Objective: To explore the utility of Principal Factor Analysis (PFA) in chromatographic data for quality control.
Method: Chromatographic fingerprints of processed root pieces of Paeonia lactiflora were determined by HPLC, the PFA was used for data processing.
Result: The quantitative differences among different growing areas and different processing batches were found with the method.
Conclusion: The method could be used in quality control for monitoring between-batch products of traditional Chinese pharmaceutical process.
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ACS Omega
December 2024
Department of Chemistry, Humboldt Universität zu Berlin, Brook-Taylor-Str. 2, Berlin 12489, Germany.
In this study, we extended a previously developed one-pot double derivatization reaction to establish the first routine isotope-coded multiplex derivatization for vitamin D and its metabolites for application in clinical environments, using commercial reagents, without the need for specialized reagents and advanced synthesis requirements. The original derivatization process consisted of using both a Cookson-type reagent and derivatization of hydroxyl groups. Initially, the analytes are derivatized by a Diels-Alder reaction using 4-phenyl-1,2,4-triazoline-3,5-dione (PTAD), followed by acetylation using acetic anhydride, catalyzed by 4-dimethylaminopyridine at room temperature.
View Article and Find Full Text PDFJ Chromatogr A
December 2024
School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. Electronic address:
The evolution of precursors to form secondary organic aerosol (SOA) is still a challenge in atmospheric chemistry. Chamber experiments were conducted to simulate the ambient OH oxidation of naphthalene and α-pinene, which are typical markers of anthropogenic and biogenic emissions. Particulate matters were sampled by quartz filters and were analyzed by comprehensive two-dimensional gas chromatography (GC×GC) coupled with a thermal desorption system (TD) and a mass spectrometer (MS).
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
September 2024
School of Pharmaceutical Sciences, Guizhou Medical University Guiyang 561113, China Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education),Guizhou Medical University Guiyang 550004, China State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University Guiyang 550004, China.
This study aimed to provide scientific evidence for predicting quality markers(Q-markers) of Xuebijing Injection by establishing high-performance liquid chromatography(HPLC) fingerprints of 25 batches of Xuebijing Injection and determining the contents of 9 major components, as well as conducting network pharmacology research. Thirty common peaks were identified by fingerprints of 25 batches of Xuebijing Injection samples, and 12 chromatographic peaks were determined, with similarity ranging from 0.970 to 0.
View Article and Find Full Text PDFArch Environ Contam Toxicol
December 2024
School of Engineering and Architecture, Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, Terracini 28, 40131, Bologna, Italy.
Concerning the entrance of oil into the Persian Gulf due to the presence of oil fields in this ecosystem, a wide investigation was carried out in 2017 to evaluate the hydrocarbons source identification and chemical fingerprinting. To this end, surface sediments were collected from the Persian Gulf. In the laboratory, compounds (n-alkanes, PAHs, hopane and sterane) were then extracted with a Soxhlet system and two steps of chromatographic columns and analyzed using a GC-MS instrument.
View Article and Find Full Text PDFJ Comput Aided Mol Des
December 2024
Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API).
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