âClick and Screen" technology for explosives detection on human

Subscriber access provided by Kaohsiung Medical University

“Click and Screen" technology for explosives detection on human hands by portable MicroNIR/Chemometrics platform Roberta Risoluti, Adolfo Gregori, Sergio Schiavone, and Stefano Materazzi Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/ acs.analchem.7b03661 • Publication Date (Web): 06 Mar 2018 Downloaded from http://pubs.acs.org on March 7, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

“Click and Screen” Technology for Explosives Detection on Human Hands by Portable MicroNIR/Chemometrics Platform Roberta Risoluti§*, Adolfo Gregori†, Sergio Schiavone† and Stefano Materazzi§

§ †

Department of Chemistry, “Sapienza” University of Rome, p.le A.Moro 5, 00185 Rome Italy Carabinieri RIS, Scientific Investigation Department, v.le Tor di Quinto 151, 00191 Rome Italy

KEYWORDS: MicroNIR, Chemometrics, Explosives, Personnel screening ABSTRACT: Portable Near-Infrared spectroscopy (MicroNIRs) coupled to chemometrics was investigated for the first time as a new tool for the on-site analysis of explosives from human hands. A novel entirely on-site approach based on the use of a particular miniaturized NIR spectrometer, was developed and validated in cooperation with the Scientific Investigation Department (Carabinieri RIS of Rome). Spectra from a number of 25 volunteers were acquired in the NIR region in reflectance mode and a model of prediction was optimized based on chemometrics tools. Results demonstrated the capabilities of the approach MicroNIR/Chemometrics to correctly identify explosive from hands and to be not affected by complexity and variability of the matrix. This study has shown that MicroNIR/Chemometrics approach can be considered as a useful, fast, non-destructive tool providing the identification of explosives manipulation in real forensic caseworks.

The screening of persons for the explosive traces detection has become more important in the last decades due to an increased threat of terroristic attacks, as some type of explosives can be prepared from easily obtainable ingredients.1 Especially at the airport, check points or other security relevant public or military areas, a robust and efficient screening technology is desired which offers a high level of security and guarantees a high throughput. The most prevalent form of explosive device utilized by terrorists today is the Improvised Explosive Device (IED). IEDs are homemade, non-conventional explosives, fabricated by combining common chemicals.2 The trace explosives detection may be performed by a number of analytical techniques 3,10 including nanomaterial-based sensors. 11,12 In the meanwhile, providing automated, miniaturized and one-touch devices for the detection of specific threats, is more and more required and innovative personal screening systems to prevent terrorist attacks involving IED may be very challenging. Spectroscopic techniques 13-15 have great potential for improving the discrimination of materials that show a similar instrumental response and among these, Near Infrared (NIR) spectroscopy is gaining global attention in forensic field, 16-21 as analytical technique able to give qualitative as well as quantitative information about complex samples. To this end, the coupling with chemometric techniques, may provide useful information as results can be easily interpreted and the effect of any kind of interferences on the spectral signal may be evaluated. 22-24 Neverthless, with respect to forensic applications, the miniaturisation and portability of spectroscopic devices deserves remarkable attention. A number of handheld near-infrared scanning spectrometers

became commercially available in the last few years,25 but the main concern about portable instruments is commonly represented by lower performance scores than laboratory instruments. 26 In this work, we developed a fast real-time and easy-to-use method for the detection of IED manipulation based on MicroNIR coupled to chemometrics for the high throughput identification of explosives from human hands. Moreover, the persistence of traces evidences was evaluated over time, considering time between handling and analysis as well as the daily routines related to hands washing. The parallel obtained results from official reference method based on gas chromatography coupled to mass spectrometry (GC-MS) in cooperation with Italian Scientific Investigation Department (Carabinieri-RIS of Rome) demonstrated the proposed approach to be promising for the real time detection of explosives in forensic investigations. EXPERIMENTAL SECTION Materials Reference standard solutions of 2,4,6trinaitrotolunene (TNT), pentaerythritol tetranitrate (PETN) and 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), were purchased from Cerilliant (Italian customer –Milan) and real explosive materials of TNT, PETN, RDX and composites such as DEMEX and M75 were kindly provided by Carabinieri RIS of Rome (Scientific Investigation Department). Experimental design A proper experimental design was considered to calibrate the model based on the situations which one expects to analyze in real cases. As the prediction

