1
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Milani NBL, García-Cicourel AR, Blomberg J, Edam R, Samanipour S, Bos TS, Pirok BWJ. Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography. Anal Chim Acta 2024; 1312:342724. [PMID: 38834259 DOI: 10.1016/j.aca.2024.342724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/22/2024] [Accepted: 05/14/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND Comprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. RESULT To tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SIGNIFICANCE Data analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.
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Affiliation(s)
- Nino B L Milani
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.
| | | | - Jan Blomberg
- Shell Global Solutions International B.V., Grasweg 31, 1031 HW, Amsterdam, the Netherlands
| | - Rob Edam
- Shell Global Solutions International B.V., Grasweg 31, 1031 HW, Amsterdam, the Netherlands
| | - Saer Samanipour
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Tijmen S Bos
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands
| | - Bob W J Pirok
- Van't Hoff Institute for Molecular Science (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), the Netherlands.
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2
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Ma GM, Wang JN, Wang XC, Ma FL, Wang WX, Li SF, Liu PP, Lv Y, Yu YJ, Fu HY, She Y. AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem. Anal Chem 2024; 96:9379-9389. [PMID: 38805056 DOI: 10.1021/acs.analchem.4c00100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.
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Affiliation(s)
- Gui-Mei Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Jia-Nan Wang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Xing-Cai Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Feng-Lian Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Wen-Xin Wang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Shu-Fang Li
- Institute of Quality Standard and Testing Technology for Agro-products, Henan Academy of Agricultural Science, Zhengzhou 450002, China
| | - Ping-Ping Liu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Yi Lv
- Key Laboratory of Quality and Safety of Wolfberry and Wine for State Administration for Market Regulation, Ningxia Food Testing and Research Institute, Yinchuan 750004, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Yuanbin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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3
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Ma MH, Zhang JN, Ma XL, Wang XC, Ma FL, Liu JN, Lv Y, Yu YJ, She Y. Using UHPLC-HRMS-based comprehensive strategy to efficiently and accurately screen and identify illegal additives in health-care foods. Food Res Int 2023; 170:113015. [PMID: 37316023 DOI: 10.1016/j.foodres.2023.113015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 05/18/2023] [Accepted: 05/19/2023] [Indexed: 06/16/2023]
Abstract
Accurately and high-thoroughly screening illegal additives in health-care foods continues to be a challenging task in routine analysis for the ultrahigh performance liquid chromatography-high resolution mass spectrometry based techniques. In this work, we proposed a new strategy to identify additives in complex food matrices, which consists of both experimental design and advanced chemometric data analysis. At first, reliable features in the analyzed samples were screened based on a simple but efficient sample weighting design, and those related to illegal additives were screened with robust statistical analysis. After the MS1 in-source fragment ion identification, both MS1 and MS/MS spectra were constructed for each underlying compound, based on which illegal additives can be precisely identified. The performance of the developed strategy was demonstrated by using mixture and synthetic sample datasets, indicating an improvement of data analysis efficiency up to 70.3 %. Finally, the developed strategy was applied for the screening of unknown additives in 21 batches of commercially available health-care foods. Results indicated that at least 80 % of false-positive results can be reduced and 4 additives were screened and confirmed.
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Affiliation(s)
- Meng-Han Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Jia-Ni Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Xing-Ling Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Xing-Cai Wang
- Zhejiang University of Technology, Hangzhou 310014, China
| | - Feng-Lian Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Jia-Nan Liu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Yi Lv
- Ningxia Inspection and Research Institution of Food Control, Yinchuan 750000, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China.
| | - Yuanbin She
- Zhejiang University of Technology, Hangzhou 310014, China
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4
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Wang XC, Zhang JN, Zhao JJ, Guo XM, Li SF, Zheng QX, Liu PP, Lu P, Fu HY, Yu YJ, She Y. AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification. Anal Chem 2023; 95:638-649. [PMID: 36599407 DOI: 10.1021/acs.analchem.2c01795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.
