Toward Locard's Exchange Principle: Recent Developments in

Marisia A. Fikiet graduated with a B.S. in Chemistry from the University of ... for the analysis of forensically relevant materials has been observed...
0 downloads 0 Views 9MB Size
Review Cite This: Anal. Chem. 2019, 91, 637−654

pubs.acs.org/ac

Toward Locard’s Exchange Principle: Recent Developments in Forensic Trace Evidence Analysis Ewelina Mistek, Marisia A. Fikiet, Shelby R. Khandasammy, and Igor K. Lednev*

■ Downloaded via OPEN UNIV OF HONG KONG on January 23, 2019 at 19:17:29 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States

CONTENTS

Statistical Analysis Hair Fibers Paint Glass Gunshot Residue Explosives Conclusions Author Information Corresponding Author ORCID Notes Biographies Acknowledgments References

class characteristics provide information that can be associated with a type of group. Individual characteristics can associate a sample with a specific source. Varieties of analytical tools have been employed for forensic applications. Current forensic practices utilize scanning electron microscopy-energy dispersive X-ray spectrometry (SEM-EDX) for characterizing size, morphology, and elemental composition of individual particles. Elemental composition can also be examined using laser-induced breakdown spectroscopy (LIBS). Chromatographic methods, such as liquid chromatography (LC) or gas chromatography (GC), can be used for extracting and separating purposes. Mass spectrometry (MS) can be utilized in order to get chemical information about analyzed samples based on the ions produced. The chemical composition of a sample can also be revealed using vibrational spectroscopic techniques, such as Raman or attenuated total reflection (ATR) Fourier transforminfrared (FT-IR) spectroscopy. These techniques are based on the vibrations of molecules in a sample. In 2015, Stoney and Stoney reviewed forensic trace evidence analysis and showed where the needs for improvement were.4 An extensive review article detailing the role of analytical chemistry in forensic analysis has been published in 2017.5 In recent years, the increasing importance of vibrational spectroscopy for the analysis of forensically relevant materials has been observed.6−8 Vibrational spectroscopy provides a nondestructive analysis for a wide range of samples. Surface enhanced Raman spectroscopy (SERS) offers even more sensitive analysis.8 Aforementioned articles also showed the increasing importance of chemometrics in analysis. Herein, recent developments in forensic trace evidence analysis between 2016 and 2018 are presented. This work includes improvements to existing techniques as well as the introduction of novel methods for the analysis of fibers, hair, paint, glass, GSR, and explosives.

637 638 639 640 641 643 646 650 651 651 651 651 651 651 651

T

race evidence materials are small quantities of chemical or physical evidence that are found at a crime scene, on a suspect, or on a victim. According to the guidelines set by the National Institute of Standards and Technology (NIST), trace evidence includes the following materials: hair, fibers, paint, glass, gunshot residue (GSR), and explosives.1 The importance of forensic trace evidence analysis is exemplified in Locard’s exchange principle. This theory states that, whenever two objects come into contact, an exchange of materials occurs between them.2 This may lead to a connection between a suspect and a crime scene or a suspect and a victim, based on transferred fragments of materials. Most current forensic practices are based on the comparison between a questioned sample and a reference sample.2 The comparison process can reveal whether two or more objects could have a common origin. This is mostly the case when physical evidence is recovered from a suspect, victim, or crime scene and is compared to a sample from known source: crime scene or suspect’s or victim’s belongings. Fibers recovered from a victim’s shirt might be compared to fibers collected from suspect’s sweatshirt, or a glass fragment recovered from suspect’s clothing may be compared to broken glass found at the crime scene. A variety of analytical methods may be used to compare the microscopic properties, examine the composition, and test the physical properties of the materials. In addition to the comparison analysis of two samples, the chemical and physical properties of the evidence can be compared to information stored in databases. There are government and private forensic databases which may assist investigators in their examination.3 There are two categories of findings that the examination of physical evidence can reveal: (i) class characteristics and (ii) individual characteristics.2 The © 2018 American Chemical Society



STATISTICAL ANALYSIS Statistical evaluation of results was found to be very useful in a research setting. It can also provide more weight to specific types of evidence in a trial setting. Likelihood ratios (LRs) are one of the currently applied statistical approaches, usually used for evaluating DNA test results.9 A LR is used to compare the fit of two models and is expressed by how many times more likely it is that the suspect is the perpetrator than if some other person is the perpetrator. In other words, it is a quantitative Special Issue: Fundamental and Applied Reviews in Analytical Chemistry 2019 Published: November 8, 2018 637

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

identify and discriminate between different hairs of the same color family.22 Manheim et al. went further and used ATR FTIR spectroscopy along with chemometrics to differentiate animal fur, human hair, and synthetic fibers.23 Also, more research is going into the physical properties of hair and variation between individuals. Srettabunjong et al. looked into hair variation in Thailand, specifically at hair cross-sectional shape, thickness of hair, and medullary index, while Ho et al. studied variation in twins.24,25 The twin study took place in Australia and investigated the heritability of traits like hair diameter and curvature. Tang et al. investigated how the age of the donor affects hair hardness, elastic modulus, and diameter.26 DelRio and Cook went even further and used atomic force microscopy (AFM) to determine indentation modulus and pull off force for hair before and after treatments.27 These studies only looked at microscopic and physical differences, not at chemical differences. Still, other research has been done on the composition of hairs. Wu et al. investigated the distribution and concentration of squalene in human hairs using both GC/MS and Raman spectroscopy.28 Hietpas et al. investigated hair composition as it pertains to postmortem root bands (PMRBs) using microscopy.29 They found that the root band is due to preferential degradation of the hair matrix and hope that their research will lead to a PMRB becoming a reliable indicator of hair that has been removed from a dead body. Quite a lot of recent research has been performed on identifying chemical modifications to hair. Both Tinoco Corrêa et al. and Boll et al. investigated hair dyes.30,31 Tinoco Corrêa et al. used nanoparticles to preconcentrate extracted dye and detect it with a composite electrode. Boll et al. utilized ATR FT-IR spectroscopy to identify dyes of different colors and manufacturers without extraction. Petzel-Witt et al. used breakdown products of melanin quantified by LC-tandem MS (LC-MS/MS) as an indicator of bleaching, permanent, and semipermanent dying processes.32 Pienpinijtham et al. also used ATR FT-IR spectroscopy to not only identify if a single hair had been dyed but also identify the presence of other hair treatments like permanent waves, straightening, and bleaching.33 They were also able to differentiate spectra from split and unsplit hair. Cardoso Santos et al. used wavelength dispersive X-ray fluorescence (WDXRF) to determine similar chemical treatments, but because of the technique being used, they needed 150 mg of hair which limits its forensic relevance.34 Kuzuhara investigated different types of permanent waving processes in more detail with Raman spectroscopy and found decreases in the amount of sulfur cross-linking when certain waving processes were used.35 Fedorkova et al. used Raman spectroscopy, microscopy, and gel filtration chromatography to investigate the effects of ultraviolet (UV) damage on hair.36 Hair can also tell a lot about geographical location through isotope ratios. Ratios of certain isotopes can be used to discriminate hair originating from different geographical locations. Chau et al. were able to use a high-temperature conversion elemental analyzer coupled with an isotope ratio MS (TC/EA-IRMS) to measure oxygen isotope values and a multicollector inductively coupled plasma MS (MC-ICPMS) to measure strontium isotope ratios in horse hair (Figure 1).37 While oxygen isotope ratios stayed relatively constant for horses who lived in different environments, strontium ratios differed.

evaluation of evidentiary weight, in order to express the association/nonassociation of two pieces of evidence. The statistical evaluation of physical evidence was already presented in 1977 by Lindley.10 The weight of forensic evidence for different physical evidence has been recently analyzed.11−14 However, Lund and Iyer expressed concerns regarding the LR approach given by the expert and that of decision makers.14 They illustrated their concepts by evaluating results for the refractive index of glass and automated comparison scores for fingerprints. Concerns of overstating the value of the evidence were also expressed in the work by Morrison and Poh.15 Multiple procedures for converting the results of analyses to interpretable LR or Bayes factors were tested. Glass was analyzed together with voice recordings and face images. This work shows the large dependence of outcomes on various criteria. While LRs remain as the most widely overviewed forms of forensic evidence evaluation, there are new models that were introduced. Current research involves a large variety of chemometric approaches as a form of statistical analysis for data.6−8,16 As presented in the review article by Kumar and Sharma, many areas of forensic expertise utilize chemometrics from document analysis to toxicology.16 Among chemometric methods, principal component analysis (PCA), an unsupervised model, is one of the most common. Unsupervised models do not use user generated groupings and only rely on the data itself to group information. On the other hand, supervised models, such as soft independent modeling of class analogy (SIMCA) or partial least squares discriminant analysis (PLSDA), take into account information provided by the user, such as in what class each piece of data belongs. This can often be helpful in the improvement of a model’s performance.



HAIR Hair analysis has several different facets. Many people view hair analysis as only comparison analysis, where a hair from an unknown source is compared microscopically to hair of known origin to see if they could have come from the same source. This is certainly one of the main uses of hair, but its scientific validity has come into question in recent years.17,18 Because of this, other avenues for hair analysis are being explored, like giving statistical relevance to hair comparisons using analytical techniques and investigating chemical modification made to hair (dying, straightening, etc.). Hair can also be used to determine drug use, since traces of substances consumed are incorporated into the shaft of the hair. Similarly, hair can be used to determine approximate location due to changes in isotope ratios in different geographic locations. Mitochondrial DNA can also be extracted from hair if the root is present. This Review is only concerned with analytical techniques used on the hair itself or an extract and not concerned with determining drug use of the hair donor or with DNA analysis. Both the excluded techniques are not within the scope of this Review. For more on the types of hair analysis examined in this Review, the reader is referred to Castillo-Peinado and Luque de Castro as well as Robertson for a general overview of hair in forensics and to Kučera et al. and Pozebon et al. for a review of elemental hair analysis.5,19−21 Since the scientific validity of microscopic hair comparison has been brought into question, some research is focused on adding statistics and other techniques to support conclusions. Mills et al. supplemented the microscopic techniques already in use with digital image analysis and chemometrics to help 638

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

cyanide.39 Instead, Coplen and Qi used a single-oven, chromium-filled elemental analyzer (Cr-EA) coupled to an IRMS on previously used hair isotope standards to determine their new oxygen and hydrogen isotope values.40 Soto et al. also re-evaluated several keratin containing standards, including standard hair, and created a calibration curve to accurately calculate between measurements taken with the old method and the new improved method.41 The majority of the new research has looked into supplementing or improving existing techniques, such as microscopy and TC/EA-IRMS, for hair analysis. Comparison microscopy has been supplemented or replaced by other analytical techniques and chemometrics to increase scientific validity of forensic hair analysis. Others look into chemical modifications of the hair that can be used on their own or to strengthen comparisons. For hair isotope analysis, much research has gone into re-evaluation of commonly used standards after the realization that current methods underestimate hydrogen isotope concentrations. All in all, hair research has continued forward despite the many skeptics that question its validity.



FIBERS Forensic fiber analysis covers two main categories, identification of the fiber and identification of the dye used. The majority of recent research on fibers falls into these two categories. Another area of fiber research is to identify signs of aged fibers. Spectroscopy is still the method of choice when investigating fibers as spectroscopic techniques are nondestructive and can analyze fibers and dyes without extraction. Different types of chemometric analysis are now starting to be added to supplement the discriminating power of the different techniques. For research done before 2016 on fiber analysis, ́ the reader is referred to reviews by Prego Meleiro and Garcia42,43 Ruiz, and Farah et al. Many researchers took full advantage of the nondestructive nature of many spectroscopic techniques and analyzed fibers and dyes without extraction. Muñoz de la Peña et al. used total excitation−emission fluorescence to analyze different synthetic fibers.44 Several different types of chemometric methods were applied with discriminant unfolded partial least-squares (DUPLS) analysis showing the best discrimination. Reichard et al. used microspectrophotometry (MSP) to determine the amount of yellow dye, expressed in % w/w, in polyester fibers without extraction.45 Chemometrics was also used, with agglomerative hierarchical clustering (AHC) analysis. MSP coupled with chemometrics was the most common method used. Sauzier et al. used this method as well as Fisher’s test to evaluate fiber to fiber comparisons.46 Starczak and Was̨ -Gubała also used MSP to evaluate how different fiber types (synthetic and natural) affect spectra for the same dye.47 AHC was also employed for discrimination purposes. Powell et al. went further and used MSP spectra to construct a spectral database and a comparison strategy.48 Lunstroot et al. used MSP along with microscopy and FT-IR spectroscopy to analyze fleeced fibers in order to evaluate variation in the black fleece population.49 Zhou et al. used both a destructive and a nondestructive technique to analyze dyed fibers.50 High performance liquid chromatography coupled with time-of-flight MS (HPLC-TOFMS) was used to identify extracted dyes while time-of-flight secondary ion MS (TOF-SIMS) was used to examine the surface and cross sections of fibers. Both methods were able to

Figure 1. (a) δ18O values of tail hair from the control horse (CH; solid circles) and transported horse (TH; open circles) across lengths of ∼430 mm. (b) 87Sr/86Sr of tail hair from the CH and TH across lengths of ∼510 mm (CH) and ∼470 mm (TH). Lengths of hair represent a temporal period of ca. 24 months (assuming a growth rate of 0.70 mm day−1). The move from Brazil to the United States of the TH is estimated to be at 340 mm; the gray box indicates uncertainty about the estimation due to variation in hair growth rate plus travel and quarantine time. Error bars for δ18O and 87Sr/86Sr values correspond to 2σ of reference materials used in this study: 0.54 and 0.0003, respectively. Reproduced from Reconstruction of travel history using coupled δ18O and 87Sr/86Sr measurements of hair, Chau, T. H.; Tipple, B. J.; Hu, L.; Fernandez, D. P.; Cerling, T. E.; Ehleringer, J. R.; Chesson, L. A. Rapid Commun. Mass Spectrom., Vol. 31, Issue 6 (ref 37). Copyright 2017 Wiley.

