{"title":"电子鼻(e-nose)设计用于arduino纳米清真鉴别","authors":"M. Kadafi, R. A. Putra","doi":"10.18860/NEU.V13I1.8903","DOIUrl":null,"url":null,"abstract":"It has been successfully designed an Electronic Nose (e-Nose) instrumentation system consisting of 6 MQ gas sensors, namely, MQ2, MQ4, MQ5, MQ7, MQ9, MQ135. The E-nose system is used to identify halal-haram food. This E-Nose system uses an Arduino Nano microcontroller. The Graphic User Interface (GUI) system is built with Visual Studio 2008. Then, the data responses will be evaluated by using 2 patterns recognition methods called Principle Component Analysis (PCA). The classification results can be explained by the value of the score plot on the PCA of the data. PC1 accounts for 19% of the variance, and PC2 accounts for 5% of the variance, data obtained is stored and displayed on personal computers in Excel format. Each sample was tested for up to ten repetitions. The data obtained from the six sensors in the e-nose was processed using Minitab 18 and it was necessary to obtain classification data on lard, pig oil, and sample B, which were fried crackers using pork oil.","PeriodicalId":17685,"journal":{"name":"Jurnal Neutrino","volume":"61 1","pages":"8-12"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"ELECTRONIC NOSE (E-NOSE) DESIGN FOR ARDUINO NANO-BASED HALAL HARAM IDENTIFICATION\",\"authors\":\"M. Kadafi, R. A. Putra\",\"doi\":\"10.18860/NEU.V13I1.8903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It has been successfully designed an Electronic Nose (e-Nose) instrumentation system consisting of 6 MQ gas sensors, namely, MQ2, MQ4, MQ5, MQ7, MQ9, MQ135. The E-nose system is used to identify halal-haram food. This E-Nose system uses an Arduino Nano microcontroller. The Graphic User Interface (GUI) system is built with Visual Studio 2008. Then, the data responses will be evaluated by using 2 patterns recognition methods called Principle Component Analysis (PCA). The classification results can be explained by the value of the score plot on the PCA of the data. PC1 accounts for 19% of the variance, and PC2 accounts for 5% of the variance, data obtained is stored and displayed on personal computers in Excel format. Each sample was tested for up to ten repetitions. The data obtained from the six sensors in the e-nose was processed using Minitab 18 and it was necessary to obtain classification data on lard, pig oil, and sample B, which were fried crackers using pork oil.\",\"PeriodicalId\":17685,\"journal\":{\"name\":\"Jurnal Neutrino\",\"volume\":\"61 1\",\"pages\":\"8-12\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Neutrino\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18860/NEU.V13I1.8903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Neutrino","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18860/NEU.V13I1.8903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
摘要
成功设计了由6个MQ气体传感器组成的电子鼻(e-Nose)仪表系统,即MQ2、MQ4、MQ5、MQ7、MQ9、MQ135。电子鼻系统用于识别清真食品。这个电子鼻系统使用Arduino纳米微控制器。图形用户界面(GUI)系统是用Visual Studio 2008构建的。然后,使用主成分分析(PCA)两种模式识别方法对数据响应进行评估。分类结果可以用数据在PCA上的得分图的值来解释。PC1占方差的19%,PC2占方差的5%,得到的数据以Excel格式存储并显示在个人电脑上。每个样品最多重复测试10次。利用Minitab 18对电子鼻内6个传感器采集的数据进行处理,需要对猪油、猪油和样品B(用猪油油炸的饼干)进行分类数据。
ELECTRONIC NOSE (E-NOSE) DESIGN FOR ARDUINO NANO-BASED HALAL HARAM IDENTIFICATION
It has been successfully designed an Electronic Nose (e-Nose) instrumentation system consisting of 6 MQ gas sensors, namely, MQ2, MQ4, MQ5, MQ7, MQ9, MQ135. The E-nose system is used to identify halal-haram food. This E-Nose system uses an Arduino Nano microcontroller. The Graphic User Interface (GUI) system is built with Visual Studio 2008. Then, the data responses will be evaluated by using 2 patterns recognition methods called Principle Component Analysis (PCA). The classification results can be explained by the value of the score plot on the PCA of the data. PC1 accounts for 19% of the variance, and PC2 accounts for 5% of the variance, data obtained is stored and displayed on personal computers in Excel format. Each sample was tested for up to ten repetitions. The data obtained from the six sensors in the e-nose was processed using Minitab 18 and it was necessary to obtain classification data on lard, pig oil, and sample B, which were fried crackers using pork oil.