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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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Bosten E, Kensert A, Desmet G, Cabooter D. Automated method development in high-pressure liquid chromatography. J Chromatogr A 2024; 1714:464577. [PMID: 38104507 DOI: 10.1016/j.chroma.2023.464577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
Method development in liquid chromatography is a crucial step in the optimization of analytical separations for various applications. However, it is often a challenging endeavour due to its time-consuming, resource intensive and costly nature, which is further hampered by its complexity requiring highly skilled and experienced scientists. This review presents an examination of the methods that are required for a completely automated method development procedure in liquid chromatography, aimed at taking the human out of the decision loop. Some of the presented approaches have recently witnessed an important increase in interest as they offer the promise to facilitate, streamline and speed up the method development process. The review first discusses the mathematical description of the separation problem by means of multi-criteria optimization functions. Two different strategies to resolve this optimization are then presented; an experimental and a model-based approach. Additionally, methods for automated peak detection and peak tracking are reviewed, which, upon integration in an instrument, allow for a completely closed-loop method development process. For each of these approaches, various currently applied methods are presented, recent trends and approaches discussed, short-comings pointed out, and future prospects highlighted.
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Affiliation(s)
- Emery Bosten
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium; Department of Pharmaceutical Development and Manufacturing Sciences, Janssen Pharmaceutica, Turnhoutseweg 30, Beerse, Belgium
| | - Alexander Kensert
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium
| | - Gert Desmet
- Department of Chemical Engineering, Free University of Brussels (VUB), Pleinlaan 2, Brussels 1050, Belgium
| | - Deirdre Cabooter
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium.
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4
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Bosten E, Van Broeck P, Cabooter D. Automated tuning of denoising algorithms for noise removal in chromatograms. J Chromatogr A 2023; 1709:464360. [PMID: 37725870 DOI: 10.1016/j.chroma.2023.464360] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/02/2023] [Accepted: 09/02/2023] [Indexed: 09/21/2023]
Abstract
Different algorithms, such as the Savitzky-Golay filter and Whittaker smoother, have been proposed to improve the quality of experimental chromatograms. These approaches avoid excessive noise from hampering data analysis and as such allow an accurate detection and quantification of analytes. These algorithms require fine-tuning of their hyperparameters to regulate their roughness and flexibility. Traditionally, this fine-tuning is done manually until a signal is obtained that removes the noise while conserving valuable peak information. More objective and automated approaches are available, but these are usually method specific and/or require previous knowledge. In this work, the L-and V-curve, k-fold cross-validation, autocorrelation function and residual variance estimation approach are introduced as alternative automated and generally applicable parameter tuning methods. These methods do not require any previous information and are compatible with a multitude of denoising methods. Additionally, for k-fold cross-validation, autocorrelation function and residual variance estimation, a novel implementation based on median estimators is proposed to handle the specific shape of chromatograms, typically composed of alternating flat baselines and sharp peaks. These tuning methods are investigated in combination with four denoising methods; the Savitsky-Golay filter, Whittaker smoother, sparsity assisted signal smoother and baseline estimation and denoising using sparsity approach. It is demonstrated that the median estimators approach significantly improves the denoising and information conservation performance of relevant smoother-tuner combinations up to a factor 4 for simulated datasets and even up to a factor 10 for an experimental chromatogram. Moreover, the parameter tuning methods relying on residual variance estimation, k-fold cross-validation and autocorrelation function lead to similar small root-mean squared errors on the different simulated datasets and experimental chromatograms. The sparsity assisted signal smoother and baseline estimation and denoising using sparsity approach, which both rely on the use of sparsity, systematically outperform the two other methods and are hence most appropriate for chromatograms.
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Affiliation(s)
- Emery Bosten
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium; Janssen Pharmaceutica, Department of Pharmaceutical Development and Manufacturing Sciences, Turnhoutseweg 30, Beerse, Belgium
| | - Peter Van Broeck
- Janssen Pharmaceutica, Department of Pharmaceutical Development and Manufacturing Sciences, Turnhoutseweg 30, Beerse, Belgium
| | - Deirdre Cabooter
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium.
