In this chapter, we explore the integration of symbolic reasoning and neural network-based methods within the domain of food science. We provide a comprehensive overview of symbolic methods such as food ontologies, knowledge graphs, and their construction, emphasizing their role in enhancing data interoperability and supporting complex food computing tasks. We then discuss neural network-based techniques for extracting food information from textual and image data, food representation learning through embeddings, and present various methodologies for food category classification, nutrition estimation, and ingredient substitution. Practical applications such as personalized nutrition, dietary management, food logging, and recipe creation using generative AI are also discussed, showcasing the transformative impact of neurosymbolic methods in food computing.