Abstract
Achieving high classification accuracy on trace chemical residues in active spectroscopic sensing is challenging due to the limited amount of training data available to the classifier. Such classifiers often rely on physics-based models for generating training data though these models are not always accurate when compared to measured data. To overcome this challenge, we developed a physics-guided neural network (PGNN) for predicting chemical reflectance for a set of parameterized inputs that is more accurate than the state-of-the-art physics-based signature model for chemical residues. After training the PGNN, we use it to generate a library of predicted spectra for training a classifier. We compare the classification accuracy when using this PGNN library versus a library generated by the physics-based model. Using the PGNN, the average classification accuracy increases from 0.623 to 0.813 on real chemical reflectance data, including data from chemicals not included in the PGNN training set.
Publication Date
4-7-2021
Document Type
Article
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Recommended Citation
Cara P. Murphy and John P. Kerekes, "Physics-guided neural network for predicting chemical signatures," Appl. Opt. 60, 3176-3181 (2021) https://doi.org/10.1364/AO.420688
Campus
RIT – Main Campus
Comments
© 2021 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.