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Omics Technologies Applied to Agriculture and Food

Metabolomic approach for the authentication of berry fruit juice by liquid chromatography quadrupole timeof-#ight mass spectrometry coupled to chemometrics jiukai zhang, Qiuhao Yu, Haiyan Cheng, Yiqiang Ge, Han Liu, Xingqian Ye, and Ying Chen J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b01682 • Publication Date (Web): 10 Jul 2018 Downloaded from http://pubs.acs.org on July 11, 2018

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Journal of Agricultural and Food Chemistry

Title: Metabolomic approach for the authentication of berry fruit juice by liquid chromatography

quadrupole

time-of-flight

mass

spectrometry

coupled

to

chemometrics

Authors: Jiukai Zhanga, QiuhaoYua,b, Haiyan Chengc, Yiqiang Ged, Han Liua, XingqianYeb, Ying Chen*a

Affiliations: a

Agro-Product Safety Research Center, Chinese Academy of Inspection and

Quarantine, Beijing 100176, People’s Republic of China b

College of Biosystems Engineering and Food Science, Fuli Institute of Food Science,

Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou 310058, People’s Republic of China c

AB Sciex (China) Co., Ltd., Beijing 100102, People’s Republic of China

d

China Rural Technology Development Center, Beijing 100045, People’s Republic of

China

*Corresponding author: Ying Chen E-mail: [email protected] Tel: +86 10 53897910 Fax: +86 10 53897910 1

ACS Paragon Plus Environment


Abstract Berry fruit juice, which is represented by blueberry and cranberry juice, has become increasingly popular due to its reported nutritional and health benefits. However, in markets, adulteration of berry fruit juice with cheaper substitutes is frequent. In the present study, a metabolomic approach for authentication of berry fruit juices by liquid chromatography coupled with quadrupole time-of-ﬂight mass spectrometry (LC-QTOF-MS) was established. The global characterization of the berry fruit metabolome by information dependent acquisition (IDA) directed LC-MS/MS coupled to a peak mining workflow by isotope pattern matching was reported. Targeted metabolomics analysis of known juice biomarkers, such as flavonoids, anthocyanins, etc. exhibited a good separation of berry fruit juices from adulterant juices. Moreover, untargeted metabolomics analysis was carried out and subjected to chemometrics analysis. Discrimination of blueberry juice, cranberry juice, and its adulterant apple juice, grape juice was obtained by principal component analysis-discriminant analysis (PCA-DA). 18 characteristic markers discriminating berry fruit juice and its adulterants were selected by comparison of marker abundances in different juice samples. Identification of characteristic markers was accomplished by elemental formula prediction and online database searches based on accurate MS information. These results suggested that the combination of untargeted and targeted metabolomics approach has great potential for authentication of berry fruit juice. Key words: Berry fruit juice; Authentication; Metabolomics; Chemometrics; 2


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LC-QTOF-MS 1. Introduction Berry fruit juices have become popular with regards to their various health-promoting properties. These beneficial functions have been largely attributed to the presence of several taste- and health-related compounds such as organic acids, sugars, and phenolics1. In recent years, the number of food products that use blueberries and cranberries has increased. Particularly, the trade of blueberry and cranberry juices has continued to rise and has become increasingly popular. Similar to other high value-added food, berry fruit juices have become a possible object for adulteration and fraud2. The most frequent economically motivated adulteration practices included addition with water, interfusion of pulp wash or additives, and incorporation of cheaper alternative juices (such as apples and grape juices)3,4. These practices are performed either alone or together to make the authenticity detection of berry fruit juicemore complicated. Currently, a number of analytical methods have been developed to discriminate genuine fruit juice and its adulterants, from sensory evaluation (electronic tongue, electronic nose 5), non-destructive testing (infrared spectrometry 6), DNA-based method (polymerase chain reaction 7) to targeted profile detection (organic acids 8, amino acid 9 or phytochemicals 10), as well as physical element analyses (inductively coupled plasma mass spectrometry 11, and isotope ratio mass spectrometry