Mant et al. tried to tie hydrogen and oxygen isotopes in hair to water sources in a relatively small geographical area using TC/EA-IRMS.38 However, it was found that, since water is not the only source of hydrogen and oxygen, the impact of diet does not allow for differentiation of the source in such a small geographical area. It was found in 2015 that using TC/EAIRMS to find hydrogen and oxygen ratios in nitrogen containing compounds (e.g., hair) underestimates the amount of the hydrogen isotope due to formation of hydrogen 639

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

Figure 2. Spectra obtained on the “coffee rings” of the 10−4 M AO7 solution spotted on (a) a gold coated glass slide analyzed in ATR; (b) silicon analyzed in ATR; (c) BaF2 analyzed in ATR; (d) AgCl analyzed in ATR; (e) gold glass line analyses in transflection. Reprinted from Anal. Chim. Acta, Vol. 941, Prati, S.; Milosevic, M.; Sciutto, G.; Bonacini, I.; Kazarian, S. G.; Mazzeo, R. Analyses of trace amounts of dyes with a new enhanced sensitivity FTIR spectroscopic technique: MU-ATR (metal underlayer ATR spectroscopy), pp. 67−79 (ref 56). Copyright 2016, with permission from Elsevier.

identify the dyes used. While keeping the fiber is ideal for forensic analysis, sometimes, dye extractions can provide useful information especially when fibers are lightly colored. Campiglia et al. used total excitation−emission fluorescence, ultraviolet and visible (UV−vis) absorption spectroscopy, and HPLC on dye extracts.51 Multivariate curve resolution alternating least-squares (MCR-ALS) was helpful in differentiation of visibly identical dyes. HPLC was also used by Schotman et al. along with MSP on dye extracts in eight case examples.52 Groves et al. found the optimum parameters to use for high performance thin layer chromatography (HPTLC) of dye extracts and optimized the solvent for the dye extraction process.53,54 Germinario et al. used pyrolysis-GC/MS (Py-GC/ MS) to investigate triarylmethane dyes which are used in fiber dyes.55 Prati et al. stepped away from chromatography and used a new type of ATR spectroscopy called metal underlayerATR (MU-ATR) spectroscopy on dye extracts.56 A comparison of spectra from their analysis can be seen in Figure 2. They used this new method to detect a 10−5 M concentration of extracted dyes. Aging of fibers is also of forensic interest. There were two different types of aging: aging due to environmental exposure and aging due to regular use. Brinsko et al. and Ueland et al. focused on environmental exposure aging.57,58 Brinsko et al. simulated conditions in the laboratory and used a variety of spectroscopic and microscopic techniques to study aging of natural fibers. Ueland et al. stored fibers outdoors in two

different seasons and used ATR FT-IR spectroscopy combined with chemometrics. Bianchi et al. studied aging due to washing and UV exposure to simulate real use with Raman spectroscopy.59 Heider el al. went further and used fluorescence microscopy to determine types of washing detergent used.60 Overall, analytical methods for fiber examination are moving toward nondestructive approaches, but chromatography is still being used when extraction is necessary. Also, chemometrics was used in almost all of the recent research cases, especially when paired with spectroscopic techniques. Fiber analysis has grown with the times and has been able to incorporate many different analytical methods.



PAINT The majority of forensic paint analysis can be put into two main categories: car paint and artistic paint. Car paint analysis is usually used for hit and run incidents and other types of motor vehicle related accidents. Art paint analysis usually involves the analysis of forgeries. Of course, there are other types of paint, such as house paint and spray paint, that are also researched. Recent research on paint analysis has been heavily utilizing chemometrics and databases to help in the examination. Spatially offset Raman spectroscopy (SORS) is the newest technique being employed. Spectroscopy has been the analytical method of choice along with microscopy for years. Recently, direct analysis in real-time MS (DART-MS) has started to be more utilized. For more information on paint 640

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

analysis in general, the readers are referred to Buzzini and Suzuki.61 For more information on art and forensics, the readers are referred to Yu and Butler, and Sloggett.62,63 Automotive clear coats are also starting to get more attention in research. Maric et al. used Raman spectroscopy along with chemometrics to characterize and classify clear coats, pointing out that it is the most accessible layer and the most often encountered.64 Marić et al. also used DART-MS to classify clear coats.65 Van der Pal et al. studied the degradation of automotive clear coats using a range of different infrared (IR) techniques and chemometrics.66 They determined that spectra should be taken from the middle of a clear coat to avoid encountering degraded material. Perera et al. went further with the chemometrics and found search prefilters for clear coats in the Paint Data Query (PDQ) database with ATR FT-IR spectroscopic data.67 Lavine et al. have studied search prefilters for PDQ searches of FT-IR spectroscopic data extensively. They have used these prefilters to assist in correct manufacturer information, to enhance leads, and for model Ford vehicles made between 2000 and 2006.68−70 Many other types of chemometrics, other than prefilter settings, have been used. Thoonen et al. used automated color analysis and chemometrics to aid in the classification of car paint using microscopy.71 Michalska el al. used Raman spectroscopy and LRs to help with comparisons of paint chip samples.72 Identification of pigments used in automotive paints is also a common type of analysis. Ferreira et al. investigated the best parameters for analysis of Raman spectra of car paint chips to decrease variability and increase the quality of spectra.73 Lv et al. used Raman spectroscopy and the combination of Raman and FT-IR spectroscopy to identify pigments in car paints.74,75 Chen and Wu used a different technique, DART coupled to a Q-orbitrap tandem MS (DART-Q-orbitrap), and compared it with FT-IR spectroscopic analysis.76 Huang and Beauchemin went further and used solid sampling electrothermal vaporization coupled to inductively coupled plasma optical emission spectrometry (SS-ETV-ICPOES) to investigate not only color but also manufacturer and year of production using chemometrics.77 Kwofie el al. also used chemometrics to differentiate color and manufacturer but used IR spectroscopy instead.78 Ferreira et al. used hyperspectral imaging visible/near-infrared (HSI-VIS/NIR) spectroscopy and chemometrics for discriminating between different colors of car paint.79 Most colors were able to be 100% differentiated, but black car paint gave much lower classification power. Gomes de Oliveira et al. used both Raman and ATR FT-IR spectroscopy to investigate aging in paint chip cross sections.80 Rather than differentiate by color, Zhang et al. used optical coherence tomography (OCT) to distinguish cross-sectional and transverse images of paint chips.81 Current research in paint analysis for art purposes is going toward nondestructive analysis of multilayer systems such as paintings. Bertasa et al. used micro-ATR FT-IR spectroscopy on real and simulated samples to identify micrometer thick layers in cross sections.82 Matousek and coauthors have done quite a large amount of work on SORS of painted systems with an emphasis on restoration and forgery.83−86 They with others have investigated the determination of the thickness of painted layers, paint mixtures, hidden painted images, and a portable SORS prototype for use at the scene. An example of an optical microscope image and chemical images can be seen in Figure 3. For more information on SORS and its uses, the reader is referred to a review by Matousek et al.87 Paint analysis usually

Figure 3. Optical microscope image and chemical images: (a) optical microscope image of the uncovered sample; (b) optical microscope image of the covered sample; (c) normal Raman map of (a) (M1); (d) normal Raman map of (b) (M2); micro-SORS map of (b) (M3). Adapted from Botteon, A.; Conti, C.; Realini, M.; Colombo, C.; Matousek, P. Anal. Chem. 2017, 89, 792−798 (ref 85). Copyright 2017 American Chemical Society.

focuses on color and pigment differentiation, but differences in the binder used can also be relevant. Bower et al. used ATR FT-IR spectroscopy to differentiate between authentic and forged stamps by age of the stamp using oil binders.88 Besides automotive and artistic paint, there are other types of paint that are relevant in forensic analysis. Specifically, spray paint often found in hate crimes or vandalism has been researched recently. Jost et al. evaluated the aging of spray paint using both FT-IR and Raman spectroscopy, while Muehlethaler et al. went on and used FT-IR spectroscopy to study the aging of spray paint on actual case work samples.89,90 Germinario et al. characterized spray paint using Py-GC/MS, FT-IR spectroscopy, and micro-Raman spectroscopy.91 Zieb̨ aPalus and Kowalski investigated which technique, Raman or ATR FT-IR spectroscopy, is best for evaluating spray paints in situ on different substrates.92 Besides spray paint, house paints can also be relevant to forensics. Lambert et al. employed common component and specific weight analysis (CCSWA) to help differentiation of red house paints using both Raman and FT-IR spectra.93 Paint related to automobiles is still one of the most prolific areas of research, but the focus has been moved to identification of clear coats and use of databases and chemometrics and then to the identification of pigments. In the art world, a novel approach is using SORS to look underneath the first layer of paint. Different kinds of chemometrics have been combined with almost all the analytical techniques used. Chemometric approaches have helped to extract even more from data including make, model, and production year of cars. Research on paint has continued forward and advances toward the future.



GLASS Glass is a hard, amorphous substance made of sand (silicon oxides), various metal oxides, soda (sodium carbonate), and lime (calcium oxide).2 The most common metal oxides in soda-lime glass are sodium, calcium, magnesium, and aluminum. Some glasses contain specific silica or boron oxides. Different glass types are also received through various glass 641

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

lesions.103 This is especially important for forensic anthropologists in cases which involve glass tools. Although contact lenses have had limited importance during forensic investigations and trials, Zwerling reported a case which utilized the examination of a contact lens from an exhumed body.104 Surprisingly, this evidence provided factual proof which refuted the defendant’s testimony in the murder trial. LIBS has already demonstrated advantages for the analysis of glass evidence.94 It is a quantitative technique which can be utilized for the analysis of the elemental composition of glass. LIBS is a technique that is almost nondestructive and has good sensitivity and discrimination. However, this technique has some drawbacks, such as limited precision and repeatability. Gupta et al. examined the intra- and interday variations of LIBS using glass samples.105 Statistical analyses showed that this technique could be used in order to investigate evidence reliably on different days. LIBS together with electron paramagnetic resonance (EPR) spectroscopy was used to examine the elemental content of borate glass.106 In this work, quantitative analysis of copper was extensively studied using double pulse LIBS (DP-LIBS) (Figure 4). Acharya and Pujari overviewed neutron and proton based nuclear analytical techniques for numerous forensic applications.107 Particle induced gamma-ray emission (PIGE) was found to be particularly useful for the quantification of concentrations of low atomic number elements in barium borosilicate glass.108 Examination of glass from car windows is very important during investigations of automotive accidents. LA-ICPMS showed promise for the analysis of glass from car windows.109 The technique was especially useful when combined with linear discriminant analysis (LDA). Discrimination was possible based on car manufacturer, glass manufacturer, and glass thickness. Car window glass was also studied using other techniques to distinguish between different glass manufacturers.110 In this study, the following methods were used: refractive index, X-ray fluorescence (XRF), and X-ray absorption fine structure (XAFS). XRF was found to be the most powerful of these techniques. It measured component concentration successfully. Statistical analysis was used, and cluster analysis in particular was performed. The LR approach was used with LA-ICPMS glass analysis data.111,112 LR is a quantitative evaluation of the weight of evidence, which expresses the association/nonassociation of two pieces of evidence. This approach showed good accuracy, discrimination, and calibration in the study on LA-ICPMS databases for glass.111 The method has a disadvantage as it requires large databases for accurate representation of the relevant population. Van Es et al. in their study on the LR approach in LA-ICPMS for glass analysis found small rates of misleading evidence.112 This demonstrates satisfactory performance of the method for the evaluation of the strength of evidence for LA-ICPMS measurements of glass. Multiobjective genetic optimization algorithms of fuzzy rulebased classifiers were proposed as another decision support for the identification of glass evidence.113 This approach was effective for intelligent decision support. Thomas et al. used an inverse prediction approach for multiple applications.114 Glass composition from experimental physical properties was studied. First, forward models were made via least squares regression, and then, the inverse predictions followed. It can be seen that since the last reviews new research methods were developed for forensic glass analysis. A lot of research was done to study new techniques or improve existing

