Wide-band laser photo-acoustic spectroscopy and machine learning for breath air analysis
Yury V. Kistenev,1 , Alexey V. Borisov,1, Vladimir V.Prishepa,1, Igor K. Lednev,2, Han Jin, 3,4
1 Tomsk State University, Tomsk 634050, Russian Federation
2 University at Albany, SUNY, Albany, NY 12222, USA
3 Institute of Micro-Nano Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
4 National Engineering Research Center for Nanotechnology, Shanghai, 200241, P. R. China
Abstract
The report is devoted to applications of wide tuning range photo-acoustic laser spectroscopy and machine learning for breath air analysis to detect a specific disease. Breath air analysis can be conducted through the chemical-composition-based and pattern-recognition-based approaches. For the former approach implementation, we use deep neural networks [1] and original chemometrics’ methods: (a) a combination of the standard addition method with multivariate curve resolution called HAMAND [2]; (b) criterium based on reducing a spectrum complexity (RSC) [3] to provide exhaled air chemical composition. The latter approach is typical for supervised machine learning algorithms. We will compare both approaches.
File with abstract
Speaker
Yury V. Kistenev
Tomsk State University, Tomsk, 634050, Russia
Russia
Discussion
Ask question