12

). With

regards to the targeted property of all the aforementioned methods, only a few specific adulteration practices can be successfully detected. Therefore, analytical approaches 3



with a more global overview into fruit juices constituents are required. Metabolomics is defined as comprehensive, simultaneous analysis of numerous metabolites as a whole in biological systems, and has emerged in diverse many research areas. In food science, metabolomics has been extensively used for detection of food authenticity, such as authentication ofdairy products13, alcoholic beverages14, coffee15, traditional Chinese medicine food 16, honey17, olive oil18 and meat19, etc. The advances of metabolomics has been improved by the generation of high resolution analytical techniques, such as mass spectrometry (MS) and nuclear magnetic resonance (NMR), which facilitated the analysis of varieties of metabolites at different concentration levels20,21. Multivariate data analysis, either unsupervised or supervised, is needed for procession and interpretation of complicated metabolomics data. Metabolomics is also underpinned by a number of MS databases that provide both descriptive and spectral information on chemicals components found in metabolomes20. In the present study, a metabolomic approach for authentication of berry fruit juices by liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was explored. The aim of the study was to identify potential robust biomarkers in combination of targeted and untargeted metabolomics approach. Blueberry and cranberry juices were selectedas ‘model juices’ in the present study. The capability to detect the adulteration of apple and grape juice was also investigated. Targeted identification of characteristic metabolites was accomplished by the information dependent acquisition (IDA) directed LC-MS/MS. Complicated 4


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LC-MS data from untargeted metabolomics were analyzed by automated data-mining algorithm and further projected to chemometrics analysis, such as principal component analysis-discriminant analysis (PCA-DA) and orthogonal partial least squares-discriminant

analysis

(OPLS-DA).

Furthermore,

identification

of

characteristic markers for berry fruit juice authentication was accomplished by accurate MS and MS/MS data. 2. Materials and methods 2.1 Samples and sample treatment In total 96 different varieties of blueberry (n=18), cranberry (n=18), apple (n=30) and grapes (n=30) were collected from fruit planting base in Shandong, Liaoning, Guizhou, Jilin, Jiangsu, China and were identified by local expert on germplasm resources. Afterwards, the fresh juices were prepared in the laboratory from those fruits correspondingly. The other 24 concentrated juices including 12 blueberry juices and 12 cranberry juices were bought from Chinese retail markets and the species authenticity of those juices was confirmed by DNA barcoding method (data not shown). Simulated adulterate samples were made by adding lab made apple juice and grape juice to blueberry juice and cranberry juice at 10%, 20%, 30%, 40% and 50% (v/v), respectively. The quality control (QC) samples were also prepared from lab made fresh juice. Fresh pressed blueberry juice, cranberry juice, apple juice and grape juice were randomly selected and mixed together with a centration of 25% (v/v) for each type of juice. In total, 12 QC samples were made. All samples(50mL) were centrifuged at 5



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8000 rpm for 20 min at room temperature and filtered through a 0.2 µm PTFE membrane (Jinteng Corp., Tianjin, China) in order to remove solid components. After 20-fold diluted with deionized water, all samples were stored at -80°C until use. 2.2 Chemicals and reagents Ultrapure water was obtained from a Milli-Q purification system (Millipore, Bedford, MA, USA). Methanol of LC-MS grade, formic acid, 30 pure standards, including anthocyanins, flavonoids, and phenolic acids, namely quercetin, quercetin-3-β-D-glucoside,

quercetin-3-O-arabinoside,

quercetin-3-O-galactoside,

cyanidin-3-O-glucoside,

cyanidin-3-O-arabinoside,

cyanidin-3-O-galactoside,

cyanidin

chloride,

malvidin-3-galactoside,

procyanidin

B2,

kaempferol,

kaempferol-3-O-glucoside, kaempferol-3-O-galactoside, kaempferol-3-O-rutinoside, luteolin,

luteolin-7-O-β-D-glucoside,

myricetin,

myricetin-3-O-β-D-galactopyranoside, trans-cinnamaldehyde, catechin, epicatechin, chlorogenic acid, salicylic acid, coumaric acid, caffeic acid, ferulic acid, sinapic acid, vanillic acid, syringic acid, and 4-hydroxybenzoic acid were supplied by Sigma-Aldrich (ST. Louis, MO, USA). All the purity of above 30 standards was ≥ 90%. 2.3 High-resolution LC-MS/MS analysis Chromatographic separation was performed using on a Shimadzu Prominence UFLC XR system equipped with a C18 column (2.1×100 mm, 2.6 µm; Phenomenex, LA, USA). The injection volume was 2.0 µl. The mobile phase consisted of 0.1% formic acid/water (A) and 0.1% formic acid/methanol (B). The gradient elution was 6