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Affiliation(s)
- Xing-Cai Wang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Jia-Ni Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Juan-Juan Zhao
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Xiao-Meng Guo
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Shu-Fang Li
- Institute of Quality Standard and Testing Technology for Agro-products, Henan Academy of Agricultural Science, Zhengzhou 450002, China
| | - Qing-Xia Zheng
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Ping-Ping Liu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Peng Lu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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5
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Chemometric strategy for aligning chemical shifts in 1H NMR to improve geographical origin discrimination: A case study for Chinese Goji honey. Microchem J 2022. [DOI: 10.1016/j.microc.2021.107062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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6
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Critical comparison of background correction algorithms used in chromatography. Anal Chim Acta 2022; 1201:339605. [DOI: 10.1016/j.aca.2022.339605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 11/19/2022]
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7
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Song JJ, Wang X, Wang YY, Zhang YY, Yu YJ. High-throughput identification of volatile and semi-volatile organic compounds in archaeological samples by gas chromatography–mass spectrometry combined with advanced chemometrics methodology. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105289] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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8
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Zhang Q, Zhang YY, Liu Z, Zhang YM, Lu N, Hai GQ, Shao SZ, Zheng QX, Zhang X, Fu HY, Bai CC, Yu YJ, She Y. Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics. J Sep Sci 2020; 43:2794-2803. [PMID: 32386337 DOI: 10.1002/jssc.201901138] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 04/15/2020] [Accepted: 04/22/2020] [Indexed: 11/08/2022]
Abstract
There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.
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Affiliation(s)
- Qian Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China
| | - Yu-Ying Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China
| | - Zhi Liu
- Institute of Quality and Standards for Agricultural Products, Zhejiang Academy of Agricultural Sciences, Hangzhou, P. R. China
| | - Yue-Ming Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China
| | - Ning Lu
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China
| | - Guo-Qing Hai
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China
| | - Sheng-Zhi Shao
- Institute of Quality and Standards for Agricultural Products, Zhejiang Academy of Agricultural Sciences, Hangzhou, P. R. China
| | - Qing-Xia Zheng
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, P. R. China
| | - Xia Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Chang-Cai Bai
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan, P. R. China.,Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, P. R. China
| | - Yuanbin She
- Zhejiang University of Technology, Hangzhou, P. R. China
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9
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Bos TS, Knol WC, Molenaar SR, Niezen LE, Schoenmakers PJ, Somsen GW, Pirok BW. Recent applications of chemometrics in one- and two-dimensional chromatography. J Sep Sci 2020; 43:1678-1727. [PMID: 32096604 PMCID: PMC7317490 DOI: 10.1002/jssc.202000011] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/20/2020] [Accepted: 02/21/2020] [Indexed: 12/28/2022]
Abstract
The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.
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Affiliation(s)
- Tijmen S. Bos
- Division of Bioanalytical ChemistryAmsterdam Institute for Molecules, Medicines and SystemsVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Wouter C. Knol
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Stef R.A. Molenaar
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Leon E. Niezen
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Peter J. Schoenmakers
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Govert W. Somsen
- Division of Bioanalytical ChemistryAmsterdam Institute for Molecules, Medicines and SystemsVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Bob W.J. Pirok
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
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10
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Li Z, Zhang X, Liao J, Fan X, Cheng Y. An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry. J Pharm Anal 2020; 11:88-95. [PMID: 33717615 PMCID: PMC7930630 DOI: 10.1016/j.jpha.2020.01.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 12/28/2019] [Accepted: 01/13/2020] [Indexed: 11/30/2022] Open
Abstract
Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine. However, this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness. In this work, we proposed a multiple reaction monitoring (MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment. The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards, based on which the large-scale fingerprinting method was efficiently established. This approach was exemplified on QiShenYiQi Pill (QSYQ), a traditional Chinese medicine-derived drug product, and its robustness was systematically evaluated by four indices: clustering analysis by principal component analysis, similarity analysis by the congruence coefficient, the number of separated peaks, and the peak area proportion of separated peaks. Compared with conventional ultraviolet-based fingerprints, the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples, but also higher robustness under different chromatographic conditions (i.e., flow rate, apparent pH, column temperature, and column). The result also showed for such large-scale fingerprints including a large number of peaks, the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm. This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive, robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine. MRM fingerprints are proposed for quality assessment of traditional medicine. MRM fingerprints show favorable robustness, coverage and discriminatory capacity. Similarity analysis methods for such large-scale fingerprints are proposed.
