Rapid and Nondestructive Techniques for Quality Assessment of Tofu: A Comprehensive Review.

Abstract

Tofu is a traditional soy-based food of global importance, requiring stringent quality control to ensure safety and support industrial modernization. However, real-time monitoring in industrial-scale production is challenging because traditional methods are subjective, time-consuming, and destructive. In response to this challenge, a suite of rapid detection technologies based on optical, electrochemical, sensor, and physical field principles has emerged as a transformative solution. These technologies enhance detection speed, accuracy, and functionality, thus enabling the intelligent upgrading of the tofu industry. This review comprehensively summarizes and critically evaluates recent advancements in rapid detection methods for tofu, encompassing near-infrared spectroscopy, Raman spectroscopy, hyperspectral imaging, electronic nose/tongue, low-field nuclear magnetic resonance, ultrasonic testing, and photoelectrochemical sensors. For each technology, the underlying principles, application performance, and representative case studies are elucidated. In addition, persistent challenges such as model robustness, signal interference, and lack of standardization are analyzed. Finally, future trends are foreseen, with emphasis placed on multi-technology integration, intelligent automation driven by artificial intelligence, and the development of portable and online systems. This work aims to serve as a valuable reference for guiding future research and promoting the practical application of rapid detection technologies in the tofu industry.