Unilever Foods Innovation Centre

• Consumers were segmented on their WTP for animal- versus plant-based products • Across meat and dairy categories, two WTP-based clusters consistently emerged • Pro animal-based cluster was smaller and reported wider price premiums • Pro plant-based cluster was larger and reported narrower price premiums • Clusters differed on psychographics including behavior change stage A gradual shift from animal-based consumption toward more plant-based diets is crucial for public and planetary health. However, one practical barrier for this protein transition includes the (perceived) financial accessibility of plant-based alternatives. We further explore this tension by examining whether consumers in a large multi-country sample can be reliably distinguished based on their relative willingness to pay for animal- versus plant-based products (WTP Δ ) across meat and dairy categories. We subsequently profiled emergent segments on psychographic characteristics, including their readiness to reduce animal-based consumption (i.e., behavior change stage). Participants from the USA (N = 1614), the UK (N = 1664), and the Netherlands (N = 1967) completed an online survey that measured their WTP for animal- and plant-based variants of diverse meat and dairy products. Segments were identified using a k -means algorithm on standardized WTP Δ scores across countries. Consistently among product categories, two clusters emerged: a pro animal-based cluster and a pro plant-based cluster. Pro animal-based consumers (25.2% for meat and 19.8% for dairy) exhibited a higher relative valuation of animal-based products (M WTPΔMeat = 1.35; M WTPΔDairy = 1.64), while pro plant-based consumers (74.8% for meat and 80.2% for dairy) reported a higher WTP for plant-based alternatives (M WTPΔMeat = -0.46; M WTPΔDairy = -0.41). For both categories, the pro animal-based cluster expressed a lower behavior change stage, lower intention to consume plant-based alternatives, greater perceived barriers for reducing animal-based intake, and more older and rurally-situated consumers. Our findings allude to the potential utility of tailoring behavioral interventions to emergent cluster profiles.

BackgroundThe complex ecosystem on skin comprising tens of thousands of microorganisms plays an important role in health and disease. The last decade in particular has witnessed a surge in microbiome research, which has been elucidating the role of the microbiota in numerous skin pathologies. Of relevance to the current study, are recent evidences implicating the microbiome in skin pigmentary conditions. Melasma is one such refractory, hyperpigmentary condition with a poorly understood pathogenesis. The present study was carried out to characterize the nature of microbial dysbiosis and its impact on microbial community structure in melasma subjects.ResultsThe clinical assessment of melasma carried out using biophysical, biochemical and biomarker-based measures confirmed significant changes in melasma lesions, most notably, those linked to redox, inflammation and barrier properties. A deep characterization of the skin microbiome in melasma from human face, indicated significant differences between lesional and peri-lesional areas. Of the 377 genera identified through an agglomeration of all OTUs at the Genus level through 16S rRNA sequencing, 344 were common, while 12 were unique to lesional and 21 unique in peri-lesional areas. A significant decrease was observed in alpha diversity in melasma lesion as compared to peri-lesion areas, with an accompanying decrease in number of interconnections among them. The differences in the microbiome also appeared to correlate with several clinical parameters, notably with the melasma severity measured through modified MASI (mMASI) scoring. The observed changes in both host and microbiome, point to a potential role for the latter in melasma pathogenesis.ConclusionsOur study indicates that there are significant differences in the microbiome between lesional and peri-lesional areas of melasma subjects, with associated changes in microbial community structures. Additionally, the observed changes were seen to correlate with measured clinical parameters. These findings provide the opportunity to further probe the nature of host and microbiome links that may underlie the phenotypic manifestation, as well as provide effective routes for managing this recalcitrant disorder.

The combination of near-infrared spectroscopic techniques (HSI or hyperspectral imaging) with advanced image analysis allows noninvasive, quantitative analysis of slow mixing phenomena. This is illustrated in a small study to monitor the preparation of homemade mayonnaise, specifically concerning the major challenge of “breaking” the emulsion. The context of the phenomenon in terms of the physics of mixing is explained as well. This study demonstrates that the Kullback–Leibler divergence metric, when applied to HSI data, enables quantitative tracking of emulsion mixing dynamics and can be used to quantify emulsion homogeneity and identify phase inversion during mayonnaise preparation if manual oil addition proceeds too quickly. This approach holds promise for real-time monitoring of emulsion quality in industrial settings where the state of the emulsion cannot always be determined visually.

