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Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression

[Image: see text] The components in polymer-bonded explosive X, including cyclotetramethylene-tetranitramine, paraffin, and polytetrafluoroethylene, were determined using near-infrared (NIR) spectroscopy. Using partial least squares as the multivariate calibration method, quantitative calibration mo...

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Autores principales: Su, Pengfei, Liang, Wenhao, Zhang, Gao, Wen, Xiaoyan, Chang, Hai, Meng, Zihui, Xue, Min, Qiu, Lili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444221/
https://www.ncbi.nlm.nih.gov/pubmed/34549117
http://dx.doi.org/10.1021/acsomega.1c02745
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author Su, Pengfei
Liang, Wenhao
Zhang, Gao
Wen, Xiaoyan
Chang, Hai
Meng, Zihui
Xue, Min
Qiu, Lili
author_facet Su, Pengfei
Liang, Wenhao
Zhang, Gao
Wen, Xiaoyan
Chang, Hai
Meng, Zihui
Xue, Min
Qiu, Lili
author_sort Su, Pengfei
collection PubMed
description [Image: see text] The components in polymer-bonded explosive X, including cyclotetramethylene-tetranitramine, paraffin, and polytetrafluoroethylene, were determined using near-infrared (NIR) spectroscopy. Using partial least squares as the multivariate calibration method, quantitative calibration models for components in X were verified internally and externally. The possible combinations of eight general spectral pretreatment methods and different bands of the scanning spectral region (12,500–4000 cm(–1)) were established. The models were analyzed, evaluated, and optimized via the fitting effect. The data were combined with the mathematical meaning of the model spectral pretreatment methods and the chemical significance of the modeling spectral bands. Prediction performance offered optimal quantitative calibration models. Paired bilateral Student’s t tests show that there is no significant difference between the values obtained by NIR and chemical analysis methods, and the NIR method has good accuracy. Moreover, the precision of the NIR method is better than that of the chemical method, and the analysis time is reduced from 2 days to a few minutes.
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spelling pubmed-84442212021-09-20 Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression Su, Pengfei Liang, Wenhao Zhang, Gao Wen, Xiaoyan Chang, Hai Meng, Zihui Xue, Min Qiu, Lili ACS Omega [Image: see text] The components in polymer-bonded explosive X, including cyclotetramethylene-tetranitramine, paraffin, and polytetrafluoroethylene, were determined using near-infrared (NIR) spectroscopy. Using partial least squares as the multivariate calibration method, quantitative calibration models for components in X were verified internally and externally. The possible combinations of eight general spectral pretreatment methods and different bands of the scanning spectral region (12,500–4000 cm(–1)) were established. The models were analyzed, evaluated, and optimized via the fitting effect. The data were combined with the mathematical meaning of the model spectral pretreatment methods and the chemical significance of the modeling spectral bands. Prediction performance offered optimal quantitative calibration models. Paired bilateral Student’s t tests show that there is no significant difference between the values obtained by NIR and chemical analysis methods, and the NIR method has good accuracy. Moreover, the precision of the NIR method is better than that of the chemical method, and the analysis time is reduced from 2 days to a few minutes. American Chemical Society 2021-08-27 /pmc/articles/PMC8444221/ /pubmed/34549117 http://dx.doi.org/10.1021/acsomega.1c02745 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Su, Pengfei
Liang, Wenhao
Zhang, Gao
Wen, Xiaoyan
Chang, Hai
Meng, Zihui
Xue, Min
Qiu, Lili
Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression
title Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression
title_full Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression
title_fullStr Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression
title_full_unstemmed Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression
title_short Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression
title_sort quantitative detection of components in polymer-bonded explosives through near-infrared spectroscopy with partial least square regression
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444221/
https://www.ncbi.nlm.nih.gov/pubmed/34549117
http://dx.doi.org/10.1021/acsomega.1c02745
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