Abstract

The high demand and related high cost of minced meat make it vulnerable to adulteration. The practice of adulteration can have detrimental implications for individuals' well-being and can provide significant challenges for religious communities adhering to dietary regulations. For example, individuals who follow the Islamic faith are forbidden from consuming pork, hence emphasizing the significance of swiftly determining the existence of pork in the meat they ingest. This study aimed to examine the use of an imaging technology for the detection and quantification of pork adulteration in raw minced beef. The initial approach employed for the categorization of unadulterated beef, beef contaminated with pork, and unadulterated pork is artificial neural networks, which attains a remarkable accuracy rate of 99.01%. The second model employed deep neural networks to evaluate the level of adulteration in minced beef with pork. This model achieved a root mean square error (RMSE) value of 0.01681 and a coefficient of determination (R2) value of 0.98890.

Original languageEnglish
Title of host publication2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages809-814
Number of pages6
ISBN (Electronic)9798350309225
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Lombok, Indonesia
Duration: 14 Nov 202315 Nov 2023

Publication series

Name2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings

Conference

Conference2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Country/TerritoryIndonesia
CityLombok
Period14/11/2315/11/23

Keywords

  • Food security
  • imaging system
  • minced beef
  • neural network
  • pork adulteration

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