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performed as following protocol: 0–2 min, 2% B; 2–14 min, 2%–95% B; 14–17 min, 95% B; 17–17.1 min, 2% B; 17.1–20 min, 2% B. The flow rate was set at 0.3 mL min-1.The column temperature was 40°C. Juice samples were analyzed in a mode of full scan using a hybrid QTOF mass spectrometer (TripleTOF 5600; AB SCIEX, Singapore) equipped with a DuoSpray ion source in positive and negative ionization modes. The mass range was set from m/z 50 to 1000. Column effluent was directed to the ESI. Parameters in positive ESI mode were set as follows: source voltage: +5500V, declustering potential (DP): 60V, source temperature: 550°C. The information-dependent acquisition (IDA) function of Analyst 1.6 software (AB SCIEX) was used for collecting TOF-MS and TOF-MS/MS data. The IDA settings are as follows: charge monitoring to exclude multiply charged ions and isotopes; dynamic background subtraction (DBS); curtain gas flow, 25 psi; nebulizer (GAS1), 50 psi; and heater gas (GAS2), 50 psi. A calibrant delivery system (CDS) coupled to the DuoSpray source was used to maintain the mass accuracy. 2.4 Data processing and chemometric analysis MarkerView software 1.2.1 (AB SCIEX) was used for processing (data mining, alignment, normalization, and PCA-DA) of the LC-QTOF-MS records. An automated algorithm was employed to perform data mining in the retention time (RT) from 1 to 18 min. The parameters for peak detection were described in our previous study16. Pareto scaling was conducted to preprocess the metabolomic data before PCA.The ion response in different sample groups was shown by plotting profiles for selected peaks from loading plot, and characteristic ions present withinonly one group were 7



selectedas potential biomarkers16. The validation of the obtained PCA-DA model was performed by QC samples in order to ensure the performance of the models. Stepwise, OPLS-DA was chosen to predict the unknown adulterated juice by using SIMCA software 14 (Umetrics). The training set consisted of 38 pure fruit juice samples, while the testing set consisted of 20 adulterated samples. The quality of the models was validated by recognition ability (R2) and prediction ability (Q2). R2 value is thepercentage of successfully classified samples in training set. Q2 value represents the percentage of correctly classified samples in the test set using the model developed in the above training step22. 2.5Targeted metabolite identification The first step of the metabolomic analysis was to gather information about a number of known biomarkers in berry fruit juice samples from literatures. Totally, 43 compounds, including anthocyanins, flavonoids, and phenolic acids, which were commonly found in fruit juices, were selected and characterized as part of the targeted analysis (Table 1)23-29. Extracted Ion Chromatogram (XIC) manager add-on in PeakView software (AB Sciex, Concord, Canada) was used for isotope pattern matched peak mining of data files of berry fruit juice samples. Parameters for data mining experiments were as follows: RT window, 1-15 min; minimum intensity counts≥200; S/N ratio≥3; isotope pattern matching≥80%; MS/MS spectra of all tentative identifications were obtained and matched with hits in the Chemspider (http://www.chemspider.com/), Metlin (https://metlin.scripps.edu/), and Massbank (http://www.massbank.jp/index.html) 8


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online databases. Entries without experimental MS/MS spectra were excluded30. Totally, 30 pure standards available in markets, such as catechin, quercetin 3-β-D-glucoside, trans-cinnamaldehyde, cyanidin 3-O-glucoside, kaempferol, etc. were analyzed using the same conditions to verify the identification. 2.6Untargeted biomarker identification The biomarkers identification in untargeted metabolomics was supported by Formula Finder and Fragments Pane add-on for PeakView software. The proposed molecular formula of each marker was calculated by Formula Finder software. The confirmation of potential compound was conducted as follows: the experimental masses must be within 5 ppm of its theoretical masses, and the intensity difference between isotopic peaks and its theoretical distribution must be less than 20%. Atoms for the molecular formula predictionwere set as follows: C (n≤50), H (n≤50), O (n≤20), N (n≤10),and S (n≤5). The experimental MS/MS data were compared to reference data obtained from MassBank, Chemspider, and Metlin online databases. The most likelycandidate metabolites were chosen from the above databases, according to theconsistency with literatures and probability in the samples under study. In additional, the fragmentation information of the selected compound was verified using Fragments Pane by analyzing MS/MS spectra. The molecular structures of compounds were imported into Fragments Pane for theoretical fragmentation prediction and matching with experimental data. Furthermore, 30 available pure fruit juice standards were also analyzed to verify the identification. 9



