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The waste generated by the brewing industry, particularly brewer’s spent grain (BSG) and wastewater, presents challenges for sustainable management practices. While BSG is traditionally utilized as cattle feed, this option is not universally accessible. This study considered the environmental impact of a novel, laboratory-based process for converting BSG into biochar that also utilizes brewing wastewater, as compared to disposing of BSG and cleaning chemical wastewater. The study employed a carbon footprint assessment approach to quantify the greenhouse gas (GHG) emissions associated with each disposal method, using one unprocessed kg of BSG as the functional unit. The results indicated that landfilling BSG generated approximately 3 kg CO2 equivalent (CO2e) per kg of unprocessed BSG, whereas biochar production reduced emissions to 1.18 kg CO2e per kg of BSG. The study concluded that diverting BSG from landfills to biochar production presents a viable strategy for minimizing environmental impacts associated with BSG disposal. However, several factors must be considered in the development of a biochar production facility, including biochar transportation. These elements may contribute more GHG emissions than landfilling if not properly designed. 

Abstract Our food system is complex, multifaceted, and in need of an upgrade. Population growth, climate change, and socioeconomic disparities are some of the challenges that create a systemic threat to its sustainability and capacity to address the needs of an evolving planet. The mission of the AI Institute of Next Generation Food Systems (AIFS) is to leverage the latest advances in AI to help create a more sustainable, efficient, nutritious, safe, and resilient food system. Instead of using AI in isolation, AIFS views it as the connective tissue that can bring together interconnected solutions from farm to fork. From guiding molecular breeding and building autonomous robots for precision agriculture, to predicting pathogen outbreaks and recommending personalized diets, AIFS projects aspire to pave the way for infrastructure and systems that empower practitioners to build the food system of the next generation. Workforce education, outreach, and ethical considerations related to the emergence of AI solutions in this sector are an integral part of AIFS with several collaborative activities aiming to foster an open dialogue and bringing closer students, trainees, teachers, producers, farmers, workers, policy makers, and other professionals. 

The growth and activity of adherent cells can be enabled or enhanced through attachment to a solid surface. For food and beverage production processes, these solid supports should be food-grade, low-cost, and biocompatible with the cell of interest. Solid supports that are edible can be a part of the final product, thus simplifying downstream operations in the production of fermented beverages and lab grown meat. We provide proof of concept that edible filamentous fungal pellets can function as a solid support by assessing the attachment and growth of two model cell types: yeast, and myoblast cells. The filamentous fungus Aspergillus oryzae was cultured to produce pellets with 0.9 mm diameter. These fugal pellets were inactivated by heat or chemical methods and characterized physicochemically. Chemically inactivated pellets had the lowest dry mass and were the most hydrophobic. Scanning electron microscope images showed that both yeast and myoblast cells naturally adhered to the fungal pellets. Over 48 h of incubation, immobilized yeast increased five-fold on active pellets and six-fold on heat-inactivated pellets. Myoblast cells proliferated best on heat-treated pellets, where viable cell activity increased almost two-fold, whereas on chemically inactivated pellets myoblasts did not increase in the cell mass. These results support the use of filamentous fungi as a novel cell immobilization biomaterial for food technology applications. 

The past decade witnessed rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve the classification and prediction of these properties. However, the barriers to collecting large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the de facto passive learning approach, advanced approaches (AL, SSL, and the hybrid) can greatly reduce the number of labeled samples, with some cases decreasing the number of labeled samples by more than half.