Summary of Study ST001374

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000940. The data can be accessed directly via it's Project DOI: 10.21228/M86396 This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST001374
Study TitleUntargeted Metabolomics for fruit juice authentication
Study SummaryUse of Information Dependent Acquisition mass spectra and Sequential Window Acquisition of all Theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication. LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry. Objectives To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study. Methods IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets. Results IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%. Conclusion SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.
Institute
Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences
Last NameXu
First NameLei
AddressNo.12 Zhongguancun South St.,Haidian District Beijing P.R.China
Emailxulei@cau.edu.cn
Phone+8618811583506
Submit Date2020-05-08
Raw Data AvailableYes
Raw Data File Type(s)wiff
Analysis Type DetailLC-MS
Release Date2020-05-22
Release Version1
Lei Xu Lei Xu
https://dx.doi.org/10.21228/M86396
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR000940
Project DOI:doi: 10.21228/M86396
Project Title:Untargeted Metabolomics for fruit juice authentication
Project Summary:Introduction LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry. Objectives To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study. Methods IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets. Results IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%. Conclusion SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.
Institute:Institute of Quality Standard & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences
Last Name:Xu
First Name:Lei
Address:No.12 Zhongguancun South St.,Haidian District Beijing P.R.China
Email:xulei@cau.edu.cn
Phone:+8618811583506

Subject:

Subject ID:SU001448
Subject Type:Other
Subject Species:Malus domestica;Citrus sinensis
Taxonomy ID:3750;2711
Species Group:Plants

Factors:

Subject type: Other; Subject species: Malus domestica;Citrus sinensis (Factor headings shown in green)

