Food risk assessment plays an important role in protecting public health worldwide. Stakeholders involved in food risk assessment, such as national authorities, agencies, non‐governmental ...organisations (NGOs), industry and consumers, need to properly understand the terminology of food risk assessment effectively. In this respect, the first part of the EU‐FORA work programme (WP1) aimed to provide insights into the actual translation of two essential terms used in food risk assessment. ‘Hazard’ and ‘risk’ were first identified and compared between the English version of various food regulations and their equivalents in the national legislation of EU Member States. The comparison and critical evaluation revealed several inconsistencies. These inconsistencies could lead to misinterpretations, followed by errors in conducting risk assessments or communicating risks. We recommend that consistency is restored and maintained so that the message is properly communicated. The second part of the work programme (WP2) was focused on a specific area within chemical risk assessment (CRA). In this context, special attention was given to the impact of the food matrix on the bioaccessibility and bioavailability of heavy metals and metalloids. After collection and careful selection of data from scientific journals, a database with information on the bioaccessibility and bioavailability of cadmium (Cd), lead (Pb), mercury (Hg) and arsenic (As) in different food matrices was created for future statistical analyses related to dietary exposure.
Addressing the elusive specificity of cysteine cathepsins, which in contrast to caspases and trypsin-like proteases lack strict specificity determining P1 pocket, calls for innovative approaches. ...Proteomic analysis of cell lysates with human cathepsins K, V, B, L, S, and F identified 30,000 cleavage sites, which we analyzed by software platform SAPS-ESI (Statistical Approach to Peptidyl Substrate-Enzyme Specific Interactions). SAPS-ESI is used to generate clusters and training sets for support vector machine learning. Cleavage site predictions on the SARS-CoV-2 S protein, confirmed experimentally, expose the most probable first cut under physiological conditions and suggested furin-like behavior of cathepsins. Crystal structure analysis of representative peptides in complex with cathepsin V reveals rigid and flexible sites consistent with analysis of proteomics data by SAPS-ESI that correspond to positions with heterogeneous and homogeneous distribution of residues. Thereby support for design of selective cleavable linkers of drug conjugates and drug discovery studies is provided.
For risk communication, it is important to understand the difference between "hazard" and "risk". Definitions can be found in Codex Alimentarius and the European Union (EU) General Food Regulation ...(EC) No. 178/2002. The use of these terms as synonyms or their interchange is a recurrent issue in the area of food safety, despite awareness-raising messages sent by EFSA (European Food Safety Authority) and other interested entities. A quick screening of the EU's food regulations revealed several inconsistencies. Hence, it was considered necessary to further investigate if regulations could act as a source for this problem. A software tool was developed to support the detection and listing of inconsistent translations of "hazard" and "risk" in certain EU food regulations. Subsequently, native-speaking experts working in food safety from each EU country were asked to provide their individual scientific opinion on the prepared list. All data were statistically analysed after applying numerical scores (1-5) describing different levels of consistency. Results showed that the most common problem was the interchange of "hazard" with "risk" and vice versa. This lack of consistency can create confusion that can further translate into misjudgments at food risk assessment and communication levels.
Grinding with pearl mill capable of solid particles grinding in emulsions or greases from granulation of approximately 30–1
μm was studied on the basis of statistically planned experiments. The ...fractional factorial design for five factors was implemented. The data were used for modelling to develop back-propagation neural network and incomplete higher order polynoms. The obtained models were used for determination of the correlations among selected variables and for prediction of optimal values. Energy consumption and time were of our special interest and were directly dependent on the granualisation of the particles, i.e. smaller particles demand more energy and longer milling time. On the basis of the developed models and selected size of particles, the energy consumption and the time of milling could be predicted. The problems inherent in the modelling with mentioned models were discussed in detail.
