen grains larger than approx. 15 µm can be measured by FTIR microspectroscopy using conventional light sources, while smaller grains may be measured by employing synchrotron light sources. Pollen grains that were larger than 50 µm were too thick for FTIR imaging since the grain constituents absorbed almost all infrared light. Chemical images of pollen were obtained on sectioned samples, unveiling the distribution and concentration of proteins, carbohydrates, sporopollenins and lipids within pollen substructures. The comparative analysis of pollen species revealed that, compared with other Pinales pollens, Cedrus atlantica has a higher relative amount of lipid nutrients, as well as different chemical composition of grain wall sporopollenin. The pre-processing and data analysis, namely extended multiplicative signal correction and principal component analysis, offer simple estimate of imaging spectral data and indirect estimation of physical properties of pollen. The vibrational microspectroscopy study demonstrates that detailed chemical characterization of pollen can be obtained by measurement of an individual grain and pollen ultrastructure. Measurement of phenylpropanoid components in pollen grain wall could be used, not only for the reconstruction of past environments, but for assessment of diversity of plant species as well. Therefore, analysis of extant and extinct pollen species by vibrational spectroscopies is suggested as a valuable tool in biology, ecology and palaeosciences.