Lin Hong-Dar, He Mao-Quan, Lin Chou-Hsien
Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.
Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712-0273, USA.
Sensors (Basel). 2025 Apr 24;25(9):2698. doi: 10.3390/s25092698.
Ractopamine, a beta-agonist used to enhance lean meat yield, poses health risks with excessive consumption. To comply with global trade policies, Taiwan permits imports of North American (USA. and Canadian) pork containing ractopamine, raising concerns over unclear labeling and potential misidentification as Taiwan pork. Given the high demand for pork, consumers need a reliable way to verify meat authenticity. To address this issue, this study proposes a smartphone-based visual detection system for meat cut and pork origin classification, extending to ractopamine detection. Consumers can use mobile devices in retail settings to analyze pork images and make informed decisions. The system employs a three-stage process: first, applying a black elliptical mask to extract the outer ROI (region of interest) for meat cut classification; then, using a black square mask to obtain the inner ROI for pork origin classification; and finally, determining ractopamine presence in North American pork. Experimental results demonstrate MobileNet's superior accuracy and efficiency, achieving a 96% CR (classification rate) for meat cut classification, a 79.11% average CR and 90.25% F1 score for pork origin classification, and an 80.67% average CR and 80.56% F1 score for ractopamine detection. These findings confirm the system's effectiveness in enhancing meat authenticity verification and market transparency.
莱克多巴胺是一种用于提高瘦肉率的β-激动剂，过量食用会带来健康风险。为了符合全球贸易政策，台湾允许进口含有莱克多巴胺的北美（美国和加拿大）猪肉，这引发了人们对标签不清晰以及可能被误认作台湾猪肉的担忧。鉴于猪肉的高需求，消费者需要一种可靠的方法来验证肉类的真实性。为解决这一问题，本研究提出了一种基于智能手机的视觉检测系统，用于肉类切块和猪肉产地分类，并扩展到莱克多巴胺检测。消费者可以在零售环境中使用移动设备分析猪肉图像并做出明智的决策。该系统采用三阶段流程：首先，应用黑色椭圆形掩码提取用于肉类切块分类的外部感兴趣区域（ROI）；然后，使用黑色方形掩码获取用于猪肉产地分类的内部ROI；最后，确定北美猪肉中莱克多巴胺的存在情况。实验结果表明，MobileNet具有卓越的准确性和效率，肉类切块分类的分类率达到96%，猪肉产地分类的平均分类率为79.11%，F1分数为90.25%，莱克多巴胺检测的平均分类率为80.67%，F1分数为80.56%。这些发现证实了该系统在增强肉类真实性验证和市场透明度方面的有效性。
