Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies

Comprehensive review of dysphagia and technological advances in dysphagia food

As the global population ages, dysphagia is becoming increasingly common among the elderly, posing serious risks such as choking, aspiration pneumonia, and even death. Leveraging advanced technologies to develop specialized food products for those with dysphagia not only serves the economic interests of the elderly food market but also significantly enhances the nutritional health and social satisfaction of this demographic. This review examines the causes and early symptoms of dysphagia, the development of texture-modified foods (TMFs), and the relevant regulations and standards. It also addresses the key factors influencing the swallowing of foods, focusing on rheology and tribology. Most texture-modified foods currently available do not provide an enjoyable eating experience for the elderly or those with dysphagia. The integration of artificial intelligence (AI) and mathematical modeling with food additive manufacturing technology appears promising for improving foods designed for the elderly and those with dysphagia. This paper highlights the critical benefits and potential applications of AI, mathematical modeling, and food additive manufacturing in creating dysphagia-friendly foods and provides a conceptual system for designing diets based on AI for dysphagic foods. AI and mathematical model-based food processing technology enable the food industry to achieve digitalization and large-scale customization, potentially revolutionizing the approach to dietary management in dysphagia.

ACE inhibitory peptides from enzymatic hydrolysate of fermented black sesame seed: Random forest-based optimization, screening, and molecular docking analysis

In this study, black sesame seeds were fermented by Lactobacillus Plantarum NCU116 and then hydrolyzed using acid protease to improve Angiotensin-I-converting enzyme (ACE) inhibitory activity. The random forest-particle swarm optimization (RF-PSO) model was applied to predict the ACE inhibitory activity during the hydrolysis process based on the experimental data. After separating by adsorption chromatography, gel filtration chromatography, and reversed phased-high performance liquid chromatography and then screening in silico method, eight peptides were identified from fermented black sesame seed hydrolysates as ITAPHW, SLPNYHPSPR, QYLPR, IRPNGL, YHNAPIL, LSYPR, GFAGDDAPRA, and LDPNPRSF with IC50 values of 51.69 μM, 146.67 μM, 655.02 μM, 752.60 μM, 1.02 mM, 2.01 mM, 1.97 mM, and 3.43 mM, respectively. ITAPHW and SLPNYHPSPR exhibited high antioxidant activity and inhibited the ACE activity in a non-competitive pattern. Molecular docking revealed that the strong ACE inhibition of ITAPHW and SLPNYHPSPR is probably attributed to the interaction with Zn2+ of ACE.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.