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Artificial Intelligence

Artificial Intelligence (AI) Discovery of Generally Recognized As Safe (GRAS) Food Coating Materials

Fruit coating can minimize substantial privation and promote a sustainable supply chain, thus attracting attention in the field of food packaging and postharvest. Although considerable study has reported fruit coating materials with different functions for different fruits, it's still challenging to design a fruit coating plan rapidly and precisely to satisfy the demand of industry and consumers. This is because current methods of finding fruit coating materials are based on extensive experiments, which are costly, time-consuming, and highly dependent on the experience of researchers. To address these challenges, we plan to develop an AI tool to help food researchers and horticulturists screen for suitable coating materials as a possible solution. We want to build a framework network on fruit, market demand, and material properties and function based on the reported studies. The graph neural network algorithm will learn the relationship between coating materials and different demands to build a general recommender system for predicting potential coating materials. We will also test the physicochemical properties and functions of the synthetic coating materials based on the recommender system to verify the accuracy of the system predictions. We believe through this pioneering and synergistic approach, innovative fruit coatings with optimal properties will be identified. Speaker: Peihua Ma, PhD

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