A Survey on Food Ingredient Substitutions

TL;DR

This survey reviews computational methods for ingredient substitution in recipes, emphasizing datasets, techniques, contextual factors, applications, and safety, and discusses future directions like neuro-symbolic AI and knowledge graphs.

Contribution

It provides a comprehensive overview of AI-based approaches for ingredient substitution, highlighting key datasets, techniques, and future research directions in food computation.

Findings

Identifies key datasets and sources for ingredient substitution research.

Summarizes AI techniques and approaches used in substitution models.

Highlights future research directions including neuro-symbolic methods and knowledge graphs.

Abstract

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to support ingredient substitution research; (ii) techniques and approaches applied to solve substitution problems (iii) contextual information of ingredients considered, such as nutritional content, flavor, and pairing potential; (iv) applications for which substitution models have been developed, including dietary restrictions, constraints, and missing ingredients; (v) safety and transparency of substitution models, focusing on user trust and health concerns. The survey also highlights promising directions for future research, such as integrating neuro-symbolic techniques for deep learning and utilizing knowledge graphs for improved reasoning, aiming to guide advancements in food computation and ingredient substitution.