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Affiliation(s)
- Zhenhao Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiaohui Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yiyu Cheng
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
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11
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Liu Y, Lin J. A general-purpose signal processing algorithm for biological profiles using only first-order derivative information. BMC Bioinformatics 2019; 20:611. [PMID: 31775621 PMCID: PMC6882060 DOI: 10.1186/s12859-019-3188-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 11/04/2019] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Automatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences. RESULTS We developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted. CONCLUSIONS We have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.
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Affiliation(s)
- Yuanjie Liu
- College of Information and Electrical Engineering, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China.
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China.
| | - Jianhan Lin
- College of Information and Electrical Engineering, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Haidian, Beijing, 100083, People's Republic of China
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12
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Liu P, Zhou H, Zheng Q, Lu P, Yu Y, Cao P, Chen W, Chen Q. An automatic UPLC-HRMS data analysis platform for plant metabolomics. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:2038-2040. [PMID: 31150147 PMCID: PMC6790358 DOI: 10.1111/pbi.13180] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 05/25/2019] [Accepted: 05/29/2019] [Indexed: 05/21/2023]
Affiliation(s)
- Pingping Liu
- Zhengzhou Tobacco Research Institute of CNTCZhengzhouHenanChina
| | - Huina Zhou
- Zhengzhou Tobacco Research Institute of CNTCZhengzhouHenanChina
| | - Qingxia Zheng
- Zhengzhou Tobacco Research Institute of CNTCZhengzhouHenanChina
| | - Peng Lu
- Zhengzhou Tobacco Research Institute of CNTCZhengzhouHenanChina
| | - Yong‐Jie Yu
- College of PharmacyNingxia Medical UniversityYinchuanNingxiaChina
| | - Peijian Cao
- Zhengzhou Tobacco Research Institute of CNTCZhengzhouHenanChina
| | - Wei Chen
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanHubeiChina
| | - Qiansi Chen
- Zhengzhou Tobacco Research Institute of CNTCZhengzhouHenanChina
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13
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Wei F, Lamichhane S, Orešič M, Hyötyläinen T. Lipidomes in health and disease: Analytical strategies and considerations. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.115664] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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14
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Zhang YM, Zhang YY, Zhang Q, Lv Y, Sun T, Han L, Bai CC, Yu YJ. Automatic peak detection coupled with multivariate curve resolution–alternating least squares for peak resolution in gas chromatography–mass spectrometry. J Chromatogr A 2019; 1601:300-309. [DOI: 10.1016/j.chroma.2019.04.065] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/22/2019] [Accepted: 04/23/2019] [Indexed: 12/22/2022]
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15
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Jiang CK, Ma JQ, Apostolides Z, Chen L. Metabolomics for a Millenniums-Old Crop: Tea Plant ( Camellia sinensis). JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2019; 67:6445-6457. [PMID: 31117495 DOI: 10.1021/acs.jafc.9b01356] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Tea cultivation and utilization dates back to antiquity. Today it is the most widely consumed beverage on earth due to its pleasant taste and several beneficial health properties attributed to specific metabolites. Metabolomics has a tremendous potential to correlate tea metabolites with taste and health properties in humans. Our review on the current application of metabolomics in the science of tea suggests that metabolomics is a promising frontier in the evaluation of tea quality, identification of functional genes responsible for key metabolites, investigation of their metabolic regulation, and pathway analysis in the tea plant. Furthermore, the challenges, possible solutions, and the prospects of metabolomics in tea science are reviewed.
