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J Chromatogr A. 2018 Mar 16;1541:12-20. doi: 10.1016/j.chroma.2018.02.017. Epub 2018 Feb 10.

Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability.

Author information

1
College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China.
2
Ningxia Institute of Cultural Relics and Archeology, Yinchuan 750001, China.
3
Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China.
4
College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan 750004, China. Electronic address: yongjie.yu@163.com.
5
College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China. Electronic address: sheyb@zjut.edu.cn.

Abstract

Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis.

KEYWORDS:

Flaxseed; Geographical origin discrimination; Peak resolution; Two-way encoding PLS model; Untargeted metabolic profiling analysis

PMID:
29448994
DOI:
10.1016/j.chroma.2018.02.017
[Indexed for MEDLINE]

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