用颜色和化学特征检测掺假猪肉

Q2 Arts and Humanities
Armil Monsura, A. R. Fernando, Rafael Ventura, Denise Antoinette Bañas, Dorothy Joy de Castro, John Michael Molleno, Krystal Mae Denise Rama, Eirron Carl Ramirez, Benjamin Norbert Vitug
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引用次数: 1

摘要

菲律宾的猪肉主要来自当地,并不是所有这些肉都来自消毒良好的屠宰场。一些非屠宰死亡的猪尸体,被称为掺假肉或双死肉,进入市场,与合法和安全的肉混在一起,只卖给毫无戒心的消费者。这些对肉类加工者和消费者的健康构成危害。防止双死肉在市场上扩散的长期做法之一是人工检查,根据肉的感官特性分析肉。然而,这种解决方案容易受到主观性和人为评价错误的限制。在这项研究中,支持者设计了一个系统来识别肉类样品是否掺假。该系统旨在通过颜色和化学决定因素提供快速准确的双死肉检测,至少与人类相当,甚至比人类更好。该系统的应用可以取代传统的电子传感器人工评估,避免不同评估人员对肉类的评估存在偏差,消除肉类检验人员和消费者的健康风险。该系统采用三个传感器:颜色传感器、甲烷传感器和pH值传感器。微控制器实现逻辑回归分类器。模型效果良好,经验证准确率为91.40%,ROC下面积为0.95。实现后,该模型的准确率为93.18%,而人类评估者对相同肉类样本的评估准确率为84.09%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Adulterated Pork Meat via Color and Chemical Characteristics
Pork in the Philippines is mostly sourced locally, and not all these meats come from well-sanitized slaughterhouses. Some carcasses of pigs that have died other than by slaughtering, called adulterated or double-dead meat, make their way to markets and get mixed up with legal and safe meat, only to be sold to unsuspecting consumers. These pose health hazards to meat handlers and consumers. One of the longtime practices in preventing the proliferation of double-dead meat in the markets is the manual inspection that analyzes the meat based on its organoleptic properties. This solution, however, is prone to limitations of subjectivity and human evaluation error. In this study, the proponents designed a system that will identify whether the meat sample is adulterated or not. The system is aimed at providing fast and accurate detection of double-dead meat using color and chemical determinants that is at least comparable to, or better than, humans. The application of the system can be utilized to replace the traditional human evaluation using electronic sensors as to avoid bias in the evaluation of the meat by different assessors as well as to eliminate the health risks on the meat inspectors and the consumers. The system utilizes three sensors: color sensor, methane sensor and pH level sensor. A microcontroller implements a logistic regression classifier. The model performs well, with an accuracy of 91.40% after validation, with an area of 0.95 under its ROC. When realized, the model has an accuracy of 93.18% compared to 84.09% of the human assessor's evaluation of the same meat samples.
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来源期刊
Platonic Investigations
Platonic Investigations Arts and Humanities-Philosophy
CiteScore
0.30
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