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 10

ability of a model heavily relies on the collection of a representative dataset, several aspects were taken into consideration for samples collection, to ensure the independency and the representativeness of the model. In particular, three main experiments were scheduled to check the model to be reliable: a) the ability of the approach to detect explosives in complex matrices, thus the matrix effect on the instrumental signal at different amount of residual explosive; b) the effect of the interferences from other explosives materials, thus the possibility to simultaneously detect more than one explosive; c) the persistence over time of residuals after daily routines related to hands washing. A number of 25 volunteers were involved in this study: 10 subjects were considered for model development in order to simulate real caseworks and 5 subjects for the validation. Finally, 10 subjects were asked for explosives handling to check the prediction ability of the model. The experimental design was built as follows: a preliminary step was performed by the mean of reference standard materials in order to calibrate the spectral response and to evaluate the matrix effect on explosive identification. To this end, hands of 15 subjects were fortified with increasing amounts of explosives (10, 50, 100 µg) to simulate handling and spectra were recorded and processed by Principal Component Analysis (PCA). In a second stage, the performances of the model for each explosive were tested on the same type of specimen as the calibration set, but fully independent batches; to this end, 10 different subjects were considered for manipulation of real samples from recent seizures of law enforcement, three for each explosives TNT, RDX, and PETN and three for explosive materials containing PETN and RDX as M75 and DEMEX. In order to statistically evaluate the explosive dispersion on the entire hand during manipulation, a total of 60 spectra in reflectance mode were recorded from each subject: 30 of these prior to explosive deposition (five from each finger and five from the palm) and the remaining 30 on the same points after contact with explosives. Principal Component Analysis was used as chemometric tool to compare results using the dataset from 300 spectra while a comprehensive model of prediction for all the investigated explosives simultaneously was developed by Partial Least Square Discriminant Analysis (PLS-DA) to evaluate the matrix effect in the explosive discrimination on human hands. Finally, the ability of the MicroNIR/Chemometrics approach to correctly detect traces of explosives on human hands was evaluated over time for a period of 24 h after handling, considering all daily routines related to hands involvement. Consequently, every subject was asked to handle a quantity of explosive ranging from 1 to 3 g and to wash their hands. Spectra were recorded after 2, 5 and 24 h. Validation of the achieved results was performed by GC-MS as reference official method to confirm the explosive detection.

advantage of these compact systems is related to the particularly geometry and optical resolution, allowing to achieve comparable outcomes as the reduction in size does not compromise the performances of the spectrometer. The MicroNIR device is developed and distributed by Viavi Solutions (JDSU Corporation, Milpitas, USA) and operates in the spectral region 900-1700 nm. This ultra-compact and lowcost device consist of a linear variable filter (LVF) as dispersing element directly connected to a 128-pixel linear indium gallium arsenide (InGaAs) array detector and two tungsten light bulbs as radiation source. The input aperture size is 2.5 mm x 3.0 mm. In this study, spectra were collected in the reflectance mode at a nominal spectral resolution of 6.25 nm. Spectralon was used as NIR reflectance standard (blank), with a 99% diffuse reflectance, while a dark reference is required to be obtained from a fixed place in the room. The acquisitions were performed with an integration time of 10 ms, resulting in a total measurement time of 2.5 s per sample. MicroNIR instrument control was performed by the use of the MicroNIR Pro software (JDSU Corporation, Milpitas, USA) while all chemometric treatments were carried out by using VJDSU Unscrambler Lite (Camo software AS, Oslo, Norway). The effect of a number of mathematical pre-treatments was investigated: in particular, among scatter-correction methods, Standard Normal Variate transform (SNV), 27 Multiplicative Scatter Correction (MSC) 28 and normalization 29 were evaluated, while Savitzky-Golay (SG) polynomial derivative filters 30 was considered as spectral derivation techniques. Among these, the Multiplicative Scatter Correction (MSC) was selected, as it provided a clear distinction of all the investigated explosives.

MicroNIR/Chemometrics platform The MicroNIR instrument represents the core of the this novel approach as it is a very ultra compact and portable (45 mm in diameter and 42 mm in height) compared to other commercialized devises, weighting about 60 g, and entirely powered (5 V) and controlled via USB port of a portable computer. As a consequence, after model development and optimization of all the statistically treatments, the approach is easy to be performed by not skilled person. In addition, the main

RESULTS AND DISCUSSION

GC-MS parameters The GC-MS system was a Perkin Elmer (Waltham, MA) using a HP-5MS (30 m x 0.25 mm x 0.25 mm) as capillary separation column. Electron impact (EI) ionization was employed at a voltage of 70 eV. The carrier gas was helium delivered at a constant flow of 1 mL/min. The oven temperature program was initially 60°C for 5 min, ramped to 100°C at 15°C/min and maintained for 1 min and then ramped to 200°C at 25°C/min for 1 min. The inlet temperature was 175 °C, the interface temperature was 290°C, and the temperature of the ion source and quadrupole were 230°C and 150°C. Mass spectral data was collected in the scan mode from m/z 44 to 400. The injection was made in split mode with a split ratio of 60:1. Experimental safety Permeability of the explosives after handling was estimated by GC-MS. To this aim, urine samples of each subject were collected over the period of exposure, up to 48 h and no traces of explosives and its metabolites were recovered.

