Wang Guangzhi, Guo Yuchen, Yu Yang, Shi Yan, Ying Yuxiang, Men Hong
School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Institute of advanced sensor technology, Northeast Electric Power University, Jilin 132012, China.
School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Research Team, Northeast Electric Power University, Jilin 132012, China; Institute of advanced sensor technology, Northeast Electric Power University, Jilin 132012, China.
Food Chem. 2025 Apr 15;471:142794. doi: 10.1016/j.foodchem.2025.142794. Epub 2025 Jan 6.
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.
猪肉的新鲜度对于风味、营养和消费者健康至关重要。当前的比色传感器阵列（CSA）检测系统面临着传感器开发成本高、识别准确率低以及平台使用受限等挑战。在此，我们开发了一种CSA和ColorNet框架来检测猪肉新鲜度。通过选择敏化pH指示剂和醛/酮指示剂设计了53点CSA。为了优化传感器，使用欧几里得距离法用表现出更敏感响应的染料识别24个阵列点。ColorNet捕捉猪肉新鲜度的颜色信息，能够以99.5%的准确率进行实时检测。为了实际部署和移动应用，使用梯度激活映射开发了一种精简的12点CSA，保持了99%的识别率，与24点CSA相当。所提出的CSA和模型确保了消费者的健康和安全，为猪肉行业的质量监测和控制提供了强有力的技术支持。
