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

IF 8.5 1区 农林科学 Q1 CHEMISTRY, APPLIED
Guangzhi Wang , Yuchen Guo , Yang Yu , Yan Shi , Yuxiang Ying , Hong Men
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引用次数: 0

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.
ColorNet:一个基于人工智能的框架,用于使用比色传感器阵列进行猪肉新鲜度检测
猪肉的新鲜度对猪肉的风味、营养和消费者健康至关重要。当前的比色传感器阵列(CSA)检测系统面临着与传感器开发成本高、识别精度低以及平台使用限制相关的挑战。在此,我们开发了一个CSA和ColorNet框架来检测猪肉新鲜度。通过选择敏化pH指标和醛酮指标设计53点CSA。为了优化传感器,采用欧几里得距离法鉴定出24个具有较灵敏响应的染料阵列点。ColorNet捕获了猪肉新鲜度的颜色信息,允许以99.5 %的准确率进行实时检测。对于实际部署和移动应用程序,使用梯度激活映射开发了改进的12点CSA,保持了99 %的识别率,与24点CSA相当。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。
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来源期刊
Food Chemistry
Food Chemistry 工程技术-食品科技
CiteScore
16.30
自引率
10.20%
发文量
3130
审稿时长
122 days
期刊介绍: Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.
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