Rapid differentiation and quality control of tobacco products using Direct Analysis in Real Time Mass Spectrometry and Liquid Chromatography Mass Spectrometry

Sensitive quantitation of ultra-trace toxic aconitines in complex matrices by perfusion nano-electrospray ionization mass spectrometry combined with gas-liquid microextraction

The accurate analysis of ultra-trace (e.g. <10-4 ng/mL) substances in complex matrices is a burdensome but vital problem in pharmaceutical analysis, with important implications for precise quality control of drugs, discovery of innovative medicines and elucidation of pharmacological mechanisms. Herein, an innovative constant-flow perfusion nano-electrospray ionization (PnESI) technique was developed firstly features significant quantitative advantages in high-sensitivity ambient MS analysis of complex matrix sample. More importantly, double-labeled addition enrichment quantitation strategies of gas-liquid microextraction (GLME) were proposed for the first time, allowing highly selective extraction and enrichment of specific target analytes in a green and ultra-efficient (>1000-fold) manner. Using complex processed Aconitum herbs as example, PnESI-MS directly enabled the qualitative and absolute quantitative analysis of the processed Aconitum extracts and characterized the target toxic diester alkaloids with high sensitivity, high stability, wide linearity range, and strong resistance to matrix interference. Further, GLME device was applied to obtain the highly specific enrichment of the target diester alkaloids more than 1000-fold, and accurate absolute quantitation of trace aconitine, mesaconitine, and hypaconitine in the extracts of Heishunpian, Zhichuanwu and Zhicaowu was accomplished (e.g., 0.098 pg/mL and 0.143 pg/mL), with the quantitation results well below the LODs of aconitines from any analytical instruments available. This study built a systematic strategy for accurate quantitation of ultra-trace substances in complex matrix sample and expected to provide a technological revolution in many fields of pharmaceutical research.

Quantitative analysis of pyrolysis characteristics and chemical components of tobacco materials based on machine learning

To investigate the quantitative relationship between the pyrolysis characteristics and chemical components of tobacco materials, various machine learning methods were used to establish a quantitative analysis model of tobacco. The model relates the thermal weight loss rate to 19 chemical components, and identifies the characteristic temperature intervals of the pyrolysis process that significantly relate to the chemical components. The results showed that: 1) Among various machine learning methods, partial least squares (PLS), support vector regression (SVR) and Gaussian process regression (GPR) demonstrated superior regression performance on thermogravimetric data and chemical components. 2) The PLS model showed the best performance on fitting and prediction effects, and has good generalization ability to predict the 19 chemical components. For most components, the determination coefficients R 2 are above 0.85. While the performance of SVR and GPR models was comparable, the R 2 for most chemical components were below 0.75. 3) The significant temperature intervals for various chemical components were different, and most of the affected temperature intervals were within 130°C-400°C. The results can provide a reference for the materials selection of cigarette and reveal the possible interactions of various chemical components of tobacco materials in the pyrolysis process.

Applications of ambient ionization mass spectrometry in 2022: An annual review

The development of ambient ionization mass spectrometry (AIMS) has transformed analytical science, providing the means of performing rapid analysis of samples in their native state, both in and out of the laboratory. The capacity to eliminate sample preparation and pre-MS separation techniques, leading to true real-time analysis, has led to AIMS naturally gaining a broad interest across the scientific community. Since the introduction of the first AIMS techniques in the mid-2000s, the field has exploded with dozens of novel ion sources, an array of intriguing applications, and an evident growing interest across diverse areas of study. As the field continues to surge forward each year, ambient ionization techniques are increasingly becoming commonplace in laboratories around the world. This annual review provides an overview of AIMS techniques and applications throughout 2022, with a specific focus on some of the major fields of research, including forensic science, disease diagnostics, pharmaceuticals and food sciences. New techniques and methods are introduced, demonstrating the unwavering drive of the analytical community to further advance this exciting field and push the boundaries of what analytical chemistry can achieve.

Rapid quantification of cannabidiol from oils by direct analysis in real time mass spectrometry

This work is the first to describe the use of Direct Analysis in Real Time Mass Spectrometry (DART-MS) for the rapid quantification of cannabidiol (CBD) in CBD oils. For this study, self-prepared samples spiked with CBD in hemp seed oil as well as commercial CBD oils from the Austrian market with different CBD contents were analyzed. CBD concentrations were between 5 and 30% (m/m) for the spiked samples as well as between 5 and 15% (m/m) for the real samples. The performance of quantification by means of DART-MS was assessed against a validated liquid chromatography-mass spectrometry (LC-MS) method. The correlation of the quantification results of both methods was high with a correlation factor greater than 0.98 and a maximum bias of 9.8%. Furthermore, the relative standard deviation values of the DART-MS measurments were below the tolerable limit of 12%. These results demonstrate that quantification of CBD by DART-MS is reliable and hence suitable as a rapid and cost-effective alternative method for quality control of CBD content in CBD oils.

Measuring Liquid Droplet Size in Two-Phase Nozzle Flow Employing Numerical and Experimental Analyses

The flavoring process ensures the quality of cigarettes by endowing them with special tastes. In this process, the flavoring liquid is atomized into particles by a nozzle and mixed with the tobacco in a rotating drum. The particle size of the flavoring liquid has great influence on the atomization effect; however, limited research has addressed the quantitation of the liquid particle size in two-phase nozzle flow. To bridge this research gap, the authors of this study employed numerical and experimental techniques to explore the quantitative analysis of particle size. First, a simulation model for the flavoring nozzle was established to investigate the atomization effect under different ejection pressures. Then, an experimental test is carried out to compare the test results with the simulation results. Lastly, the influencing factors of liquid particle size in two-phase nozzle flow were analyzed to quantify particle size. The analysis results demonstrated that there was a cubic correction relationship between the simulation and experiment particle size. The findings of this study may provide a reliable reference when evaluating the atomization effect of flavoring nozzles.