Boosting structural food science using X-ray and neutron techniques

Knowledge about food structures at different length scales is key for the continued development of sustainable, tasty and healthy foods. It is critical to control, model and predict the supramolecular architecture of foods along the whole value chain: from raw materials, to their changes during processing, all the way to how products form structures during consumption and digestion. Today, advanced physical methods enable us to obtain structural information from the nanoscale-to the microscale with unprecedented resolution. The structural details can then relate to the mesoscale and microscale functionalities, important for the appeal and consumption of food products. X-ray and neutron techniques expand and strengthen the food structure characterisation toolbox. They enable in situ and in operando investigations with greater detail as well as new types of measurements that are not possible with other techniques. The knowledge gained will complement compositional and functional data obtained by other techniques, providing robustness to the interpretation of complex structural information. There are several intrinsic scientific challenges to overcome: from the lack of relevant sample environments to advanced data processing and modelling tools that consider the complexity of the food. The new frontier in food structural science can be gained through interdisciplinary collaborations not only in academia but also from the wider innovation ecosystem. This review showcases how the use of X-ray and neutron techniques is already leading to transformational knowledge in structural food science with a perspective that points to the future of this new multidisciplinary discipline

Pattern recovery by SLOPE

SLOPE is a popular method for dimensionality reduction in high-dimensional regression. Its estimated coefficients can be zero, yielding sparsity, or equal in absolute value, yielding clustering. As a result, SLOPE can eliminate irrelevant predictors and identify groups of predictors that have the same influence on the response. The concept of the SLOPE pattern allows us to formalize and study its sparsity and clustering properties. In particular, the SLOPE pattern of a coefficient vector captures the signs of its components (positive, negative, or zero), the clusters (groups of coefficients with the same absolute value), and the ranking of those clusters. This is the first paper to thoroughly investigate the consistency of the SLOPE pattern. We establish necessary and sufficient conditions for SLOPE pattern recovery, which in turn enable the derivation of an irrepresentability condition for SLOPE given a fixed design matrix X. These results lay the groundwork for a comprehensive asymptotic analysis of SLOPE pattern consistency

What do we know about software analytics research? : A critical review of secondary studies

Software analytics (SA) is often proposed as a tool to support software engineering (SE) tasks. Several secondary studies on SA have been published, some published within the same calendar year. This presents an opportunity to take a meta-perspective and examine how the field of SA has been conceptualized and synthesized so far. By analyzing how SA is defined, which topics are emphasized, what search strategies are employed, and to what extent primary studies overlap, we aim to identify gaps, trends, and redundancies in the current body of secondary studies. Such insights can inform the design and focus of future secondary studies. We identified five secondary studies on SA published from 2015 to 2023 that cover primary research from 2000 to 2021. Despite similarities in objectives and overlapping search timeframes, the secondary studies have negligible overlap in their included primary studies. Each secondary study presents a distinct perspective, and collectively, the five secondary studies offer a fragmented rather than cohesive view of the research landscape. We present a structured overview of the identified secondary studies in terms of their objectives, research quality, and findings. This overview helps readers navigate and leverage existing research. The analysis also indicates that there is potential for further secondary research to build a more cohesive and comprehensive understanding of the SA literature

Pathways to quality of life : Roles of customer orientation, organizational commitment, leadership, and job engagement among hotel staff facing job insecurity

This study examined pathways to quality-of-life (QOL) among hotel staff facing job insecurity, considering the roles of customer orientation, organizational commitment, leadership, and job engagement. A research model was developed and tested using path analysis on survey data, comparing groups with high and low job insecurity. Results showed the model explained 43.3 % of variance in QOL. Customer orientation, organizational commitment, and transformative leadership had significant indirect effects on QOL through job satisfaction. Job engagement aspects had mixed direct and indirect effects. The high and low job insecurity groups showed some differences in path coefficients. Necessary condition analysis found customer orientation, dedication, and absorption were necessary for QOL in the full sample. Sufficient configurations for high QOL differed between job insecurity groups. Deep learning comparing alternative models supported the research model. Findings highlight the importance of job satisfaction as a mediator, reveal nuances in effects of job engagement, and demonstrate differences based on job insecurity level. This study provides insights for enhancing hotel employees’ well-being in the face of job insecurity. Implications for hotel management are discussed

Pea protein isolates adsorption on phospholipid bilayer interfaces : a quartz crystal microbalance and neutron reflectometry study

Legumin and vicilin, the main storage proteins from peas, are increasingly employed as functional ingredients for food. The purpose of this work was to better understand the interactions between these proteins and selected lipids, due to their potential consequences on interfacial functionality of protein preparations. Legumin and vicilin isolates were obtained after isoelectric precipitation followed by further purification using size exclusion chromatography. The interactions were studied using DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine): DOPE (1,2-dioleoyl-sn-glycero-3 phosphoethanolamine) as mixed supported lipid bilayers, using quartz crystal microbalance with dissipation monitoring (QCM-D) and complementary neutron reflectometry (NR) experiments. This approach, fundamental in nature, allowed to observe interactions at the molecular level in the presence of phospholipids interfaces. QCM-D data suggested that legumin did not show any significant affinity for the investigated lipid interface. On the contrary there were changes in the bilayer with vicilin injection. NR experiments also supported these observations, and modeling of the experimental data also suggested a structural reorganization of the bilayer after vicilin injection and rinsing. This unique fundamental study of legumin and vicilin leads to the hypothesis that vicilin forms complexes with phospholipids bilayers which can be dispersed and removed by rinsing. All together, this study adds to the current debate on the importance of endogenous and non-endogenous phospholipids presence in affecting surface functionality of pea protein isolates

Metabolome fingerprinting elucidates chemodiversity in Ornithogalum, Loncomelos, and Melomphis (Asparagaceae)

This research employed GC-MS metabolomics profiling on bulbs of three genera i.e., Ornithogalum, Loncomelos, and Melomphis (Asparagaceae), to elucidate their taxonomic relationships. The analysis of 30 specimens across 8 species (Ornithogalum comprising 3 species: O. cuspidatum, O. neurostegium, O. orthophyllum; Loncomelos including 4 species: L. arcuatum, L. brachystachys, L. bungei, L. kurdicum; and Melomphis persica) from Iran revealed 147 detected metabolites, classified in several main superclasses e.g., organic oxygen compounds (40 %), organic acids (23 %), and lipids (18 %). Additionally, the metabolites were grouped into classes, with carbohydrates comprising the majority (42 %), followed by carboxylic acids (17 %) and fatty acids (14 %). Multivariate statistics including the variable importance in projection, and hierarchical clustering heat map discerned metabolites differentiating the genera. Projections to latent structures discriminant analysis revealed that Ornithogalum exhibited significant metabolic divergence from the closely related taxa Loncomelos and Melomphis, aligning with their established phylogenetic divisions. Overall, this work significantly expands knowledge of the chemodiversity of these taxa. The findings provide a phylogenetic framework to guide further ethno-pharmacological, phytochemical, and evolutionary research on these geophyte taxa