Application of artificial intelligence in the advancement of sensory evaluation of food products

Background

Artificial intelligence (AI) is increasingly being integrated into sensory evaluation in food science to overcome limitations of traditional methods such as subjectivity, variability, and dependence on human panels. By incorporating data from chemical analysis, imaging, and consumer feedback, AI enables more objective, reproducible, and scalable assessments of sensory attributes, including taste, aroma, texture, and appearance.

Scope and approach

This review provides a comprehensive overview of AI applications in the sensory evaluation of food products. It includes key AI technologies, such as machine learning, computer vision, natural language processing, and intelligent sensors, and their roles in predicting, simulating, and personalizing sensory attributes. AI transformations of sensory analysis through data-driven modeling, multimodal integration, and digital simulation are highlighted. Additionally, ethical and technical challenges associated with the adoption of AI in sensory science are addressed.

Key findings and conclusions

AI enables advanced modeling of human sensory perception by linking analytical data with consumer sensory responses. Machine learning and deep learning facilitate predictive analysis of sensory traits; computer vision and e-sensing technologies replicate human visual and chemical perception; and natural language processing provides insights from consumer-generated content. These technologies also support real-time, personalized sensory experiences. While AI presents powerful tools for innovation in food design and evaluation, issues such as data quality, model transparency, and ethical use must be addressed. This review emphasizes the need for interdisciplinary collaboration to ensure inclusive, explainable, and human-centered development of AI-based sensory systems.