DICP OpenIR
Quantitative structure-retention relationships model for retention time prediction of veterinary drugs in food matrixes
Noreldeen, Hamada A. A.1,2,3; Liu, Xingyu1; Wang, Xiaolin1; Fu, Yanqing1,2; Li, Zaifang1,2; Lu, Xin1; Zhao, Chunxia1; Xu, Guowang1
Corresponding AuthorZhao, Chunxia(zhaocx@dicp.ac.cn) ; Xu, Guowang(xugw@dicp.ac.cn)
KeywordQuantitative structure-retention relationships Liquid chromatography-mass spectrometry Illegal additives Food matrixes
Source PublicationINTERNATIONAL JOURNAL OF MASS SPECTROMETRY
2018-11-01
ISSN1387-3806
DOI10.1016/j.ijms.2018.09.022
Volume434Pages:172-178
Funding ProjectNational Natural Science Foundation of China[21775147] ; National Natural Science Foundation of China[21675154] ; National Natural Science Foundation of China[21575140]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
WOS SubjectPhysics, Atomic, Molecular & Chemical ; Spectroscopy
WOS Research AreaPhysics ; Spectroscopy
WOS KeywordPERFORMANCE LIQUID-CHROMATOGRAPHY ; ATOMIC PHYSICOCHEMICAL PARAMETERS ; RESOLUTION MASS-SPECTROMETRY ; TANIMOTO SIMILARITY INDEX ; ION CHROMATOGRAPHY ; GAS-CHROMATOGRAPHY ; TRAINING SETS ; QSRR APPROACH ; FACTOR RATIO ; BEHAVIOR
AbstractQuantitative structure-retention relationships (QSRR) is a technique used in the prediction of the retention time of compounds based on their structure and chromatographic behavior. In this study, an easy and usable QSRR model was established based on multiple linear regression (MLR) to predict three kinds of illegal additives in food matrixes. For this purpose, 95 drugs were chosen, including a training set of 62 drugs, a test set of 30 drugs, and a real sample set of 3 drugs. The molecular descriptors for each compound were obtained by free softwares of advanced chemistry development (ACD) and toxicity estimation software tool (TEST). After that, the MLR-based QSRR model was established, both internal and external validation was used for validation of this model. The result indicated that the following descriptors have great influence on the predicted retention time: ACDlogP, ALOGP, ALOGP2, Hy, Ui, ib, BEHp1, BEHp2, GATS1m, GATS2m. The correlation coefficient for fitting model revealed a strong correlation between the drug retention time and selected molecular descriptors (R-2 = 0.966). Moreover, the four validation methods (leave-one-out, k-fold cross-validation, test set, and real sample set) indicated the high reliability of this model. In conclusion, this method provided a more suitable and usable model for research work in several branches of analytical chemistry, especially in the field of food safety to improve the ability of retention time prediction for illegal additives. (C) 2018 Elsevier B.V. All rights reserved.
Language英语
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
WOS IDWOS:000449709900025
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/166705
Collection中国科学院大连化学物理研究所
Corresponding AuthorZhao, Chunxia; Xu, Guowang
Affiliation1.Chinese Acad Sci, CAS Key Lab Separat Sci Analyt Chem, Dalian Inst Chem Phys, Dalian 116023, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Natl Inst Oceanog & Fisheries, Marine Chem Lab, Marine Environm Div, Hurghada 84511, Egypt
Recommended Citation
GB/T 7714
Noreldeen, Hamada A. A.,Liu, Xingyu,Wang, Xiaolin,et al. Quantitative structure-retention relationships model for retention time prediction of veterinary drugs in food matrixes[J]. INTERNATIONAL JOURNAL OF MASS SPECTROMETRY,2018,434:172-178.
APA Noreldeen, Hamada A. A..,Liu, Xingyu.,Wang, Xiaolin.,Fu, Yanqing.,Li, Zaifang.,...&Xu, Guowang.(2018).Quantitative structure-retention relationships model for retention time prediction of veterinary drugs in food matrixes.INTERNATIONAL JOURNAL OF MASS SPECTROMETRY,434,172-178.
MLA Noreldeen, Hamada A. A.,et al."Quantitative structure-retention relationships model for retention time prediction of veterinary drugs in food matrixes".INTERNATIONAL JOURNAL OF MASS SPECTROMETRY 434(2018):172-178.
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