Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection  Primary Sjögren's Syndrome Associated with Interstitial Lung Disease

Wu, Xue; Chen, Chen; Chen, Mei Xiao; Luo, Nan Cai; Lv, Yi Xiao; Shi, Mei Ya; Yang, Jie; Meng, Yan Xin; Chen, Cheng; Su, Mei Jin; Wu, Lijun

doi:10.2139/ssrn.4125268

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…In an individual session, we observe that fitting the model takes most time for LR and least for KNN, while their accuracies are comparable with the exception of the last combination of data. Tables 2 and 3 also illustrate how different machine learning models are differently accurate, as it is also found in other studies [71,72].…”

Section: Resultssupporting

confidence: 73%

Classification of Cannabinoid Spectra Using Machine Learning

Kabra

Serra

Lozano

et al. 2022

Preprint

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Vibrational spectroscopy, encompassing Raman and Infrared (IR) spectroscopy, is a powerful technique that probes the intrinsic vibrations of a molecule, thus providing a unique chemical signature for that molecule. This information is beneficial to differentiate between two similarly structured molecules since their vibrational fingerprint will be different. In an effort to introduce an automated spectroscopic data analysis tool, we explore different Machine Learning (ML) algorithms to identify the chemical structure from the simulated Raman and IR spectra of 22 similar molecules belonging to the class of cannabinoids. In this study, we investigate the best ML approach by using representative synthetic IR/Raman data obtained from quantum chemical calculations of the selected molecular structures. We account for the experimental variability of the spectra by adding different kinds of noise and backgrounds to the simulated spectra such that they mimic experimental conditions such as fluorescence background as well as Gaussian noise. This methodology is used to setup the database to train the ML algorithms. We report the accuracy of the different ML algorithms and the time taken to process the algorithms in differentiating the cannabinoid varieties.

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Section: Resultssupporting

confidence: 73%