Quantitative Analysis of Nitric Acid Using Raman Spectroscopy Based on Machine Learning
Aiming at the problem that the traditional methods in a modern production line of pentaerythrite tet-ranitrate(PETN)with intelligent detection technologies cannot achieve high precision quantitative analysis of the nitric acid Raman spectra due to the complicated changes of concentration,a high precision quantitative anal-ysis method based on machine learning was proposed.Three machine learning algorithms including partial least squares regression,supporting vector regression and random forest regression were firstly applied for construc-ting nitric acid quantitative models,which took the pre-processed spectra after smoothing,background subtrac-ting and normalization as input and nitric acid concentration as output.After optimization of the hyper-parame-ters in the three models by using GridSearchCV method coupled with 5-folds cross verification,the three models had all achieved the value of R2＞0.995 in the test packages and the random forest regression model was con-firmed as optimal,of which the mean square error was as low as 0.356.Experimental results showed that non-destructive,high precision detection for nitric acid concentration could be achieved by adopting Raman spectros-copy coupled with machine learning.
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