Due to the instability of alpha type HMX at low concentrations, it belongs to the impurity crystal form. To ensure the functional effectiveness, operational reliability and management safety of HMX, it is necessary to quantify the low content of the unstable alpha-HMX crystal form in the composite explosive. In this study, low-concentration alpha-HMX is quantitatively analyzed in a mixture of alpha- and beta-HMX. First, terahertz time-domain spectroscopy (THz-TDS) is used to obtain the absorption spectrum of the alpha/beta-HMX element in the frequency range of 0.2-2.0 THz, and the characteristic frequency is selected. The absorption coefficient data in the frequency band of 0.7-1.3 THz are considered as the sample data for quantitative analysis. Finally, support vector machine (SVM) algorithm is used to establish a regression model, and principal component analysis (PCA) is employed for feature extraction. Grid search (GS), genetic algorithm (GA) and particle swarm optimization (PSO) are utilized for parameter optimization in support vector regression (SVR). These algorithms are combined to establish six regression models, and their effectiveness is assessed. The experimental results show that all the six methods can predict the content of alpha-HMX components with a small error and a high prediction accuracy. Compared to GA-SVR and PSO-SVR models, the PCA-GA-SVR and PCA-PSO-SVR models exhibit higher prediction accuracy and stability. The test set of the PCA-GA-SVR model reveals an average absolute error of 0.880%. It has the highest prediction accuracy, and the coefficient of determination (R-2) reaches 0.9996. This indicates that PCA and SVR can be effectively used in the detection of low-concentration HMX components and can serve as a reliable basis for the quantitative analysis of other explosives.
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