Biniam Kebede

Common beans ( Phaseolus vulgaris L.) are essential raw material for the canning industry. This article reviews recent advances in assessing canning quality and the integration of artificial intelligence (AI) into breeding methodologies aimed at developing genotypes with superior yield and canning‐quality traits. Cultivars destined for canning must consistently meet strict quality standards in addition to high agronomic performance. Conventional phenotypic quality parameters, such as washed drained weight, processing quality index, sensory properties, and texture, are central to predicting canning performance. However, their assessment is labor‐intensive, costly, and often limited to advanced filial generations, making early selection challenging. Recent progress in artificial intelligence, imaging, and data analysis provides new opportunities to evaluate canning traits at early stages of breeding, complementing conventional sensory and laboratory evaluations. These innovations enable breeders to optimize selection pipelines, reduce dependency on external facilities, and accelerate the release of superior cultivars. The review highlights the potential of AI coupled with nondestructive imaging to transform canning‐quality assessment by offering high‐throughput, cost‐effective, and scalable tools that improve prediction accuracy. Future directions include harmonizing evaluation protocols, developing cultivars that combine nutritional enrichment with drought tolerance and canning quality, and expanding genotype testing across multiple environments. The integration of AI with traditional breeding strategies offers a promising pathway to enhance both the efficiency and sustainability of dry bean improvement programs, ensuring cultivars that align with market requirements and consumer expectations.

Legumes are rich in dietary fiber, plant proteins, micronutrients, and bioactive compounds, offering a sustainable and affordable addition to the diet. However, the extent to which legume-induced modulation of the gut microbiota contributes to glycemic regulation in type 2 diabetes (T2D), relative to microbiota-independent physiological mechanisms, remains insufficiently defined. This narrative review synthesizes current evidence on legume-based interventions and their effects on gut microbiota composition and function in relation to glycemic control. Relevant studies were identified through structured searches of MEDLINE and the Cochrane Central Register of Controlled Trials via Ovid, complemented by manual screening of reference lists. Seventeen studies (three human and 14 animal trials) were considered in this review. Human studies report that consuming legumes is associated with improved glucose tolerance and blood glucose levels, although effects on microbiota composition are variable and modest. Conversely, animal studies demonstrate improvements in insulin sensitivity, glucose tolerance, and microbial diversity with higher dose legume interventions. Legume consumption has been associated with enrichment of beneficial microbial taxa, such as Bifidobacterium , Akkermansia , Ruminococcus , and Bacteroides , as well as increased concentrations of microbial metabolites such as short-chain fatty acids (SCFAs). These microbial features are implicated in metabolic pathways relevant to insulin signaling and glycemic regulation; however, current human evidence does not establish that microbiota alterations causally mediate glycemic improvements. Well-designed, adequately powered clinical studies incorporating functional microbiome analyses and formal mediation approaches are required to clarify microbiota-dependent and microbiota-independent mechanisms.

The global demand for Plant-based meat analogues (PBMAs) is rising due to increasing vegetarianism, health and environmental concerns, and animal-welfare issues. However, PBMAs encounter several challenges that limit widespread acceptance, including inadequate physico-chemical, sensory, and nutritional attributes, and concerns about over-processing and potential food-safety risks. To address these challenges, initiatives such as using high-quality raw materials, blending various plant protein sources, and employing innovative processing techniques are underway. Consumer acceptance of PBMAs also depends on the type of in-home cooking method used to prepare these foods, and the goal in many cases is for the quality attributes after cooking to resemble that of meat. The objective of this review is to compare the effects of in-home cooking methods on the quality of meat products to better understand how PBMAs will perform relative to their animal-based counterparts. Here, research articles that evaluate the impact of different cooking methods (e.g., baking and pan-frying) on the quality of meat and PBMAs were systematically summarized and compared to demonstrate trends in changes related to the physicochemical, sensory, and nutritional attributes of these products. The results revealed that the type of cooking method significantly influenced consumers’ acceptance of meat and PBMAs. Changes in physical properties such as cooking loss and water holding capacity (WHC) can affect texture and were dependent on cooking temperatures. However, these changes were not independent of the protein source (PBMAs) or muscle type (meat). For example, cooking at 70 °C decreased the amount of sulfhydryl content in PBMAs compared to pork and beef burgers (20 µmol/g vs. 65 µmol/g protein, respectively). Conversely, when cooking temperatures (> 120 °C) were used, maillard reaction resulted in favourable sensory (appearance and flavour) attributes in both types of products, however, in the presence of sugars and the amino acid asparagine, concerns about the presence of chemical hazards such as acrylamide increased. Microwave cooking also increased WHC and oil absorption properties of plant proteins which improved the mouthfeel of PMBAs. In conclusion, there is a need to optimize in-home cooking techniques to enhance desirable textures and flavours in PBMAs which will improve consumer acceptability of these products.

This study sheds light on the metabolic activity of Lp. Plantarum and provides insights into the flavour potential of oats via solid state fermentation, while also underscoring the importance of machine learning in capturing non-linear changes.