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5
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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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6
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Ma XL, Wang XC, Zhang JN, Liu JN, Ma MH, Ma FL, Lv Y, Yu YJ, She Y. A study of flavor variations during the flaxseed roasting procedure by developed real-time SPME GC-MS coupled with chemometrics. Food Chem 2023; 410:135453. [PMID: 36682286 DOI: 10.1016/j.foodchem.2023.135453] [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: 10/14/2022] [Revised: 01/07/2023] [Accepted: 01/08/2023] [Indexed: 01/11/2023]
Abstract
Volatile compound variations during the roasting procedure play an essential role in the flaxseed-related product. In this work, we proposed a new strategy to high-throughput characterize the dynamic variations of flavors in flaxseed. Volatile compounds released at various roasting times were comprehensively investigated by a newly developed real-time solid-phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). Raw data files were analyzed by our advanced GC-MS data analysis software AntDAS-GCMS. Chemometric methods such as principal component analysis and partial least squares-discrimination analysis have realized the differences of samples with various roasting times. Finally, a total of 51 compounds from 11 aromas were accurately identified and confirmed with standards, and their variations as a function of roasting time were studied. In conclusion, we provided a new solution for the online monitoring of volatile compounds during the industrial roasting process.
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Affiliation(s)
- Xing-Ling Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Xing-Cai Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Jia-Ni Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, 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, Yinchuan 750004, China
| | - Meng-Han Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Feng-Lian Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Yi Lv
- Key Laboratory of Quality and Safety of Wolfberry and Win 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, Yinchuan 750004, China.
| | - Yuanbin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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7
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Kensert A, Collaerts G, Efthymiadis K, Van Broeck P, Desmet G, Cabooter D. Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms. J Chromatogr A 2021; 1646:462093. [PMID: 33853038 DOI: 10.1016/j.chroma.2021.462093] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/15/2021] [Accepted: 03/19/2021] [Indexed: 12/25/2022]
Abstract
Enhancement of chromatograms, such as the reduction of baseline noise and baseline drift, is often essential to accurately detect and quantify analytes in a mixture. Current methods have been well studied and adopted for decades and have assisted researchers in obtaining reliable results. However, these methods rely on relatively simple statistics of the data (chromatograms) which in some cases result in significant information loss and inaccuracies. In this study, a deep one-dimensional convolutional autoencoder was developed that simultaneously removes baseline noise and baseline drift with minimal information loss, for a large number and great variety of chromatograms. To enable the autoencoder to denoise a chromatogram to be almost, or completely, noise-free, it was trained on data obtained from an implemented chromatogram simulator that generated 190.000 representative simulated chromatograms. The trained autoencoder was then tested and compared to some of the most widely used and well-established denoising methods on testing datasets of tens of thousands of simulated chromatograms; and then further tested and verified on real chromatograms. The results show that the developed autoencoder can successfully remove baseline noise and baseline drift simultaneously with minimal information loss; outperforming methods like Savitzky-Golay smoothing, Gaussian smoothing and wavelet smoothing for baseline noise reduction (root mean squared error of 1.094 mAU compared to 2.074 mAU, 2.394 mAU and 2.199 mAU) and Savitkzy-Golay smoothing combined with asymmetric least-squares or polynomial fitting for baseline noise and baseline drift reduction (root mean absolute error of 1.171 mAU compared to 3.397 mAU and 4.923 mAU). Evidence is presented that autoencoders can be utilized to enhance and correct chromatograms and consequently improve and alleviate downstream data analysis, with the drawback of needing a carefully implemented simulator, that generates realistic chromatograms, to train the autoencoder.
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Affiliation(s)
- Alexander Kensert
- University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, 3000 Leuven, Belgium; Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium
| | - Gilles Collaerts
- University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, 3000 Leuven, Belgium
| | - Kyriakos Efthymiadis
- University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, 3000 Leuven, Belgium; Vrije Universiteit Brussel, Department of Computer Science, Artificial Intelligence Laboratory, Pleinlaan 9, 1050 Brussel, Belgium
| | - Peter Van Broeck
- Janssen Pharmaceutica, Department of Pharmaceutical Development and Manufacturing Sciences, Turnhoutseweg 30, Beerse, Belgium
| | - Gert Desmet
- Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium
| | - Deirdre Cabooter
- University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, 3000 Leuven, Belgium.
