Literature DB >> 32544651

Quantitative analysis of bioactive components in walnut leaves by UHPLC-Q-Orbitrap HRMS combined with QAMS.

Chaonan Su1, Caihong Li2, Kang Sun3, Wenjing Li4, Rongxia Liu5.   

Abstract

Walnut leaves are rich in phenolic components with antibiotic and antioxidative effects. However, few studies have reported the quantitative analysis of active components in walnut leaf. In this study, a novel method for quantifying the active components in walnut leaves was developed by combining ultra-high performance liquid chromatography-hybrid quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) with quantitative analysis of multi-components by a single marker (QAMS). In total, 13 bioactive components were analyzed by a single marker, quercetin. To evaluate the accuracy of this method, an auxiliary quantification method with 13 reference standards was established and validated. The standard method differences (SMDs) of the quantification results between QAMS and the auxiliary method were lower than 20%, indicating that the QAMS method can accurately determine the active components in walnut leaves. This method can provide a reference to address the absence of reference standards for analyzing other foods and herbs.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  4-Hydroxybenzoic acid (PubChem CID: 135); Esculetin (PubChem CID: 5281416); Hyperozide (PubChem CID:5281643); Isoquercitroside (PubChem CID: 5484006); Kaempferol (PubChem CID: 5280863); Myricitrin (PubChem CID: 5281673); Neochlorogenic acid (PubChem CID: 5280633); QAMS; Quantification; Quercetin (PubChem CID: 5280343); Quercitrin (PubChem CID: 5280459); Quinic acid (PubChem CID: 6508); UHPLC-Q-Orbitrap HRMS; Walnut leaf

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Year:  2020        PMID: 32544651     DOI: 10.1016/j.foodchem.2020.127180

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  2 in total

1.  Fuzzy identification of bioactive components for different efficacies of rhubarb by the back propagation neural network association analysis of UPLC-Q-TOF/MSE and integrated effects.

Authors:  Jia-Qian Chen; Yan-Yan Chen; Xia Du; Hui-Juan Tao; Zong-Jin Pu; Xu-Qin Shi; Shi-Jun Yue; Gui-Sheng Zhou; Er-Xin Shang; Yu-Ping Tang; Jin-Ao Duan
Journal:  Chin Med       Date:  2022-04-26       Impact factor: 4.546

2.  Quantification of Chemical Groups and Quantitative HPLC Fingerprint of Poria cocos (Schw.) Wolf.

Authors:  Yu Yang; Xing-Lin Huang; Zhong-Min Jiang; Xue-Fang Li; Yan Qi; Jie Yu; Xing-Xin Yang; Mei Zhang
Journal:  Molecules       Date:  2022-09-27       Impact factor: 4.927

  2 in total

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