Study on rapid prediction of low concentration o-nitrotoluene in mononitrotoluene mixture by near infrared spectroscopy combined with novel calibration strategies

• Published in: Microchemical journal 2024-05, Vol.200, p.110347, Article 110347
• Main Authors: Huo, Xue-Song, Chen, Pu, Li, Jing-Yan, Xu, Yu-Peng, Liu, Dan, Chu, Xiao-Li
• Format: Article
• Language: English
• Subjects: Chemometrics; NIR spectroscopy; O-nitrotoluene; Virtual sample
• ISSN: 0026-265X; 1095-9149
• DOI: 10.1016/j.microc.2024.110347

[Display omitted] •The Segmented modeling method proposed in this study can be used to explore the concentration range of samples for building the model with the best prediction performance.•The Virtual sample generation method based on background difference compensation is proposed, which can effectively reduce the demand of the model for samples.•The segmented model method and Virtual sample generation method can provide some reference ideas for similar applications of near-infrared spectroscopy. The determination of the o-nitrotoluene (o-MNT) content in separation process of mononitrotoluene (MNT) is of interest, since it affects the purity of m-nitrotoluene (m-MNT) and p-nitrotoluene (p-MNT). However, the analytical techniques traditionally used for its content determination are tedious and time consuming. Therefore, we explored the analysis of spectral data based on near-infrared spectroscopy (NIRS) and chemometrics, and extracted the spectral features of the o-MNT based on the interval selection algorithm. The calibration models for the o-MNT content based on samples with different concentration ranges were developed by PLS. Among them, the calibration model based on samples with 0.01–0.5 % concentration range has the best prediction performance. The calibration model was established with the determination coefficient of prediction (R2) of 0.959, root mean squared error of prediction (RMSEP) of 0.011 and ratio of standard deviation of the calibration set to standard error of prediction (RPD) of 4.899 for o-MNT. It is sufficient to meet the fast detection needs of the o-MNT content for process control in chemical industry. In addition, in order to reduce the demand of the model on samples and corresponding reference values, we explored the virtual sample generation method based on background difference compensation. The calibration model based on virtual samples was established with R2 of 0.74, RMSEP of 0.028 and RPD of 1.951. This study shows that the method based on NIRS and chemometrics has strong prediction performance for o-MNT in separation process of MNT, which is a guideline for controlling product purity of m-MNT and p-MNT. And the virtual sample generation method proposed in this study can significantly reduce the sample demand of calibration model.