TNT removal from culture media by three commonly available wild plants growing in the Caribbean

Correa-Torres, Sandra Natalia; Pacheco‐Londoño, Leonardo C.; Espinosa-Fuentes, Eduardo; Rodriguez, Lolita; Souto-Bachiller, Fernando A.; Hernández‐Rivera, Samuel P.

doi:10.1039/c1em10602c

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Recently, vetiver has been shown to effectively remove explosives from wastewater effluents from an industrial munition facility, with RDX, HMX, and DNAN transformation products detected in root and aerial tissues (Panja et al 2018). Studies have also identified the cool season orchardgrass (Dactylis glomerata; Duringer et al 2010), and species native to the Caribbean (Correa-Torres et al 2012). Still, perhaps a contentious issue, is the use of human food crop species for in-the-field remediation, for example, some studies have used sunflower (Helianthus annuus) which would perhaps require constraints to be in place to prevent plant materials entering the human food chain.…”

Section: Suitable Plant Speciesmentioning

confidence: 99%

Right on target: using plants and microbes to remediate explosives

Rylott

Bruce

2019

International Journal of Phytoremediation

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While the immediate effect of explosives in armed conflicts is frequently in the public eye, until recently, the insidious, longer-term corollaries of these toxic compounds in the environment have gone largely unnoticed. Now, increased public awareness and concern are factors behind calls for more effective remediation solutions to these global pollutants. Scientists have been working on bioremediation projects in this area for several decades, characterizing genes, biochemical detoxification pathways, and field-applicable plant species. This review covers the progress made in understanding the fundamental biochemistry behind the detoxification of explosives, including new shock-insensitive explosive compounds; how field-relevant plant species have been characterized and genetically engineered; and the major roles that endophytic and rhizospheric microorganisms play in the detoxification of organic pollutants such as explosives.

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Section: Suitable Plant Speciesmentioning

confidence: 99%

Right on target: using plants and microbes to remediate explosives

Rylott

Bruce

2019

International Journal of Phytoremediation

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“…The intensive use of high explosives (HEs) in military operations and mining excavations has contributed to soil contamination. Providing that HEs and their decomposition products that are highly persistent, mutagenic, and classified as Group C human carcinogens threaten human health [1,2], research on the timely detection of HEs has continued to receive considerable attention over the past few years. The methods currently used to detect HEs include gas chromatography-mass spectroscopy (GC-MS), gas chromatography-chemiluminescence (GC-CL), ion mobility spectrometry (IMS) [3], immunosensors [4], electrophoresis [5], fluorescence [6], high-pressure liquid chromatography (HPLC) [7,8], HPLC/mass spectrometry [7], and photo-assisted electrochemical detection [9].…”

Section: Introductionmentioning

confidence: 99%

Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence

et al. 2020

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A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

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“…These are considered as a contamination source which can generate poisoning of population humans and animals, and producing genetic diseases that threaten human health. [1,2] In order to monitor the quality of HE-contaminated soils, this study focuses on the detection of HE in soil using mid-infrared (MIR) laser spectroscopy with a Quantum Cascade Laser (QCL) source as a remote method of analysis. [3][4][5][6][7][8][9][10][11][12][13] QCL spectroscopy first demonstrated in 1994 by Faist et al [14] offers several benefits over conventional or thermal source MIR spectroscopy, such as room temperature operation, small beam sizes, long lifetimes, low energy consumption, long-term power stability, and fine-tuning of the output frequency.…”

Section: Introductionmentioning

confidence: 99%

Self-Simulated Learning Artificial Intelligence for the Detection of High Explosives in Soil by Mid Infrared Laser Spectroscopy

Villareal-González¹,

Galán-Freyle²,

Pacheco‐Londoño³

et al.

Authorea

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A tunable mid-infrared (MIR) laser (quantum cascade laser, QCL) was used for the detection of TNT and RDX in soil samples at a concentration range from 0 to˜20% w/w. This type of sensing is complicated due to the complexity of the matrix, i.e., the diversity of compounds contained in soil. Thus, the high explosives (HE) detection in soil by QCL was assisted with an Artificial Intelligence (AI) system. AI managed to predict these HE in seven kinds of soils using minimum information Machine Learning (ML). The models were generated only from neat HE and soil spectra, without necessity using experimental spectra of the mixes. AI used these neat spectra to simulate the spectra of HEs/soil mixes. The simulated data was used to train the ML models and then were tested with real spectra of HEs/soils mixes. The method was designated as "Self-Simulated Learning Artificial Intelligence" (SSLAI). This methodology has advantages for applications in field scenarios where the matrices are unknown because SSLAI models do not need to be trained with real samples a priori. Models would only have to be fed with spectra for the neat components to train itself. The methodology was tested with mixes of seven soils and two explosives. Test samples were classified into three concentrations ranges: high concentration test (Test H > 10% w/w), medium concentration test (10% w/w > Test M > 3% w/w), and low concentration test (Test L < 3% w/w). The results show that it is possible to correctly predict these two HE/soil mixes from the simulated data. Specifically, for TNT and RDX, SSLAI achieved a high precision in the prediction for the high and medium concentration tests (Test H and Test M). However, for both samples with concentrations below 3% w/w (Test L), the number of false positives increased, and the precision was reduced.

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TNT removal from culture media by three commonly available wild plants growing in the Caribbean

Cited by 4 publications

References 12 publications

Right on target: using plants and microbes to remediate explosives

Right on target: using plants and microbes to remediate explosives

Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence

Self-Simulated Learning Artificial Intelligence for the Detection of High Explosives in Soil by Mid Infrared Laser Spectroscopy

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