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7IR and terahertz spectroscopy and machine learning for medical and ecological applications
Yu. Kistenev*, V. Prishepa, V. Skiba1, V. Nikolaev, G. Rasponin, D. Makashev, A. Borisov
LMIML Laboratory, Tomsk State University, Tomsk, Russia
IR and THz spectra are associated with "molecular fingerprints" because these spectral ranges contain useful information about vibration, rotation, intramolecular and intermolecular modes of large number of molecules [1]. This information allows conducting a sample qualitative and quantitative analysis. Terahertz time domain spectroscopy (THz-TDS) is based on measurement of electric field of transmitted through the sample femtosecond terahertz pulses 0,3]. THz-TDS has a spectral resolution of several GHz that is enough for solid or dried liquid samples analysis. THz high-resolution absorption spectroscopy has spectral resolution of several kHz that enough for gas mixture analysis. Symmetric molecules have no strong absorption lines in THz range, polar possess absorption in both spectral ranges. Therefore, spectral analysis of a sample by a THz spectrometer is complementary to IR spectrometry. But combination of THz and IR spectra means that data are presented in high dimension feature space. In this case, data are highly correlated. It makes inefficient data analysis using conventional methods like Multivariate Curve Resolution [4], Univariate Calibration 0. Machine learning methods are becoming the best tool to solve the discussed tasks.
Several applications of THz and IR spectroscopy combined with machine learning methods will be presented.
The work was conducted with the financial support of the Ministry of Science and Higher Education of Russia (Agreement No. 075-15-2024-557 dated 04/25/2024).
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