Abstract: |
The stabilizer measurements are an important part of the propellant status assessment. To achieve rapid and non destructive inspection of stabilizers, the feasibility of qualitative and quantitative analysis of stabilizers by UV vis diffuse reflectance spectroscopy are investigated. A spectral measurement system using fiber optic sensing is built to acquire the spectra of three stabilizer samples. The spectra are classified and identified by Support Vector Machine (SVM), and Principal Component Analysis (PCA) is applied to visualize the clustering trend of the samples.In combination with chemometrics, two quantitative prediction models are developed by partial least squares regression (PLSR) and principal component regression (PCR) using the spectral intensity of N methyl 4 nitroaniline (MNA) in the 470~500 nm band as characteristic data. The results show that SVM can classify three types of stabilizer samples, and the accuracy of the classification in the test set reaches 100%. The coefficients of determination (R2) of the two quantitative prediction models are 0.993 9 and 0.994 6, respectively, and the root mean square error (RMSE) of the external validation of the models is 0.000 23 at maximum, allowing quantitative analysis of MNA. The method has great potential for application in the field of stabilizer detection and can be extended to other detection fields. |