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

Dagmawi Delelegn Tegegn; Dagmawi Delelegn Tegegn; Italo Francesco Zoppis; Sara Manzoni; Cezar Sas; Edoardo Lotti

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Paper

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

Topics: Neural Networks for Biosignal Data; Pattern Recognition & Machine Learning for Biosignal Data

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSTEC, 169-176, 2021

Authors: Dagmawi Delelegn Tegegn ^{1

;

2} ; Italo Francesco Zoppis ¹ ; Sara Manzoni ¹ ; Cezar Sas ³ and Edoardo Lotti ²

Affiliations: ¹ Department of Computer Science, University of Milano-Bicocca, Milan, Italy ; ² SeleTech Engineering Srl, Milan, Italy ; ³ Independent Researcher, Italy

Keyword(s): Convolutional Neural Network (CNN), Near-Infrared (NIR), Quantitative Analysis.

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 t est 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. (More)

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Paper citation in several formats:

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 (BIOSTEC 2021) - BIOSIGNALS; ISBN 978-989-758-490-9; ISSN 2184-4305, SciTePress, pages 169-176. DOI: 10.5220/0010244100002865

@conference{biosignals21,
author={Dagmawi Delelegn Tegegn. and Italo Francesco 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 (BIOSTEC 2021) - BIOSIGNALS},
year={2021},
pages={169-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010244100002865},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOSIGNALS
TI - Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum
SN - 978-989-758-490-9
IS - 2184-4305
AU - Tegegn, D.
AU - Zoppis, I.
AU - Manzoni, S.
AU - Sas, C.
AU - Lotti, E.
PY - 2021
SP - 169
EP - 176
DO - 10.5220/0010244100002865
PB - SciTePress