Developing an electronic nose for formalin detection in meatballs using Support Vector Machine (SVM) method and Raspberry Pi 4

被引:0
|
作者
Sanjaya, W. S. Mada [1 ,2 ]
Roziqin, Akhmad [3 ]
Temiesela, Agung Wijaya [1 ]
Zaman, M. Fauzi Badru [1 ]
Taqwim, Ahsani [1 ]
Opialisti, Intan [1 ]
Sintia, Putri [1 ]
Mulyawan, Andri [1 ]
Anggraeni, Dyah [1 ,2 ]
Sa'adah, Tsamrotus [1 ,2 ]
机构
[1] UIN Sunan Gunung Djati, Fac Sci & Technol, Dept Phys, Bandung, Indonesia
[2] CV Bolabot, Bolabot Techno Robot Inst, Bandung, Indonesia
[3] UIN Sunan Gunung Djati, Fac Educ & Teaching, Dept Madrasah Ibtidaiyah Teacher Edu, Bandung, Indonesia
关键词
electronic nose; formalin; Support Vector Machine (SVM); LDA; Raspberry Pi; CLASSIFICATION; NETWORK;
D O I
10.1088/1402-4896/ad6a9e
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
This study presents the development of an Electronic Nose system using Arduino Mega and Raspberry Pi 4, capable of accurately detecting odors and gases. Previously, sensor analysis revealed that variations in meatball and formalin mixtures yield diverse sensor responses, with some sensors exhibiting high sensitivity to formalin. Additionally, Linear Discriminant Analysis (LDA) demonstrated clear separation among different classes, facilitating comprehensive data analysis. The study also evaluated the performance of the SVM model, showing precise SVM parameter optimization with high accuracy for classification, achieving up to 100% accuracy at C = 0.1, kernel RBF, and gamma 0.1. These findings highlight the potential of the developed system to effectively detect formalin in meatballs, providing valuable insights for ensuring food safety and quality assurance. Overall, the optimal selection of the C parameter plays a key role in enhancing SVM model performance and contributes to advancing detection technology in food industry applications.
引用
收藏
页数:11
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