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...
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| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08672-1 |
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| 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 |