硫化促进剂太赫兹光谱数据扩充策略与定量检测OA

To achieve rapid,non-destructive and accurate detection of vulcanization accelerator content in rubber products,this study adopted terahertz time-domain spectroscopy technology,combined with data augmentation and chemometric methods,to conduct quantitative analysis of vulcanization accelerators in multi-component rubber mixtures.Aiming at the problems of serious spectral overlap of rubber mixtures,small sample size which was prone to model overfitting and poor generalization ability,a data augmenta-tion strategy based on data fusion and Least Squares Gaussian Fitting(LSGF)was proposed,and a quan-titative model of Genetic Algorithm-optimized Support Vector Regression(GA-SVR)was constructed.To reduce data dimensionality and improve modeling efficiency,the Variable Space Iterative Shrinkage Approach(VISSA)was used to extract features from the original and augmented spectra.The results show that data augmentation can significantly improve the predictive performance of the model.Among them,the LSGF method has the best effect;after VISSA feature extraction,the model accuracy is further improved,and the correlation coefficient Rp of the LSGF-augmented data in the prediction set reaches as high as 0.982 6,with the RMSEP as low as 0.002 3.This method can provide technical reference for rubber formula optimization and green and sustainable development of the industry.