mb_sample_id local_sample_id Juice type Acquisition method
SA09988420181222-OJ-IDA-140% IDA
SA09988520181222-OJ-IDA-130% IDA
SA09988620181222-OJ-IDA-120% IDA
SA09988720181222-OJ-IDA-150% IDA
SA09988820181222-OJ-IDA-160% IDA
SA09988920181222-OJ-IDA-180% IDA
SA09989020181222-OJ-IDA-170% IDA
SA09989120181222-OJ-IDA-110% IDA
SA09989220181222-OJ-IDA-90% IDA
SA09989320181222-OJ-IDA-50% IDA
SA09989420181222-OJ-IDA-40% IDA
SA09989520181222-OJ-IDA-20% IDA
SA09989620181222-OJ-IDA-60% IDA
SA09989720181222-OJ-IDA-70% IDA
SA09989820181222-OJ-IDA-190% IDA
SA09989920181222-OJ-IDA-80% IDA
SA09990020181222-OJ-IDA-100% IDA
SA09990120181222-OJ-IDA-200% IDA
SA09990220191201-A+O Commercial samples-O1-50% IDA
SA09990320191201-A+O Commercial samples-O2-10% IDA
SA09990420191201-A+O Commercial samples-O2-20% IDA
SA09990520191201-A+O Commercial samples-O1-40% IDA
SA09990620191201-A+O Commercial samples-O1-30% IDA
SA09990720191201-A+O Commercial samples-O1-10% IDA
SA09990820191201-A+O Commercial samples-O1-20% IDA
SA09990920191201-A+O Commercial samples-O2-30% IDA
SA09991020191201-A+O Commercial samples-O2-40% IDA
SA09991120191201-A+O Commercial samples-O3-40% IDA
SA09991220191201-A+O Commercial samples-O3-50% IDA
SA09991320191201-A+O Commercial samples-O3-30% IDA
SA09991420191201-A+O Commercial samples-O3-20% IDA
SA09991520191201-A+O Commercial samples-O2-50% IDA
SA09991620191201-A+O Commercial samples-O3-10% IDA
SA09991720181222-OJ-IDA-10% IDA
SA09991820181222-OJ-IDA-30% IDA
SA09991920191227-A+O-Commercial-O1-40% IDA
SA09992020191227-A+O-Commercial-O1-50% IDA
SA09992120191227-A+O-Commercial-O1-30% IDA
SA09992220191227-A+O-Commercial-O1-20% IDA
SA09992320191227-A+O-Commercial-O2-30% IDA
SA09992420191227-A+O-Commercial-O1-60% IDA
SA09992520191227-A+O-Commercial-O1-10% IDA
SA09992620191227-A+O-Commercial-O2-20% IDA
SA09992720191227-A+O-Commercial-O2-10% IDA
SA09992820191227-A+O-Commercial-O1-80% IDA
SA09992920191227-A+O-Commercial-O1-70% IDA
SA09993020191227-A+O-Commercial-O2-50% IDA
SA09993120191227-A+O-Commercial-O2-40% IDA
SA09993220191227-A+O-Commercial-O3-70% IDA
SA09993320191227-A+O-Commercial-O2-60% IDA
SA09993420191227-A+O-Commercial-O3-80% IDA
SA09993520191227-A+O-Commercial-O3-90% IDA
SA09993620191227-A+O-Commercial-O3-100% IDA
SA09993720191227-A+O-Commercial-O3-50% IDA
SA09993820191227-A+O-Commercial-O3-60% IDA
SA09993920191227-A+O-Commercial-O3-40% IDA
SA09994020191227-A+O-Commercial-O2-80% IDA
SA09994120191227-A+O-Commercial-O2-70% IDA
SA09994220191227-A+O-Commercial-O3-20% IDA
SA09994320191227-A+O-Commercial-O3-30% IDA
SA09994420181222-OJ-SWATH-40% SWATH
SA09994520181222-OJ-SWATH-50% SWATH
SA09994620181222-OJ-SWATH-20% SWATH
SA09994720181222-OJ-SWATH-60% SWATH
SA09994820181222-OJ-SWATH-30% SWATH
SA09994920181222-OJ-SWATH-150% SWATH
SA09995020181222-OJ-SWATH-170% SWATH
SA09995120181222-OJ-SWATH-160% SWATH
SA09995220181222-OJ-SWATH-180% SWATH
SA09995320181222-OJ-SWATH-190% SWATH
SA09995420181222-OJ-SWATH-200% SWATH
SA09995520181222-OJ-SWATH-140% SWATH
SA09995620181222-OJ-SWATH-130% SWATH
SA09995720181222-OJ-SWATH-90% SWATH
SA09995820181222-OJ-SWATH-80% SWATH
SA09995920181222-OJ-SWATH-100% SWATH
SA09996020181222-OJ-SWATH-110% SWATH
SA09996120181222-OJ-SWATH-120% SWATH
SA09996220181222-OJ-SWATH-70% SWATH
SA09996320181222-OJ-SWATH-10% SWATH
SA10006020191201-A+O Commercial samples-A1-1100% IDA
SA10006120191201-A+O Commercial samples-A3-3100% IDA
SA10006220191227-A+O-Commercial-A1-11100% IDA
SA10006320191201-A+O Commercial samples-A1-2100% IDA
SA10006420191227-A+O-Commercial-A1-14100% IDA
SA10006520191227-A+O-Commercial-A1-15100% IDA
SA10006620191227-A+O-Commercial-A1-13100% IDA
SA10006720191227-A+O-Commercial-A1-12100% IDA
SA10006820191201-A+O Commercial samples-A3-2100% IDA
SA10006920191227-A+O-Commercial-A1-3100% IDA
SA10007020191227-A+O-Commercial-A1-2100% IDA
SA10007120191201-A+O Commercial samples-A3-5100% IDA
SA10007220191201-A+O Commercial samples-A2-1100% IDA
SA10007320191227-A+O-Commercial-A1-1100% IDA
SA10007420191201-A+O Commercial samples-A2-5100% IDA
SA10007520191201-A+O Commercial samples-A2-2100% IDA
SA10007620191201-A+O Commercial samples-A2-3100% IDA
SA10007720191201-A+O Commercial samples-A2-4100% IDA
SA10007820191227-A+O-Commercial-A1-4100% IDA
SA10007920191227-A+O-Commercial-A1-5100% IDA
Showing page 1 of 6     Results:    1  2  3  4  5  Next  Last     Showing results 1 to 100 of 597

Collection:

Collection ID:CO001443
Collection Summary:Introduction LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry. Objectives To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study. Methods IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets. Results IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%. Conclusion SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.
Collection Protocol Filename:xulei_cau_20200508_204508_PR_CO_Protocol.docx
Sample Type:Fruit juice

Treatment:

Treatment ID:TR001463
Treatment Summary:Fruit juices (50 ml) were centrifuged (15 min, 18000g, 4 °C) and filtered through a 0.45 μm and a 0.22 μm PES membrane filter in series before injection. A pooled sample (comprising an aliquot of apple or orange juice samples in the study, usually called quality control, QC, sample) was used for the fruit juice metabolomics study. Mixtures of orange juice adulterated with apple juice (n=6) were prepared at 1%, 2%, 5%, 10%, 15% and 20%. Authentic apple and orange juice samples combining with these mixtures were used for the fruit juice authenticity determination study. And the pooled quality control samples from the authentic apple and orange juices were injected 5 times of interval to ensure the stability and repeatability of the system. For the commercial samples , six brands of commercial apple (n=60) and three brands of commercial orange (n=39) juices were purchased from local markets, and the adulterated samples of orange juice by every one of the apple juices with different proportions (1%, 2%, 5%, 10%, 15% and 20%, n=216) were used for the test of potential markers and model construction/validation.

Sample Preparation:

Sampleprep ID:SP001456
Sampleprep Summary:Fruit juices (50 ml) were centrifuged (15 min, 18000g, 4 °C) and filtered through a 0.45 μm and a 0.22 μm PES membrane filter in series before injection. A pooled sample (comprising an aliquot of apple or orange juice samples in the study, usually called quality control, QC, sample) was used for the fruit juice metabolomics study. Mixtures of orange juice adulterated with apple juice (n=6) were prepared at 1%, 2%, 5%, 10%, 15% and 20%. Authentic apple and orange juice samples combining with these mixtures were used for the fruit juice authenticity determination study. And the pooled quality control samples from the authentic apple and orange juices were injected 5 times of interval to ensure the stability and repeatability of the system. For the commercial samples , six brands of commercial apple (n=60) and three brands of commercial orange (n=39) juices were purchased from local markets, and the adulterated samples of orange juice by every one of the apple juices with different proportions (1%, 2%, 5%, 10%, 15% and 20%, n=216) were used for the test of potential markers and model construction/validation.

Combined analysis:

Analysis ID AN002294
Analysis type MS
Chromatography type Reversed phase
Chromatography system SCIEX ExionLC
Column Agilent Eclipse Plus C18,RRHD (100 x 2.1mm,1.8um)
MS Type ESI
MS instrument type Triple TOF
MS instrument name ABI Sciex 6600 TripleTOF
Ion Mode POSITIVE
Units Peak area

Chromatography:

Chromatography ID:CH001685
Chromatography Summary:All samples were subjected to reversed-phase chromatography using a UHPLC system, including ExionLC solvent valve, ExionLC AD pump, ExionLC autosampler, ExionLC controller, ExionLC AC column oven (SCIEX, Redwood City, CA, USA). Mobile phases A was 0.2% formic acid in water and B was acetonitrile, respectively. Chromatographic separation was performed at a flow rate of 300 μL/min using a UHPLC column (Eclipse Plus C18, RRHD 1.8 μm, 2.1*100 mm, Agilent, USA) maintained at 40 °C. Solvent gradient was as follow: 0−10 min, 5%−35% B; 10−12 min, 35−100% B; 12−15 min, 100% B; 15−15.01 min, 100%−5% B; 15.01−18 min, 5% B. Injection volume was 2 μL for all samples.
Instrument Name:SCIEX ExionLC
Column Name:Agilent Eclipse Plus C18,RRHD (100 x 2.1mm,1.8um)
Flow Gradient:0-10 min, 5%-35% B; 10-12 min, 35-100% B; 12-15 min, 100% B; 15-15.01 min, 100%-5% B; 15.01-18 min,
Flow Rate:300ul/min
Solvent A:100% water; 0.2% formic acid
Solvent B:100% acetonitrile
Chromatography Type:Reversed phase

MS:

MS ID:MS002138
Analysis ID:AN002294
Instrument Name:ABI Sciex 6600 TripleTOF
Instrument Type:Triple TOF
MS Type:ESI
MS Comments:Mass spectrometric analysis was performed with a Q-TOF MS (TripleTOF 6600, SCIEX, Redwood City, CA, USA) operating in the positive ion mode using a DuoSpray ion source. TOF MS scan and product ion scan for each sample were acquired by two different methods (including the QC samples): IDA-MS and SWATH-MS. The instrument was operated in the high sensitivity mode for product ion scan, and automatically calibrated every 6 sample injections using APCI positive calibration solution delivered via a calibration delivery system (SCIEX, Redwood City, CA, USA). The other experiment parameters for TOF MS scan were set as follows: curtain gas, 25 (arbitrary units); ion source gas 1, 50 (arbitrary units); ion source gas 2, 50 (arbitrary units); temperature, 500 °C; ion spray voltage floating, 5.5 kV; declustering potential, 60 V; collision energy, 10 eV. The IDA (cycle time 545 ms) was composed of a TOF MS scan (accumulation time, 50 ms; CE, 10 eV) and 15 dependent product ion scans (accumulation time, 30 ms each; CE, 35 eV) in the high-sensitivity mode with dynamic background subtraction. The SWATH (cycle time 545 ms) was composed of a TOF MS scan (accumulation time, 50 ms; CE, 10 eV) and a series of product ion scans (accumulation time, 30 ms each; CE, 35 eV) of 15 Q1 windows of 60 Da from m/z 100−1000 in the high-sensitivity mode. Mass ranges of TOF MS was m/z 100-1000 and product ion scans was m/z 50−1000 for both these two methods. QC samples were acquired throughout the run to monitor the instrument stability. The LC−MS data was acquired using Analyst TF 1.7.1 (SCIEX, Redwood City, CA, USA). For the fruit juice metabolomics study evaluation, UHPLC-Q-TOF data of apple juices and orange juices were processed by MarkerView 1.3.1 (SCIEX, Redwood City, CA, USA). The parameters were set as follow: RT tolerance 0.5 min, mass tolerance 10 ppm, minimum RT peak width 6 scans, noise threshold 100. And the list of MS1 features was exported for further analysis using Microsoft Office Excel 2016 (Microsoft Corporation, Redmond, Washington, USA). The extracted MS1 feature with detection rate greater 80% among the five replicate injections of QC sample were defined as detected ones. To compare the MS2 quantity and quality acquired by IDA-MS and SWATH-MS in the fruit juice metabolomics study, the data files were processed by PeakView 2.2 for in-house MS/MS phenol library matching, and the freely available MS-DIAL software Version 3.40 (http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/) for MassBank of North America (MONA, http://mona.fiehnlab.ucdavis.edu/) library (after filtering based on instrument type) putative annotation. And the annotation level is level 2 (Putatively annotated compounds) according to Metabolomics Standards Initiative standards. MS-DIAL is designed as a universal program for MS data processing that supports any mass spectrometry approach. It is vendor independent and supports data conversion from file formats of many instrument manufacturers. It also supports any data acquisition method, from nominal or accurate mass analysis to data-dependent or data-independent MS/MS. For the fruit juice authenticity study comparison, the UHPLC-Q-TOF data of orange juices, apple juices and orange juices adulterated with different proportions (1%, 2%, 5%, 10%, 15% and 20%) of apple juice acquired by IDA-MS and SWATH-MS were processed by MarkerView Version 1.3.1, and the parameters were set as in the fruit juice metabolomics. The exported MS1 peak list was analyzed by Microsoft Office Excel 2016 to filter the MS1 features whose detection rate were lower than 80% or relative standard deviations of the quality control group were higher than 30%. Then the table was uploaded to Metaboanalyst (https://www.metaboanalyst.ca/), and the missing values were replaced by a small value, none filtering for features was used. After log transformation and auto scaling, principal component analysis (PCA) and hierarchical clustering analysis (HCA), student’s t-test, as well as fold change analysis were conducted. And SIMCA 14.1 (Umetrics, Sweden) was used for the orthogonal projection to latent structures discriminant analysis (OPLS-DA) mode construction. The MS2 spectra of potential markers were exported from MarkerView 1.3.1 to MS-FINDER software (version 3.16) for the potential marker annotation by matching with the in-built databases, such as Massbank, GNPS, HMDB and FooDB. And the MoNA database from Massbank of North American (http://mona.fiehnlab.ucdavis.edu) was downloaded and searched by the MS-FINDER simultaneously. The METLIN database was searched using MassHunter PCDL Version B.07.00 (Agilent, USA). The parameters, MS1 tolerance and MS2 tolerance, were set as 10 ppm and 15 ppm. The standard confirmation of potential markers was conducted by comparing the retention time as well as the MS2 fragments. For the potential marker test and model construction/validation in commercial samples, the peak area table of the potential markers was exported from MultiQuant 3.0.3 (SCIEX, Redwood City, CA, USA), and imported to the Excel tool, DD-SIMCA, for model construction and validation. MarkerView; Excel; Metaboanalyst;SIMCA;MS-FINDER
Ion Mode:POSITIVE
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