{"title":"基于变分模态分解和小波变换特征的橄榄油红外光谱分类","authors":"Omer Faruk Karaaslan, G. Bilgin","doi":"10.1109/SIU.2019.8806409","DOIUrl":null,"url":null,"abstract":"Nowadays, it becomes important to determine the chemical structure without damaging the samples. As a result of the use of infrared, the spectras are obtained both quickly and without any special sample preparation process, and they contain specific characteristics. In this study, features of Fourier Transform Infrared spectra acquired from olive oil samples are extracted by Wavelet Transform (WT) and Variational Mode Decomposition (VMD) that does not require a main function.Afterwards, these attributes are classified in comparison by using the powerful classifiers, support vector machines (SVM) and random forests (RF). Experimental studies have shown that the features obtained by two proposed methods increase the classification performance.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of FTIR Spectra of Olive Oil with Features of Variational Mode Decomposition and Wavelet Transform\",\"authors\":\"Omer Faruk Karaaslan, G. Bilgin\",\"doi\":\"10.1109/SIU.2019.8806409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, it becomes important to determine the chemical structure without damaging the samples. As a result of the use of infrared, the spectras are obtained both quickly and without any special sample preparation process, and they contain specific characteristics. In this study, features of Fourier Transform Infrared spectra acquired from olive oil samples are extracted by Wavelet Transform (WT) and Variational Mode Decomposition (VMD) that does not require a main function.Afterwards, these attributes are classified in comparison by using the powerful classifiers, support vector machines (SVM) and random forests (RF). Experimental studies have shown that the features obtained by two proposed methods increase the classification performance.\",\"PeriodicalId\":326275,\"journal\":{\"name\":\"2019 27th Signal Processing and Communications Applications Conference (SIU)\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 27th Signal Processing and Communications Applications Conference (SIU)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2019.8806409\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 27th Signal Processing and Communications Applications Conference (SIU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2019.8806409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of FTIR Spectra of Olive Oil with Features of Variational Mode Decomposition and Wavelet Transform
Nowadays, it becomes important to determine the chemical structure without damaging the samples. As a result of the use of infrared, the spectras are obtained both quickly and without any special sample preparation process, and they contain specific characteristics. In this study, features of Fourier Transform Infrared spectra acquired from olive oil samples are extracted by Wavelet Transform (WT) and Variational Mode Decomposition (VMD) that does not require a main function.Afterwards, these attributes are classified in comparison by using the powerful classifiers, support vector machines (SVM) and random forests (RF). Experimental studies have shown that the features obtained by two proposed methods increase the classification performance.