The exploitation of illicit substances, such as drugs and explosives, is on the rise. Special attention must therefore be considered to reduce the transportation and storage of these illicit substances by improving the capability of detection, even when hidden from view. Although analytical methods of detection for both drugs and explosives have improved over time, biological detectors, such as canines, are still commonly used. In comparison to humans, these canines have a larger number of olfactory receptors and a greater olfactory epithelium surface area, providing them with a more enhanced olfaction than that of humans. The premise for the detection of illicit drugs and explosives is based on the premise that these substances though hidden, will emit volatile organic compounds (VOCs). These VOCs are not often the parent drug or explosive, they are essentially a chemical associated with the source and provide a reliable indication of the illicit substance. Previous successful research has been conducted on the identification of the active odors present in the headspace of cocaine, methamphetamine, and MDMA but instead for marijuana and heroin there have been minimum success. Thus, in the present research a method using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME-GC-MS) was optimized to identify the VOCs makeup of heroin and marijuana to further identify the active odor compound(s) responsible for the alert response of biological detectors (canines). A mixture of acetic acid and acetylsalicylic acid was identified as target odor mimic for heroin by certified detector canines, while a mixture of limonene and caryophyllene was recognized as odor mimic for marijuana by conducting ORTs. The training aids developed successfully mimic the scent of the actual illicit substance and can be used to improve the capabilities of both drug and explosive detection canines. Additionally, as growing threat of improvised explosives has created a worldwide concern and emphasized the requirement of a greater spectra of canine training aids that covers the complete range of explosives available, a new approach for the creation of training aids for IEDs have been evaluated. The use of a dynamic collection system have proved to be an option to develop fast and reliable canine training aids for IEDs.

Human scent has been the focal point of diverse scientific interests and research initiatives for the past several years. The knowledge gained about its composition has favored the advancement of multiple disciplines, and promoted the development of a wide variety of applications. Among these applications is the use of human scent as a resource for Forensic investigations, where scent profiles are often used as evidence to associate individuals to the scene of a crime. The characteristic nature of individual human scent has enabled this type of evidence to be used as a biometric tool for the differentiation of subjects. Nevertheless, the present study discusses a new perspective towards human scent's role and application in Forensic investigations. The foundation of this new perspective consists of employing human scent’s biometric quality to classify individuals using common traits. In this research study, underarm and hand odor samples were collected from Caucasian, Hispanic and East Asian individuals, of both genders. Subjects were also organized into 3 different age groups: 18-30, 35-50 and 55+ years. Headspace Solid Phase Micro-extraction Gas Chromatography Mass Spectrometry (HS-SPME-GC-MS) was used to create individual scent profiles for the evaluation of subject classification by age, gender and race/ethnicity. Individual classification was assessed through the identification of qualitative and quantitative patterns in the volatile organic compound (VOC) constituents that characterize human scent. Principal Component and Linear Discriminant analyses of the collected scent profiles, led to the identification and validation of characteristic VOC marker combinations for age, gender and race/ethnicity. Statistical analysis facilitated group classification and differentiation on the basis of these traits. Moreover, this study also evaluated the use of solvent extraction as a complementary technique to HS-SPME for human scent analysis. Findings from this assessment revealed that the simultaneous consideration of data from both extraction techniques favors an enhancement of the classification of subjects by means of human scent. The discoveries achieved in this study represent a significant step for human scent as a forensic tool. The outcome of this research has cleared a new path for further human scent investigation, and highlighted its further relevance to forensic applications.