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2.7 Method validation To evaluate the validity of the developed method, the adulterated fruit juices obtained in the laboratory, which were prepared from fresh-pressed fruit juices purchased from retail markets, were used as the unknown samples. The confirmation of characteristic biomarkers for adulterated juices was conducted by extracting the accurate mass of relevantions from the raw data set. In the next step, supervised statistical analysis methods, such as OPLS-DA model was also constructed for classification and prediction purposes. 3. Results and discussion 3.1. Targeted metabolomics analysis Following IDA directed LC-MS/MS, isotope pattern matched peak mining was performed.Forty-one metabolites representing 3 compound classes, i.e. anthocyanins, flavonoids, and phenolic acids, were identified in berry juice, and its adulterant (Table 2). The different juice samples were separated into four clusters based on the 41-metabolite peak intensities and visualized in a heat map (Figure 1). In general, the anthocyanins and flavonoids were more abundant in berry fruit juice than in apple juice and grape juice. Differing flavonoid glycosylation patterns were visualized in the heat map. Both apple and grape juice lacked delphinidin-3-O-glucoside, petunidin-3-O-arabinoside,

peonidin-3-O-glucoside/-arabinoside,

and

kaempferol-3-O-rutinoside. Significant differences of phenolic acids were also found based on the peak intensity. Earlier studies have shown that flavonoids (e.g., anthocyanins and quercetin glycosides), hydroxycinnamic acids, and flavan-3-ols are 10


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important constituents of many berry fruits31,32. Several members of these compound groups were identified as sensory-active features in those studies33. Therefore, the special phenolic compounds identified in the targeted metabolomics could be employed as potential biomarkers for authentication of berry fruit juice and its substitutions. Multivariate analysis by OPLS-DA score plot also showed that blueberry juice, cranberry juice could be discriminated from apple and grape juice using 41 targeted metabolite intensities identified by the IDA LC-QTOF-MS (Supporting information Figure S1), indicating the distinguishing capacity of the targeted metabolites. Classic data processing in compounds analysis was to obtain experimental MS/MS masses from peak detection. However, the present study exhibited a reverse manner whereby theoretical masses from the in-house library were applied to extract peaks based on isotope pattern matching from experimental data. Although many researches have documented the phytochemicals screening in berry fruit in conjunction with targeted analysis23,29,34, our present study reported for the first comprehensive characterization of the berry fruit metabolome by IDA directed LC-MS/MS coupled to a peak mining workflow, which could be employed as a reference for the other metabolites identification. 3.2Untargeted metabolomics analysis To obtain comprehensive information on the metabolitesof the berry fruit juice and its adulterant juice, untargeted metabolomics analysis was conducted using UPLC-QTOF-MS. The chromatographical elution of different kinds of fruit juice 11



were achieved in 20 min. A UPLC system with gradient elutionmode was employed to ensure the maximized chromatographicperformance. The characteristic of the base peak chromatogram(BPC) of fruit juice samples in the positive ion mode were shown in Figure 2. The peak number, position, and height differed significantly from each other. Chemical compositions differing between 4 groups of juice samples were in the ranges of 5–10 min and 11–16 min. 8000 peaks of positive ionization data matrices were obtained by data mining and aligning procedures, To lower the dimensionality of the data matrices and minimize the signal redundancy, only peaks representing monoisotopic ions (signals with the lowest m/z value with an isotope pattern) were selected (4088 peaks in positive ionisation data) and subjected to chemometric analysis. The UPLC-QTOF-MS data of different juice samples was subjected to PCA-DA statistical model to screen characteristic discrimination markers. Pareto scaling of data was performed to modify the weights of the respective variables. According to the result of the pre-experiment, only positive model data were further used in the next metabolomic analysis, for the reason that more pronounced samples clustering, and increased number of metabolites were obtained in positive ionization mode than in negative ionization mode. Furthermore, in order to investigate the data reliability, QC samples were employed to validate the chromatographic and mass detection system. A PCA-DA scores plot exhibited a tight clustering among the QC samples in positive mode, and the position of the QC sample was near the coordinate origin35. These results clearly confirmed the stability and reproducibility of the analytical method.As 12