MicroNIR exploratory tool The preliminary evaluation of the contribution of the matrix (hands) on the signal was performed by comparing the acquired spectra of fingers and palm from 10 subjects before and after explosive deposition. A typical behavior of the spectral signal due to the complexity of the matrix is reported in figure 1. The Principal Component Analysis (PCA) was performed on MicroNIR spectra in


Page 3 of 10


1 2 3 reflectance mode of non-fortified hands and fortified hands with increasing amount of explosives to evaluate whether the 4 complexity of the matrix may affect explosive detection. 5 Blank% In order to improve sensitivity, not all the spectral range was Blank%+%TNT% 6 Blank%+%RDX%% considered for the PCA analysis, as not all the wavelengths Blank%+%PETN% 7 contribute equally to the signal. In particular, the following spectral ranges were selected as a result of a preliminary 8 chemometric investigation: from 1091.7 to 1212.1 nm, from 9 1333.4 to 1400.4 nm and from 1500.7 to 1650.1 nm. 10 The improvement of chemometric techniques to spectroscopic TNT# 11 data is associated to the ability of enhancing the signal related to the investigated molecule, with respect to redundancy and 12 Wavelength%(nm)% noise. This process will increase the signal to noise in the 13 MicroNIR outcomes. All the recorded spectra were baseline Figure 1. Acquired spectra in reflectance mode of blank (hands TNT# 14 TNT# corrected and pretreated by MSC and the resulting scores plots without explosives, blue), blank with TNT (red), RDX (green) 15 are reported in figure 2. Regardless of the type of explosive and PETN (orange). TNT# TNT# considered, each PCA model provided a clear discrimination 16 of the molecule according to PC 1 (overall explained variance ! 17 TNT (a) ! TNT# not less than 87.3 % for all the explosives) as shown in figure ! 18 1. The location of the samples corresponding to the fortified ! ! TNT# ! ! ! ! TNT# 19 and non fortified revealed a good robustness of the model that ! ! ! ! ! resulted not affected by the complexity of the hands ! 20 ! ! ! composition and variability. Despite the quantification of ! 21 ! ! RDX$ ! ! residual explosives might sound not very useful from a 22RDX$ ! forensic point of view and does not represent the aim of the ! 23 ! Blank (hands) application, it provided the sensitivity of the MicroNIR Blank + 10 µg TNT approach. Therefore, the achieved results suggest to develop a 24 RDX$ Blank + 50 µg TNT comprehensive tool to simultaneously discriminate explosives 25RDX$ Blank + 100 µg TNT from human hands. To this aim, Principal Components 26 Analysis was applied to the entire data set of collected spectra PC 1 (93.6 %) 27 ! and results are shown in figure 2. As shown in figure 3, the RDX$ ! 28 interpretation of the scores plot provides several important ! RDX$ ! RDX (b) ! results. At first, a good correlation among samples belonging ! 29 ! !!! to the same specimens could be observed, as no dispersion of ! ! 30 ! !! ! the data was achieved, suggesting a correct reproducibility of ! ! 31 ! !! ! the method. ! !! 32 The exploratory analysis demonstrated the ability of the ! ! ! ! MicroNIR approach to simultaneously identify the presence of ! ! 33 ! ! ! explosive material on hands according to PC1 (65.11 % of ! 34 ! ! ! Blank (hands) ! RDX based explosives PETN based explosives TNT 35 ! Blank + 10 µg RDX ! ! Blank + 50 µg RDX 36 ! Blank + 100 µg RDX ! 37 38 PC 1 (87.3 %) 39 (c) PETN 40 ! 41 PETN%PETN% ! ! 42 ! ! ! PETN% 43 ! ! 44 ! ! ! 45 ! Blank (hands) ! 46 Blank + 10 µg PETN ! PC 2 (26.4 %) 47 Blank + 50 µg PETN Figure 3. Resulting scores plot obtained by PCA of spectra from Blank + 100 µg PETN 48 explosives handling of TNT (green), RDX (red), PETN (blue), 49 DEMEX (light blue) an M75 (orange). PC 1 (90.6%) 50 Figure 2. Scores plot obtained by PCA of spectra from simulated explained variance) and to predict the chemical composition of explosives handling with 10 µg (blue), 50 µg (red) and 100 µg 51 the explosive moving along PC2 (26.45 % explained (green) of TNT (a), RDX (b) and PETN (C). 52 variance). In addition, the PCA model allowed to distinct 53 clusters corresponding to three main groups of samples 54 55 56 57 58 ACS Paragon Plus Environment 59 60 3.50E+01%

