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Understanding precisely what is in food products is not always straightforward due to food fraud, differing labelling regulations, naming inconsistencies and the hierarchical nature of ingredients. Despite this, the need to detect and substitute ingredients in consumer food products is far-reaching. The cultivation and production of many ingredients is unsustainable, and can lead to widespread deforestation and biodiversity loss. Understanding the presence and replaceability of these ingredients is an important step in reducing their use. Furthermore, certain ingredients are critical to consumer food products, and identifying these ingredients and evaluating supply-chain resilience in the event of losing access to them is vital for food security analysis. To address these issues, we first present a novel machine learning approach for detecting the presence of unlabelled ingredients. We then characterise the unsolved problem of proposing viable food substitutions as a directed link prediction task and solve it with a graph neural network (GNN). [ABSTRACT FROM AUTHOR]

This document discusses the emergence of deep learning as a transformative tool in the field of operations research (OR). Deep learning, a specialized category of machine learning algorithms, uses multiple layers to uncover hidden patterns and insights from large datasets. It has gained popularity due to its superior predictive performance compared to traditional machine learning models. The integration of deep learning applications into OR has the potential to enhance decision-making, productivity, sustainability, and optimization processes. However, there are challenges to address, such as data availability, interpretability, and computational cost. The document highlights the need for practical research and provides future research directions in areas such as supply chain management, demand forecasting, inventory optimization, and decision-making processes. Overall, deep learning has the potential to transform the field of OR, and this document aims to provide new perspectives for further research and practical applications. [Extracted from the article]