As food safety issues gain increasing attention, the need for rapid, accurate, and low‐cost detection techniques becomes essential. Colorimetric sensors, known for their rapid response, high sensitivity, low cost, and readily observable results, are playing an increasingly important role in food quality inspection. This review article provides an in‐depth discussion on the development and application of colorimetric sensing technology in the field of food detection and safety. The review outlines the fundamental principles of colorimetric sensors and provides a critical assessment of research on array dyes, substrates, and system optimization. In recent years, the optimization of functionalized precious metal nanoparticles and nanoenzymes has pushed detection limits down to the parts per billion level. Porous polymer substrates modified with synergistic flexible materials combine high adsorption, strong anti‐interference, and excellent mechanical properties, providing support for integrated applications in wearable sensors and smart packaging. The integration of data acquisition and analysis techniques based on color changes is also discussed. Through an overview of the application of sensors in food safety testing, food quality control, and food production and processing monitoring, this review article highlights the significant potential of colorimetric sensor arrays (CSAs) in enhancing food safety and quality. The review also identifies current challenges, with major obstacles including environmental interference, matrix effects, and limited industrial validation. The review concludes that combining advanced sensor materials, portable and stable optical devices with online analytical platforms for real‐time data extraction and simple analysis will enable CSAs to develop into a powerful on‐site tool for ensuring food quality and safety.

The quality of green tea deteriorates the longer it is stored. However, there is a lack of accurate and rapid methods for determining the storage period of tea. In this study, hyperspectral imaging (HSI) was used to determine the storage period of green teas stored at 4 °C (set 1) and 25 °C (set 2), and to quantify and visualize the main chemical components (e.g. catechins). In this study, three prediction algorithms were compared, in which partial least squares discriminant analysis outperformed the other models in qualitative discrimination, with 98% and 96% correct discrimination for two sets, respectively. Moreover, quantitative models for ester catechins, simple catechins, and total catechins were developed with Rp > 0.90 and RPD > 1.0, indicating that the models were reliable. Further, a more intuitive visualization of catechin content was achieved. In conclusion, the HSI provides a rapid, non-destructive method to determine the freshness of stored green tea.