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The detection of explosives and explosive devices based on the volatile compounds they emit is a long-standing tool for law enforcement and physical security. Toward that end, solid-phase microextraction (SPME) combined with gas chromatography-mass spectrometry (GC-MS) has become a crucial analytical tool for the identification of volatiles emitted by explosives. Previous SPME studies have identified many volatile compounds emitted by common explosive formulations that serve as the main charge in explosive devices. However, limited research has been conducted on initiators like fuses, detonating cords, and boosters. In this study, a variety of SPME fiber coatings (i.e., polydimethylsiloxane (PDMS), polydimethylsiloxane/divinylbenzene (PDMS/DVB), divinylbenzene/carboxin/polydimethylsiloxane (DVB/CAR/PDMS), and carboxin/polydimethylsiloxane (CAR/PDMS)) were employed for the extraction and analysis of volatiles from Composition C-4 (cyclohexanone, 2-ethyl-1-hexanol, and 2,3-dimethyl-2,3-dinitrobutane (DMNB)) and Red Dot double-base smokeless powder (nitroglycerine, phenylamine). The results revealed that a PDMS/DVB fiber was optimal. Then, an assortment of explosive items (i.e., detonation cord, safety fuse, slip-on booster, and shape charge) were analyzed with a PDMS/DVB fiber. A variety of volatile compounds were identified, including plasticizers (tributyl acetyl citrate, N-butylbenzenesulfonamide), taggants (DMNB), and degradation products (2-ethyl-1-hexanol). [ABSTRACT FROM AUTHOR]

Since its introduction, gas chromatography (GC) coupled to vacuum ultraviolet spectrophotometry (VUV) has been shown to complement mass spectrometry (MS) for materials such as petrochemicals, explosives, pesticides, and drugs. In forensic chemistry, opioids are commonly encountered but rarely are the samples pure. This work focuses on GC-VUV analysis applied to naturally occurring (e.g., morphine), semi-synthetic (e.g., heroin), and synthetic (fentanyl) opioids as well as common adulterants and diluents (e.g., lidocaine and quinine). The specificity of the VUV spectra were examined visually as well as via descriptive statistical methods (e.g., correlation coefficients and sums of square residuals). Multivariate pattern recognition techniques (principal component analysis and discriminant analysis (DA)) were used to prove the opioid spectra can be reliably differentiated. The accuracy of the DA model was 100% for a test set of VUV spectra. Finally, three "street" heroin samples were analyzed to show "real-world" performance for forensic analyses. These samples contained adulterants such as caffeine, as well as by-products of heroin manufacture. [ABSTRACT FROM AUTHOR]

The microtexture and elemental composition of the backing of electrical tapes have been shown to be highly discriminating. In this study, the organic composition of electrical tape was evaluated as a complementary means of distinguishing tape brands. The backing and adhesive of 72 rolls of electrical tape were analyzed via Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR FTIR) and discriminant analysis was used to classify all samples by brand. Generally, the accuracy for FTIR data (88–99%) was higher than that for elemental data (86–94%). FTIR spectra from the adhesive layer were the most discriminating. In separate studies, two fragments of blast-damaged tape were correctly assigned to their brand of origin and discriminant analysis was used to quantitatively associate or exclude tape samples from two bombing cases. [ABSTRACT FROM AUTHOR]

Simple Summary: Volatile organic compounds (VOCs) in urine have been shown to be potential biomarkers for breast cancer. However, how urinary VOCs change upon the course of tumor progression has never been studied. The aim of our study was to identify changes in VOC profiles corresponding to mammary tumor (triple negative cells) presence and progression in mice models of induced breast cancer. Urine samples were collected from mice prior to tumor injection and from days 2–19 after. VOC models constructed by linear discriminant analysis had high ability to distinguish tumor-bearing mice from control and determine the week of urine collection after tumor injection. Principal component regression analysis demonstrated that VOCs could predict the number of days since tumor injection. VOCs identified from these analyses correspond to metabolic pathways dysregulated by breast cancer and previous biomarker investigations. It is anticipated that these findings can be translated into human research for early detection of breast cancer recurrence. Previous studies have shown that volatile organic compounds (VOCs) are potential biomarkers of breast cancer. An unanswered question is how urinary VOCs change over time as tumors progress. To explore this, BALB/c mice were injected with 4T1.2 triple negative murine tumor cells in the tibia. This typically causes tumor progression and osteolysis in 1–2 weeks. Samples were collected prior to tumor injection and from days 2–19. Samples were analyzed by headspace solid phase microextraction coupled to gas chromatography–mass spectrometry. Univariate analysis identified VOCs that were biomarkers for breast cancer; some of these varied significantly over time and others did not. Principal component analysis was used to distinguish Cancer (all Weeks) from Control and Cancer Week 1 from Cancer Week 3 with over 90% accuracy. Forward feature selection and linear discriminant analysis identified a unique panel that could identify tumor presence with 94% accuracy and distinguish progression (Cancer Week 1 from Cancer Week 3) with 97% accuracy. Principal component regression analysis also demonstrated that a VOC panel could predict number of days since tumor injection (R2 = 0.71 and adjusted R2 = 0.63). VOC biomarkers identified by these analyses were associated with metabolic pathways relevant to breast cancer. [ABSTRACT FROM AUTHOR]