The Intersection of Food Security and Mental Health in the Pursuit of Sustainable Development Goals.

Food insecurity, a multifaceted global challenge, intertwines with mental health concerns, necessitating nuanced strategies for sustainable solutions. The intricate web of challenges posed by these intersections has made it imperative to delineate a strategic way forward, incorporating solutions and robust policy recommendations. This study aims to comprehensively examine the intricate relationship between food security and its intersection with mental health on a global scale, offering insights into case studies, responses, and innovative approaches to inform effective strategies for addressing these pressing challenges. This study involved an analysis of a literature search, mainly between 2013 and 2023, with an updated addition of relevant 2024 studies. Examining responses across regions unveils varied interventions, from targeted social safety net programs in West Africa to technology-driven solutions in Asia. Success stories, such as Ghana's sustainable agricultural practices and Canada's income transfer programs, underscore the efficacy of multifaceted approaches. Innovative initiatives like community food programs offer promising alternatives to traditional food banks. Furthermore, international cooperation and policy innovations, exemplified by the European Union's "Farm to Fork Strategy", demonstrate the potential for collective action in addressing food insecurity. By prioritizing integrated strategies, global collaboration, and evidence-based policymaking, we lay the groundwork for sustainable development where communities thrive nutritionally and mentally. We emphasize continuous research and evaluation and incorporating mental health support into community programs to pave the way for a future where communities are not only food-secure but also mentally resilient.

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models.

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.