Salt is an important ingredient in the production of meat product. Any reduction of salt requires a special treatment. This study was conducted to evaluate the effect of salt reduction on the growth ...of microorganisms in Kranjska sausages during their shell-life and to carry out a sensory assessment. The 18 lots of sausages were prepared under salt-reduced (1.6%) and control (2.3%) salt concentrations, directly on the production line. A total of 85 sausages were analysed and the data were used for the comparisons of groups (ANOVA) and to detect the significant variables (polynomial models) influenced on the total number of microorganisms (TNMs). The significant differences were determined between the lots (representing the microbiological status of the stuffing), between the salt-reduced samples and control samples, and between the different humidity levels. The correlations and significant relationships were determined between the TNMs and the lots, the salt concentrations, and the relative humidity. The polynomial models were to general to be used for the prediction. For sensory analysis implemented on 40
day 18 sausages were assessed. The reduction of salt resulted in lower scores in the sensory evaluation. The less-salted sausages contained more microorganisms.
Bacterial, fungal and archaeal microbiota was analysed in 143 chicken faecal samples from a single poultry farm. After DHPLC (denaturing high performance liquid chromatography) 15 bacterial groups, ...10 fungal groups and a single archaeal species were differentiated.
Samples were grouped into two clusters with significantly different frequencies of C. difficile positive and negative samples in each cluster. Acidaminococcus intestini, described here for the first time as a part of poultry faecal microbiota, was significantly more likely present in C. difficile negative samples, while presence/absence of some other microorganisms (Enterococcus cecorum, Lactobacillus galinarum, Moniliella sp. and Trichosporon asahii) was close to significance. Two other groups not reported previously for poultry, Coprobacillus sp. and Turicibacter sp. did not differ significantly between C. difficile positive and negative samples. Differences in microbiota diversity depend on animal age, but not on the presence of C. difficile. With machine learning (WEKA J48) we have defined specific combinations of microbial groups predictive for C. difficile colonisation. Microbial groups associated with C. difficile colonisation in poultry are different than those reported for humans and include bacteria as well as fungi. Also with this approach A. intestini was found to be most strongly related to C. difficile negative samples.
The influence of different ratios of individual components (silicone surfactant, hydrophilic and lipophilic phase) on the viscoelastic behaviour of semisolid lipophilic emulsion systems was studied. ...The creams were prepared according to a preliminary experimental design (mixture design). The content of all three phases was varied: surfactant (1–5%), purified water (from 40 to 90%) and white petrolatum (5–59%). Oscillatory rheometry was used as the most appropriate experimental method for the evaluation of the emulsions. The rheological properties were influenced by the ratio of the components. For highly concentrated systems the predominant elastic response in the whole frequency range was measured. The cross-over point is characteristic for the concentrated systems. For the low concentrated systems, viscous behaviour is predominant. Rheometry has also been employed to follow and evaluate physical stability as one of the critical factors of emulsion systems. The dynamic rheological parameter, tan
δ, has been chosen as the basis for developing mathematical models (linear, polynomial, neural) to forecast stability related to the content of the separate components in emulsion systems. After testing the linear models their non agreement to statistical criteria for a good model
F
reg,
F
lof, CC, DC, and RMS was found. The two-level neural network has been proven to be a statistically acceptable model, as were the polynomials of the second order. The two-level neural network model was also evaluated and the results have shown a great degree of reliability. The prediction of tan
δ using a neural network model was found to be of great interest for the contemporary pharmaceutical formulation design because a lot of additional testing can be omitted.
Previously published data (
Gašperlin et al., 1998) on viscoelastic behaviour of lipophilic semisolid emulsion systems and the prediction of their physical stability by neural network modelling are ...analysed in further detail. Most attention has been paid to viscosity, which with storage (G′) and loss modulus (G′′), is one of the most important rheological parameters influenced by structure. Complex dynamic viscosity (
η*) was measured by oscillatory rheometry. The viscosity dependence of the lipophilic semisolid emulsions on the ratio of the particular components was defined by the neural network (error back-propagation algorithm), linear and incomplete polynomial models of higher orders. Polynomial models were used to complement the neural network model and to determine the relationship between variables. Since the viscosity was expressed in the whole measured frequency range, modelling was more complex and indirect modelling was introduced. The determined models were tested and the results confirm their usefulness for the explanation and prediction of the rheological characteristics of emulsion systems. The trained and tested neural network model proved to be a highly effective and applicable tool for predicting the viscosity of a lipophilic semisolid emulsion system of given composition.
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