Abstract

A solution of spectroscopic inverse problems, implying determination of target parameters of the research object via analysis of spectra of various origins, is an overly complex task, especially in case of strong variability of the research object. One of the most efficient approaches to solve such tasks is use of machine learning (ML) methods, which consider some unobvious information relevant to the problem that is present in the data. Here, we compare ML approaches to the problem of nanocomplex concentrations determination in human urine via optical absorption spectra, perform preliminary analysis of the data array, find optimal parameters for several of the most popular ML methods, and analyze the results.

© 2021 Optical Society of America

Machine learning model with physical constraints for diffuse optical tomography

Yun Zou, Yifeng Zeng, Shuying Li, and Quing Zhu
Biomed. Opt. Express 12(9) 5720-5735 (2021)

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Ashwin P. Rao, Phillip R. Jenkins, Ryan E. Pinson, John D. Auxier II, Michael B. Shattan, and Anil K. Patnaik
Appl. Opt. 62(6) A83-A109 (2023)

Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

Yongni Shao, Yong He, and Jingyuan Mao
Appl. Opt. 46(25) 6391-6396 (2007)

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier, Michael B. Shattan, and Anil K. Patnaik
Appl. Opt. 61(7) D30-D38 (2022)

Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy

Huan Liu, Jun Zhu, Huan Yin, Qiangqiang Yan, Hong Liu, Shouxin Guan, Qisheng Cai, Jiawen Sun, Shun Yao, and Ruyi Wei
Appl. Opt. 61(10) 2834-2841 (2022)