The way food is presented can significantly influence liking, satiation, and emotional responses to food. This study explored these effects across two separate experiments by examining the impact of plate size (small vs. large) and plating style (high-stacked vs. spread) on participants’ liking, satisfaction, fullness, and food-evoked emotions when consuming chicken salad. In the first experiment, conducted online (n = 192), we used interactive 360-degree videos to simulate real-life experiences of chicken salads under the different conditions. The second experiment expanded this research into a real-life cafeteria setting (n = 176) where participants actually consumed the chicken salads. In this setting, salads served on a large plate with high-stacked plating received the highest ratings for liking, compared to the other conditions: small plate-high-stacked, small plate-spread, and large plate-spread. This condition also evoked the most positive food-related emotions, such as happiness, satisfaction, and relaxation, and was perceived as closest to the “ideal portion size.” Notably, the real-life experiment provided a better discrimination between the experimental conditions, with more intense and higher ratings on food-evoked emotions, liking, and willingness to pay compared to the online context. Real-life eating encompasses social interactions, sensory stimulation and post-ingestive effects, offering a richer and more accurate representation of actual eating experiences. These findings highlight the importance of real-life multi-modal measurement environments for obtaining accurate measures of food perception, acceptance and eating behaviours.

Prussian Blue Analogues (PBAs), which are characterized by their open structure, high stability, and non-toxic properties, have recently been the subject of research for various applications, including their use as electrode precursors for capacitive deionization, gas storage, and environmental purification. These materials can be readily tailored to enhance their affinity towards gases for integration with sensing devices. An improved understanding of PBA-gas interactions is expected to enhance material development and existing sensor deposition schemes greatly. The use of inverse gas chromatography (IGC) is a robust approach for examining the relationship between porous materials and gases. In this study, the adsorption properties of (functionalized) hydrocarbons, i.e., probe molecules, on the copper hexacyanoferrate (CuHCF) lattice were studied via IGC, demonstrating that alkylbenzenes have a higher affinity for this material than n-alkanes. This difference was rationalized by steric hindrance, π–π interactions, and vapour pressure effects. Along the same line, the five isomers of hexane showed decreasing selectivity upon increased steric hindrance. Enthalpy values for n-pentane, n-hexane and n-heptane were lower than that of toluene. The introduction of increased probe masses resulted in a surface coverage of 46% for toluene. For all n-alkane probe molecules this percentage was lower. However, the isotherms of these probes did not show saturation points and the observed linear regime proves beneficial for gas sensing. Our work demonstrates the versatility of CuHCF for gas sensing purposes and the potential of IGC to characterize the adsorption characteristics of such a porous nanomaterial.

The phase behavior is investigated for systems composed of a large number of macromolecular components N, with N ≥ 2. Liquid-liquid phase separation is modeled using a virial expansion up to the second order of the concentrations of the components. Formal analytical expressions for the spinodal manifolds in N dimensions are derived, which simplify their calculation (by transforming the original problem into inequalities that can be evaluated numerically using linear programming techniques). In addition, a new expression is obtained to calculate the critical manifold and composition of the coexisting phases. The present analytical procedure complements previous attempts to handle spinodal decomposition for many components using a statistical approach based on random matrix theory. The results are relevant for predicting the effects of polydispersity on phase behavior in fields like polymer or food science and liquid-liquid phase separation in the cytosol of living cells.

Models predicting lipid oxidation in oil-in-water (O/W) emulsions are a requirement for developing effective antioxidant solutions. Existing models do, however, not include explicit equations that account for composition and structural features of O/W emulsions. To bridge this gap, a mechanistic kinetic model for lipid oxidation in emulsions is presented, describing the emulsion as a one-dimensional three phase (headspace, water, and oil) system. Variation in oil droplet sizes, overall surface area of oil/water interface, oxidation of emulsifiers, and the presence of catalytic transition metals were accounted for. For adequate predictions, the overall surface area of oil/water interface needs to be determined from the droplet size distribution obtained by dynamic and static light scattering (DLS, SLS). The kinetic model predicted well the formation of oxidation products in both mono- and polydisperse emulsions, with and without presence of catalytic transition metals.