Machine learning algorithms to control concentrations of carbon nanocomplexes in biological medium via optical absorption spectroscopy: how to choose and what to expect?

Posted on 13.09.2021 - 16:15
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 nanocomplexes concentrations determination in human urine via optical absorption spectra, perform preliminary analysis of the data array, find optimal parameters for several most popular ML methods, and analyze the results.

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Sarmanova, Olga; Laptinskiy, Kirill; Burikov, Sergey; Khmeleva, Maria; Fedyanina, Anna; Tomskaya, Alexandra; et al. (2021): Machine learning algorithms to control concentrations of carbon nanocomplexes in biological medium via optical absorption spectroscopy: how to choose and what to expect?. The Optical Society. Collection. https://doi.org/10.6084/m9.figshare.c.5572812.v2
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Applied Optics

AUTHORS (9)

Olga Sarmanova
Kirill Laptinskiy
Sergey Burikov
Maria Khmeleva
Anna Fedyanina
Alexandra Tomskaya
Aleksandr Efitorov
Sergey Dolenko
Tatiana Dolenko

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