Human scent characterization: A review.

Human scent has long been cited as a probable parameter that can be exploited as a biometric measure. Identifying the scent of individual persons using specially trained canines is a well-known forensic method which is frequently used in criminal investigations. To date there has been limited research on the chemical components present in human scent and their usefulness in distinguishing between people. This review delivers insight into studies which have dealt with human scent in forensics. Sample collection methods, sample preparation, instrumental analysis, compounds identified in human scent and data analysis techniques are discussed. Methods for sample collection and preparation are presented, but to date, there is no available validated method. Instrumental methods are presented and from the overview it is clear that gas chromatography combined with mass spectrometry is the method of choice. New developments such as two-dimensional gas chromatography offer exiting possibilities to collect more information. Given the amount and complexity of data, data processing is used to extract the relevant information to discriminate people. Finally, sensors offer new opportunities for the characterization of human scent.

Evaluation of a Commercial Electronic Nose Based on Carbon Nanotube Chemiresistors.

Recently a hand-held, carbon-nanotube-based electronic nose became available on the market. Such an electronic nose could be interesting for applications in the food industry, health monitoring, environmental monitoring, and security services. However, not much is known about the performance of such an electronic nose. In a series of measurements, the instrument was exposed to low ppm vapor concentrations of four volatile organic compounds with different scent profiles and polarities. Detection limits, linearity of response, repeatability, reproducibility, and scent patterns were determined. The results indicate detection limits in the range of 0.1-0.5 ppm and a linear signal response in the range of 0.5-8.0 ppm. The repeatability of the scent patterns at compound concentrations of 2 ppm allowed the identification of the tested volatiles based on their scent pattern. However, the reproducibility was not sufficient, since different scent profiles were produced on different measurement days. In addition, it was noted that the response of the instrument diminished over time (over several months) possibly by sensor poisoning. The latter two aspects limit the use of the current instrument and make future improvements necessary.

Experimental Flight Patterns Evaluation for a UAV-Based Air Pollutant Sensor.

The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0-2.9m/s) to high (2.1-5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.

Silicon dioxide and titanium dioxide particles found in human tissues.

Silicon dioxide (silica, SiO2, SAS) and titanium dioxide (TiO2) are produced in high volumes and applied in many consumer and food products. As a consequence, there is a potential human exposure and subsequent systemic uptake of these particles. In this study we show the characterization and quantification of both total silicon (Si) and titanium (Ti), and particulate SiO2 and TiO2 in postmortem tissue samples from 15 deceased persons. Included tissues are liver, spleen, kidney and the intestinal tissues jejunum and ileum. Low-level analysis was enabled by the use of fully validated sample digestion methods combined with (single particle) inductively coupled plasma high resolution mass spectrometry techniques (spICP-HRMS). The results show a total-Si concentration ranging from <2 to 191 mg Si/kg (median values of 5.8 (liver), 9.5 (spleen), 7.7 (kidney), 6.8 (jejunum), 7.6 (ileum) mg Si/kg) while the particulate SiO2 ranged from <0.2 to 25 mg Si/kg (median values of 0.4 (liver), 1.0 (spleen), 0.4 (kidney), 0.7 (jejunum, 0.6 (ileum) mg Si/kg), explaining about 10% of the total-Si concentration. Particle sizes ranged from 150 to 850 nm with a mode of 270 nm. For total-Ti the results show concentrations ranging from <0.01 to 2.0 mg Ti/kg (median values of 0.02 (liver), 0.04 (spleen), 0.05 (kidney), 0.13 (jejunum), 0.26 (ileum) mg Ti/kg) while particulate TiO2 concentrations ranged from 0.01 to 1.8 mg Ti/kg (median values of 0.02 (liver), 0.02 (spleen), 0.03 (kidney), 0.08 (jejunum), 0.25 (ileum) mg Ti/kg). In general, the particulate TiO2 explained 80% of the total-Ti concentration. This indicates that most Ti in these organ tissues is particulate material. The detected particles comprise primary particles, aggregates and agglomerates, and were in the range of 50-500 nm with a mode in the range of 100-160 nm. About 17% of the detected TiO2 particles had a size <100 nm. The presence of SiO2 and TiO2 particles in liver tissue was confirmed by scanning electron microscopy with energy dispersive X-ray spectrometry.

Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles.

For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO2. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of the resulting air quality data. Recently, electrochemical sensors and their integration with unmanned aerial vehicles (UAVs) have attempted to fill these gaps through various experiments, none of which have considered the influence of a UAV when calibrating the sensors. Accordingly, this research attempts to improve the reliability of NO2 measurements detected from electrochemical sensors while on board an UAV by introducing rotor speed as part of the calibration model. This is done using a DJI Matrice 100 quadcopter and Alphasense sensors, which are calibrated using regression calculations in different environments. This produces a predictive r-squared up to 0.97. The sensors are then calibrated with rotor speed as an additional variable while on board the UAV and flown in a series of flights to evaluate the performance of the model, which produces a predictive r-squared up to 0.80. This methodological approach can be used to obtain more reliable NO2 measurements in future outdoor experiments that include electrochemical sensor integration with UAV's.