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shown in Figure 3, samples were clearly clustered into five groups: blueberry juice (n=30), cranberry juice (n=24), apple juice (n=30), grape juice (n=30), quality control (QC, n=12). These results indicated that fruit juices could be distinguished based on the levels or presence of their metabolites. To screen the potential characteristic markers that contributed most to discriminate the four fruit juice groups, the ion response in different groups was analyzed by plotting intensity profiles for selected peaks in the loading plot. As shown in Figure 4, the intensity of ion A (m/z 291.1, RT 8.2 min) were relatively high in all apple juice samples, but undetectable in other juice samples, indicating that components corresponding to ion A could be selected as potential characteristic markers of apple juice. Similarly, the response of ion B (m/z 441.1, RT 6.6min), C (m/z 577.1, RT 8.3 min), D (m/z 335.0, RT 5.3 min), were relatively high in blueberry juice, cranberry juice, and grape juice, indicating the possibilityfor themto be used as characteristic markers. In the same way, 18 ions were selected as potential characteristic biomarkers in different juices, and an overview of these markers information is provided in Table 3. 3.3 Identification of marker compounds The identification of discriminating marker compounds could be accomplished by QTOF hybrid mass spectrometer, due to its high resolution, mass accuracy and full-spectrum acquisition capabilities2. The main procedure included elemental composition identification, molecular formula prediction, structural formula identification, and isomer confirmation, etc. Using above procedure, 16 of 18 markers 13



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were identified, either tentatively or confirmed by standards (Table 3). Results showed that some biomarkers of blueberry and cranberry juice were tentatively identified as anthocyanins. The aglycone moiety of 5 anthocyanins, including cyanidin, delphinidin, peonidin,petunidin, and malvidin, was determined by its positive fragment ions at m/z 287, 303, 301,317, and 331, respectively. These anthocyanins were all monoglycosides, such as galactoside, glucoside, and arabinoside. Previous study showed that the elution order of monoglycosides on C-18 column was as follows: galactoside, glucoside, arabinoside 36. In this way, most of the characteristic anthocyanins could be deduced. In addition, some characteristic flavonoids were also identified as biomarkers of berry juice. For two compounds that eluted at retention times 8.6 min (No.1) and 8.5 min (No.5) with m/z 481.0977 and 481.1145, respectively, the Formula finder software suggested the same formula C21H20O13. A search of the above molecular provided 3 isomers hits in the database: gossypin, myricetin-3-glucoside and myricetin-3-galactoside. However, only myricetin glycosides might be present in fruit juices, and this was supported by the result in targeted metabolomics. What’s more, the fragmentation pattern matched well with the online database. The cleavage of the glucosidic bond in protonated molecules at m/z 481.1145 ([M+H]+) may have produced the ion at m/z 319.0429 ([M+H-C6H12O5]+) by the extra loss of one hexose. According to retention time order on the C-18 column of the glycosides, biomarker No.1

