Chromatographic Fingerprinting Based on Column Switching Technology for Quality Evaluation of Tianmeng Oral Liquid

Separation power was limited when the conventional high-performance liquid chromatography (HPLC) fingerprinting method based on a single column was used to analyze very complex traditional Chinese medicine (TCM) preparations. In this research, a novel HPLC fingerprinting method based on column switching technology by using a single pump was established for evaluating the quality of Tianmeng oral liquid (TMOL). Twelve batches of TMOL samples were used for constructing HPLC fingerprints. Compared with the 16 common peaks in fingerprinting with a single column, 25 common peaks were achieved with two columns connected through a six-way valve. The similarity analysis combined with bootstrap method was applied to determine the similarity threshold, which was 0.992 to distinguish expired samples and unexpired samples. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were also applied to classify the TMOL samples, and results revealed that expired and unexpired samples are classified into two categories. The HPLC fingerprinting based on column switching technology with better separation power and higher peak capacity could characterize chemical composition information more comprehensively, providing an effective and alternative method to control and evaluate the quality of TMOL, which would offer a valuable reference for other TCM preparations.

Lepton identification in Belle II using observables from the electromagnetic calorimeter and precision trackers

We present a major overhaul to lepton identification for the Belle II experiment, based on a novel multi-variate classification algorithm. Boosted decision trees are trained combining measurements from the electromagnetic calorimeter (ECL) and the tracking system. The chosen observables are sensitive to the different physics that governs interactions of hadrons, electrons and muons with the calorimeter crystals. Dedicated classifiers are used in various detector regions and lepton momentum ranges. The tree output is eventually combined with classifiers that rely upon independent measurements from other sub-detectors. Using simulation, the performance of the new algorithm is compared against the method used for analysis of the 2018 Belle II data, namely a likelihood discriminator based on the ratio of energy measured in the ECL over the momentum measured by the trackers. In the low momentum region, we largely improve the lepton-pion separation power, decreasing misidentification probability by a factor of 10 for electrons, and 2 for muons at fixed identification efficiency.

Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data

In Oryza sativa, indica and japonica are pivotal subpopulations, and other subpopulations such as aus and aromatic are considered to be derived from indica or japonica. In this regard, Oryza sativa accessions are frequently viewed from the indica/japonica perspective. This study introduces a computational method for indica/japonica classification by applying phenotypic variables to the logistic regression model (LRM). The population used in this study included 413 Oryza sativa accessions, of which 280 accessions were indica or japonica. Out of 24 phenotypic variables, a set of seven phenotypic variables was identified to collectively generate the fully accurate indica/japonica separation power of the LRM. The resulting parameters were used to define the customized LRM. Given the 280 indica/japonica accessions, the classification accuracy of the customized LRM along with the set of seven phenotypic variables was estimated by 100 iterations of ten-fold cross-validations. As a result, the classification accuracy of 100% was achieved. This suggests that the LRM can be an effective tool to analyze the indica/japonica classification with phenotypic variables in Oryza sativa.

LC-NMR for Natural Products Analysis: A Journey from an Academic Curiosity to a Robust Analytical Tool

LC–NMR combines the advantage of the outstanding separation power of liquid chromatography (LC) and the superior structural elucidating capability of nuclear magnetic resonance (NMR). NMR has proved that it is a standout detector for LC by providing maximum structural information about plant originated extracts particularly in its isolating ability of isomeric (same molecular formula) and/or isobaric (same molecular weight) compounds as compared to other detectors. The present review provides an overview of the LC–NMR developmental trends and its application in natural products analysis. The different LC–NMR operational modes are described, as well as how technical improvements assist in establishing this powerful technique as an important analytical tool in the analysis of complex plant-derived compounds. On-flow, stop-flow and loop-storage modes, as well as the new offline mode LC–SPE–NMR and capLC-NMR configurations that avoid the ingestion of expensive deuterated solvents throughout the experiment are mentioned. Utilization of cryogenic probe and microprobe technologies which are the other important promising approaches for guaranteeing the sensitivity issues are also described. Concluding remarks and future outlooks are also discussed.

An optimal trapdoor zeolite for exclusive admission of CO2 at industrial carbon capture operating temperatures

Molecular trapdoor chabazites with lowered Si/Al ratios show elevated operating temperatures for carbon capture and significantly improved separation power.