Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum

Dagmawi Delelegn Tegegn, Dagmawi Delelegn Tegegn, Italo Zoppis, Sara Manzoni, Cezar Sas, Edoardo Lotti

Abstract

Advances in Near-infrared (NIR) spectroscopy technology led to an increase of interest in its applications in various industries due to its powerful non-destructive quantization tool. In this work, we used a one-dimensional CNN to determine simultaneously quantities of organic materials in a mixture using their NIR infrared spectra. The coefficient of determination (R2) and the root mean square error (RMSE) is used to test the performance of the model. We used six materials to make pairwise combinations with distinct quantities of each pair. We obtained 13 different pairwise mixtures, afterward, their near-infrared spectrum profiles is extracted. The model predicted for each mixture their percentage of composition with a result of 0.9955 R2 and RMSE 0.0199. Furthermore, we examined the performance of our model when predicting unseen composition percentages with unseen mixtures. To do so, two scenarios are carried out by filtering the training and testing set: the first one where we test on unseen composition percentage (UP) of mixtures, and the second one where we test on unseen composition percentage of unseen mixtures (UPM). The model achieved an R2 of 0.947 and 0.627 scores respectively for UP and UPM.

Download


Paper Citation


in Harvard Style

Tegegn D., Zoppis I., Manzoni S., Sas C. and Lotti E. (2021). Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-490-9, pages 169-176. DOI: 10.5220/0010244101690176


in Bibtex Style

@conference{biosignals21,
author={Dagmawi Delelegn Tegegn and Italo Zoppis and Sara Manzoni and Cezar Sas and Edoardo Lotti},
title={Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,},
year={2021},
pages={169-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010244101690176},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,
TI - Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum
SN - 978-989-758-490-9
AU - Tegegn D.
AU - Zoppis I.
AU - Manzoni S.
AU - Sas C.
AU - Lotti E.
PY - 2021
SP - 169
EP - 176
DO - 10.5220/0010244101690176