Machine Vision-Assisted Self-Verifying Bipolar Electrochromic Distance Sensor for Instrument-Free Detection

Abstract Distance-based electrochromic visual sensing has attracted considerable attention for point-of-care testing (POCT) due to its simple operation and intuitive signal readout. However, the analytical reliability of existing platforms is frequently limited by environmental disturbances and poorly defined electrochromic signal boundaries, which can lead to inaccurate interpretation of visual responses. Herein, we report a machine vision-assisted dynamic self-verifying bipolar electrochromic distance sensor for the highly sensitive visual detection of ochratoxin A (OTA). A closed bipolar electrode (CBPE) architecture was rationally designed to generate two spatially separated electrochromic distance signals through coupled anodic and cathodic redox reactions, enabling an intrinsic self-verification mechanism by continuously evaluating the consistency between dual distance-based signals. To achieve objective signal interpretation, a machine vision-based analytical algorithm was integrated into the sensing system to automatically perform electrochromic boundary identification, bidirectional distance measurement, signal-ratio calculation, and quantitative analysis. Compared with conventional image-processing methods based on color intensity extraction, the developed approach provides standardized and automated quantification of spatially resolved electrochromic signals without subjective visual judgment. Under optimized conditions, the sensor exhibited a linear response between the color-change distance and the logarithm of OTA concentration over a range of 0.04−850 ng/mL, with a detection limit of 0.003 ng/mL. By synergistically integrating electrochromic signal generation with machine vision-enabled intelligent interpretation, the proposed strategy establishes a self-validating analytical framework that transforms conventional distance-based visual sensing from empirical image readout into an automated and quantitatively reliable analytical process, providing a versatile platform for portable food safety monitoring, environmental analysis, and point-of-care diagnostics.