Characterisation of friction behaviour of intact soft solid foods and food boli

Methodologies to quantify friction forces between soft solid foods or food boli and (model) oral surfaces are desired to better understand how changes in food properties during oral processing affect sensory perception. In this short communication, friction forces (FF) occurring between polydimethylsiloxane (PDMS) surfaces and intact soft solid foods/boli were quantified. As models for intact foods, we used gelatine gels varying in composition and particle size, and sausages were used as an example for real foods. Friction forces measured during the relative motion of intact foods against a rough PDMS surface (“oral surface”), strongly depended on the composition of the food. Friction forces were significantly lower for PDMS against emulsion-filled gels, than for PDMS against unfilled gels, likely due to the lubricating effect of released oil from the gel. Moreover, sausages, displayed significantly higher friction forces than gelatine gels when moving against the PDMS probe, presumably linked to differences in the surface of the foods. The friction forces observed for the PDMS probe moving against food boli were dependent on particle size and saliva quantity; boli with larger particle sizes showed significantly lower friction forces, whereas the addition of saliva to food boli first increased friction forces, but with increasing amount decreased the friction forces significantly. We conclude that the presented methodology is able to quantify the friction behaviour of intact soft solid foods and boli directly, taking into account (i) the effect of composition and added fillers, (ii) serum or oil release and (iii) bolus particle size.</p

PEtab -- interoperable specification of parameter estimation problems in systems biology

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been -- so far -- no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies. Specifications of PEtab, the PEtab Python library, as well as links to examples, and all supporting software tools are available at https://github.com/PEtab-dev/PEtab, a snapshot is available at https://doi.org/10.5281/zenodo.3732958. All original content is available under permissive licenses

Clustering of oil droplets in foods : Implications on rheology, tribology and sensory perception

Many foods with a high palatability include significant quantities of fat. Fat influences next to the sensory perception and palatability also the microstructure of foods. It is thus challenging to reduce the fat content in many food items without negative impact on texture or perception. Fats and oils are mostly found as droplets in both solid and liquid matrices. Those droplets can be distributed homogeneously or inhomogeneously throughout a system. Previous research has shown that inhomogeneity (clustering) in the distribution of fat, in model systems, changes the fat related sensory perception and changes the mechanical properties. However, so far, the processes to obtain an inhomogeneous (clustered) fat distribution neither allow control over the process nor incorporation into foods. Therefore, this research aimed to understand the influence of fat phase inhomogeneity through controlled clustering of oil droplets on physical-chemical properties (rheology, tribology, cluster morphology), in-mouth behaviour and sensory perception of liquid o/w emulsions, emulsion gels and emulsion-filled gels. The findings of this research can help to control the microstructure of foods to design the physicochemical and sensory properties of foods. Further, the insights might help in reducing the fat content of foods without negative impact on mechanical properties and sensory perception.&nbsp; Key conclusions/findings Oil droplet cluster size and interaction strength (within oil droplet clusters) can be controlled in liquid o/w emulsions, and semi-solid emulsion filled gels. Two methodologies provide good control over the size and interaction strength: hetero-aggregation and clustering with cross-linkers. For hetero-aggregation, the cluster size is controlled by the volume ratio of charged droplets and the strength by the charge of the emulsifier. For chemical clustering, the extent of clustering depends on cross-linker content. With regards to oil droplet inhomogeneity in gels, this process was controlled for overall strongly bound clusters (chemically and strong electrostatic interactions). An alternative approach is the incorporation of oil-rich or oil depleted filler particles into a gel matrix, introducing an inhomogeneity on a larger length scale. Inhomogeneity strongly affects rheological/mechanical properties and sensory perception of fat-related attributes. Clustering of oil droplets in o/w emulsions significantly increases viscosity due to entrapment of water in the oil droplet cluster leading to an increase in effective volume fraction and improved lubrication, allowing to enhance the perception of fat-related attributes. Emulsions with inhomogeneous oil droplet distribution were shown to be perceived as more thick and creamy. In gels, inhomogeneity mainly affects the modulus but can make gels also more brittle. If strong cluster interactions are present, graininess perception is enhanced. The effects on sensory perception in gels are less distinct. When taking increases in gel stiffness and brittleness into consideration, clustering in gels can be used to reduce the overall fat content.&nbsp; Tribological characterisations of fat containing foods correlate to their sensory properties but depend on the presence of saliva When taking interactions between emulsions and saliva into account, tribological parameters correlate stronger with fat-related perception, then without saliva. We developed a new tribological setup to measure the friction properties of intact foods and boli samples. Material properties of emulsion gels can be directed by designing droplet-droplet interactions The rheological properties at small and large deformations of emulsion gels can be designed to vary between soft glassy and gel-like materials, by increasing the interaction strength between oil droplets. Overall, we conclude that by changing the oil droplet distribution in foods and the droplet-droplet interactions, the microstructure, physicochemical and sensory properties can be changed controllably

