{"title":"Identification of organic compounds using artificial neural networks and refractive index","authors":"Innocent Kirigiti, N. Aminah, Samson Thomas","doi":"10.2298/jsc230201049k","DOIUrl":null,"url":null,"abstract":"Identification of chemical compounds has many applications in science and technology. However, this process still relies heavily on the knowledge and experience of chemists. Thus, the development of techniques for faster and more accurate chemical compound identification is essential. In this work, we demonstrate the feasibility of using artificial neural networks to accurately identify organic compounds through the measurement of refractive index. The models were developed based on refractive index measurements in different wavelengths of light, from UV to the far-infrared region. The models were trained with about 250,000 records of experimental optical constants for 60 organic compounds and polymers from published literature. The models performed with accuracies of up to 98%, with better performance observed for refractive index measurements across the visible and IR regions. The proposed models could be coupled with other devices for autonomous identification of chemical compounds using a single-wavelength dispersive measurement","PeriodicalId":17489,"journal":{"name":"Journal of The Serbian Chemical Society","volume":"1 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Serbian Chemical Society","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.2298/jsc230201049k","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Identification of chemical compounds has many applications in science and technology. However, this process still relies heavily on the knowledge and experience of chemists. Thus, the development of techniques for faster and more accurate chemical compound identification is essential. In this work, we demonstrate the feasibility of using artificial neural networks to accurately identify organic compounds through the measurement of refractive index. The models were developed based on refractive index measurements in different wavelengths of light, from UV to the far-infrared region. The models were trained with about 250,000 records of experimental optical constants for 60 organic compounds and polymers from published literature. The models performed with accuracies of up to 98%, with better performance observed for refractive index measurements across the visible and IR regions. The proposed models could be coupled with other devices for autonomous identification of chemical compounds using a single-wavelength dispersive measurement
期刊介绍:
The Journal of the Serbian Chemical Society -JSCS (formerly Glasnik Hemijskog društva Beograd) publishes articles original papers that have not been published previously, from the fields of fundamental and applied chemistry:
Theoretical Chemistry, Organic Chemistry, Biochemistry and Biotechnology, Food Chemistry, Technology and Engineering, Inorganic Chemistry, Polymers, Analytical Chemistry, Physical Chemistry, Spectroscopy, Electrochemistry, Thermodynamics, Chemical Engineering, Textile Engineering, Materials, Ceramics, Metallurgy, Geochemistry, Environmental Chemistry, History of and Education in Chemistry.