production processes. Therefore, specific types of glass can be distinguished by their unique chemical and/or physical properties. Current forensic practices for glass analysis are based on the comparison of glass fragments to check if they could have the same origin.2 Physical fitting may be used to compare fragments, recovered respectively from a crime scene and a suspect. A possible common origin could be indicated if the irregular edges of broken glass fit together. To characterize glass properties, the examinations of density and refractive index are the most common methods. However, these are class characteristics and can only exclude glass fragments not of the same origin. These properties cannot provide conclusive information on the common origin of two or more glass pieces. Trace elemental composition is another very attractive property of glass for forensic investigators.2 Elemental profiles can be obtained using a high-energy laser beam. The Technical Support Working Group possesses the glass evidence reference database.3 This database provides the elemental composition of glass samples through analyses using two plasma mass spectrometers. The comparison of unknown glass pieces to information contained in the database cannot determine the origin of the unknown piece. However, it can be helpful for the exclusion of evidence. Besides linking a suspect to a crime scene, glass analysis can be helpful in reconstructing crime scene events.2 This can be investigated by the examination of glass fractures. Fracture patterns can indicate the direction of impact. The history and methods for forensic glass analysis were reviewed in the book edited by Katz and Halámek.94 An overview for the examination of trace evidence was published by Stoney and Stoney in 2015.4 Notably, laser ablation inductively coupled plasma MS (LA-ICPMS) was found to be advantageous for the examination of physical evidence.95,96 Here, in this section, more up-to-date applications of this method and other techniques for glass analysis will be discussed. Fracture patterns in glass and glassy polymers were extensively studied by Baca et al.97 The study demonstrated that, even though fractures made under controlled conditions are different, there is a lack of mathematical or statistical analysis of such data. Fracture patterns were found to be specifically important for investigations at shooting scenes.98−100 Fracture patterns could be essential in order to reconstruct a scene, as the patterns can differ between bullet and weapon types. Patterns can also lead to estimation of the angle for perforation or ricochet. It was also reported that analysis of glass can provide information on the weapon used, based on examination of the wounds.101 Further research was done using SEM-EDX applied to glass analysis. Michalska et al. proposed a new method for the sample preparation of glass for SEM-EDX analysis.102 Currently applied techniques include an embedding procedure that is limiting, especially for small samples. The proposed technique was a nonembedding procedure based on the visual selection of a piece of glass using a stereomicroscope. The results showed that no significant difference was observed for the analysis of elemental glass composition using both sample preparation techniques. The nonembedding method of sample preparation shows potential, especially in cases involving limited evidence size. Moreover, the method allows for reuse of the same portion of glass for additional analyses. SEM-EDX was also utilized for detecting glass particles on bone 642

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

The composition of GSR and its potential false positives is crucial information for forensic investigators. Tucker et al. sought to address this concern in their study on brake pads.119 Brake pads have been identified as a potential false positive for three-component GSR particles. It was concluded in this study that, while this was a concern in the past, modern day brake pads do not appear to produce many three-component particles, and indeed, none were found during the course of the study. Goudsmits et al. also published an article discussing GSR composition.120 In this study, a 20 compound shortlist for OGSR was created and a potential scheme by which to classify OGSR was introduced. MS for GSR analysis has been investigated in several studies. Castellanos et al. used MS imaging in order to investigate GSR from skin swabs.116 They found their method to be effective for the characterization of skin swabs containing IGSR and OGSR with minimal sample destruction and no sample preparation. Meanwhile, Perez et al. used laser electrospray MS to analyze the residues found inside spent cartridge casings.121 They were able to achieve high classification rates for the ammunition manufacturers investigated by means of PCA. Evans-Nguyen et al. used direct ambient ionization MS to analyze solid samples directly with no sample preparation steps.122 This method was effective for the identification of all GSR components chosen. Gassner and Weyermann used LCMS to compare the effectiveness of different sampling substrates for OGSR collection.123 They found that adhesive tape, which is the typical collection method for IGSR, collected the most OGSR in comparison to the other substrates. Pigou et al. performed a study using GC/MS which investigated the potential formation of artifacts during the analysis of propellant from ammunition cartridges.124 They found that the formation of artifacts was possible and stated that while these artifacts do not interfere with identifying propellant conclusively, such artifact formation could lead to problems when trying to correlate the analyzed sample to a reference sample. Different variations of atomic absorption spectrometry (AAS) have also been applied for the analysis of GSR. Cid et al. focused their research on the detection of tin in GSR using flame furnace-AAS (FF-AAS) modified with a subcritical fluid nebulizer.125 It is normally not easy to detect tin using atomic spectrometry techniques, but with this modified setup, researchers were able to both quantify and detect tin in aqueous solutions with a very impressive enhancement in detection ability. They were notably able to detect tin in GSR samples. Meanwhile, Yüksel et al. utilized graphite furnace AAS (GFAAS) for the detection of lead, barium, and antimony.126 They were successful in the detection of these components in hand swabs containing GSR. They also reported their findings concerning the length of time for the retention of these components on shooters’ hands. Aliste and Chávez conducted a study utilizing GFAAS for the analysis of GSR traces found in nasal mucus.127 They found that, while less GSR is found in nasal mucus than on the hands of an individual, the retention time for GSR was comparatively longer in the mucus. Nasal mucus containing GSR was also investigated by Merli et al., who used instrumental neutron activation analysis (INAA) for the detection of barium and antimony from GSR.128 They found that INAA was a suitable method for detection, and proposed it as an advancement over typical GSR analysis methods. SEM-EDX is the typical analysis method for GSR; Terry et al. recently published a study in which SEM-EDX was utilized

Figure 4. (A) The picture of borate glasses is taken with additives of copper concentration levels from 0.1 to 0.5 mol %. (B) Copper spectral marker line Cu(II) 589.79 nm peaks for different concentrations of copper in borate glass matrix. (C) Plot of DPLIBS calibration curve for different copper concentrations in mol %. Reprinted from Talanta, Vol. 154, Khalil, A. A. I.; Morsy, M. A. Quantitative determination of copper in a glass matrix using double pulse laser-induced breakdown and electron paramagnetic resonance spectroscopic techniques, pp. 109−118 (ref 106). Copyright 2016, with permission from Elsevier.

methods for glass examination. Moreover, new approaches for statistical analyses and the interpretation of results were presented in order to strengthen the outcomes of different methods of glass evidence analysis.



GUNSHOT RESIDUE GSR is produced upon the discharge of a firearm and is comprised of organic GSR (OGSR) from propellant and lubricants, and inorganic GSR (IGSR) which is comprised of components from the propellant, cartridge casing, firearm, and primer.115,116 For further reading regarding GSR analysis and other information regarding its role in a forensic science context, the reader is referred to the reviews published by Blakey et al. in 2018 and Brożek-Mucha in 2017.117,118 643

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

in order to investigate the contributions from cartridge cases to GSR composition.129 In this work, researchers also attempted to differentiate between GSR stemming from lead-free and lead containing primers. Kara et al. employed SEM-EDX to analyze GSR from different ammunition types, and the researchers also provided information that may be useful in assessing environmental concerns surrounding GSR.130 Gandy et al. notably developed a color test for the detection of OGSR.131 This color test is noteworthy because it allows for subsequent analysis of a sample using SEM-EDX in order to analyze any IGSR present. Studies concerned with the sampling of GSR particles are also an area of interest for forensic researchers. Gassner et al. published a study in which the storage, transfer, persistence, and sampling processes surrounding OGSR were investigated.132 Meanwhile, Taudte et al. published a communication, which detailed their work regarding the potential for smokeless powder degradation on different types of sample collection tools.133 Recently, two studies were also published which dealt with the effect of skin in correlation to GSR.134,135 Kara published a study in which swabs of GSR were assessed and observations about the amount of GSR recovered were made on the basis of skin coloration and other physical parameters surrounding an individual.134 Kara found that skin color had no bearing on GSR recovery. However, other physical attributes did influence the quantity of GSR recovered. Burnett’s study investigated whether or not skin debris on GSR tape lifts was detrimental for SEM analysis.135 It was concluded that skin debris and oils can obscure GSR particles, and the author suggested that Na/Ca bleach treatment may be an effective solution when encountering such cases. Finally, Taudte et al. conducted a study in which they attempted to develop an optimized method for the collection and analysis of OGSR and IGSR particles.136 It was concluded that the best method involved collection of GSR with adhesive covered aluminum stubs, followed by SEM-EDX analysis and an extraction procedure, and finally ultrahigh-performance LC (UHPLC) analysis. Vibrational spectroscopy techniques have also been used for the analysis of GSRs. López-López et al. used SERS to investigate smokeless powder and macroscopic GSR particles.137 They found that SER spectra of the smokeless powders were mostly correlated to stabilizers. Ortega-Ojeda et al. performed a study in which they used short wave infrared hyperspectral imaging in order to better visualize GSR patterns on cotton targets.138 They performed this study using both nontoxic and traditional ammunition. Their method proved to be nondestructive and applicable irrespective of the ammunition type used. Sarraguça et al. meanwhile performed a study in which Fourier transform near-infrared (FT-NIR) spectroscopy was used for the analysis of GSR.139 This analysis was used to estimate firing distance for the two types of ammunition cartridges studied. Finally, Bueno et al. released a study that used Raman spectroscopic mapping in order to investigate GSR and false positive residues on adhesive tape substrates.140 This study served to emphasize the value of Raman spectroscopy for the analysis of GSR. Figure 5 depicts the hyperspectral map of an IGSR particle. Other firing distance focused studies were conducted using a variety of techniques. Hofer et al. published a two part study in which they investigated analysis methods for firing distance determination.141,142 In the first study, they examined unburned propellant particles using IR luminescence and

Figure 5. Chemical imaging for IGSR detection. (a) The visual image of a 15 μm diameter (approximately) IGSR particle. The red rectangle defines the mapped area. x- and y-axes plot the coordinates of the particle as compared to the arbitrary center of the montage image. (b) The chemical map of image (a), generated as the intensity of the Raman band located at approximately 1575 cm−1 in the z-direction. This band was assigned to graphite and found to be characteristic of IGSR. Reprinted by permission from Springer Nature: Springer, Anal. Bioanal. Chem., Raman microspectroscopic mapping as a tool for detection of gunshot residue on adhesive tape, Bueno, J.; Halámková, L.; Rzhevskii, A.; Lednev, I. K. (ref 140). Copyright 2018.

correlated GSR shooting distance to GSR particle density.141 In the second part of their study, they found a method they claimed improves upon that presented in the first.142 This new method involved IR visualization followed by subsequent transfer of OGSR from targets onto thin layer chromatography (TLC) plates using solvent. The plates were then sprayed with diphenylamine, which reacts with the nitrate groups in OGSR to provide good visualization of the particles. This method applied the same correlation between particle density and firing distance as used in the first study, and in this case, imaging software was used to count pixels in order to determine the particle density. Buking et al. developed a microfluidic paperbased analytical device to be used for the identification of bullet holes and for the determination of firing distance through measurement of lead concentration using the device.143 This device is advantageous because of its affordability and novelty. Finally, Zapata et al. used multispectral imaging in order to determine approximate firing distances.144 They found that in assessing multispectral images by counting pixels they were able to correlate the relationship between GSR particles and distance. In a study which specifically investigated gunshot wounds from “intermediate” distances of 5, 15, and 30 cm, Giraudo et al. fired into human calf sections and used micro-computed tomography (microCT) to examine the wounds.145 They concluded that, while this method would be suitable for helping to differentiate between entry and exit wounds when body parts are covered by clothing, it is not to be looked upon as a go to method for GSR identification or characterization (as it is not a confirmatory method). Finally, Brożek-Mucha published a study in which GSR from a submachine gun fired at close range (with and without a silencer) was assessed using a variety of methods including SEM-EDX, microscopy, and IR spectroscopy.146 Studies regarding GSR patterns were also recently published. Greely and Weber performed a very interesting study modeled after a real life case.147 This study showed that GSR can be deposited on other surfaces inside a room even after firing through glass. The authors stated that these findings emphasize the idea that collecting GSR from victims of a shooting crime is virtually useless, as this evidence may not really elucidate anything. Berger et al., who optimized and assessed the method 644

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

Figure 6. Photomicrographs and EDX spectra of parts of clean range ammunition of a 0.40 pistol. Reprinted from Microchem. J. Vol. 129, Costa, R. A.; Motta, L. C.; Destefani, C. A.; Rodrigues, R. R. T.; do Espı ́rito Santo, K. S.; Aquije, G. M. F. V.; Boldrini, R.; Athayde, G. P. B.; Carneiro, M. T. W. D.; Romão, W. Gunshot residues (GSR) analysis of clean range ammunition using SEM/EDX, colorimetric test and ICP-MS: A comparative approach between the analytical techniques, pp. 339−347 (ref 153). Copyright 2016, with permission from Elsevier.

