Global trends in the application of artificial intelligence in sensory science: A bibliometric analysis in the context of food science

The growing demand for more personalized, functional, and sensorially appealing foods has driven the adoption of computational approaches in the food sector. In this context, the application of artificial intelligence to sensory science emerges as a promising alternative to automate evaluations, predict consumer preferences, and enhance innovation in food products. The aim of this study was to map the international scientific output on the topic through a bibliometric analysis of 735 articles indexed in the Web of Science database between 1991 and 2025. The Bibliometrix (R) and VOSviewer software tools were used to extract indicators of productivity, impact, collaboration, and thematic structure. The results show a significant growth in publications since 2016, with a strong concentration in the last 5 years. China, India, and Brazil rank among the most productive countries, with notable contributions from authors based in Australia and East Asia. The co-occurrence analysis revealed seven thematic clusters, highlighting approaches based on machine learning, neural networks, computer vision, and electronic sensors. This study contributes to the understanding of the field’s evolution and provides insights to inform research and development strategies in the food sector.