3.00E+01%

%%Reflectance%

2.50E+01%

2.00E+01%

1.50E+01%

1.00E+01%

5.00E+00%

1300%

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

1400%

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

1500%

1600%

1700%

PC#1#(93.6#%)#

PC#1#(93.6#%)# PC#1#(93.6#%)#

PC#1#(87.3#%)#

PC#1#(87.3#%)#

PC#1#(87.3#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

Positive

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! !! !! ! !! !! !! !! !! !! !! !! !! ! !! !! !! !! ! !! !! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! PC#1#(93.6#%)# ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

Hands&before&handling& Hands#before#handling# PETN&handling& PETN#handling# M75&handling& M75#handling# TNT&handling& TNT#handling# RDX&handling& RDX#handling# DEMEX&handling& DEMEX#handling#

Negative

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! !

! 1200%

PC 1 (65.1 %) PC#1#(65.11#%)#

PC#1#(11.1#%)#

PC PC#2#(11.1#%)# 2PC#1#(11.1#%)# (11.1 %)

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

1100%

! !

PC 2 (2.7 %)

PC#1#(2.7#%)#

PC#1#(2.7#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! !! !! ! ! ! ! ! ! ! ! ! ! !! !! !! !! ! !! !! !! !! !! !! !! !! !! ! !! !! !! !! ! !! !! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !

PC#1#(11.1#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

PC 2 (9.3 %) PC#2#(9.3#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

PC#2#(9.3#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

PC#2#(9.3#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! !! !! ! !! !! !! !! !! !! !! !! !! ! !! !! !! !! ! !! !! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !

PC#1#(2.7#%)#

PC#1#(2.7#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

1000%

! !

0.00E+00% 900%

PC#2#(26.45#%)#

PC#1#(90.6#&)# PC#1#(90.6#&)#

PC#1#(90.6#%)# PC#1#(90.6#&)#


CONCLUSIONS For the first time an ultra compact portable devise (MicroNIR) was applied for the on-site analysis of explosives from human hands to propose an innovative personal screening system to prevent terrorist attacks. In order to develop a reliable, rapid, highly sensitive model able to identify explosives manipulation, a comprehensive experimental design was carried out taking into account the contribution of a complex matrix to the spectral response by Principal Component Analysis. A number of five explosive materials were considered in this study and a model of prediction was developed and validated by PLS-DA. The results and correlation with the parallel reference official method demonstrated the ability of the MicroNIR to correctly identify 90 9090"

90" 90"

80 8080"

80" 80"

70 7070"

70" 70"

60 6060"

60" 60"

50 50

50" 50"

10 1010" 00

0"

PETN

20" 20" 10" 10"

100 100%

0 00

0"0"

100% 100

100"

9090%

90"

70"

70% 70%

%%Correct%classifica3on%rate%

6060%

%"Correct"classificaDon"rate"

5050% 4040%

40% 40%

3030% 2020%

30% 30%

1010%

50"

Prediction

96.0

96.7

100.0

99.7

100.0

89.0

98.4

80.0

97.7 95.1

100.0 91.7

50% 50% 40% 40%

30"

20

80%

70%

8080%

80"

7070%

70"


6060%

70% 70%

50%

5050%

60% 60%

40%

4040%

50% 50%

30%

3030%

40% 40%

20%

2020%

30% 30%

10%


60%

1010%

20% 20%

0%

0

10% 10%

0%0%

0%

2 5 5" 2 2"Time%(h)% 5 24 5%5% (h)Time"(h)" 22 Time 55 24 Time 24 Time%(h)% Time%(h)% (h)

100% 100% 100% 100%

24%

24

9090%

24"

90% 90%

24% 24%

Time (h) TimeTNT# (h)

80

90% 90% 80%

RDX$ PETN%

70

80% 80% 70%

70% 70%

RDX$ RDX$

60

70% 70% 60%

60% 60%

50

60% 60% 50%

50% 50%

40

50% 50% 40%

40% 40%

30

40% 40% 30%

30% 30%

20

30% 30% 20% 20% 20% MicroNIR% 10% 20% MicroNIR% 20% GCDMS% 10% 10% GCDMS% 0% 10% 10% MicroNIR% MicroNIR% 0% 0% GCDMS% GCDMS%

60"

10 0

50"

40"

0%0% 30"

100% 100%

10"

0%0%

0%

2%

0"

2%2%

2 2% 2 Time%(h)% Time%(h)% 2 2" (h) Time

5%

5%

55 5" 24 5 24

Time"(h)" Time (h) (h) Time 5%5%

Time%(h)% TNT# 22 Time%(h)%

55

24%

24%

24% 24%

24%

24

PETN TNTDEMEX DEMEX DEMEX

90%

80%

70%

100"