Influence of clustering of protein-stabilised oil droplets with proanthocyanidins on mechanical, tribological and sensory properties of o/w emulsions and emulsion-filled gels

This study aimed to determine the effect of clustering of protein-stabilised oil droplets with proanthocyanidins on mechanical, tribological and sensory properties of o/w emulsions and emulsion-filled gels. Whey protein-stabilised oil droplets in o/w emulsions were crosslinked with proanthocyanidins, which led to the controlled formation of dense clusters of strongly-interacting oil droplets, in a size range from 2 to 110 μm. With increasing degree of clustering of oil droplets, the viscosity of o/w emulsions increased by up to three orders of magnitude. Clustering of oil droplets decreased friction coefficients. Clustering led to an increase in perceived creaminess, coating and thickness intensity. The changes in fat-related sensory perception were an interplay of both flow- and friction behaviour. In emulsion-filled gelatine gels, crosslinking of oil droplets increased Young's modulus and decreased fracture strain and stress. With increasing cluster size, gels were perceived as harder and more grainy than emulsion-filled gels with non-clustered oil droplets. Creaminess of emulsion-filled gels did not increase upon clustering, as hardness also increased. When Young's modulus and perceived hardness of the gels were matched, gels containing clustered oil droplets tended to be perceived more creamy (not significant, p = 0.07) and significantly less watery than gels with non-clustered oil droplets. We relate these effects to the role of the emulsion droplets as structuring agents and an increase of the effective volume fraction by clustering of oil droplets. We conclude that clustering of protein-stabilised oil droplets with proanthocyanidins in o/w emulsions and emulsion-filled gels can be used to modify flow- and texture properties with positive effects on perception of fat-related sensory attributes.</p

Clustering of oil droplets in o/w emulsions: Controlling cluster size and interaction strength

Clustering of oil droplets changes the rheological properties of oil-in-water (o/w) emulsions and can be used as a tool to structure foods. The aim of this study was to manipulate both oil droplet cluster size and cluster strength in liquid o/w emulsions, and to investigate the effect of these parameters on the rheological properties. Clustered emulsions were prepared using three different methods: (i) clustering by protein-proanthocyanidin interactions, (ii) clustering by hetero-aggregation of oppositely-charged emulsion droplets, and (iii) enzymatic clustering of protein-stabilised droplets using transglutaminase. Clustering by protein-proanthocyanidin interactions allowed to control oil droplet cluster size from 1 to 140 μm. Clusters decreased in size upon both an increase and decrease in pH, but were stable against changes in ionic strength. Hetero-aggregation of oppositely-charged oil droplets (gelatine/whey protein and gelatine/DATEM) allowed to control cluster size from 1 to 40 μm. Clusters showed a strong decrease in size in response to changes in pH and a small decrease in size with increasing ionic strength. Enzymatic clustering did not allow to control cluster size. Cluster strength of proanthocyanidin-stabilised clusters was found to be higher than that of hetero-aggregated clusters. Stabilisation of clusters was likely induced by different protein-proanthocyanidin interactions such as H-bridges, π-π stacking, and hydrophobic interactions, whereas hetero-aggregation is based on electrostatic interactions. Upon clustering, emulsion viscosity increased by up to three orders of magnitude. We conclude that protein-proanthocyanidin interactions and hetero-aggregation are effective methods to tune droplet cluster size and strength in o/w emulsions, and that cluster size and interaction strength control the rheological properties of o/w emulsions with clustered oil droplets.</p

Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland

Background: In December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant was estimated to have a transmission fitness advantage of around 40–80 % (Volz et al., 2021; Leung et al., 2021; Davies et al., 2021). Aim: This study aims to estimate the transmission fitness advantage and the effective reproductive number of B.1.1.7 through time based on data from Switzerland. Methods: We generated whole genome sequences from 11.8 % of all confirmed SARS-CoV-2 cases in Switzerland between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the B.1.1.7 variant and quantify the variant's transmission fitness advantage on a national and a regional scale. Results: We estimate B.1.1.7 had a transmission fitness advantage of 43–52 % compared to the other variants circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants. Specifically, we estimate the reproductive number for B.1.1.7 was 1.24 [1.07–1.41] from 01 January until 17 January 2021 and 1.18 [1.06–1.30] from 18 January until 01 March 2021 based on the whole genome sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive number was 1.14 [1.00–1.26] based on all confirmed cases. For reference, Switzerland applied more non-pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on 01 March 2021. Conclusion: The observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated doubling time of around 2–3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.ISSN:1878-0067ISSN:1755-436