Offline Solid-Phase Extraction and Separation of Mineral Oil Saturated Hydrocarbons and Mineral Oil Aromatic Hydrocarbons in Edible Oils, and Analysis via GC with a Flame Ionization Detector

A method was developed for the determination of mineral oil saturated hydrocarbons (MOSH) and mineral oil aromatic hydrocarbons (MOAH) in edible oils, achieving similar limits of quantification than those obtained by online extraction methodologies, i.e., 0.5 mg/kg. The isolation of MOSH and MOAH was performed in a silver nitrated silica gel stationary phase prior to their analysis by gas chromatography–flame ionization detector (GC-FID). To improve the sensitivity, the simulated on-column injection method, using a suitable liner, was optimized. The method was validated at 0.5, 10.0 and 17.9 mg/kg, and recoveries ranged from 80 to 110%. Intra and inter-day precision were evaluated at the same levels, and relative standard deviation (RSD) was lower than 20%. The method was applied to a total of 27 samples of different types of oil previously analyzed in an accredited laboratory, detecting MOSH up to 79.2 mg/kg and MOAH up to 22.4 mg/kg.

Simultaneous Quantification of 17 Cannabinoids by LC–MS-MS in Human Plasma

In recent years, the surge in use of tetrahydrocannabinol (THC) and cannabidiol (CBD) has increased the need for sensitive and specific analytical assays to measure the said compounds in patients, to establish dose–effect relationships and to gain knowledge of their pharmacokinetics and metabolism. We developed and validated an online extraction high-performance liquid chromatography-tandem mass spectrometry (HPLC–MS-MS) method for simultaneous quantification of 17 cannabinoids and metabolites including THC and its metabolites, CBD and its metabolites and other minor cannabinoids in human plasma. CBD-glucuronide (CBD-gluc) standard was produced in-house by isolation of CBD-gluc from urine of patients using pure CBD oil. For calibration standards and quality control samples, human plasma was spiked with cannabinoids at varying concentrations within the working range of the respective compound and 200 µL of the plasma was extracted using a simple one-step protein precipitation procedure. The extracts were analyzed using online trapping LC/LC–atmospheric pressure chemical ionization–MS-MS running in the positive multiple reaction monitoring mode. The lower limit of quantification ranged from 0.78 to 7.8 ng/mL, and the upper limits of quantification were between 100 and 2,000 ng/mL. Inter-day analytical accuracy and imprecision ranged from 90.4% to 111% and from 3.1% to 17.4%, respectively. The analysis of plasma samples collected during clinical studies showed that (3R-trans)-cannabidiol-7-oic acid (7-CBD-COOH) was the major human metabolite with 5960% (59.6-fold) of CBD followed by 7-hydroxy-CBD (177%), CBD-gluc (157%) and 6α-hydroxy-CBD (39.8%); 6β-hydroxy-CBD was not detected in any of the samples. In the present study, we developed and validated a robust LC–MS-MS assay for the simultaneous quantification of cannabinoids and their metabolites, which has been used to measure >5,000 samples in clinical studies. Moreover, we were able to quantify CBD-gluc and showed that 7-CBD-COOH, 7-hydroxy-CBD and CBD-gluc are the major CBD metabolites in human plasma.

Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking

Three-dimensional (3D) zigzag-line welding seams are found extensively in the manufacturing of marine engineering equipment, heavy lifting equipment, and logistics transportation equipment. Currently, due to the large amount of calculation and poor real-time performance of 3D welding seam detection algorithms, real-time tracking of 3D zigzag-line welding seams is still a challenge especially in high-speed welding. For the abovementioned problems, we proposed a method for the extraction of the pose information of 3D zigzag-line welding seams based on laser displacement sensing and density-based clustering point cloud segmentation during robotic welding. after thee point cloud data of the 3D zigzag-line welding seams was obtained online by the laser displacement sensor, it was segmented using theρ-Approximate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. In the experiment, high-speed welding was performed on typical low-carbon steel 3D zigzag-line welding seams using gas metal arc welding. The results showed that when the welding velocity was 1000 mm/min, the proposed method obtained a welding seam position detection error of less than 0.35 mm, a welding seam attitude estimation error of less than two degrees, and the running time of the main algorithm was within 120 ms. Thus, the online extraction of the pose information of 3D zigzag-line welding seams was achieved and the requirements of welding seam tracking were met.

A rapid analysis of antioxidants in Sanghuangporus baumii by online extraction-HPLC-ABTS

Rapid discovery of antioxidants from natural product by online OLE-HPLC-ABTS.

ANNUAL URBAN BUILT-UP CHANGE AREA ONLINE EXTRACTION USING LANDSAT TIME SERIES DATA

Urban built-up area change information in multiple periods is a pivotal factor in global climate change application and sustainable development research. Due to spatial-temporal expression of land cover types, processing speed and operability, built-up area change information extraction using Landsat time series data is still a challenging task. To provide insights into the inter-annual dynamic of land use change, focusing on how time series characteristics improves recognition of urban change and how much online extraction convenience is facilitated, this paper presents a new methodology to built-up change area extraction using inter-annual time series of Landsat images. The central premise of the approach is that time series characteristics are firstly expressed by spectral index. The logistic algorithm is then used in time series trajectory modelling of land cover types for annual urban built-up change area extraction. Finally, the individual steps of the whole process, including image selection, time series trajectory modelling and results display, are converted to web service for online processing. The further comparison is also conducted between the proposed method and post-classification comparison method. Results show that the online processing mode has strengths regarding the provision of functionality to user-end, the automation of recurring tasks or the sharing of workflows. Results also demonstrate that the proposed method improves the accuracy of annual urban built-up change area extraction.