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IRIS è il sistema di gestione della ricerca dell’Università degli Studi di Milano-Bicocca.Il repository BOA è il modulo del sistema dedicato alla raccolta e alla disseminazione della produzione scientifica di Ateneo. Le pubblicazioni sono ricercabili per parola chiave attraverso il box nella barra orizzontale in alto oppure, mediante il pulsante Sfoglia, per "Afferenza", "Autore", "Titolo", "Tipologia" o "Settore scientifico-disciplinare".
IRIS contiene inoltre alcuni moduli dedicati alla registrazione dei progetti e dei contratti, e alla reportistica avanzata per le attività di valutazione della ricerca
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) [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) [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 Joint Conference on Biomedical Engineering Systems and Technologies | |
| ISBN: | 978-989-758-490-9 | |
| Appare nelle tipologie: | 02 - Intervento a convegno |
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