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8
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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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9
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Chen H, Shi Q, Fu H, Hu O, Fan Y, Xu L, Zhang L, Lan W, Sun D, Yang T, She Y. Rapid detection of five pesticide residues using complexes of gold nanoparticle and porphyrin combined with ultraviolet visible spectrum. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:4464-4473. [PMID: 32399965 DOI: 10.1002/jsfa.10487] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/30/2020] [Accepted: 05/13/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUD Pesticides are widely used to control insect infestation and weeds in agriculture. However, concerns about the pesticide residues in agricultural products have been raised in recent years because of public interest in health and food quality and safety. Thus, rapid, convenient, and accurate analytical methods for the detection and quantification of pesticides are urgently required. RESULTS A nanohybrid system composed of gold nanoparticles (AuNPs) and tetrakis(N-methyl-4-pyridiniumyl) porphyrin (TMPyP) was used as an optical probe for the detection and quantification of five pesticides (Paraquat, Dipterex, Dursban, methyl thiophanate and Cartap). The method is based on the aggregation effect of pesticides on the carboxyl group modified by AuNPs. Subsequently, with the help of particle swarm optimization-optimized sample weighted least squares-support vector machine (PSO-OSWLS-SVM), all the pesticides could be successfully quantified. In addition, partial least squares discriminant analysis (PLS-DA) was applied and the five pesticides were satisfactorily recognized based on data array obtained from the ultraviolet visible (UV-visible) spectra of AuNP-TMPyP complex. Furthermore, the quantitative and qualitative analysis of the five pesticides could be also achieved in the complex real samples, in which all the relative standard deviations (RSDs) were less than 0.3‰ and all the linear absolute correlation coefficients were more than 0.9990. Furthermore, recognition rate of the training set and the prediction set based on multiplicative scatter correction (MSC), or second-order derivative (2nd derivative) UV-visible spectra in PLS-DA model could reach 100%. CONCLUSION This method was successfully applied for the rapid and accurate determination of multicomponent pesticide residues in real food samples. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Qiong Shi
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Ou Hu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Yao Fan
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, P. R. China
| | - Lu Xu
- College of Material and Chemical Engineering, Tongren University, Tongren, P. R. China
| | - Lei Zhang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, P. R. China
| | - Wei Lan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Donglei Sun
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Tianming Yang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan, P. R. China
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, P. R. China
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10
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Creydt M, Fischer M. Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity. Molecules 2020; 25:E3972. [PMID: 32878155 PMCID: PMC7504784 DOI: 10.3390/molecules25173972] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 08/27/2020] [Accepted: 08/28/2020] [Indexed: 12/11/2022] Open
Abstract
Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.
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Affiliation(s)
- Marina Creydt
- Hamburg School of Food Science-Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany;
- Center for Hybrid Nanostructures (CHyN), Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Markus Fischer
- Hamburg School of Food Science-Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany;
- Center for Hybrid Nanostructures (CHyN), Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
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11
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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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12
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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: 32] [Impact Index Per Article: 8.0] [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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13
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Zhang YY, Zhang Q, Zhang YM, Wang WW, Zhang L, Yu YJ, Bai CC, Guo JZ, Fu HY, She Y. A comprehensive automatic data analysis strategy for gas chromatography-mass spectrometry based untargeted metabolomics. J Chromatogr A 2020; 1616:460787. [DOI: 10.1016/j.chroma.2019.460787] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/09/2019] [Accepted: 12/11/2019] [Indexed: 02/01/2023]
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14
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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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15
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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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16
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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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17
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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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18
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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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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: 11] [Impact Index Per Article: 1.8] [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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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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22
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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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23
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Myint L, Kleensang A, Zhao L, Hartung T, Hansen KD. Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics. Anal Chem 2017; 89:3517-3523. [PMID: 28221771 PMCID: PMC5362739 DOI: 10.1021/acs.analchem.6b04719] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 02/21/2017] [Indexed: 12/20/2022]
Abstract
As mass spectrometry-based metabolomics becomes more widely used in biomedical research, it is important to revisit existing data analysis paradigms. Existing data preprocessing efforts have largely focused on methods which start by extracting features separately from each sample, followed by a subsequent attempt to group features across samples to facilitate comparisons. We show that this preprocessing approach leads to unnecessary variability in peak quantifications that adversely impacts downstream analysis. We present a new method, bakedpi, for the preprocessing of both centroid and profile mode metabolomics data that relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to detect peaks. This new method reduces this unnecessary quantification variability and increases power in downstream differential analysis.
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Affiliation(s)
- Leslie Myint
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Andre Kleensang
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental
Health and Engineering, Johns Hopkins Bloomberg
School of Public Health, Baltimore, Maryland 21205, United States
| | - Liang Zhao
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental
Health and Engineering, Johns Hopkins Bloomberg
School of Public Health, Baltimore, Maryland 21205, United States
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental
Health and Engineering, Johns Hopkins Bloomberg
School of Public Health, Baltimore, Maryland 21205, United States
- University of Konstanz, CAAT-Europe, 78457 Konstanz, Germany
| | - Kasper D. Hansen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
- McKusick-Nathans
Institute of Genetic Medicine, Johns Hopkins
University School of Medicine, Baltimore, Maryland 21205, United States
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24
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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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25
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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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