parts of the “clean range cartridge” investigated can be seen in Figure 6. Studies involving the use of markers in GSR have also been conducted in recent years. Melo Lucena et al. developed a twodimensional metal−organic framework, [Dy(DPA)(HDPA)], using hydrothermal synthesis.154 This marker works on the basis of its luminescent properties under UV lighting. Lucena et al. published another study concerned with luminescent GSR markers in 2017, which investigated how the firearm discharge process affects luminescent markers.155 Arouca et al. published a follow up to their previous work investigating luminescent GSR markers.156 Their study covered four aspects of interest to forensic investigators, and they attempted to determine firing distance, position of the shooter, and pistol type, as well as study transfer of luminescent GSR through shaking hands. Finally, Destefani et al. conducted a study which assessed the toxicity of a potential GSR marker consisting of europium complexed with other components.157 They found overall that this potential marker was of “average toxicity” and was not any more toxic than the typical inorganic compounds found in GSR. The aging of GSR has been studied and published in a two part study by Gallidabino et al. in 2017.158,159 This study used a method known as headspace sorptive extraction (HSSE). LIBS has also been featured prominently in several recently published studies. López-López et al. performed a study which focused on the potential use of LIBS for the visualization of GSR patterns.160 They concluded that, while LIBS was successful for pattern visualization, it should not be used as a replacement for SEM-EDX analysis. Fambro et al. published two studies which investigated the potential for LIBS to be used for the analysis of GSR stemming from ammunition having lead-free composition.161,162 They utilized LIBS in conjunction with SEM-EDX for both of these studies and

of Total Nitrite Pattern Visualization, conducted another study concerned with GSR patterns.148 Several studies have been conducted to assess the overall prevalence of GSR in different circumstances. Lucas et al. conducted a study in which they assessed the distribution of GSR and residues similar to GSR in a random population sampling of individuals.149 They collected 289 samples in total and analyzed them using SEM-EDX. They found that only one individual had three-component GSR particles found on him. However, Pb/Sb component particles were found in 8% of the samples. Cook published a study which investigated the prevalence of GSR particles on the hands of police officers after their start-of-shift procedure handling their handguns.150 They found that some officers had fairly high levels of GSR contamination after handling their firearms. It was also discovered that washing hands using soap and water or the use of a hand gel resulted in the reduction of GSR particles that were found. Ali et al. also performed a study concerned with the prevalence of GSR particles in varying locations from four police stations in Pittsburgh.151 They used SEM-EDX and LC-MS/MS in this study. They concluded that, while GSR could potentially undergo secondary transfer onto suspects through contact with the areas investigated in this study, the odds are very small. Bell and Seitzinger used ion mobility spectrometry (IMS) in their recent study in order to assess the background levels of OGSR found in a survey of 73 people.152 This study is remarkable as it boasts a large sample population and deals specifically with OGSR. Costa et al. performed a study in which they compared a variety of techniques including inductively coupled plasma MS (ICPMS), SEM-EDX, and a colorimetric test for the investigation of GSR from “clean range” cartridges.153 They found that ICPMS performed very well. Their SEM-EDX results were also elucidating, and photomicrographs along with the EDX spectra of the different 645

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

Figure 7. Secondary electron image and EDX spectrum for a GSR particle from a Fiocchi Zero Pollution Primer sample. (a) Image acquired at 4000× magnification, an accelerating voltage of 20 kV, and working distance of 10 mm. (b) Energy dispersive X-ray spectrum indicating the elemental composition of the sample. (c) Atoms present in the spectrum (carbon (red): C; oxygen (green): O; aluminum (blue): Al; silicon (yellow): Si; potassium (purple): K) mapped over the sample surface to illustrate relative locations. Image scaling in micrometers. Reprinted from Fambro, L. A.; Vandenbos, D. D.; Rosenberg, M. B.; Dockery, C. R. Appl. Spectrosc. Vol. 71, Issue 4, pp. 699−708 (ref 162), copyright 2017 by SAGE. Reprinted by Permission of SAGE Publications, Ltd.

found that, on the basis of the evidence, including the GSR distribution, the case appeared to be more likely a homicide than a suicide. Burnett and Lebiedzik also modeled their study on the case of a shooting in which the firearm was discharged from inside the car out of a car window.167 Overall, many important research advances have been made for the detection, identification, and characterization of both IGSR and OGSR in recent years. As is evidenced by the extensive pool of recent papers presented here, many researchers are applying new and novel ideas to the field of GSR analysis. The future for this “hot topic” type of evidence certainly seems to be promising.

emphasized the potential for LIBS to be used as a prescreening method before analysis using SEM-EDX. An example of the types of SEM-EDX maps used to correlate and compare with LIBS data in their 2017 study can be seen in Figure 7. This map of a GSR particle shows the elemental composition using SEM-EDX. Finally, Trejos et al. performed a study in which they analyzed GSR using LIBS and electrochemical methods.163 They also presented LIBS as a potential prescreening tool prior to analysis using SEM-EDX. Electrochemical methods were also used in a study by Hashim et al., in which they used cyclic voltammetry with a screen printed carbon electrode to detect Cu(II) in GSR.164 They compared their results to those obtained using inductively coupled plasma optical emission spectroscopy (ICP-OES). They validated the voltammetry method using ICP-OES and reported an accuracy value of 94%. Erol et al. applied capillary electrophoresis for the successful identification of the presence of nitrate and nitrite in GSR samples.165 They were able to do this rapidly and with good limits of detection. Finally, studies modeled on real-life cases emphasize the relevance of GSR for forensic investigators. Brożek-Mucha and Zdeb conducted a study regarding a purported suicide case in which the weapon was a machine gun.166 The GSR in this case was studied using a variety of techniques. Overall, researchers



EXPLOSIVES Explosives are a categorization of forensic evidence which can be subdivided into many other subclassifications based on different parameters.168 Explosives can be separated by their composition into the following seven categorizations: aromatic nitramines, nitrate esters, initiating explosives, fuel/oxidant formulations, peroxides, nitric compounds, and salt formulations.168 Many of these classifications have been covered in the following review of papers. It must be noted that due to the varied structures of explosives many techniques have been applied for explosives analysis. Thus, this section makes mention of many different methods and approaches. 646

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

for NB and DNB, and 1 ppb for DNT. The accuracy for this method was determined to be greater than 81% and was observed to be 96% when the quantity of printed sensor material used was doubled. LDA was used for the differentiation of the explosives, and the simplicity of this chemometric technique was noted. Devi et al. and Qian et al. both developed fluorescence-based sensors for detection of TNT.178,179 Devi et al. created mercaptopropionic acid capped CdSe quantum dots conjugated with mercaptoethylamine capped gold nanoparticles for TNT detection. Qian et al. used red emitting CdTe quantum dots capped with L-cysteine and green emitting CdTe quantum dots capped with 3mercaptopropionic acid interfaced in different manners with SiO2 in order to achieve detection. This method was touted as affordable, able to produce high resolution images, and convenient, as it was successfully integrated into a paper filter design. Aptasensors are often used in immunology applications as they are based on aptamers which are single-stranded short pieces of DNA or RNA which can associate selectively with antigens.180 For further information regarding aptasensors for the detection of explosives and other forensic materials, the reader is referred to the review by Gooch et al. published in 2017.180 Roushani et al. combined gold nanoparticles and fullerene (C60) to assist in the creation of a TNT aptasensor.181 This aptasensor possessed a limit of detection of 0.17 fM for TNT. Sempionatto et al. devised a wearable ring sensor for the detection of explosives and nerve agent.182 This ring sensor operates on the basis of chronoamperometric analysis and fast square-wave voltammetry. The sensor was able to detect explosives and nerve agent in both liquid and vapor form. However, liquid forms seemed to be preferable for analysis by the sensor. The wearable ring sensor, its mechanisms of operation, and graphical depictions of the detection of target compounds can be seen in Figure 8. The key advantage of the sensor ring is the fact that it is able to be worn and thus is small and portable. Wu et al. developed a chemiresistive gas sensing method for the discrimination of components found in improvised explosive devices (IEDs).183 IEDs are of relevance in a forensic context because they are easy to construct and often employed in acts of terrorism. Canines are often used for the detection of explosives, and a review discussing their role in this field was published by Hayes et al. in 2018.184 A review regarding chemical sniffing instruments was published by Giannoukos et al. in 2016; this publication provides a comprehensive overview regarding “electronic noses” and their role in a security context.185 Artificial sniffing is a technique which attempts to recreate the sense of olfaction (in humans or other species) using arrays or specially designed systems. Guo et al. investigated the sensing of explosive vapors using an electronic nose setup comprised of an array of silicon nanowires, an optoelectronic Schottky junction, and ZnO.186 They successfully used this setup to detect and differentiate between urea, paranitrotoluene, RDX, picric acid, black powder, ammonium nitrate (AN), TNT, and DNT. Verbitskiy et al. developed a fluorescence-based sensor for use in an electronic nose system geared toward the detection of vaporous nitroaromatics.187 Researchers also used their fluorophores to design two sensor prototypes for use with the handheld artificial sniffing system known as Nitroscan. Yew et al. used electrochemical methods to detect TNT and DNT.188 Graphene sheets were created under different

The manner in which explosives samples are collected is a crucial factor in explosives investigations. Yu et al. used a contact heater system to recover traces of the high explosives triacetone triperoxide (TATP) and ethylene glycol dinitrate (EGDN) from porous materials.169 Porous materials are difficult substrates from which to recover explosive traces. The contact heater system was tested on a variety of substrates “spiked” with explosives. The contact heater method has proven to be readily compatible with GC/MS and LC-MS for subsequent confirmatory analysis. Chaffee-Cipich et al. addressed the issue of sample collection in the context of airport security by assessing contact between four common sample collection traps and seven relevant surfaces.170 This study was performed using a combination of computational methods and physical experiments. It was found that, the smoother the surface and trap, the more likely it was that a particle would be sampled successfully. Explosives detection has been successfully achieved though the optimization of specific chemical reactions, very often reactions involving a color change. Choodum et al. developed a colorimetric kit which was able to detect the prevalently used explosive trinitrotoluene (TNT).171 The colorimetric test was created by trapping potassium hydroxide reagent within a polyvinyl alcohol (PVA) hydrogel. The kits are cost-effective, can be kept frozen for up to 3 months, may be reused up to 12 times, and allow for the rapid detection of TNT. These advantages combined with the ease of quantitation using digital image colorimetry present the hydrogel kit as very promising tool. Ma et al. developed a test based on the reaction of UV light with carbazolyl radicals in a chloroform solution.172 The compound 2,4,6-trinitrophenol (TNP) was targeted, because of the difficulty in distinguishing TNP from TNT. This test provides a specific and sensitive way to identify TNP which requires very little instrumentation. Many sensors were developed for the detection of explosives. These sensors mainly utilized the fluorescence properties of their components in order to detect explosive traces, and nanomaterials were often incorporated into their design. One notable review by Yang and Dou provides an overview regarding the use of 1D inorganic nanomaterials for the detection of explosive vapors.173 Another notable article which discusses fluorescence-based sensors for explosives was published by Sathish et al.174 Zhu et al. developed a twomember array system using fluorescence-based sensors and successfully discriminated between several classes of explosives.175 The advantage to this method was the ease of data processing and the simplicity of assembling the array in comparison to other setups. Wang et al. created a fluorescence spot (“fluo-spot”) for the detection of the explosives octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), pentaerythritol tetranitrate (PETN), and 1,3,5-trinitroperhydro1,3,5-triazine (RDX).176 This method was based on quenching of the fluorescent dye 4-(dicyanomethylene)-2-methyl-6-(4dimethylaminostyryl)-4H-pyran or DCM after its reaction with NO2• or NO2+ from the explosives. Researchers also developed a DCM spray and tested it on the real life substrates of a dollar bill and cardboard. Bolse et al. expanded upon their previous work with fluorescence sensor arrays produced using aerosol jet printing.177 They were successfully able to use the printed sensors to detect and differentiate between the nitroaromatic explosives 1,3-dinitrobenzene (DNB), 2,4-dinitrotoluene (DNT), and nitrobenzene (NB). The limit of detection for the vaporous form of these explosives was approximately 3 ppb 647

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

Figure 8. (A) Ring-based sensor platform for detecting explosives and nerve agent threats in both vapor and liquid phases. Images showing (a) ring polymeric case with the integrated electronics and replaceable screen-printed electrodes based on (from left to right) a carbon working electrode (WE 1), an Ag/AgCl reference electrode (RE), a carbon/Prussian-blue working electrode (WE 2), and a carbon counter electrode (CE); (b) bottom view of integrated circuit board and coin battery compartment capable of performing square wave voltammetry (SWV) and chronoamperometry (CA); (c) ring sensor worn on wearer’s middle finger. All scales: 15 mm. (B) Schematic representation of the redox detection processes of the different chemical threats on the ring sensor platform, and corresponding vapor-phase detection, showing multiplexed (a) vapor detection of DNT (red) on carbon WE 1 and corresponding background SWV (black) in vapor phase; (b) peroxides detection on the carbonPrussian blue WE 2 (red) along with the background CA (black); (c) MPOx vapor detection on carbon WE 1 (red) along with the background SWV response (black). Adapted from Sempionatto, J. R.; Mishra, R. K.; Martı ́n, A.; Tang, G.; Nakagawa, T.; Lu, X.; Campbell, A. S.; Lyu, K. M.; Wang, J. ACS Sens. 2017, 2, 1531−1538 (ref 182). Copyright 2017 American Chemical Society.