90"

80"

70"

60"

40"

0"

0% 00% 0 0 0%0%0 00%0

0"

2% 2%

2% 5% 24% Time%(h)% 2 222" 5 55" 5 242424%24% 2424" 5% 5% Time%(h)% 2 Time 24 Time"(h)" 5 Time (h) Time%(h)%(h) 5% 24% Time%(h)% Time%(h)% DEMEX% 22Time 55 24 24 24% Time (h)(h) 5% TNT#

80% 80% 70% 70%

RDX$

RDX

60%

50%

40%

40%

30%

30%

20% MicroNIR% 20% MicroNIR% GCDMS% 10%GCDMS% MicroNIR% GCDMS% 10% MicroNIR% 0% GCDMS% 0%

30% 40% 30% 20%

70%

60%

0 0% 0

0%

50%

10% GCDMS% 20% MicroNIR% 10% 0% GCDMS% 10%0% 0%

50%

40%

100%

0

0%

2%

2

Time%(h)% Time%(h)%

5%

5

24

24%

22 (h) 55 24 Time 24 explosive manipulation and in 00 the Time meanwhile, to (h)(h) DEMEX% 0 2 Time 5 24 RDX$ simultaneously differentiate the explosive involved perfectly DEMEX RDX Time (h) in agreement with reference methods. Moreover, the method 0 2 5 24 proved affected neither by the variability of human 0 to be not 2Time (h) 5 24 Time (h) time since handling, as it has showed the hands, nor by the RDX$ RDX same outcomes of the reference methods and suggesting to be a very promising tool to a sensitive, inexpensive and simpler 40%

30%

0% 0%

2% 2%

0%

0%

30%

2%

5% 5%

Time%(h)%

Time%(h)%

5%

90%

24% 24%

80%

24%

20%

20%

100% 100%

0%

0%

2%

0%

2%

Time%(h)% Time%(h)%

5%

24%

5%

90%

24%

80%

100%

90%

80%

70%



70%

60%

50%

40%

30%

20%

90%

80%

70%

50%

20%

ACS Paragon Plus Environment 10%

0%

0%

0

2%

2 Time%(h)%

Time (h)

5%

5

24%

24

0%

00 0%

20%

50%

40%

30%

20% 10%MicroNIR% MicroNIR% GCDMS% GCDMS% 0%

0

30%

GCDMS% 0% 0%

30%

60%

0%

40%

10% MicroNIR% 10%

40%

70%

60%

60%

50%

2%

DEMEX

20% 30% 20% MicroNIR% 10%

60%

0%

60%

50%

70%80% 70% 80%

10%

The figure 3 also confirms that the model permits to identify explosives with an average predictive accuracy of 93.1 % for

90% 90%

M75$ Time Time (h) (h)

10%

!

100% 100%

2%2%

40% 50% 40% 30%

70%

M75

DEMEX

GC6MS"

50% 40%

80%

0

0%

10" MicroNIR"

60% 50% 50% 60%

80%

30%

0%

70% 60% 60% 70%

90%

40%

20"

RDX 4. Evaluation Time Time (h) (h) Figure of the persistence of residual traces of explosives on hands over time by MicroNIR approach (blue) and GC-MS (orange). Data are expressed as percentage of correct classification objects. RDX$

50%

30"

80%90% 80% 90%

24 24

60%

MicroNIR% MicroNIR" 20% MicroNIR% GCDMS% MicroNIR% GC6MS" MicroNIR% GCDMS% MicroNIR% GCDMS% 10% GCDMS% GCDMS%

50"

100% 90% 90% 100%

24"

100%

2424"

M75$ PETN% Time DEMEX% Time (h) DEMEX% TNT#(h)

TNT M75 DEMEX

20"

0 0"0%0 0

2% 2 2" Time%(h)% 5% 5"5 2 Time 5 24 24%24% 5%5% (h) DEMEX% 22Time 55 24 24 Time%(h)% Time"(h)" Time%(h)%(h)

2%2%

80% 80%

100"

90"

80% 80%

2%2%

TNT RDX PETN RDX RDX

9090%

90% 90%

0"

5%


90%

100% 100%

M75

0 0% 0" 0 00 M75

100% 100

2%

GC6MS"

30"

10

10"

0%

MicroNIR"

GCDMS% GCDMS% 40"

30 30%

20"

0 0 00 0%0%

100% 100

MicroNIR% GCDMS% MicroNIR% MicroNIR%

50"

40 40%

0

40"

0"

100%

60"

50 50%

30% 30% 20% MicroNIR% 20" MicroNIR" 20% 20% GCDMS% MicroNIR% MicroNIR% 10% GC6MS" 10" GCDMS% GCDMS% 10% 10% 0% 0" 0%0% 0%0%

