Trace explosive detection based on fluorescence sensing and similarity measures for time series classification

Abstract Currently, almost all explosives involved in bombings are nitro compounds, especially 2,4,6-trinitrotoluene (TNT) is the most widely used. In order to detect and prevent potential explosive threats in time, it is of great significance to detect trace TNT quickly and conveniently. We investi...

Full description

Saved in:
Bibliographic Details
Main Authors: Weize Shi, Yabin Wang, Piaotong Liu, Xin Li
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-025-08672-1
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849238780224995328
author Weize Shi
Yabin Wang
Piaotong Liu
Xin Li
author_facet Weize Shi
Yabin Wang
Piaotong Liu
Xin Li
author_sort Weize Shi
collection DOAJ
description Abstract Currently, almost all explosives involved in bombings are nitro compounds, especially 2,4,6-trinitrotoluene (TNT) is the most widely used. In order to detect and prevent potential explosive threats in time, it is of great significance to detect trace TNT quickly and conveniently. We investigated a fluorescence sensor and designed a trace explosive fluorescence detection system for detecting TNT acetone solutions and common chemical reagents. Experiments were conducted on the detection of TNT acetone solution with different concentrations, common chemical reagents, the influence of different injection volumes and injection flow rates, and the influence of UV irradiation time. In addition, the time series similarity measures, including the Pearson correlation coefficient, Spearman correlation coefficient, Dynamic Time Warping (DTW) distance, and Derivative Dynamic Time Warping (DDTW) distance, were used to classify the detection results. The results show that the limit of detection (LOD) of the fluorescent sensor for TNT acetone solution is 0.03 ng/μL, and the response time is less than 5 s. Moreover, the fluorescent sensor is specific, reversible and repeatable, and the recovery response time is less than 1 min. In addition, the method of integrating the calculation of Spearman correlation coefficient and DDTW distance can effectively classify the detection results.
format Article
id doaj-art-9cc9f4be89ff4a8a889db59723ed6ccc
institution Kabale University
issn 2045-2322
language English
publishDate 2025-07-01
publisher Nature Portfolio
record_format Article
series Scientific Reports
spelling doaj-art-9cc9f4be89ff4a8a889db59723ed6ccc2025-08-20T04:01:24ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-08672-1Trace explosive detection based on fluorescence sensing and similarity measures for time series classificationWeize Shi0Yabin Wang1Piaotong Liu2Xin Li3School of Mechatronical Engineering, Beijing Institute of TechnologySchool of Mechatronical Engineering, Beijing Institute of TechnologySchool of Mechatronical Engineering, Beijing Institute of TechnologySchool of Mechatronical Engineering, Beijing Institute of TechnologyAbstract Currently, almost all explosives involved in bombings are nitro compounds, especially 2,4,6-trinitrotoluene (TNT) is the most widely used. In order to detect and prevent potential explosive threats in time, it is of great significance to detect trace TNT quickly and conveniently. We investigated a fluorescence sensor and designed a trace explosive fluorescence detection system for detecting TNT acetone solutions and common chemical reagents. Experiments were conducted on the detection of TNT acetone solution with different concentrations, common chemical reagents, the influence of different injection volumes and injection flow rates, and the influence of UV irradiation time. In addition, the time series similarity measures, including the Pearson correlation coefficient, Spearman correlation coefficient, Dynamic Time Warping (DTW) distance, and Derivative Dynamic Time Warping (DDTW) distance, were used to classify the detection results. The results show that the limit of detection (LOD) of the fluorescent sensor for TNT acetone solution is 0.03 ng/μL, and the response time is less than 5 s. Moreover, the fluorescent sensor is specific, reversible and repeatable, and the recovery response time is less than 1 min. In addition, the method of integrating the calculation of Spearman correlation coefficient and DDTW distance can effectively classify the detection results.https://doi.org/10.1038/s41598-025-08672-1Explosive detectionTrace detectionFluorescent sensorTime series similarity
spellingShingle Weize Shi
Yabin Wang
Piaotong Liu
Xin Li
Trace explosive detection based on fluorescence sensing and similarity measures for time series classification
Scientific Reports
Explosive detection
Trace detection
Fluorescent sensor
Time series similarity
title Trace explosive detection based on fluorescence sensing and similarity measures for time series classification
title_full Trace explosive detection based on fluorescence sensing and similarity measures for time series classification
title_fullStr Trace explosive detection based on fluorescence sensing and similarity measures for time series classification
title_full_unstemmed Trace explosive detection based on fluorescence sensing and similarity measures for time series classification
title_short Trace explosive detection based on fluorescence sensing and similarity measures for time series classification
title_sort trace explosive detection based on fluorescence sensing and similarity measures for time series classification
topic Explosive detection
Trace detection
Fluorescent sensor
Time series similarity
url https://doi.org/10.1038/s41598-025-08672-1
work_keys_str_mv AT weizeshi traceexplosivedetectionbasedonfluorescencesensingandsimilaritymeasuresfortimeseriesclassification
AT yabinwang traceexplosivedetectionbasedonfluorescencesensingandsimilaritymeasuresfortimeseriesclassification
AT piaotongliu traceexplosivedetectionbasedonfluorescencesensingandsimilaritymeasuresfortimeseriesclassification
AT xinli traceexplosivedetectionbasedonfluorescencesensingandsimilaritymeasuresfortimeseriesclassification