Georgekutty Jose Maniyattu, Eldho Geegy, M. Wohlschläger, N. Leiter, M. Versen, C. Laforsch
{"title":"多层感知器的发展，以识别塑料荧光寿命成像显微镜","authors":"Georgekutty Jose Maniyattu, Eldho Geegy, M. Wohlschläger, N. Leiter, M. Versen, C. Laforsch","doi":"10.31399/asm.edfa.2023-3.p031","DOIUrl":null,"url":null,"abstract":"\n Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements.","PeriodicalId":431761,"journal":{"name":"EDFA Technical Articles","volume":"205 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy\",\"authors\":\"Georgekutty Jose Maniyattu, Eldho Geegy, M. Wohlschläger, N. Leiter, M. Versen, C. Laforsch\",\"doi\":\"10.31399/asm.edfa.2023-3.p031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements.\",\"PeriodicalId\":431761,\"journal\":{\"name\":\"EDFA Technical Articles\",\"volume\":\"205 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EDFA Technical Articles\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31399/asm.edfa.2023-3.p031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EDFA Technical Articles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31399/asm.edfa.2023-3.p031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multilayer Perceptron Development to Identify Plastics Using Fluorescence Lifetime Imaging Microscopy
Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
