Application of Chemometrics to NIRS Determination of TNT in Composite Explosive
摘 要
为构建样品中的被测组分(TNT)的含量与其红外光谱之间的数学模型,从生产线上采集以及按相同方法制备了共计155个样品并采集了它们的红外光谱,根据计算所得光谱残留F值判别并剔除异常光谱。随机选取63个样品的光谱作为校正集,其余92个样品的光谱作为验证集。另外采用常规的溶剂提取-红外光谱法测定了这些样品中TNT的含量作为建模参考值。在最优模型波段(cm-1)为:9 114.1~8 331.2,7 671.6~7 189.5,6 514.5~5 666,5 102.8~4 929.3,4 744.14~4 728.71的条件下,根据校正集的光谱数据,用偏最小二乘法建立数字模型。通过交叉检验均方根误差,RMSECV-维数曲线的理想程度以及光谱主成分分析结果选取了最优模型。采用χ2检验法,以及根据预测标准差和Bias值,结合验证集样品的光谱和数据,评估了方法的精确度和准确度。从TNT含量在36.68%~46.95%内的8个样品的测定结果得出其预测值的Bias值为0.078%,SEP%为0.514%。说明方法的准确度和精密度良好,且无需使用有机试剂。
Abstract
To establish mathematical model correlating the analyte (TNT) in the explosive samples with their near infrared spectra, a total of 155 samples collected directly from the production line and self-prepared in the same way were taken, and their NIR spectra were taken with the abnormal spectra rejected according to the values of SpecResF found. 63 samples among the 155 were selected at random as the calibration set and the remainder (92 samples) were taken as the testing set. All these samples were analyzed by the conventional NIRS method with solvent extraction, to obtain values of TNT used as reference in modelling. Based on the NIRS data, PLS was applied to establish mathematical models, selecting the following optimum modelling wavelength sections:9 114.1-8 331.2 cm-1, 7 671.6-7 189.5 cm-1, 6 514.5-5 666 cm-1, 5 102.8-4 929.3 cm-1 and 4 744.14-4 728.71 cm-1. Optimal models were selected through cross testing of RMSECV and considering the ideal of the curve of RMSECV/dimension and results of PCA of the spectra. Precision and accuracy of the proposed method were evaluated by applying the χ2-testing, and on the base of values of predictive standard deviation (SEP) and Bias values. 8 samples with TNT contents in the range from (w) 36.5% to 41.9% were analyzed by the proposed new method, giving value of Bias of 0.078% and SEP% of 0.514%. In addition, no organic solvent was used in the new method.
中图分类号 O657.33 DOI 10.11973/lhjy-hx201910015
所属栏目 工作简报
基金项目 青海省基础研究计划自然科学基金青年项目(2019ZJ963Q);青海省基础研究计划自然科学基金面上项目(2018ZJ916);青海省重大科技专项(2018-GX-A8)
收稿日期 2018/9/25
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联系人作者关云山(qh-gys@163.com)
备注赵云,副教授,博士,研究方向为化学与化工
引用该论文: ZHAO Yun,WANG Xiaojun,MA Xiao,GUAN Yunshan,CUI Mingxin,HU Xin. Application of Chemometrics to NIRS Determination of TNT in Composite Explosive[J]. Physical Testing and Chemical Analysis part B:Chemical Analysis, 2019, 55(10): 1202~1207
赵云,王小军,马骁,关云山,崔明鑫,胡鑫. 化学计量学方法应用于近红外光谱法测定混合炸药中TNT含量[J]. 理化检验-化学分册, 2019, 55(10): 1202~1207
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【5】杨晓丽,何琼.CARSiPLS用于烟煤中水分与挥发分的近红外光谱测定[J].理化检验-化学分册, 2017,53(6):636-640.
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【8】付建华,周新奇,刘辉军,等.基于稀疏主成分分析的近红外光谱法鉴别黄花梨的成熟度[J].理化检验-化学分册, 2017,53(2):146-151.
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【11】JIANG C, QU H B. A comparative study of using in-line near-infrared spectra, ultraviolet spectra and fused spectra to monitor Panax notoginseng adsorption process[J]. Journal of Pharmaceutical and Biomedical Analysis, 2015,102:78-84.
【12】CATELANI T A, SANTOS J R, PÁSCOA R N M J, et al. Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis:A feasibility study[J]. Talanta, 2018,179:292-299.
【13】严衍禄,赵龙莲,韩东海,等.近红外光谱分析基础与应用[M].北京:中国轻工业出版社, 2005.
【14】严衍禄,陈斌,朱大洲.近红外光谱分析的原理、技术与应用[M].北京:中国轻工业出版社, 2013.
【15】程士超,蔺向阳,李燕,等.基于NIR光谱的固体推进剂代用料混合特性[J].固体火箭技术, 2016,39(3):373-377.
【16】程士超,蔺向阳,李燕,等.近红外光谱法快速测试改性双基推进剂组分的均匀性[J].含能材料, 2016,24(4):343-347.
【17】富明,肖淑华,刘伟,等.近红外光谱法快速测定端羟基聚丁二烯固体推进剂药浆中功能组分含量[J].北京化工大学学报(自然科学版), 2014,41(6):47-51.
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