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Affiliation(s)
- Chen-Kai Jiang
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture and Rural Affairs , Tea Research Institute of the Chinese Academy of Agricultural Sciences , Hangzhou 310008 , China
| | - Jian-Qiang Ma
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture and Rural Affairs , Tea Research Institute of the Chinese Academy of Agricultural Sciences , Hangzhou 310008 , China
| | - Zeno Apostolides
- Department of Biochemistry, Genetics and Microbiology , University of Pretoria , Pretoria 0002 , South Africa
| | - Liang Chen
- Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture and Rural Affairs , Tea Research Institute of the Chinese Academy of Agricultural Sciences , Hangzhou 310008 , China
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16
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He M, Hong L, Zhou Y. Multi-scale Gaussian/Haar wavelet strategies coupled with sub-window factor analysis for an accurate alignment in nontargeted metabolic profiling to enhance herbal origin discrimination capability. J Sep Sci 2019; 42:2003-2012. [PMID: 30919573 DOI: 10.1002/jssc.201801077] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 03/20/2019] [Accepted: 03/22/2019] [Indexed: 12/27/2022]
Abstract
Metabolic dataset can provide an overview of different herbal origin, which is conducted by some statistical procedures. Such results often deviate to a certain degree, due to peaks shifts in chromatographic signals. In order to solve this problem, an improved algorithm of combining sub-window factor analysis with the mass spectrum information is proposed. The algorithm uses a peak detection approach derived either from multi-scale Gaussian function or Haar wavelet to locate the peaks with different application scope; the candidate drift points at each peak are estimated by Fast Fourier transform cross correlation; Specifically, the best drift points at each candidate peaks are confirmed by sub-window factor analysis and mass spectrum information in nontargeted metabolic profiling. Finally, the peak regions were aligned against a reference chromatogram, and the non-peak regions were used linear interpolation. The chromatographic signals of 30 Bupleurum samples were aligned as an illustration of this algorithm, and they could be well distinguished using some statistical procedures. The result demonstrates that the presented method is stronger than other mass-spectra based algorithms, when facing the alignment of some co-eluted peaks.
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Affiliation(s)
- Min He
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, P. R. China
| | - Liang Hong
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, P. R. China
| | - Yu Zhou
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, P. R. China
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17
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Yu YJ, Zheng QX, Zhang YM, Zhang Q, Zhang YY, Liu PP, Lu P, Fan MJ, Chen QS, Bai CC, Fu HY, She Y. Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics. J Chromatogr A 2018; 1585:172-181. [PMID: 30509617 DOI: 10.1016/j.chroma.2018.11.070] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/06/2018] [Accepted: 11/25/2018] [Indexed: 02/06/2023]
Abstract
Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.
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Affiliation(s)
- Yong-Jie Yu
- 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
| | - Qing-Xia Zheng
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Yue-Ming Zhang
- 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
| | - Qian Zhang
- 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
| | - Yu-Ying Zhang
- 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
| | - Ping-Ping Liu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Peng Lu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Mei-Juan Fan
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Qian-Si Chen
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Chang-Cai Bai
- 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
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South Central University for Nationalities, Wuhan, 430074, China.
| | - Yuanbin She
- Zhejiang University of Technology, Hangzhou, 310014, China.
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18
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Kanginejad A, Mani-Varnosfaderani A. Chemometrics advances on the challenges of the gas chromatography–mass spectrometry metabolomics data: a review. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2018. [DOI: 10.1007/s13738-018-1461-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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19
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Han L, Zhang YM, Song JJ, Fan MJ, Yu YJ, Liu PP, Zheng QX, Chen QS, Bai CC, Sun T, She YB. Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability. J Chromatogr A 2018; 1541:12-20. [DOI: 10.1016/j.chroma.2018.02.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 01/23/2018] [Accepted: 02/07/2018] [Indexed: 10/18/2022]
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20
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Chemometric Analysis of the Volatile Compounds Generated by Aspergillus carbonarius Strains Isolated from Grapes and Dried Vine Fruits. Toxins (Basel) 2018; 10:toxins10020071. [PMID: 29415459 PMCID: PMC5848172 DOI: 10.3390/toxins10020071] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/01/2018] [Accepted: 02/03/2018] [Indexed: 12/31/2022] Open
Abstract
Ochratoxin A (OTA) contamination in grape production is an important problem worldwide. Microbial volatile organic compounds (MVOCs) have been demonstrated as useful tools to identify different toxigenic strains. In this study, Aspergillus carbonarius strains were classified into two groups, moderate toxigenic strains (MT) and high toxigenic strains (HT), according to OTA-forming ability. The MVOCs were analyzed by GC-MS and the data processing was based on untargeted profiling using XCMS Online software. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was performed using extract ion chromatogram GC-MS datasets. For contrast, quantitative analysis was also performed. Results demonstrated that the performance of the OPLS-DA model of untargeted profiling was better than the quantitative method. Potential markers were successfully discovered by variable importance on projection (VIP) and t-test. (E)-2-octen-1-ol, octanal, 1-octen-3-one, styrene, limonene, methyl-2-phenylacetate and 3 unknown compounds were selected as potential markers for the MT group. Cuparene, (Z)-thujopsene, methyl octanoate and 1 unknown compound were identified as potential markers for the HT groups. Finally, the selected markers were used to construct a supported vector machine classification (SVM-C) model to check classification ability. The models showed good performance with the accuracy of cross-validation and test prediction of 87.93% and 92.00%, respectively.
