Infrared spectroscopy has proved to be a powerful tool for solving organic chemistry problems and finds a widening field in many industries. Infrared absorption and its relation to the molecular structure of organic material are discussed to give the essential background for detailed descriptions of techniques adopted in this work. Existing spectral analysis approaches rely on pre-processing and feature selection methods to remove signal artifacts based on prior experiences. This work introduces a data-driven deep learning approach and successfully applies it to predict organic powders’ mixtures. In particular, in this work, we use a convolutional neural network to predict different composition percentages of mixed organic powders. We show that using specific pre-processing steps, such as Savitsky Golay smoothing and derivatives, can increase the accuracy of the results.

Delelegn, T., Zoppis, I., Manzoni, S., Mognato, A., Reguzzoni, I., Lotti, E. (2021). Rapid Analysis of Powders Based on Deep Learning, Near-Infrared and Derivative Spectroscopy. In Proceedings of the 1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) (pp.1-12). CEUR-WS.

Rapid Analysis of Powders Based on Deep Learning, Near-Infrared and Derivative Spectroscopy

Zoppis, IF;Manzoni, S;Mognato, A;
2021

Abstract

Infrared spectroscopy has proved to be a powerful tool for solving organic chemistry problems and finds a widening field in many industries. Infrared absorption and its relation to the molecular structure of organic material are discussed to give the essential background for detailed descriptions of techniques adopted in this work. Existing spectral analysis approaches rely on pre-processing and feature selection methods to remove signal artifacts based on prior experiences. This work introduces a data-driven deep learning approach and successfully applies it to predict organic powders’ mixtures. In particular, in this work, we use a convolutional neural network to predict different composition percentages of mixed organic powders. We show that using specific pre-processing steps, such as Savitsky Golay smoothing and derivatives, can increase the accuracy of the results.
No
paper
Convolutional Neural Network; Near-Infrared (NIR); Quantitative Analysis; Savitsky Golay;
English
1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries, AIABI 2021 - 30 November 2021
Delelegn, T., Zoppis, I., Manzoni, S., Mognato, A., Reguzzoni, I., Lotti, E. (2021). Rapid Analysis of Powders Based on Deep Learning, Near-Infrared and Derivative Spectroscopy. In Proceedings of the 1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) (pp.1-12). CEUR-WS.
Delelegn, T; Zoppis, I; Manzoni, S; Mognato, A; Reguzzoni, I; Lotti, E
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/374025
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