ColorNet: An AI-based framework for pork freshness detection using a colorimetric sensor array

Abstract

Pork freshness is crucial for flavour, nutrition and consumer health. The current colorimetric sensor array (CSA) detection systems face challenges related to high sensor development costs, low recognition accuracy and limitations in the platform use. Herein, we developed a CSA and ColorNet framework to detect pork freshness. The 53-point CSA was designed by selecting sensitised pH indicators and aldehyde/ketone indicators. To optimize the sensor, the Euclidean distance method was used to identify 24 array points with dyes that exhibited more sensitive responses. The ColorNet captured the color information of pork freshness, allowing real-time detection with a 99.5 % accuracy. For practical deployment and mobile applications, a refined 12-point CSA was developed using gradient activation mapping, maintaining a 99 % recognition rate, which is comparable with the 24-point CSA. The proposed CSA and model ensure consumer health and safety, providing strong technical support for quality monitoring and control in the pork industry.