Supervised Learning For Selective Multi-species Quantification From Noisy Infrared Spectroscopy Data
Al ibrahim, Emad
Loading…
Permalink
https://hdl.handle.net/2142/116495
Description
Title
Supervised Learning For Selective Multi-species Quantification From Noisy Infrared Spectroscopy Data
Author(s)
Al ibrahim, Emad
Contributor(s)
Farooq, Aamir
Issue Date
2022-06-22
Keyword(s)
Mini-symposium: Machine Learning
Abstract
A supervised learning approach is implemented to extract information from noisy vibrational spectroscopy data. Our method tackles two of the main problems in any commercial sensing application: sensitivity and selectivity. First, an encoder takes in noisy spectra of complex mixtures and learns reduced representations referred to as embeddings. The learned embeddings are then used in the decoder to filter out noise and unwanted species. Embeddings are also simultaneously used as input to a regression network for the prediction of concentrations and baseline shift. The model was applied for gas sensing using Fourier-Transform Infrared spectroscopy (FTIR) data. We focus on identifying common volatile organic compounds (VOCs) in a realistic scenario. The multitask nature of the model gives better results compared to single task denoising followed by regression and classical techniques like non-negative linear regression. The denoising capability was also compared to other denoising methods like Savitzky-Golay filters (SVG) and wavelet transformations (WT).
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