of different low explosives using a wide variety of techniques with a focus on AN-based explosives.193 Two interesting studies were performed concerning pipe bombs, which are the most popular type of IED.194,195 Oxley et al. studied the fragmentation process of pipe bombs using fragment surface area distribution mapping, aka fragment weight distribution mapping. 194 Bors and Goodpaster performed a study in which total vaporization solid phase microextraction GC/MS (TV-SPME/GC/MS) was used to map post-blast residues from smokeless powders on pipe bomb fragments.195 They were successfully able to detect and separate the three target smokeless powder components selected with an analysis time of less than 5 min. Tang et al. also performed a study involving mapping which utilized multiphoton electron extraction spectroscopy (MEES) for the detection of explosives.196 This technique was touted for the fact that sample analyses could take place under ambient conditions in a matter of seconds, with no sample pretreatment. Pawłowski et al. conducted a study concerned with lab protocols for the analysis of forensic evidence and the potential for the contamination of such evidence with explosive traces.197 Finally, Lees et al. performed a study which utilized multispectral imaging (MSI) to detect a variety of explosives.198 This study dealt largely with the topic of explosives transfer, which is a sorely neglected area of forensic explosives research. Advantages of this method were its simple and yet innovative design. Matyás ̌ et al. conducted a comprehensive study on erythritol tetranitrate utilizing multiple analytical approaches.199 It was

conditions and compared on the basis of their potential as electrode materials for the detection of TNT and DNT. AFM is a high-resolution microscopy technique involving a scanning probe which provides topographic three-dimensional images of samples.189 Pandey et al. published a review in 2017, which details AFM analysis for forensic applications, including explosives.189 Also in 2017, DelRio and Cook published a study in which AFM was used to analyze explosive particles along with other forensic samples.27 This study was unique because it focused primarily on the mechanics surrounding the evidence rather than on chemical or morphological characterization. Other techniques have also been utilized for the analysis of explosives. Andrasko et al. notably investigated a wide variety of explosives using GC coupled with UV spectrophotometry (GC-UV).190 This method had a good limit of detection of around 1 ng for the explosives investigated. The main advantage of this method was that it allowed for the detection not only of explosive compounds but also of their decomposition products. Yu et al. used LC-UV to assess the best manner to preserve and store soil samples containing the explosives PETN, RDX, and TNT.191 Papp and Csikai investigated neutron activation and reflection as a means to detect explosives concealed within other materials.192 In this study, researchers found that neutron reflection and activation could provide an effective solution to the problem of detecting concealed explosives contained in plastics. Bagchi et al. published a comprehensive study which discussed the analysis 648

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

together they provided comprehensive information about the residues analyzed. Raman spectroscopy offered identification of the energetic salts; ion chromatography (IC) provided information regarding respective anions, and reversed-phase HPLC (RP-HPLC) provided information about organic components. The nondestructive nature and lack of sample preparation needed for Raman spectroscopy were found to be beneficial for the analysis of AN fuel oil on banknotes.208 Raman hyperspectral imaging, in conjunction with independent component analysis, was utilized for the analysis of banknotes after an ATM explosion. SERS offers even more sensitive analysis.8 The detection of secondary explosive molecules at concentrations as low as 10 μM was possible using Ag decorated ZnO nanostructures.209 He et al. proposed the use of the hydrophobic condensation effect on biomimetic SERS platforms for ultrasensitive detection of explosives.210 For further information, recent applications of Raman spectroscopy and SERS for forensics have been reviewed extensively by Khandasammy et al. and Fikiet et al.7,8 Abdul-Karim et al. proposed the combination of imaging with scanning electron microscopy (SEM) and focused ion beam.211 This method allowed researchers to conduct morphological analyses of post-blast particles and probe the internal structure of the residues. They found that particles of post-blast explosives have characteristic shapes, sizes, and internal structures. However, even though the method is promising, it required validation using other techniques. Liquid-based separation in connection with MS techniques was found to be very useful for many forensic applications. Moini published a review on recent liquid separation MS analyses in forensics which includes explosives.212 As this review includes articles published between 2015 and 2017, we do not include studies evaluated therein. MS analysis allows analysts to combine different features in order to optimize results for specific samples. DART-MS was found to be useful for explosives evidence analysis.213 The same group conducted a study which applied DART-MS with chemometric analysis to classify commercial spices used in improvised explosives by type and brand.214 DART-MS was also found to be useful for potential by-products from homemade nitrate ester explosive synthesis.215 In this case, the matrix effects and potential impurities contained in homemade explosives are important to be taken into account. Fraga et al. studied calcium ammonium nitrate, which is frequently used in homemade explosives, using ICPMS.216 Chemometrics was applied to classify samples from different manufacturers based on elemental composition. Bailey et al. applied liquid extraction surface analysis (LESA) coupled with high resolution MS and MS/MS to detect explosives in contaminated artificial fingermarks.217 Fingermarks were also analyzed using infrared laser ablation coupled to vacuum capture with GC/MS.218 The infrared laser ablation was used to extract different analytes, including explosives, from fingermarks deposited on a plastic surface. This technique showed potential. Meanwhile, infrared thermal desorption (IRTD) allowed for nanogram level detection of explosive substances and narcotics.219 Negative ion micro-fabricated glow discharge plasma desorption/ionization MS (NIMFGDP-MS) was studied for nitrogen-based explosives and successfully applied to identify traces in open air.220 Isotope ratio MS (IRMS) and GC-IRMS were useful for the analysis of TATP.221 However, in comparison to this study, Howa et al. observed greater ranges in both carbon and hydrogen isotope ratios of acetone and a large contribution of

proven that each technique offers unique advantages and can provide valuable forensic information. The advantages and limitations of vibrational spectroscopy regarding the analysis of explosives for forensic purposes was reviewed by Muro et al.6 Since the aforementioned review article, new studies on vibrational spectroscopic analysis of explosives were published. ATR FT-IR spectroscopy was proposed as a nondestructive analysis method for post-blast residues from consumer fireworks.200 ATR FT-IR spectroscopy proved to be a promising approach for this application. In 2017, Á lvarez et al. published a study in which they utilized FT-IR photoacoustic spectroscopy to achieve 100% differentiation between four propellant brands.201 This study was significant to explosives research, as propellants are often used to make IEDs. Different vibrational spectroscopic techniques have also been compared. Elbasuney and El-Sherif examined concealed explosives and explosive-related compounds using Raman and FT-IR spectroscopic analyses.202,203 These techniques were found to provide complementary data. Raman spectroscopy allows for noninvasive, instant, and standoff identification of explosive materials and was found to be a practical tool for obtaining qualitative and quantitative information about the examined material. The standoff trace explosives detection using UV resonance Raman spectroscopy was investigated by Gares et al. and Hufziger et al.204,205 The example of the images in visible light, as well as Raman spectra and Raman images of AN and PETN, can be seen in Figure 9. More information on this technique can be found in the review article by Gares et al.206 Zapata et al. proposed a combination of Raman spectroscopy and LC in order to detect, identify, and quantify residues of explosives and energetic salts found in human handmarks.207 These methods are complementary as

Figure 9. (a) CMOS image of the 120 mg (920 mg/cm2) PETN (top) and 120 mg (760 mg/cm2) AN (bottom) drop-cast films on an aluminum plate at a 2.3 m standoff distance. (b) 229 nm Raman spectra of solid PETN (top, red) and solid AN (bottom, blue). An ∼1 nm full width at half-maximum diffracted spectral bandwidth at a 16.5° photonic crystal angle is depicted by the blue shading. (c, d) Raman images of the trace explosive sample shown in (a) at a 16.5° photonic crystal angle after 30 s and 2 min accumulations, respectively. (e) Expanded 30× microscope image of the sample in panel (a) before irradiation. Scale bar represents 1 mm. (f) 229 nm Raman spectra of solid PETN (top) and solid AN (bottom). An ∼1 nm full width at half-maximum diffracted spectral bandwidth at a 14° photonic crystal angle is depicted by the red shading. (g, h) Raman images of the trace explosives shown in (a) at a 14° photonic crystal angle after 30 s and 2 min accumulations, respectively. Both rotational angles of incidence are measured in air. Thirty second and 2 min accumulations utilize 8 × 8 and 6 × 6 pixel binning, respectively. Reprinted from Hufziger, K. T.; Bykov, S. V.; Asher, S. A. Appl. Spectrosc. Vol. 71, Issue 2, pp. 173−185 (ref 205), copyright 2017 by SAGE. Reprinted by Permission of SAGE Publications, Ltd. 649

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

country-of-purchase of acetone.222 A nitrogen and oxygen isotope ratio time series was employed for AN-based fertilizers, which are often used in homemade explosive devices.223 IMS has taken on a significant role in screening strategies for customs and airport applications as it is sensitive to organic compounds such as illicit drugs, chemical and biological warfare agents, and explosives.224 An IMS was built and tested to reduce the error of measurements.225 Du et al. proposed an approach with developmental IMS coupled to a fabricated MS platform.226 The size of this instrument gives it potential for the forensic analysis of illegal substances and explosives in the field. Field asymmetric IMS (FAIMS) with gas sensors was utilized for a multicopter-based air monitoring system (Figure 10).227 This system could be potentially employed for the

Atmospheric-pressure chemical-ionization MS (APCI-MS) in combination with other MS methods was utilized for characterization of nitrated sugar alcohols as nitrated sugar alcohols were not yet fully investigated by MS.231 APCI-MS was found to be a selective and sensitive approach for the detection of nitrated sugar alcohols. TV-SPME/GC/MS was proposed by Sauzier et al. to study the optimum recovery parameters for double-base smokeless powder residues on steel.232 Explosives analyses have advanced significantly in recent years. As explosives are arguably a very important type of evidence due to the potential harm and destruction they can cause, the research detailed in this section is indeed crucial and highly relevant. Undoubtedly, this field will continue to progress quickly and steadily in coming years.



CONCLUSIONS Analytical chemistry has been making significant progress in the development and implementation of new forensic methods. Recent work on physical evidence analysis focuses on improving the existing technologies and developing novel approaches for examination. On the basis of recent literature, there has been a pronounced movement toward nondestructive, in-field analysis of trace evidence materials. However, in cases where extraction is required, chromatography is still utilized. It is also notable that chemometric approaches were found to be useful in recent research, mostly in combination with spectroscopic methods. Many emerging technologies for the forensic examination of trace evidence materials have been highlighted herein. Spectroscopy offers multiple advantages due to the potential for nondestructive analyses, which is ideal in forensic casework. As shown in the review article by Ewing and Kazarian, IR spectroscopy and spectroscopic imaging play an important role in forensic science.233 Noninvasive hair examination was possible using ATR FT-IR spectroscopy.23,31 With regards to GSR evidence, our group is working to apply hyperspectral imaging for the detection of such particles.140 One of the most advantageous techniques for the analysis of explosives is being developed by the Asher group.204,205 They are making significant progress concerning the detection of trace explosives using standoff UV Raman spectroscopy. Matousek and co-workers introduced micro-SORS technology to interrogate paint evidence and demonstrated a great potential of this novel technology.83−86 LIBS is a spectroscopic technique which provides the elemental composition of a sample. The method is relatively simple and can be considered almost nondestructive due to the small sample size required. In recent studies, LIBS was found to be very effective for glass and GSR analyses.105,106,160−163 A nonspectroscopic technique which provides elemental composition is LA-ICPMS, which was shown by Almirall and Trejos to be propitious for the analysis of various materials.95 This Review article reveals the importance of scientific examination techniques for trace evidence materials in recent forensic applications. The needs for more efficient and improved methods for the analysis of trace evidence have been addressed here. Overall, it has been shown that a variety of scientific tools alone or in combination demonstrates potential for improving the field of trace evidence examination in forensics.