60"

0%

0%0%

70"

60 60%

60% 60%


Cross-Validation

94.0

70 70%

70% 70%


0

80"

80% 80%

20% 20%

10% 10%

90"

80 80%

% %%Correct%classifica3on%rate% Correct classification rate %%Correct%classifica3on%rate% %%Correct%classifica3on%rate% %%Correct%classifica3on%rate%

%%Correct%classifica3on%rate% %%Correct%classifica3on%rate% % Correct classification rate

80"

90%

Calibration

91.5

TNT# TNT#

80% 80%

50% 50%

100"

90 90%

90% 90%

90% 90%

60% 60%

M75$ M75$ M75$ PETN%

100% 100%

24 24"24"

100%

M75 DEMEX

24"

M75 M75 M75

%%Correct%classifica3on%rate% %%Correct%classifica3on%rate% %%Correct%classifica3on%rate% %%Correct%classifica3on%rate%

Correct Classification rate (%)

100.0

5 55 5"5" 24 24

100%

PETN RDX

5"

Time"(h)" Time"(h)" Time (h) TimeTNT# (h) PETN%

100% 100%

%%Correct%classifica3on%rate% %%Correct%classifica3on%rate%

TNT

2" 2 2"2" 22 Time Time"(h)" (h)

0"

TNT PETN TNT TNT

7070%

GC6MS" GC6MS"

30" 30"

0"0"

8080%

MicroNIR" MicroNIR"


20 2020"

GC6MS"


30 3030"

MicroNIR"

40" 40"


40 4040"


50"

%%Correct%classifica3on%rate% % Correct classification rate %%Correct%classifica3on%rate%


100" 100"

%"Correct"classificaDon"rate" %"Correct"classificaDon"rate"

% Correct classification rate

100" 100 100

00 TNT

Explosive

PETN%PETN% PETN%

PETN PETN PETN


Table 1 The prediction ability of the MicroNIR approach by PLS-DA

all the considered materials and a minimum of 41/60 objects correctly identified.

%%Correct%classifica3on%rate% % Correct classification rate

(reported in parentheses in figure 2), as a function of the explosive involved, permitting to detect PETN and RDX even when included into composite materials such as M75 and DEMEX. Validation of MicroNIR model A predictive model consisting of a Discriminant Analysis (PLS-DA) was developed and fully validated for explosive detection. Leaveone-out cross-validation was selected in order to evaluate model dimensionality. The performances of the models were carried out in terms of Root Mean-Squared Error of Calibration (RMSEC), Root Mean-Squared Error of CrossValidation (RMSECV) and Root Mean-Squared Error of Prediction (RMSEP). 31,32 Four latent variables were selected to achieved the lowest RMSECV (equal to 0.05) and representing the 67.9 % of the X explained variance and the 87.2 % of the variance in the Y. Results of the predictive model for explosives detection are reported in Table 1 as a percentage of objects correctly classified (Correct classification rate). As evident, all the processed samples were correctly classified by the optimal model in calibration and cross-validation, resulting in a minimum correct classification rate of 91.5 % or 95.1 %, respectively. Good model performances were also obtained for the validation step confirming the prediction ability of the model. In addition, the performances of the developed MicroNIR platform were checked against a number of different materials such as illicit drugs, sugars, hand and body lotions and no false positive outcomes have been observed. Study of persistency In order to simulate IED assembling, each subject handled a quantity of explosive material ranging from 1 to 3 g for 10 minutes. The optimized MicroNIR model was applied to analyze real caseworks and results were compared to those from reference official method by GC-MS. Significant results can be deduced from figure 4 that demonstrated the feasibility of this novel approach to correctly detect traces of explosives on human hands (100% of correct classification rate for all the considered materials) and permitted to simultaneously differentiate the explosive involved by the subject with no misclassified outcomes. As the time from handling increases, the chance to detect residuals decreases for both MicroNIR and reference method. Neverthless, the novel approach demonstrated comparable performances with respect to reference ones even after a period of 24 h. Among the investigated explosives, both DEMEX and M75 proved to be the most persistent on hands, even after washing while TNT revealed the worst resistance after daily activities, in agreement with a previous study. 33


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 10

2%

2%

2 2 Time%(h)%

Time%(h)%

Time Time(h) (h)

5%

5%

5 5

2424%

24%

24

2

Page 5 of 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


detection of explosives in public places. The achieved results, highlight the extremely high potential of the developed MicroNIRs platform for the fast and easy identification of explosive residues and additionally, its potential ability to detect the explosive manipulation.

AUTHOR INFORMATION Corresponding Author * Roberta Risoluti, Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy; Tel +390649913616 fax: +390649387137 e-mail address: [email protected]

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. §These authors contributed equally to the manuscript.