and

No.5

were

concluded

to

be

myricetin-3-glucoside

myricetin-3-galactoside, respectively. 14


and

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3.4Analytical method validation In order to validate the feasibility of the biomarkers in the differentiation of the four juices, admixtures of blueberry and cranberry juice with apple and grape juice were prepared at 10%, 20%, 30%, 40% and 50% in volume ratios to simulate adulteration, and the response of each biomarker in each adulterated juice sample was analyzed for each group by plotting profiles. Authentication of blueberry and cranberry juices mixed with apple juice and grape juice above 10% could be achieved by plotting profile s for selected biomarkersacross the sample set (data not shown). In the final analytical procedure, a supervised pattern recognition method, OPLS-DA model was established to predict unknown adulterated fruit juice sample. The training set was established using pure blueberry, cranberry, apple and grape juices. The predictive set was established byblueberry and cranberry juices adulterated with apple or grape juices at 10%, 20%, 30%, 40%, and 50% volume ratios. A satisfactory discrimination was shown in Figure 5. The value of recognition ability (R2) and prediction ability (Q2) obtained was on average 0.99 and 0.95, respectively. Additionally, a low difference between R2 and Q2 was presented, indicating an excellent result achieved. OPLS-DA model enabled the reliable detection of the 10% adulteration of apple and grape juice into blueberry and cranberry juice. In this study, authentication of blueberry and cranberry juice and its adulterate juices was reported using untargeted and targeted metabolomic analyses. Targeted metabolomics analysis aimed to process data sets from a predefined group of metabolites retrieved in theoretical databases. In the present study, analysis of 15



currently known juice compounds, such as flavonoids, anthocyanins, phenolic acids were carried out, and a significant difference was exhibited among berry fruit juice and their adulterant based on the 41 metabolites peak intensities. Therefore, the biomarkers from the targeted metabolomics could be employed in the authentication of berry fruits from a holistic perspective. Targeted metabolomic analysis had the advantages of detecting a selected group of known metabolites. Despite recent targeted methodologies enable detect hundreds of metabolites, however, some potential, unknown or novel biomarkers cannot be screened by this method. In comparison, untargeted metabolomics allows the analysis of a broad range of metabolites with diverse chemical and physical properties, facilitating identifying candidate biomarkers. As the result in our research, besides the same 7 phenolic compounds identified in targeted metabolomics, other characteristic markers of different juices were also selected, including trans-cinnamaldehyde, α-naphthoflavone, 2,3-Di-O-carboxymethyl-D-glucose, etc. These biomarkers can be adopted, alone or combined, to determine whether the berry fruit juices were adulterated, and which adulterants were added as well. In global terms, both approaches are complementary and used simultaneously in the present study to comprehensively investigate the differences of berry fruit juice and its adulterants. The merging of targeted and untargeted analyseswould be promoted by the future advances in LC-MS metabolomics, with the former providing more sensitive and accurate detection of predetermined metabolites, while the latter focus on detecting and identifying unknown metabolites36. 16


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4. Conclusion In the present study, a LC-QTOF-MS-based metabolomic approach was developedas a powerful tool to authenticate berry fruit juices. In the targeted metabolomic analysis, a significant difference was exhibited among berry fruit juice and their adulterant based on the 41 metabolites peak intensities visualizing in the heat map. An optimized untargeted metabolomic analysis was further developed for the comprehensively evaluation of berry fruit juices authenticity.Furthermore, 18 characteristic biomarkers were identiﬁed using elemental formula calculationand online database searches. Based on the biomarkers and OPLS-DA predictive mode,adulteration of blueberry and cranberry juices with apple and grape juices at a 10% addition level can be reliably detected. In conclusion, the results demonstrated that metabolomic, coupled to chemometric tools and global database, has potential as a reliable analytical tool for the food authentication.

Acknowledgements This work was supported by the National Key R&D Program of China (2016YFD0401104) and the Fundamental Research Funds for the Public Research Institutes of Chinese Academy of Inspection and Quarantine (2016JK005). The authors declared no conflict of interest.

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Chem 2015,407, 7197-7213. (23) Cho, M. J.; Howard, L. R.; Prior, R. L.; Clark, J. R., Flavonoid glycosides and antioxidant capacity of various blackberry, blueberry and red grape genotypes determined by high-performance liquid chromatography/mass spectrometry. J Sci Food Agric 2010,84, 2149-2158. (24) Del, R. D.; Borges, G.; Crozier, A., Berry flavonoids and phenolics: bioavailability and evidence of protective effects. Brit J Nutr 2010,104, S67. (25) Määttä-Riihinen, K. R.; Kamal-Eldin, A.; Mattila, P. H.; González-Paramás, A. M.; Törrönen, A. R., Distribution and contents of phenolic compounds in eighteen Scandinavian berry species. J Agric Food Chem 2004,52, 4477-4486. (26) Sánchez-Rabaneda, F.; Jáuregui, O.; Lamuela-Raventós, R. M.; Viladomat, F.; Bastida, J.; Codina, C., Qualitative analysis of phenolic compounds in apple pomace using liquid chromatography coupled to mass spectrometry in tandem mode. Rapid Commun Mass Sp 2004, 18, 553-563. (27) Määttä-Riihinen, K. R.; Kamal-Eldin, A.; Törrönen, A. R., Identification and quantification of phenolic compounds in berries of Fragaria and Rubus species (family Rosaceae). J Agric Food Chem 2004,52, 6178-6187. (28) Mattivi, F.; Guzzon, R.; Vrhovsek, U.; Marco Stefanini, A.; Velasco, R., Metabolite Profiling of Grape: Flavonols and Anthocyanins. J Agric Food Chem 2006, 54, 7692-702. (29) Kähkönen, M. P.; Heinämäki, J.; Ollilainen, V.; Heinonen, M., Berry anthocyanins: isolation, identification and antioxidant activities. J Sci Food Agric 21



