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EnglishAdvances 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.
Tegegn, D., Zoppis, I., Manzoni, S., Sas, C., & Lotti, E. (2021). Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum. In BIOSTEC 2021. Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 (pp.169-176). AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL : SciTePress [10.5220/0010244101690176].
| Citazione: | Tegegn, D., Zoppis, I., Manzoni, S., Sas, C., & Lotti, E. (2021). Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum. In BIOSTEC 2021. Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 (pp.169-176). AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL : SciTePress [10.5220/0010244101690176]. | |
| Tipo: | paper | |
| Carattere della pubblicazione: | Scientifica | |
| Presenza di un coautore afferente ad Istituzioni straniere: | No | |
| Titolo: | Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum | |
| Autori: | Tegegn, D; Zoppis, I; Manzoni, S; Sas, C; Lotti, E | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Lingua: | English | |
| Nome del convegno: | 14th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2021 - Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021 - 11 February 2021 through 13 February 2021 | |
| ISBN: | 978-989-758-490-9 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.5220/0010244101690176 | |
| Appare nelle tipologie: | 02 - Intervento a convegno |
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