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT We express our grateful thanks to the Italian Scientific Investigation Department (Carabinieri-RIS) of Rome for providing us with standard molecules and confiscated real samples supporting this study.

REFERENCES (1) López-López, M.; García-Ruiz, C. Trends in Analytical Chemistry 2014, 54, 36–44. (2) Doyle, S. Encyclopedia of Forensic Sciences, Second Edition, Elsevier Ltd, Wellington, New Zealand 2013, DOI:10.1016/B9780-12-382165-2.00328-7. (3) Gaurav, D.; Malik, A.K.; Rai, P.K. J. Hazard. Mater. 2009, 172, 1652-1658. (4) Gumuscu, B.; Erdogan, Z.; Guler, M.O.; Tekinay, T. Plos One 2014, 9, e99230. (5) Cortada, C.; Vidal, L.; Canals, A. Talanta 2011, 85, 25462552. (6) Garcia-Reyes, J.F.; Harper, J.D.; Salazar, G.A.; Charipar, N.A.; Ouyang, Z.; Cooks, R.G. Anal. Chem. 2011, 83, 1084-1092. (7) Brady, J.J.; Judge, E.J.; Levis, R.J. Rapid Commun. Mass Spectrom. 2010, 24, 1659-1664. (8) Fraga, C.G.; Kerr, D.R.; Atkinson, D.A. Analyst 2009, 134, 2329-2337. (9) Moore, D.S. Sens. Imag. Int. J. 2007, 8, 9-38. (10) Tabrizchi, M.; ILbeigi, V. J. Hazard. Mater. 2010, 176, 692696. (11) Akhgari, F.; Fattahi, H.; Oskoei, Y.M. Sensors and Actuators B 2015, 221, 867-878. (12) Ma, Y.; Wang, S.; Wang, L. Trends in Analytical Chemistry 2015, 65, 13-21. (13) Ali, E.M.A.; Edwards, H.G.M.; Scowen, I.J. Talanta 2009, 78, 1201-1203. (14) Elbasuneya, S.; El-Sherif, A.F. Forensic Sci. Int. 2017, 270, 83-90. (15) Izake, E.L. Forensic Sci. Int. 2010, 202, 1-8. (16) Zhou, S.; Yin, Q.; Lu, L.; Wang, Z.; Deng, G. Infrared Physics & Technology 2017, 80, 11-20. (17) Risoluti, R.; Materazzi, S.; Gregori, A.; Ripani, L. Talanta 2016, 153, 407-413. (18) Materazzi, S.; Gregori, A.; Ripani, L.; Apriceno, A.; Risoluti, R. Talanta 2017, 166, 328-335.

(19) Materazzi, S.; Risoluti, R.; Pinci, S.; Romolo, F.S. Talanta 2017, 174, 673-678. (20) Materazzi, S.; Peluso, G.; Ripani, L.; Risoluti, R. Microchemical Journal 2017, 134, 277-283. (21) Fernandez de la Ossa, M.A.; Amigo, J.M.; Garcıa-Ruiz, C. Forensic Sci. Int. 2014, 242, 228-235. (22) Forina, M. ; Casale, M.; Oliveri, P. University of Genoa, Genoa, Italy. 2009 Elsevier (23) Materazzi, S.; Risoluti, R.; Gullifa, G.; Fabiano, M.A.; Frati, P.; Santurro, A.; Scopetti, M.; Fineschi, V. J. Ther.Anal. Cal. 2017, 130, 549-557 (24) Risoluti, R.; Materazzi, S.; Sorrentino, F.; Maffei, L.; Caprari, P. Talanta 2016, 159, 425-432. (25) Sorak, D.; Herberholz, L.; Iwascek, S.; Altinpinar, S.; Pfeifer, F.; Siesler, H.W. Appl. Spectrosc. Rev. 2012, 47, 83-115. (26) O’Brien, N.A.; Hulse, C.A.; Friedrich, D.M.; Van Milligen, F.J. ; von Gunten, M.K.; Pfeifer, F.; Siesler, H.W. Next-Gener. Spectrosc. Technol. V 2012, 8374, 3-9. (27) Barnes, R.J., Dhanoa, M.S., Lister, S.J. Appl. Spectrosc. 1989, 43, 772-777. (28) Geladi, P.; MacDougall, D.; Martens, H. Appl. Spectrosc. 1985, 39, 491-500. Chemometrics: Theory and (29) Wold, S.; Sjöström, M. Applications. American Chemical Society Symposium Series 1977, 52, 243-282A. (30) Savitzky, M.J.E. Anal.Chem. 1964, 3 6, 1627-1639. (31) Mark, H.; Workman, J. Chemometrics in Spectroscopy, Elsevier, Academic Press, Amsterdam, 2007. (32) Miller, J.N.; Miller, J.C. Statistics and Chemometrics for Analytical Chemistry, Prentice Hall, Harlow, England, 2000. (33) Romolo, F.S.; Ferri, E.; Mirasoli, M.; D’Elia, M.; Ripani, L.; Peluso, G.; Risoluti, R.; Maiolini, E.; Girotti, S. Forensic Sci. Int. 2015, 246, 25-30



TOC of the manuscript

! !