2003,83, 1403–1411. (30) Chen, L.; Zhou, L.; Chan, E. C. Y.; Neo, J.; Beuerman, R. W., Characterization of The Human Tear Metabolome by LC–MS/MS. J Proteome Res 2011,10, 4876. (31) Kårlund, A.; Hanhineva, K.; Lehtonen, M.; Karjalainen, R. O.; Sandell, M., Nontargeted Metabolite Profiles and Sensory Properties of Strawberry Cultivars Grown both Organically and Conventionally. J Agric Food Chem 2015,63, 1010-1019. (32) Hanhineva, K.; Rogachev, I.; Kokko, H.; Mintz-Oron, S.; Venger, I.; Kärenlampi, S.; Aharoni, A., Non-targeted analysis of spatial metabolite composition in strawberry (Fragaria×ananassa) flowers. Phytochemistry 2008,69, 2463-2481. (33) Fitzpatrick, S. M.; Gries, R.; Khaskin, G.; Peach, D. A.; Iwanski, J.; Gries, G., Populations of the gall midge Dasineura oxycoccana on cranberry and blueberry produce and respond to different sex pheromones. J Chem Ecol 2013,39, 37. (34) Ryszard Zadernowski; Marian Naczk, ‡ And; Nesterowicz†, J., Phenolic Acid Profiles in Some Small Berries. J Agric Food Chem 2005,53, 2118-2124. (35) Kim, N.; Ryu, S. M.; Lee, D.; Lee, J. W.; Seo, E.-K.; Lee, J.-H.; Lee, D., A metabolomic approach to determine the geographical origins of Anemarrhena asphodeloides by using UPLC–QTOF MS. J Pharm Biomed Anal 2014,92, 47-52. (36) Gorrochategui, E.; Jaumot, J.; Lacorte, S.; Tauler, R., Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow. Trac-Trend Anal Chem 2016, 82, 425-442. 22


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Figure captions Figure 1Heat map representation of the fruit juice sample replicates. The analysis is based on the normalized signal abundances of the identified fruit juice metabolites across all analyzed samples. The color-coding scale indicates the relative abundance within each metabolite: blue, low abundance; red, high abundance; green/yellow, average abundance.

Figure 2 UFLC-QTOF-MS base peak chromatogram (BPC) of fruit juices: (A) blueberry juice, (B) cranberry juice, (C) apple juice, (D) grape juice.

Figure 3 Results of principal component analysis-discriminant analysis (PCA-DA) of fruit juices (blueberry, cranberry, apple, grape): (A) Scores plot, positive ionisation data; (B) loadings plot, positive ionisation data.

Figure 4 Responses of selected ion markers in fruit juices (blue filled diamond blueberry, red filled square cranberry, green filled triangle apple, purple filled circle grape): (A) m/z 291.1, RT 8.2min; (B) m/z 441.1, RT 6.6min; (C) m/z 577.1, RT 8.3 min; (D) m/z 335.0, RT 5.3 min

Figure 5 Prediction of OPLS-DA score scatter plot for adulterated juice with different adulteration levels(10%,20%,30%,40% and 50%). (A) Training model 1: blueberry juice versus apple juice, R2=0.997; Q2=0.964. (B) Training model 2: blueberry juice 23



versus grape juice, R2=0.992; Q2=0.968. (C) Training model 3: cranberry juice versus apple juice, R2=0.986; Q2=0.944. (D) Training model 4: cranberry juice versus grape juice, R2=0.989; Q2=0.942.

