关键词: 梯恩梯, 凝固点温度, 近红外光谱, 偏最小二乘法, 定量分析, 主成分分析

Abstract: In order to detect the solidifying point temperature in TNT production process in real time, the partial least squares method is used to establish a quantitative model for real-time detection of TNT solidifying point by near-infrared spectroscopy combined with chemometrics and spectroscopy. A model established by the preprocessing method with the modeling interval of 9 403.8-4 597.7 cm^-1 and the first-order derivative (17-point smoothing)+SNV is the best compared with six preprocessing methods optimized for the original spectral data. The six methods are standard normal variable transformation (SNV), multiple scattering correction (MSC), first derivative, first derivative + standard normal variable transformation (SNV), first derivative + multiple scattering correction (MSC), and second derivative. The correlation coefficient of the model is R²=0.991, and the root-mean-squares error of cross-validation is 0.178. The results of principal component analysis and model validation show that the maximum deviation between the predicted value of near infrared analysis and the measured value is 0.950 4% when the optimal model is used to predict the solidifying point temperature of nitrates on different samplers. The model has good stability and predictability, and can identify different types of samplers. The solidifying point can be determined by near infrared spectroscopy in a short time. Key

Key words: TNT, solidifyingpointtemperature, near-infraredspectroscopy, partialleastsquares, quantitativeanalysis, principalcomponentanalysis