PETN

explosi

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

RDX

based

explosi

ves

Hands&b Hands#b ! efore&ha efore#ha ! PETN&ha PETN#ha ndling& ndling# ! ndling& ndling# M75&han M75#han ! dling& dling# ! TNT&han TNT#han ! dling& dling# ! RDX&han RDX#han dling& dling# ! DEMEX&h DEMEX#h andling& andling#

RDX!

Negativ

e

Samples!

!

!

e

PC PC#1#(65 1 (65.1

! !

!

Positiv

.11#%)# %)

TNT

! !

M!75!

based

ves

TNT!

PC#2#(26 PC 2 (26.4 .45#%)#

%)

Explosives ! handling! Explosives ! handling!

! ! PETN!

! !

Negative! Positive!

! !!!DEMEX!

Explosives* handling**

Explosives ! handling!

Explosives ! handling!

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Optimization!

Results!


Page 6 of 10

Page 7 of 10

% Reflectance

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


3.50E+01

Blank Blank + TNT Blank + RDX Blank + PETN

3.00E+01

2.50E+01

2.00E+01

1.50E+01

1.00E+01

5.00E+00

0.00E+00 900

1000

1100

1200

1300

Wavelength (nm) ACS Paragon Plus Environment

1400

1500

1600

1700

PC PC#2#(11.1#%)# 2PC#1#(11.1#%)# (11.1 %)

PC#1#(11.1#%)#

! ! ! ! ! ! ! ! ! ! !! !! !! !! ! !! !! !! !! !! !! !! !! !! ! !! !! !! !! ! !! !! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !

PC#1#(11.1#%)#

PC#2#(9.3#%)#

PC 2 (9.3 %) PC#2#(9.3#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! !

! ! ! RDX ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! !! !! ! !! !! !! !! !! !! !! !! !! ! !! !! !! !! ! !! !! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

TNT#

RDX$

! !

RDX$

RDX$

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

TNT# TNT#

! Analytical Chemistry (a) ! ! ! ! ! ! ! ! ! ! ! ! Blank (hands)

! !

! !

! !

TNT

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! PC#1#(93.6#%)# ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

! !

PC#1#(2.7#%)#

PC#1#(2.7#%)#

PC#1#(2.7#%)#

PC 2 (2.7 %)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 ! 41 ! ! ! 42 ! ! 43 ! ! 44 ! ! 45 ! ! 46 ! ! 47 ! ! 48 ! ! 49 ! ! 50 ! ! 51 ! TN% ! 52 ! ! 53 ! ! ! 54 ! ! 55 ! ! 56 ! ! 57 ! ! 58 ! 59 60

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! !! !!

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

Blank + 10 µg TNT Blank + 50 µg TNT Blank + 100 µg TNT

PC#1#(93.6#%)#

PC 1 (93.6 %) PC#1#(93.6#%)# PC#1#(93.6#%)#

! ! !

! !

!!! !! ! ! ! ! ! ! ! !

! ! !

PC#1#(87.3#%)#

! ! !

! !

(b)

Blank (hands) Blank + 10 µg RDX Blank + 50 µg RDX Blank + 100 µg RDX

PC#1#(87.3#%)#

PC 1 (87.3 %)

PETN

(c)

! ! ! ! ! ! !

PC#1#(87.3#%)#

! ! ! ! ! ! ! PC#1#(90.6#&)# PC#1#(90.6#&)#

Blank (hands) Blank + 10 µg PETN Blank + 50 µg PETN Blank + 100 µg PETN


PCPC#1#(90.6#%)# 1PC#1#(90.6#&)# (90.6%)

Page 8 of 10

Page 9 of 10


RDX based explosives

Hands before handling Hands#before#handling# PETN handling PETN#handling# M75 handling M75#handling# TNT handling TNT#handling# RDX handling RDX#handling# DEMEX handling DEMEX#handling#

Negative

PC 1 (65.1 %) PC#1#(65.11#%)#

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

TNT

Positive

PETN based explosives 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


PC#2#(26.45#%)# PC 2 (26.4 %)

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Page 10 of 10

âClick and Screen" technology for explosives detection on human

Recommend Documents

âClick and Screen" technology for explosives detection on human