Figure 10. Developed measuring system. (A) Photo of the assembled system: 1, array of gas sensors; 2, the 3G modem; 3, the camera; 4, GPS antenna; 5, suction cup. (B) The hovering measuring system. (C) Side view: 6, the onboard computer. (D) The installed Field Asymmetric Ion Mobility Spectrometer: 7, without suction cups. Reproduced from Remote detection of explosives using field asymmetric ion mobility spectrometer installed on multicopter, Kostyukevich, Y.; Efremov, D.; Ionov, V.; Kukaev, E.; Nikolaev, E. J. Mass Spectrom., Vol. 52, Issue 11 (ref 227). Copyright 2017 Wiley.

detection of impurities in the air, ecology monitoring, detection of chemical warfare agents, and explosives. A major disadvantage of this system is the airflow of rotating propellers, and the wind decreases the concentration of the target gas, which disturbs the flow inside the instrument. A low-pressure air dielectric-barrier discharge (DBD) ion source, using a capillary with an inner diameter of 0.115 and 12 mm long, applicable to miniaturized mass spectrometers, was developed and tested using drug and explosive samples.228 A microwave plasma used for direct ambient ionization MS proposed by Evans-Nguyen et al. required no digestion or pretreatment of the sample.122 Another ambient ionization method is paper spray ionization (PSI). The use of the same paper substrate, possessing an inkjet printed surface with silver nanoparticles, was found to be practical for SERS and MS analyses.229 This allowed for rapid analyte identification and confirmation without sample preparation steps. PSI was also utilized with FAIMS/MS in order to reduce possible background ions from the paper.230 650

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry



Review

(3) Bowen, R.; Schneider, J. Forensic Databases: Paint, Shoe Prints, and Beyond; NIJ: Washington, DC, 2007; Vol. 258; https://www.nij. gov/journals/258/pages/forensic-databases.aspx. (4) Stoney, D. A.; Stoney, P. L. Forensic Sci. Int. 2015, 251, 159− 170. (5) Castillo-Peinado, L. S.; Luque de Castro, M. D. Talanta 2017, 167, 181−192. (6) Muro, C. K.; Doty, K. C.; Bueno, J.; Halámková, L.; Lednev, I. K. Anal. Chem. 2015, 87, 306−327. (7) Khandasammy, S. R.; Fikiet, M. A.; Mistek, E.; Ahmed, Y.; Halámková, L.; Bueno, J.; Lednev, I. K. Forensic Chem. 2018, 8, 111− 133. (8) Fikiet, M. A.; Khandasammy, S. R.; Mistek, E.; Ahmed, Y.; Halamkova, L.; Bueno, J.; Lednev, I. K. Spectrochim. Acta, Part A 2018, 197, 255−260. (9) Weir, B. S. In Encyclopedia of Forensic Sciences; Siegel, J. A., Saukko, P. J., Eds.; Academic Press: New York, 2000. (10) Lindley, D. V. Biometrika 1977, 64, 207−213. (11) Meuwly, D.; Ramos, D.; Haraksim, R. Forensic Sci. Int. 2017, 276, 142−153. (12) Aitken, C. G. G. Front. Genet. 2018, 9, 126. (13) Franco-Pedroso, J.; Ramos, D.; Gonzalez-Rodriguez, J. PLoS One 2016, 11, No. e0149958. (14) Lund, S. P.; Iyer, H. J. Res. Natl. Inst. Stan. 2017, 122, 27. (15) Morrison, G. S.; Poh, N. Sci. Justice 2018, 58, 200−218. (16) Kumar, R.; Sharma, V. TrAC, Trends Anal. Chem. 2018, 105, 191−201. (17) President’s Council of Advisors on Science and Technology. Report to the President Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods; 2016; https:// obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ PCAST/pcast_forensic_science_report_final.pdf. (18) Committee on Identifying the Needs of the Forensic Sciences Community; National Research Council. Strengthening Forensic Science in the United States: A Path Forward; The National Academic Press: Washington, DC, 2009; https://www.ncjrs.gov/pdffiles1/nij/grants/ 228091.pdf. (19) Robertson, J. Aust. J. Forensic Sci. 2017, 49, 239−260. (20) Kučera, J.; Kameník, J.; Havránek, V. Forensic Chem. 2018, 7, 65−74. (21) Pozebon, D.; Scheffler, G. L.; Dressler, V. L. Anal. Chim. Acta 2017, 992, 1−23. (22) Mills, M.; Bonetti, J.; Brettell, T.; Quarino, L. J. Microsc. 2018, 270, 27−40. (23) Manheim, J.; Doty, K. C.; McLaughlin, G.; Lednev, I. K. Appl. Spectrosc. 2016, 70, 1109−1117. (24) Srettabunjong, S.; Patompakdeesakul, P.; Limawongpranee, S. Forensic Sci. Int. 2016, 267, 196−203. (25) Ho, Y. Y. W.; Brims, M.; McNevin, D.; Spector, T. D.; Martin, N. G.; Medland, S. E. Twin Res. Hum. Genet. 2016, 19, 351−358. (26) Tang, W.; Zhang, S. G.; Zhang, J. K.; Chen, S.; Zhu, H.; Ge, S. R. Int. J. Cosmet. Sci. 2016, 38, 155−163. (27) DelRio, F. W.; Cook, R. F. Exp. Mech. 2017, 57, 1045−1055. (28) Wu, Y.; Chen, G.; Ji, C.; Hoptroff, M.; Jones, A.; Collins, L. Z.; Janssen, H.-G. Anal. Bioanal. Chem. 2016, 408, 2357−2362. (29) Hietpas, J.; Buscaglia, J.; Richard, A. H.; Shaw, S.; Castillo, H. S.; Donfack, J. Forensic Sci. Int. 2016, 267, 7−15. (30) Tinoco Corrêa, G.; Tanaka, A. A.; Pividori, M. I.; Boldrin Zanoni, M. V. J. Electroanal. Chem. 2016, 782, 26−31. (31) Boll, M. S.; Doty, K. C.; Wickenheiser, R.; Lednev, I. K. Forensic Chem. 2017, 6, 1−9. (32) Petzel-Witt, S.; Meier, S. I.; Schubert-Zsilavecz, M.; Toennes, S. W. Drug Test. Anal. 2018, 10, 768−773. (33) Pienpinijtham, P.; Thammacharoen, C.; Naranitad, S.; Ekgasit, S. Spectrochim. Acta, Part A 2018, 197, 230−236. (34) Cardoso Santos, M.; Sperança, M. A.; Verbi Pereira, F. M. XRay Spectrom. 2018, 47, 252−257. (35) Kuzuhara, A. Int. J. Cosmet. Sci. 2016, 38, 201−209.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Igor K. Lednev: 0000-0002-6504-531X Notes

The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the U.S. Department of Justice. The authors declare no competing financial interest. Biographies Ewelina Mistek is a Ph.D. student in Chemistry at the University at Albany and a National Institute of Justice Graduate Research fellow in STEM. Ewelina obtained an A.P. degree in Chemical and Biotechnical Science from the Business Academy Aarhus, University of Applied Sciences, Denmark, and a B.S. in Forensic and Analytical Science from the Robert Gordon University, U.K. In 2016, she started her Ph.D. study under the mentorship of Professor Igor Lednev. Her work involves the application of vibrational spectroscopy and statistical data analysis for the development of new forensic methods for the identification and characterization of body fluid traces. She is a recipient of the Coblentz Student Award. Marisia A. Fikiet graduated with a B.S. in Chemistry from the University of Connecticut in 2013 and got her M.A. in Forensic Science from the University of New Haven in 2015. She is currently a Ph.D. candidate at the University at Albany, SUNY in the Lednev Lab. She specializes in Raman spectroscopy and surface enhanced Raman spectroscopy with an emphasis on forensic applications. Her thesis focuses on novel applications for Raman spectroscopy for use in serology. Shelby R. Khandasammy received a double degree in 2016 from the University of Central Florida, with a B.S. in Forensic Science and B.A. in English cum laude. In 2017, she began the pursuit of a Ph.D. in Chemistry in the Lednev Research Group. Her research is primarily focused on the study of gunshot residues using vibrational spectroscopic analyses. She is particularly interested in the development of new methods in forensic science for the analysis of explosives and gunshot residues. Igor K. Lednev is a professor at the University at Albany. His expertise and experience are in the development and application of novel laser spectroscopy for biomedical research and forensic purposes. Dr. Lednev served as an advisory member for the White House Subcommittee on Forensic Science and on editorial boards of four scientific journals including Forensic Chemistry and the Journal of Raman Spectroscopy. Dr. Lednev is a fellow of the Society for Applied Spectroscopy and the Royal Society of Chemistry. He is a recipient of the Society for Applied Spectroscopy Gold Medal Award. Lednev has coauthored over 210 peer-reviewed publications.



ACKNOWLEDGMENTS This project was supported by Award Nos. 2017-R2-CX-006 and 2016-DN-BX-0166 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. We give our thanks to Nicholas Morabito for providing pictures used in the Table of Contents graphic.



REFERENCES

(1) NIST: National Institute of Standards and Technology. Trace Evidence; https://www.nist.gov/topics/trace-evidence. (2) Saferstein, R. Criminalistics: An Introduction to Forensic Science, 11th ed.; Pearson Education: Boston, 2015. 651

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

(70) Lavine, B. K.; White, C.; Allen, M. Microchem. J. 2016, 129, 173−183. (71) Thoonen, G.; Nys, B.; Vander Haeghen, Y.; De Roy, G.; Scheunders, P. Forensic Sci. Int. 2016, 259, 210−220. (72) Michalska, A.; Martyna, A.; Zadora, G. Forensic Sci. Int. 2018, 282, 60−73. (73) Ferreira, K. B.; Oliveira, A. G. G.; Gomes, J. A. Spectrosc. Lett. 2017, 50, 102−110. (74) Lv, J.; Zhang, W.; Liu, S.; Chen, R.; Feng, J.; Zhou, S.; Liu, Y. Environ. Forensics 2016, 17, 59−67. (75) Lv, J. G.; Liu, S.; Feng, J. M.; Liu, Y.; Zhou, S. D.; Chen, R. Pigm. Resin Technol. 2016, 45, 294−300. (76) Chen, T.-H.; Wu, S.-P. Forensic Sci. Int. 2017, 277, 179−187. (77) Huang, L.; Beauchemin, D. J. Anal. At. Spectrom. 2017, 32, 1601−1607. (78) Kwofie, F.; Perera, U. D. N.; Allen, M. D.; Lavine, B. K. Talanta 2018, 186, 662−669. (79) Ferreira, K. B.; Oliveira, A. G. G.; Gonçalves, A. S.; Gomes, J. A. Forensic Chem. 2017, 5, 46−52. (80) Gomes de Oliveira, A. G.; Wiercigroch, E.; de Andrade Gomes, J.; Malek, K. Anal. Methods 2018, 10, 1203−1212. (81) Zhang, N.; Wang, C.; Sun, Z.; Mei, H.; Huang, W.; Xu, L.; Xie, L.; Guo, J.; Yan, Y.; Li, Z.; Xu, X.; Xue, P.; Liu, N. Forensic Sci. Int. 2016, 266, 239−244. (82) Bertasa, M.; Possenti, E.; Botteon, A.; Conti, C.; Sansonetti, A.; Fontana, R.; Striova, J.; Sali, D. Analyst 2017, 142, 4801−4811. (83) Conti, C.; Realini, M.; Colombo, C.; Botteon, A.; Bertasa, M.; Striova, J.; Barucci, M.; Matousek, P. Philos. Trans. R. Soc., A 2016, 374, 20160049. (84) Conti, C.; Botteon, A.; Colombo, C.; Realini, M.; Matousek, P. Anal. Chem. 2017, 89, 11476−11483. (85) Botteon, A.; Conti, C.; Realini, M.; Colombo, C.; Matousek, P. Anal. Chem. 2017, 89, 792−798. (86) Realini, M.; Conti, C.; Botteon, A.; Colombo, C.; Matousek, P. Analyst 2017, 142, 351−355. (87) Matousek, P.; Conti, C.; Realini, M.; Colombo, C. Analyst 2016, 141, 731−739. (88) Bower, N. W.; Blanchet, C. J. K.; Epstein, M. S. Appl. Spectrosc. 2016, 70, 162−173. (89) Jost, C.; Muehlethaler, C.; Massonnet, G. Forensic Sci. Int. 2016, 258, 32−40. (90) Muehlethaler, C.; Massonnet, G.; Hicks, T. Sci. Justice 2016, 56, 61−72. (91) Germinario, G.; van der Werf, I. D.; Sabbatini, L. Microchem. J. 2016, 124, 929−939. (92) Zięba-Palus, J.; Kowalski, R. Vib. Spectrosc. 2018, 95, 57−61. (93) Lambert, D.; Muehlethaler, C.; Esseiva, P.; Massonnet, G. Forensic Sci. Int. 2016, 263, 39−47. (94) Kammrath, B. W.; Koutrakos, A. C.; McMahon, M. E.; Reffner, J. A. In Forensic Science: A Multidisciplinary Approach; Katz, E., Halámek, J., Eds.; Wiley-VCH: Weinheim, Germany, 2016. (95) Almirall, J. R.; Trejos, T. Elements 2016, 12, 335−340. (96) Duckworth, D. C. In Encyclopedia of Encyclopedia of Spectroscopy and Spectrometry, 3rd ed.; Elsevier: Boston, 2017. (97) Baca, A. C.; Thornton, J. I.; Tulleners, F. A. J. Forensic Sci. 2016, 61, S92−S101. (98) Mattijssen, E. J. A. T.; Pater, K. D. H.; Stoel, R. D. J. Forensic Sci. 2016, 61, 1456−1460. (99) Hirakawa, S.; Saimoto, A.; Ishimatsu, T. J. Forensic Sci. 2016, 61, 1080−1084. (100) Harshey, A.; Srivastava, A.; Yadav, V. K.; Nigam, K.; Kumar, A.; Das, T. Egypt. J. Forensic Sci. 2017, 7, 20. (101) Aromatario, M.; Cappelletti, S.; Bottoni, E.; Fiore, P. A.; Ciallella, C. Leg. Med. 2016, 18, 1−6. (102) Michalska, A.; Zadora, G.; Martyna, A. Anal. Lett. 2016, 49, 1884−1895. (103) Montoriol, R.; Guilbeau-Frugier, C.; Chantalat, E.; Roumiguié, M.; Delisle, M.-B.; Payré, B.; Telmon, N.; Savall, F. Int. J. Legal Med. 2017, 131, 1347−1354.