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21
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Jia YW, Sun SY, Yang L, Wang D. Salient space detection algorithm for signal extraction from contaminated and distorted spectrum. Analyst 2018; 143:2656-2664. [DOI: 10.1039/c7an01941f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The proposed algorithm minimizes the influence of baseline distortion and exhibits good anti-noise ability and high real-time performance.
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Affiliation(s)
- Y. W. Jia
- School of Mechanical Engineering
- Tianjin University of Technology
- China
- Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System
- China
| | - S. Y. Sun
- School of Mechanical Engineering
- Tianjin University of Technology
- China
| | - L. Yang
- School of Mechanical Engineering
- Tianjin University of Technology
- China
| | - D. Wang
- School of Computer Science and Engineering
- Tianjin University of Technology
- China
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22
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Fu HY, Guo XM, Zhang YM, Song JJ, Zheng QX, Liu PP, Lu P, Chen QS, Yu YJ, She Y. AntDAS: Automatic Data Analysis Strategy for UPLC–QTOF-Based Nontargeted Metabolic Profiling Analysis. Anal Chem 2017; 89:11083-11090. [DOI: 10.1021/acs.analchem.7b03160] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Hai-Yan Fu
- School
of Pharmaceutical Sciences, South Central University for Nationalities, Wuhan 430074, China
| | - Xiao-Ming Guo
- School
of Pharmaceutical Sciences, South Central University for Nationalities, Wuhan 430074, China
| | | | - Jing-Jing Song
- Ningxia Institute of Cultural Relics and Archeology, Yinchuan 750001, China
| | - Qing-Xia Zheng
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Ping-Ping Liu
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Peng Lu
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Qian-Si Chen
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | | | - Yuanbin She
- ZhengJiang University of Technology, Hangzhou 310014, China
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23
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Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data. J Chromatogr A 2017; 1523:265-274. [PMID: 28927937 DOI: 10.1016/j.chroma.2017.09.023] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 09/07/2017] [Accepted: 09/08/2017] [Indexed: 12/18/2022]
Abstract
In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography - mass spectrometry (LC-MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies.
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24
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Fu HY, Hu O, Zhang YM, Zhang L, Song JJ, Lu P, Zheng QX, Liu PP, Chen QS, Wang B, Wang XY, Han L, Yu YJ. Mass-spectra-based peak alignment for automatic nontargeted metabolic profiling analysis for biomarker screening in plant samples. J Chromatogr A 2017; 1513:201-209. [DOI: 10.1016/j.chroma.2017.07.044] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Revised: 07/10/2017] [Accepted: 07/11/2017] [Indexed: 11/25/2022]
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25
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Zheng QX, Fu HY, Li HD, Wang B, Peng CH, Wang S, Cai JL, Liu SF, Zhang XB, Yu YJ. Automatic time-shift alignment method for chromatographic data analysis. Sci Rep 2017; 7:256. [PMID: 28325916 PMCID: PMC5428255 DOI: 10.1038/s41598-017-00390-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 02/22/2017] [Indexed: 01/26/2023] Open
Abstract
Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.
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Affiliation(s)
- Qing-Xia Zheng
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, 430074, China.
| | - He-Dong Li
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, 430074, China
| | - Bing Wang
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Cui-Hua Peng
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Sheng Wang
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Jun-Lan Cai
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Shao-Feng Liu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Xiao-Bing Zhang
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Yong-Jie Yu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China. .,School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, 430074, China. .,Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, 750004, China. .,College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China.
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26
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Cheng Z, Zhang X, Liu X, Wang S, Ma L. Classification of Different Dried Vine Fruit Varieties in China by HS-SPME-GC-MS Combined with Chemometrics. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-0848-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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