TY - GEN
T1 - Minced Meat Classification using Digital Imaging System Coupled with Machine Learning
AU - Stendafity, Selfi
AU - Hatta, Agus M.
AU - Setiadi, Iwan C.
AU - Sekartedjo,
AU - Rahmadiansah, Andi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Meat is widely recognized as a prevalent dietary choice due to its intricate nutritional profile, and its production constitutes a significant component of the global cattle industry. Due to the significant market demand for meat and the pursuit of maximizing profitability, meat products are often adulterated with additional ingredients. A diverse amount of meat can be effectively detected by the comparison model of machine learning. This study provides a comprehensive analysis of the performance of low-cost imaging for classifying minced meat product. In this study, a variety of raw minced meat, including beef, lamb, and pork, was utilized. We investigated the feasibility of low-cost imaging system coupled with machine learning classifier such as Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and AdaBoost Classifier. The findings indicate that the K-nearest neighbors (KNN) model exhibits superior performance, with an overall classification accuracy of 98.2%. The K-Nearest Neighbors (KNN) model exhibits a notable level of precision and accuracy across all classes. Therefore, this study confirms that machine learning algorithm provide robust features for classifying minced meat from the image data.
AB - Meat is widely recognized as a prevalent dietary choice due to its intricate nutritional profile, and its production constitutes a significant component of the global cattle industry. Due to the significant market demand for meat and the pursuit of maximizing profitability, meat products are often adulterated with additional ingredients. A diverse amount of meat can be effectively detected by the comparison model of machine learning. This study provides a comprehensive analysis of the performance of low-cost imaging for classifying minced meat product. In this study, a variety of raw minced meat, including beef, lamb, and pork, was utilized. We investigated the feasibility of low-cost imaging system coupled with machine learning classifier such as Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and AdaBoost Classifier. The findings indicate that the K-nearest neighbors (KNN) model exhibits superior performance, with an overall classification accuracy of 98.2%. The K-Nearest Neighbors (KNN) model exhibits a notable level of precision and accuracy across all classes. Therefore, this study confirms that machine learning algorithm provide robust features for classifying minced meat from the image data.
KW - Classification
KW - Color Imaging
KW - Food Security
KW - Machine Learning
KW - Minced Meat
UR - http://www.scopus.com/inward/record.url?scp=85186511095&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427687
DO - 10.1109/ICAMIMIA60881.2023.10427687
M3 - Conference contribution
AN - SCOPUS:85186511095
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 804
EP - 808
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -