Advances in Bioinspired Sensing—Electronic Nose and Eye Technologies for Food Quality Assessment: A Review

With the rising demand for food safety and quality, the development of intelligent, nondestructive sensing technologies has become increasingly essential. Bio-inspired systems, such as the electronic nose (E-nose) and electronic eye (E-eye), which emulate human olfactory and visual perception, have emerged as transformative tools for food quality assessment (FQA). This review provides a comprehensive overview of recent advancements in E-nose and E-eye technologies, focusing on their sensing principles, data acquisition methods, analytical capabilities, and integrated applications. Our study groups the existing technologies into three: 1) gas sensor technologies in E-nose systems; 2) image sensor technologies in E-eye systems; and 3) the fusion of E-nose and E-eye through multimodal data integration strategies. The discussion highlights the role of machine learning (ML) and deep learning (DL) algorithms in enhancing the analytical pipeline, from preprocessing tasks to complex pattern recognition and final decision-making. These technologies enhance data handling and classification accuracy while enabling consistent, real-time decision support in practical food assessment scenarios. In addition, the review addresses key challenges and outlines the potential avenues for future development. This article aims to serve as a foundational reference for researchers and practitioners advancing real-time food monitoring systems through bio-inspired sensing and intelligent data integration.