(36) Fedorkova, M. V.; Brandt, N. N.; Chikishev, A. Y.; Smolina, N. V.; Balabushevich, N. G.; Gusev, S. A.; Lipatova, V. A.; Botchey, V. M.; Dobretsov, G. E.; Mikhalchik, E. V. J. Photochem. Photobiol., B 2016, 164, 43−48. (37) Chau, T. H.; Tipple, B. J.; Hu, L.; Fernandez, D. P.; Cerling, T. E.; Ehleringer, J. R.; Chesson, L. A. Rapid Commun. Mass Spectrom. 2017, 31, 583−589. (38) Mant, M.; Nagel, A.; Prowse, T. J. Forensic Sci. 2016, 61, 884− 891. (39) Gehre, M.; Renpenning, J.; Gilevska, T.; Qi, H.; Coplen, T. B.; Meijer, H. A. J.; Brand, W. A.; Schimmelmann, A. Anal. Chem. 2015, 87, 5198−5205. (40) Coplen, T. B.; Qi, H. Forensic Sci. Int. 2016, 266, 222−225. (41) Soto, D. X.; Koehler, G.; Wassenaar, L. I.; Hobson, K. A. Rapid Commun. Mass Spectrom. 2017, 31, 1193−1203. (42) Prego Meleiro, P.; García-Ruiz, C. Appl. Spectrosc. Rev. 2016, 51, 278−301. (43) Farah, S.; Kunduru, K. R.; Tsach, T.; Bentolila, A.; Domb, A. J. Polym. Adv. Technol. 2015, 26, 785−796. (44) Muñoz de la Peña, A.; Mujumdar, N.; Heider, E. C.; Goicoechea, H. C.; Muñoz de la Peña, D.; Campiglia, A. D. Anal. Chem. 2016, 88, 2967−2975. (45) Reichard, E. J.; Bartick, E. G.; Morgan, S. L.; Goodpaster, J. V. Forensic Chem. 2017, 3, 21−27. (46) Sauzier, G.; Reichard, E.; van Bronswijk, W.; Lewis, S. W.; Goodpaster, J. V. Forensic Chem. 2016, 2, 15−21. (47) Starczak, R.; Wąs-Gubała, J. Dyes Pigm. 2016, 132, 58−63. (48) Powell, R.; van Bronswijk, W.; Coumbaros, J. Forensic Sci. Int. 2018, 287, 54−62. (49) Lunstroot, K.; Ziernicki, D.; Vanden Driessche, T. Sci. Justice 2016, 56, 157−164. (50) Zhou, C.; Beck, K. R.; Hinks, D.; Crawford, A.; Blake, S. AATCC J. Res. 2016, 3, 25−32. (51) Campiglia, A. D.; Rex, M.; Muñoz de la Peña, A.; Goicoechea, H. C. Anal. Methods 2016, 8, 8314−8321. (52) Schotman, T. G.; Xu, X.; Rodewijk, N.; van der Weerd, J. Forensic Sci. Int. 2017, 278, 338−350. (53) Groves, E.; Palenik, S.; Palenik, C. S. Forensic Chem. 2018, 8, 104−110. (54) Groves, E.; Palenik, C. S.; Palenik, S. Forensic Sci. Int. 2016, 268, 139−144. (55) Germinario, G.; Rigante, E. C. L.; van der Werf, I. D.; Sabbatini, L. J. Anal. Appl. Pyrolysis 2017, 127, 229−239. (56) Prati, S.; Milosevic, M.; Sciutto, G.; Bonacini, I.; Kazarian, S. G.; Mazzeo, R. Anal. Chim. Acta 2016, 941, 67−79. (57) Brinsko, K. M.; Sparenga, S.; King, M. J. Forensic Sci. 2016, 61, 1215−1227. (58) Ueland, M.; Howes, J. M.; Forbes, S. L.; Stuart, B. H. Spectrochim. Acta, Part A 2017, 185, 69−76. (59) Bianchi, F.; Riboni, N.; Trolla, V.; Furlan, G.; Avantaggiato, G.; Iacobellis, G.; Careri, M. Talanta 2016, 154, 467−473. (60) Heider, E. C.; Mujumdar, N.; Campiglia, A. D. Anal. Bioanal. Chem. 2016, 408, 7935−7943. (61) Buzzini, P.; Suzuki, E. J. Raman Spectrosc. 2016, 47, 16−27. (62) Yu, J.; Butler, I. S. Appl. Spectrosc. Rev. 2015, 50, 152−157. (63) Sloggett, R. Aust. J. Forensic Sci. 2015, 47, 253−259. (64) Maric, M.; van Bronswijk, W.; Pitts, K.; Lewis, S. W. J. Raman Spectrosc. 2016, 47, 948−955. (65) Marić, M.; Marano, J.; Cody, R. B.; Bridge, C. Anal. Chem. 2018, 90, 6877−6884. (66) van der Pal, K. J.; Sauzier, G.; Maric, M.; van Bronswijk, W.; Pitts, K.; Lewis, S. W. Talanta 2016, 148, 715−720. (67) Perera, U. D. N.; Nishikida, K.; Lavine, B. K. Appl. Spectrosc. 2018, 72, 886−895. (68) Lavine, B. K.; White, C. G.; Ding, T. Appl. Spectrosc. 2018, 72, 476−488. (69) Lavine, B. K.; White, C. G.; Allen, M. D.; Weakley, A. Appl. Spectrosc. 2017, 71, 480−495. 652

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

(104) Zwerling, C. S. J. Forensic Sci. 2016, 61, 534−539. (105) Gupta, A.; Curran, J. M.; Coulson, S.; Triggs, C. M. Forensic Chem. 2017, 3, 36−40. (106) Khalil, A. A. I.; Morsy, M. A. Talanta 2016, 154, 109−118. (107) Acharya, R.; Pujari, P. K. Forensic Chem. 2018, DOI: 10.1016/ j.forc.2018.01.002. (108) Chhillar, S.; Acharya, R.; Mishra, R. K.; Kaushik, C. P.; Pujari, P. K. J. Radioanal. Nucl. Chem. 2017, 312, 567−576. (109) Lee, S.-W.; Ryu, J.-S.; Min, J.-S.; Choi, M.-Y.; Lee, K.-S.; Shin, W.-J. Rapid Commun. Mass Spectrom. 2016, 30, 1612−1618. (110) Funatsuki, A.; Takaoka, M.; Shiota, K.; Kokubu, D.; Suzuki, Y. Anal. Sci. 2016, 32, 207−213. (111) Corzo, R.; Hoffman, T.; Weis, P.; Franco-Pedroso, J.; Ramos, D.; Almirall, J. Talanta 2018, 186, 655−661. (112) van Es, A.; Wiarda, W.; Hordijk, M.; Alberink, I.; Vergeer, P. Sci. Justice 2017, 57, 181−192. (113) Gorzałczany, M. B.; Rudziński, F. In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE), June 8−10, 2016; pp 78−83. (114) Thomas, E. V.; Lewis, J. R.; Anderson-Cook, C. M.; Burr, T.; Hamada, M. S. J. Qual. Technol. 2017, 49, 228−243. (115) Wallace, J. S. Chemical Analysis of Firearms, Ammunition, and Gunshot Residue; CRC Press: Boca Raton, FL, 2008. (116) Castellanos, A.; Bell, S.; Fernandez-Lima, F. Anal. Methods 2016, 8, 4300−4305. (117) Blakey, L. S.; Sharples, G. P.; Chana, K.; Birkett, J. W. J. Forensic Sci. 2018, 63, 9−19. (118) Brożek-Mucha, Z. Anal. Bioanal. Chem. 2017, 409, 5803− 5811. (119) Tucker, W.; Lucas, N.; Seyfang, K. E.; Kirkbride, K. P.; Popelka-Filcoff, R. S. Forensic Sci. Int. 2017, 270, 76−82. (120) Goudsmits, E.; Sharples, G. P.; Birkett, J. W. Sci. Justice 2016, 56, 421−425. (121) Perez, J. J.; Watson, D. A.; Levis, R. J. Anal. Chem. 2016, 88, 11390−11398. (122) Evans-Nguyen, K. M.; Gerling, J.; Brown, H.; Miranda, M.; Windom, A.; Speer, J. Analyst 2016, 141, 3811−3820. (123) Gassner, A.-L.; Weyermann, C. Forensic Sci. Int. 2016, 264, 47−55. (124) Pigou, P.; Dennison, G. H.; Johnston, M.; Kobus, H. Forensic Sci. Int. 2017, 279, 140−147. (125) Cid, F.; Kochifas, P.; Mansilla, H. D.; Santander, P.; Yáñez, J.; Amarasiriwardena, D. Microchem. J. 2016, 125, 29−33. (126) Yüksel, B.; Ozler-Yigiter, A.; Bora, T.; Sen, N.; Kayaalti, Z. At. Spectrosc. 2016, 37, 164−169. (127) Aliste, M.; Chávez, L. G. Forensic Sci. Int. 2016, 261, 14−18. (128) Merli, D.; Brandone, A.; Amadasi, A.; Cattaneo, C.; Profumo, A. Int. J. Legal Med. 2016, 130, 1045−1052. (129) Terry, M.; Fookes, B.; Bridge, C. M. Forensic Sci. Int. 2017, 276, 51−63. (130) Kara, I.; Sarikavak, Y.; Lisesivdin, S. B.; Kasap, M. Environ. Forensics 2016, 17, 68−79. (131) Gandy, L.; Najjar, K.; Terry, M.; Bridge, C. Forensic Chem. 2018, 8, 1−10. (132) Gassner, A.-L.; Ribeiro, C.; Kobylinska, J.; Zeichner, A.; Weyermann, C. Forensic Sci. Int. 2016, 266, 369−378. (133) Taudte, R. V.; Roux, C.; Beavis, A. Forensic Sci. Int. 2017, 270, 55−60. (134) Kara, I.̇ Microsc. Res. Tech. 2017, 80, 1310−1314. (135) Burnett, B. R. J. Forensic Sci. 2016, 61, 1632−1638. (136) Taudte, R. V.; Roux, C.; Blanes, L.; Horder, M.; Kirkbride, K. P.; Beavis, A. Anal. Bioanal. Chem. 2016, 408, 2567−2576. (137) López-López, M.; Merk, V.; García-Ruiz, C.; Kneipp, J. Anal. Bioanal. Chem. 2016, 408, 4965−4973. (138) Ortega-Ojeda, F. E.; Torre-Roldán, M.; García-Ruiz, C. Talanta 2017, 167, 227−235. (139) Sarraguça, J. M. G.; Lima, C.; Machado, F.; Lopes, J. A.; Almeida, A.; Fernandes, L.; Magalhães, T.; Santos, A. Analyst 2016, 141, 4410−4416.

