Wearable solid-phase microextraction sampling for enhanced detection of volatile analytes in human ears.

BACKGROUND: Investigating ear at molecule level is challenging task, since there is a lack of molecular detection by traditional diagnosis techniques such as otologic endoscopy, ear swab culture, and imaging diagnostic technique. Therefore, new development of noninvasive, highly sensitive, and convenient analytical method for investigating human ears is highly needed. RESULTS: We developed a wearable sampling device for extracting trace analytes in ear by fixing solid-phase microextraction fibers into modified earmuffs (SPME-in-earmuffs). After sampling, SPME fiber was coupled with gas chromatography-mass spectrometry (GC-MS) for identification and quantification of extracted analytes. Enhanced detection of various analytes such as volatile metabolites, exposures, and therapeutic drugs of ears were demonstrated in this work. Particularly, sport-induced metabolic changes such as fatty acids, aldehyde compounds and oxidative produces were found from human ears using this method. Acceptable analytical performances were obtained by using this newly developed method for detecting ear medicines, e.g., low limit of detection (LOD, 0.005-0.021 ng/mL) and limit of quantification (LOQ, 0.018-0.071 ng/mL), excellent linear dynamic responses (R2 > 0.99, ranging from 0.050-8.00 ng/mL), good relative standard deviations (RSDs, 13.19 % âˆ¼ 21.40 %, n = 6) and accuracy (84.43-150.18 %, n = 6) at different concentrations. SIGNIFICANCE: For the first time, this work provides a simple, convenient, and wearable microextraction method for enhanced detection of trace volatiles in human ears. The enclosed space between ear and earmuff allows headspace SPME sampling of volatile analytes, and thus provides a new wearable method for monitoring ear metabolites and human exposures, showing potential applications in human health, disease diagnosis, and sport science.

Portable Mass Spectrometry Approach Combined with Machine Learning for Onsite Field Detection of Huanglongbing Disease.

Huanglongbing (HLB) is one of the most serious citrus diseases in the world. Rapid, onsite, and accurate field detection of HLB is a challenging task in analytical science for a long time. Herein, we have developed a novel HLB detection method that combines headspace solid phase microextraction with portable gas chromatography-mass spectrometry (PGC-MS) approach for onsite field detection of volatile metabolites of citrus leaves. Detectability and characteristics of HLB-affected metabolites from leaves were validated, and the important biomarkers were verified by authentic compounds. A machine learning approach based on random forest algorithm is established to model the volatile metabolites from healthy, symptomatic, and asymptomatic citrus leaves. In this work, a total of 147 citrus leaf samples were analyzed. Analytical performances of this newly developed method were investigated by in-field detection of various volatile metabolites. Results demonstrated limits of detection and quantification of 0.04-0.12 and 0.17-0.44 ng/mL for different metabolites, respectively. Linear calibration curves of various metabolites were established over a concentration dynamic range of at least three orders (R2 > 0.96). Good reproducibility was obtained for intraday (3.0-17.5%, n = 6) and interday precision (8.7-18.2%, n = 7). This new HLB field detection method provides a rapid detection with 6 min for each sample via a simple optimized procedure, including onsite sampling, PGC-MS analysis, and data process and provides a high accuracy (93.3%) for simultaneous identification of healthy, symptomatic, and asymptomatic trees. These data support the use of this new method for reliable field detection of HLB. Furthermore, metabolic pathways of HLB-affected metabolites were also proposed. Overall, our results not only provide a rapid and onsite field HLB detection method but also provide valuable information for understanding metabolic change of HLB infection.

Onsite Identification and Spatial Distribution of Air Pollutants Using a Drone-Based Solid-Phase Microextraction Array Coupled with Portable Gas Chromatography-Mass Spectrometry via Continuous-Airflow Sampling.

Hazardous air pollutants can be unintentionally and intentionally released in many cases, such as industrial emissions, accidental events, and pesticide application. Under such events, the onsite operation is highly dependent on the molecular composition and spatial distribution of air pollutants in ambient air. However, it is usually difficult for people to reach hazardous and upper sites rapidly. In this work, we designed a new drone-based microextraction sampler array in which a solid-phase microextraction (SPME) fiber was mounted on drones for remote-control sampling at different spaces and was then coupled with a portable gas chromatography-mass spectrometry (PGC-MS) approach for quickly identifying hazardous air pollutants and their spatial distribution in ambient air within minutes. Acceptable analytical performances, including good sensitivity (detection limit at nanogram per liter level), reproducibility (relative standard deviation < 20%, n = 6), analytical speed (single sample within minutes), and excellent linear dynamic response (3 orders of magnitude) were obtained for direct measurement of air samples. The drone-SPME sampling mechanism of air pollutants involving an airflow adsorptive microextraction process was proposed. Overall, this drone-SPME sampling array can access hard-to-reach and dangerous environmental sites and provide air pollution distribution in different spaces, showing versatile potential applications in environmental analysis.