A robust, routinely manageable and sensitive RP-HPLC method combined with UV (270 nm) and ESI-MS detection was established for the determination of abundant pertinent phenolic compounds (phytochemicals) from various biological matrices. Phytochemicals were extracted by aqueous methanol (80%), extracts were analysed without further purification. Baseline separation was achieved within 30 min for 19 phytochemicals and excellent sensitivity (6-42 pmol at S/N = 3) was obtained. The identity of the phytochemicals was confirmed with standard compounds and with LC-MS. The repeatabilities for the majority of the phytochemicals ranged between 3% and 6%. The practicability of the method was shown in complex biological matrices by analysing onion and soybean extracts. This generally applicable technique may serve as a valuable tool for a rapid screening and a specific measurement of phytochemicals in food extracts and biological fluids and serve as analytical instrument for future biochemical and physiological studies.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/s0021-9673(99)00597-x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A green bioanalytical spectrofluorimetric approach for estimation of Avapritinib anti-tumor drug; application to quality control and clinical studies.
Bioanalysis
January 2025
Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, Oman.
Aims: Gastrointestinal stromal tumors (GISTs) account for about 80% of the mesenchymal tumors of the GI tract. About 5000-6000 patients are diagnosed in the United States (US) alone, and up to 14.5 cases per million discovered in Europe annually.
View Article and Find Full Text PDFTowards enhancing phytoremediation: The effect of syringic acid, a plant secondary metabolite, on the presence of phenoxy herbicide-tolerant endophytic bacteria.
Sci Total Environ
January 2025
UNESCO Chair on Ecohydrology and Applied Ecology, Faculty of Biology and Environmental Protection, University of Lodz, Banacha 12/16, 90-237 Lodz, Poland.
Among emerging pollutants, residuals of phenoxy herbicides, including 2-chloro-4-methylphenoxy acid (MCPA), are frequently detected in non-targeted areas. MCPA can be removed from environmental matrices using biological remediation methods including endophyte-assisted phytoremediation. The interactions between selected plants excreting to the rhizosphere plant secondary metabolites (PSMs) and plant-associated bacteria (incl.
View Article and Find Full Text PDFQOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics.
Brief Bioinform
November 2024
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States.
Single-cell technologies have enabled the high-dimensional characterization of cell populations at an unprecedented scale. The innate complexity and increasing volume of data pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e.
View Article and Find Full Text PDFMDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks.
BMC Bioinformatics
January 2025
Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital of Capital Medical University, Beijing, 101100, China.
Background: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experimental techniques for identifying SM-miRNA associations highlight the necessity for efficient computational methodologies in this field.
Results: In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations.
Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme.
Discov Oncol
January 2025
Department of Medical Imaging, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, China.
Background: Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.
Methods: This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!