(140) Bueno, J.; Halámková, L.; Rzhevskii, A.; Lednev, I. K. Anal. Bioanal. Chem. 2018, 410, 7295−7303. (141) Hofer, R.; Graf, S.; Christen, S. Forensic Sci. Int. 2017, 273, 10−19. (142) Hofer, R.; Wyss, P. Forensic Sci. Int. 2017, 278, 24−31. (143) Buking, S.; Saetear, P.; Tiyapongpattana, W.; Uraisin, K.; Wilairat, P.; Nacapricha, D.; Ratanawimarnwong, N. Anal. Sci. 2018, 34, 83−89. (144) Zapata, F.; López-López, M.; Amigo, J. M.; García-Ruiz, C. Forensic Sci. Int. 2018, 282, 80−85. (145) Giraudo, C.; Fais, P.; Pelletti, G.; Viero, A.; Miotto, D.; Boscolo-Berto, R.; Viel, G.; Montisci, M.; Cecchetto, G.; Ferrara, S. D. Int. J. Legal Med. 2016, 130, 1257−1264. (146) Brożek-Mucha, Z. Sci. Justice 2017, 57, 87−94. (147) Greely, D.; Weber, E. J. Forensic Sci. 2017, 62, 869−873. (148) Berger, J.; Upton, C.; Springer, E. J. Forensic Sci. 2018, 1−5. (149) Lucas, N.; Brown, H.; Cook, M.; Redman, K.; Condon, T.; Wrobel, H.; Kirkbride, K. P.; Kobus, H. Forensic Sci. Int. 2016, 262, 150−155. (150) Cook, M. Forensic Sci. Int. 2016, 269, 56−62. (151) Ali, L.; Brown, K.; Castellano, H.; Wetzel, S. J. J. Forensic Sci. 2016, 61, 928−938. (152) Bell, S.; Seitzinger, L. Forensic Sci. Int. 2016, 263, 176−185. (153) Costa, R. A.; Motta, L. C.; Destefani, C. A.; Rodrigues, R. R. T.; do Espírito Santo, K. S.; Aquije, G. M. F. V.; Boldrini, R.; Athayde, G. P. B.; Carneiro, M. T. W. D.; Romão, W. Microchem. J. 2016, 129, 339−347. (154) Melo Lucena, M. A.; Oliveira Rodrigues, M.; Gatto, C. C.; Talhavini, M.; Maldaner, A. O.; Alves, S., Jr.; Weber, I. T. J. Lumin. 2016, 170, 697−700. (155) Lucena, M. A. M.; Ordoñez, C.; Weber, I. T.; Torre, M.; García-Ruiz, C.; López-López, M. Forensic Sci. Int. 2017, 280, 95− 102. (156) Arouca, A. M.; Lucena, M. A. M.; Rossiter, R. J.; Talhavini, M.; Weber, I. T. Forensic Sci. Int. 2017, 281, 161−170. (157) Destefani, C. A.; Motta, L. C.; Costa, R. A.; Macrino, C. J.; Bassane, J. F. P.; Filho, J. F. A.; Silva, E. M.; Greco, S. J.; Carneiro, M. T. W. D.; Endringer, D. C.; Romão, W. Microchem. J. 2016, 124, 195− 200. (158) Gallidabino, M.; Romolo, F. S.; Weyermann, C. Forensic Sci. Int. 2017, 272, 159−170. (159) Gallidabino, M.; Romolo, F. S.; Weyermann, C. Forensic Sci. Int. 2017, 272, 171−183. (160) López-López, M.; Alvarez-Llamas, C.; Pisonero, J.; GarcíaRuiz, C.; Bordel, N. Forensic Sci. Int. 2017, 273, 124−131. (161) Fambro, L. A.; Miller, E. T.; Vandenbos, D. D.; Dockery, C. R. Anal. Methods 2016, 8, 3132−3139. (162) Fambro, L. A.; Vandenbos, D. D.; Rosenberg, M. B.; Dockery, C. R. Appl. Spectrosc. 2017, 71, 699−708. (163) Trejos, T.; Vander Pyl, C.; Menking-Hoggatt, K.; Alvarado, A. L.; Arroyo, L. E. Forensic Chem. 2018, 8, 146−156. (164) Hashim, N. H. M.; Zain, Z. M.; Jaafar, M. Z. In MATEC Web of Conferences; 2016, 59, 04005. (165) Erol, Ö . Ö .; Erdoğan, B. Y.; Onar, A. N. J. Forensic Sci. 2017, 62, 423−427. (166) Brożek-Mucha, Z.; Zdeb, K. J. Forensic Sci. 2018, 63, 921− 929. (167) Burnett, B. R.; Lebiedzik, J. J. Forensic Sci. 2017, 62, 768−772. (168) Bell, S. Forensic Chemistry, 2nd ed.; Pearson Education: Boston, 2013. (169) Yu, H. A.; Lewis, S. W.; Beardah, M. S.; NicDaeid, N. Talanta 2016, 148, 721−728. (170) Chaffee-Cipich, M. N.; Hoss, D. J.; Sweat, M. L.; Beaudoin, S. P. Forensic Sci. Int. 2016, 260, 85−94. (171) Choodum, A.; Malathong, K.; NicDaeid, N.; Limsakul, W.; Wongniramaikul, W. Forensic Sci. Int. 2016, 266, 202−208. (172) Ma, H.; Li, F.; Zhang, Y.; Li, X.; Li, T.; Shen, F.; Zhang, M. Talanta 2016, 160, 133−137. (173) Yang, Z.; Dou, X. Adv. Funct. Mater. 2016, 26, 2406−2425. 653

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654

Analytical Chemistry

Review

(174) Sathish, V.; Ramdass, A.; Velayudham, M.; Lu, K.-L.; Thanasekaran, P.; Rajagopal, S. Dalton Trans. 2017, 46, 16738− 16769. (175) Zhu, Q.; Xiong, W.; Gong, Y.; Zheng, Y.; Che, Y.; Zhao, J. Anal. Chem. 2017, 89, 11908−11912. (176) Wang, C.; Huang, H.; Bunes, B. R.; Wu, N.; Xu, M.; Yang, X.; Yu, L.; Zang, L. Sci. Rep. 2016, 6, 25015. (177) Bolse, N.; Eckstein, R.; Habermehl, A.; Hernandez-Sosa, G.; Eschenbaum, C.; Lemmer, U. ACS Omega 2017, 2, 6500−6505. (178) Devi, S.; Kaur, R.; Paul, A. K.; Tyagi, S. Colloid Polym. Sci. 2018, 296, 427−440. (179) Qian, J.; Hua, M.; Wang, C.; Wang, K.; Liu, Q.; Hao, N.; Wang, K. Anal. Chim. Acta 2016, 946, 80−87. (180) Gooch, J.; Daniel, B.; Parkin, M.; Frascione, N. TrAC, Trends Anal. Chem. 2017, 94, 150−160. (181) Roushani, M.; Shahdost-fard, F.; Azadbakht, A. Anal. Biochem. 2017, 534, 78−85. (182) Sempionatto, J. R.; Mishra, R. K.; Martín, A.; Tang, G.; Nakagawa, T.; Lu, X.; Campbell, A. S.; Lyu, K. M.; Wang, J. ACS Sens. 2017, 2, 1531−1538. (183) Wu, Z.; Zhou, C.; Zu, B.; Li, Y.; Dou, X. Adv. Funct. Mater. 2016, 26, 4578−4586. (184) Hayes, J. E.; McGreevy, P. D.; Forbes, S. L.; Laing, G.; Stuetz, R. M. Talanta 2018, 185, 499−512. (185) Giannoukos, S.; Brkić, B.; Taylor, S.; Marshall, A.; Verbeck, G. F. Chem. Rev. 2016, 116, 8146−8172. (186) Guo, L.; Yang, Z.; Dou, X. Adv. Mater. 2017, 29, 1604528. (187) Verbitskiy, E. V.; Baranova, A. A.; Lugovik, K. I.; Khokhlov, K. O.; Cheprakova, E. M.; Shafikov, M. Z.; Rusinov, G. L.; Chupakhin, O. N.; Charushin, V. N. Dyes Pigm. 2017, 137, 360−371. (188) Yew, Y. T.; Ambrosi, A.; Pumera, M. Sci. Rep. 2016, 6, 33276. (189) Pandey, G.; Tharmavaram, M.; Rawtani, D.; Kumar, S.; Agrawal, Y. Forensic Sci. Int. 2017, 273, 53−63. (190) Andrasko, J.; Lagesson-Andrasko, L.; Dahlén, J.; Jonsson, B.H. J. Forensic Sci. 2017, 62, 1022−1027. (191) Yu, H. A.; DeTata, D. A.; Lewis, S. W.; Nic Daeid, N. Talanta 2017, 164, 716−726. (192) Papp, A.; Csikai, J. J. Radioanal. Nucl. Chem. 2016, 308, 297− 302. (193) Bagchi, S.; Chakrabortty, A.; Kuila, D. K.; Lahiri, S. C. J. Indian Chem. Soc. 2016, 93, 889−906. (194) Oxley, J. C.; Smith, J. L.; Bernier, E. T.; Sandstrom, F.; Weiss, G. G.; Recht, G. W.; Schatzer, D. J. Forensic Sci. 2018, 63, 86−101. (195) Bors, D.; Goodpaster, J. Forensic Sci. Int. 2017, 276, 71−76. (196) Tang, S.; Vinerot, N.; Fisher, D.; Bulatov, V.; Yavetz-Chen, Y.; Schechter, I. Talanta 2016, 155, 235−244. (197) Pawłowski, W.; Matyjasek, Ł.; Cieślak, K.; Karpińska, M. Forensic Sci. Int. 2017, 281, 13−17. (198) Lees, H.; Zapata, F.; Vaher, M.; García-Ruiz, C. Talanta 2018, 184, 437−445. (199) Matyás,̌ R.; Lyčka, A.; Jirásko, R.; Jakový, Z.; Maixner, J.; Mišková, L.; Künzel, M. J. Forensic Sci. 2016, 61, 759−764. (200) Martín-Alberca, C.; Zapata, F.; Carrascosa, H.; Ortega-Ojeda, F. E.; García-Ruiz, C. Talanta 2016, 149, 257−265. (201) Á lvarez, Á .; Yáñez, J.; Contreras, D.; Saavedra, R.; Sáez, P.; Amarasiriwardena, D. Forensic Sci. Int. 2017, 280, 169−175. (202) Elbasuney, S.; El-Sherif, A. F. TrAC, Trends Anal. Chem. 2016, 85, 34−41. (203) Elbasuney, S.; El-Sherif, A. F. Forensic Sci. Int. 2017, 270, 83− 90. (204) Gares, K. L.; Bykov, S. V.; Asher, S. A. J. Phys. Chem. A 2017, 121, 7889−7894. (205) Hufziger, K. T.; Bykov, S. V.; Asher, S. A. Appl. Spectrosc. 2017, 71, 173−185. (206) Gares, K. L.; Hufziger, K. T.; Bykov, S. V.; Asher, S. A. J. Raman Spectrosc. 2016, 47, 124−141. (207) Zapata, F.; Fernández de la Ossa, M. Á .; Gilchrist, E.; Barron, L.; García-Ruiz, C. Talanta 2016, 161, 219−227.

(208) Ramos Almeida, M.; Lima Logrado, L. P.; Jardim Zacca, J.; Nascimento Correa, D.; Poppi, R. J. Talanta 2017, 174, 628−632. (209) Shaik, U. P.; Hamad, S.; Ahamad Mohiddon, M.; Soma, V. R.; Ghanashyam Krishna, M. J. Appl. Phys. 2016, 119, 093103. (210) He, X.; Liu, Y.; Xue, X.; Liu, J.; Liu, Y.; Li, Z. J. Mater. Chem. C 2017, 5, 12384−12392. (211) Abdul-Karim, N.; Blackman, C. S.; Gill, P. P.; Morgan, R. M.; Matjacic, L.; Webb, R.; Ng, W. H. Anal. Chem. 2016, 88, 3899−3908. (212) Moini, M. Electrophoresis 2018, 39, 1249−1275. (213) Pavlovich, M. J.; Musselman, B.; Hall, A. B. Mass Spectrom. Rev. 2018, 37, 171−187. (214) Pavlovich, M. J.; Dunn, E. E.; Hall, A. B. Rapid Commun. Mass Spectrom. 2016, 30, 1123−1130. (215) Sisco, E.; Forbes, T. P. Talanta 2016, 150, 177−183. (216) Fraga, C. G.; Mitroshkov, A. V.; Mirjankar, N. S.; Dockendorff, B. P.; Melville, A. M. Talanta 2017, 174, 131−138. (217) Bailey, M. J.; Randall, E. C.; Costa, C.; Salter, T. L.; Race, A. M.; de Puit, M.; Koeberg, M.; Baumert, M.; Bunch, J. Anal. Methods 2016, 8, 3373−3382. (218) Donnarumma, F.; Camp, E. E.; Cao, F.; Murray, K. K. J. Am. Soc. Mass Spectrom. 2017, 28, 1958−1964. (219) Forbes, T. P.; Staymates, M.; Sisco, E. Analyst 2017, 142, 3002−3010. (220) Tian, C. Y.; Yin, J. W.; Zhao, Z. J.; Zhang, Y.; Duan, Y. X. Talanta 2017, 167, 75−85. (221) Bezemer, K. D. B.; Koeberg, M.; van der Heijden, A. E. D. M.; van Driel, C. A.; Blaga, C.; Bruinsma, J.; van Asten, A. C. J. Forensic Sci. 2016, 61, 1198−1207. (222) Howa, J. D.; Barnette, J. E.; Chesson, L. A.; Lott, M. J.; Ehleringer, J. R. Talanta 2018, 181, 125−131. (223) Grimm, B. L.; Stern, L. A.; Lowe, A. J. Talanta 2018, 178, 94− 101. (224) Fernandez-Maestre, R. Rev. Sci. Instrum. 2017, 88, 096104. (225) Hauck, B. C.; Siems, W. F.; Harden, C. S.; McHugh, V. M.; Hill, H. H., Jr. Rev. Sci. Instrum. 2016, 87, 075104. (226) Du, Z.; Sun, T.; Zhao, J.; Wang, D.; Zhang, Z.; Yu, W. Talanta 2018, 184, 65−72. (227) Kostyukevich, Y.; Efremov, D.; Ionov, V.; Kukaev, E.; Nikolaev, E. J. Mass Spectrom. 2017, 52, 777−782. (228) Usmanov, D. T.; Yu, Z.; Chen, L. C.; Hiraoka, K.; Yamabe, S. J. Mass Spectrom. 2016, 51, 132−140. (229) Fedick, P. W.; Bills, B. J.; Manicke, N. E.; Cooks, R. G. Anal. Chem. 2017, 89, 10973−10979. (230) Tsai, C.-W.; Tipple, C. A.; Yost, R. A. Rapid Commun. Mass Spectrom. 2018, 32, 552−560. (231) Ostrinskaya, A.; Kelley, J. A.; Kunz, R. R. Rapid Commun. Mass Spectrom. 2017, 31, 333−343. (232) Sauzier, G.; Bors, D.; Ash, J.; Goodpaster, J. V.; Lewis, S. W. Talanta 2016, 158, 368−374. (233) Ewing, A. V.; Kazarian, S. G. Analyst 2017, 142, 257−272.

654

DOI: 10.1021/acs.analchem.8b04704 Anal. Chem. 2019, 91, 637−654