Machine learning for food flavor prediction and regulation: models, data integration, and future perspectives

Abstract

Background

Flavor is a central attribute of food quality, shaping consumer preferences and market performance. Traditional evaluation methods, such as sensory panels and basic assays, are often constrained by subjectivity, low throughput, and limited scalability. With the rise of high-throughput technologies and multimodal datasets, machine learning (ML) has emerged as a promising tool for deciphering and regulating complex flavor systems.

Aim of review

This review examines current flavor detection techniques and the application of ML across diverse domains. It compares supervised learning models (SVM, DT), ensemble algorithms (XGBoost, LightGBM), and deep learning approaches (CNN, ANN). This review also discusses the contribution of three major data dimensions to flavor prediction, as well as future prospects in the field. ML enables precise flavor prediction, compound screening, and real-time process control. To support these tasks, researchers have developed integrated analytical systems that combine electronic nose (E-nose), electronic tongue (E-tongue), gas chromatography–mass spectrometry (GC–MS), and gas chromatography–ion mobility spectrometry (GC-IMS). Ensemble learning and deep learning models show strong performance when handling complex, nonlinear datasets. Explainable artificial intelligence (XAI) tools such as Shapley Additive Explanations (SHAP) improve model transparency by linking predictions to underlying features. ML models further enhance both prediction accuracy and generalizability. Innovations such as attention mechanisms, graph neural networks, and digital twins support dynamic flavor modulation. ML also aids in identifying key flavor compounds and genotype-phenotype relationships, accelerating breeding and formulation.

Key scientific concepts of review

ML is opening up new technological avenues in flavor science, with significant potential to predict and control flavor formation mechanisms, verify product authenticity, and support the targeted design of flavor-active compounds that align with consumer expectations for sensory appeal.