Quantifying relative moisture content in dielectric models using CW-THz spectroscopy and supervised machine learning regression

Mayuri Kashyap; Aparajita Bandyopadhyay; Karl Bertling; Amartya Sengupta; Aleksandar D. Rakiċ

doi:10.1117/12.2595006

1 August 2021 Quantifying relative moisture content in dielectric models using CW-THz spectroscopy and supervised machine learning regression

Mayuri Kashyap, Aparajita Bandyopadhyay, Karl Bertling, Amartya Sengupta, Aleksandar D. Rakiċ

Author Affiliations +

Proceedings Volume 11827, Terahertz Emitters, Receivers, and Applications XII; 1182704 (2021) https://doi.org/10.1117/12.2595006
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

Paper, as a hygroscopic dielectric material, does not have specific spectral signatures in the Terahertz (THz) range from 0.2-6 THz. However, because of its constituent materials, including dry matter, moisture, and air pockets, it absorbs THz radiation, similar to biological tissues and green leaf. Though the absorption loss is not significant, varying levels of dampness in wet paper are observed over time using continuous wave (CW) based THz Spectroscopic system to quantify the moisture content of wet paper relative to paper at ambient environment. For this purpose, effective medium theory (EMT) approaches including Bruggeman (BM), Landau–Lifshitz–Looyenga (LLL), and Complex Refractive Index (CRI) models are analysed. However, EMT models are dependent on physical and optical properties of paper and water, which are not well-defined and are dependent on assumptions, approximations and rigorous calculations. To remove such dependencies, supervised machine learning regression (SMLR) algorithms in the form of decision tree (DT), random forest (RF), and support vector regression (SVR) are investigated. The conditioning of the training parameters is dependent on spectroscopic data which reduces the processing time and improves efficiency due to elimination of approximations. Prediction efficiency of SMLR models is observed to be better than that of EMT models. RF shows the best results in terms of coefficient of determination, 𝑅² but the time required for training is more when compared to DT and SVR models. DT models show consistent performance, while predictions using different SVR models show variance with 𝑅² ranging from 0.42 to 0.98.

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Mayuri Kashyap, Aparajita Bandyopadhyay, Karl Bertling, Amartya Sengupta, and Aleksandar D. Rakiċ "Quantifying relative moisture content in dielectric models using CW-THz spectroscopy and supervised machine learning regression", Proc. SPIE 11827, Terahertz Emitters, Receivers, and Applications XII, 1182704 (1 August 2021); https://doi.org/10.1117/12.2595006

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