24


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Table 1 Compounds from berry fruit reported in literatures No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Metabolites Anthocyanins Delphinidin-3-O-galactoside Delphinidin-3-O-glucoside Delphinidin-3-O-arabinoside Cyanidin-3-O-galactoside Cyanidin-3-O-glucoside Cyanidin-3-O-arabinoside Petunidin-3-O-galactoside Petunidin-3-O-glucoside Petunidin-3-O-arabinoside Petunidin-3-O-xyloside Peonidin-3-O-galactoside Peonidin-3-O-glucoside Peonidin-3-O-arabinoside Malvidin-3-O-galactoside Malvidin-3-O-glucoside Malvidin-3-O-arabinoside Flavonoids Myricetin-pentosylhexoside Myricetin-hexoside Quercetin-3-O-galatoside Quercetin-3-O-glucoside Quercetin-3-O-arabinoside Kaempferol-3-O-galatoside Kaempferol-3-O-glucoside Kaempferol-3-O-arabinoside

Reference 23,24 23,24,25 23,24 23,24, 25,27 23,24,25,26 23,25 23,24,25 23 23,24 25 23,24 23,24,25 24,25 23,24,25 23,24 23,24,25 23,25 23,25 23,25 23,24,25,27 23,25 25 25 25

No.

Metabolites

Reference

25 26 27 28 29 30 31 32 33

Kaempferol-3-O-rutinoside Luteolin-3-O-glucoside Luteolin-3-O-galatoside Luteolin-3-O-arabinoside Vitexin Laricitrin-3-O-hexoside Procyanidin B Catechin Epicatechin Phenolic acids Chlorogenic acid Coumaroylgucaric acid Coumaroylquinicacid Hydroxyursolic acid p-Hydroxybenzoic acid Salicylic acid m-coumaric acid Caffeic acid Ferulic acid Sinapic acid

25 26 26 26 26 28 24 24,27 24,27

34 35 36 37 38 39 40 41 42 43

25


23,25 24,25 24,25 27 24,25 28 25,28 25,27 25,27 25,28


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Table 2 Metabolites identified by the technology of IDA LC-QTOF-MS in targeted metabolomic analysis Mass No.

Metabolites

m/z(MS)b

Formula

m/z(MS/MS)

error (ppm)

RTc (min)

Anthocyanin 1

Delphinidin-3-O-galactoside

C21H20O12

465.1031

303.0484

-1.2

9.1

2

Delphinidin-3-O-glucoside

C21H20O12

465.1726

303.0498

-0.3

9.2

3

Delphinidin-3-O-arabinoside

C20H18O11

435.0914

303.0493

1.0

9.6

a

4

Cyanidin-3-O-galactoside

C21H20O11

449.1063

287.0561

2.1

7.1

5

Cyanidin-3-O-glucosidea

C21H20O11

449.1066

287.0545

0.6

9.5

6

Cyanidin-3-O-arabinoside

C20H18O10

419.0971

287.0543

0.5

9.7

7

Petunidin-3-O-galactoside

C22H22O12

479.1168

317.0645

1.6

7.3

8

Petunidin-3-O-glucoside

C22H22O12

479.1195

317.0661

-0.2

9.8

9

Petunidin-3-O-arabinoside

C21H20O11

449.1063

317.0653

-1.6

10.3

10

Petunidin-3-O-xyloside

C21H20O11

449.1071

317.0656

-1.0

9.9

11

Peonidin-3-O-galactoside

C22H22O11

463.1238

301.0690

-0.2

7.2

12

Peonidin-3-O-glucoside

C22H22O11

463.0847

301.0690

-1.3

7.6

13

Peonidin-3-O-arabinoside

C21H20O10

433.1078

317.0647

1.2

9.5

14

Malvidin-3-O-galactosidea

C23H24O12

493.1341

331.0803

0.9

6.7

15

Malvidin-3-O-glucoside

C23H24O12

493.1997

331.0823

-0.7

7.8

16

Malvidin-3-O-arabinoside

C22H22O11

463.1229

331.0808

-0.8

8.7

C26H28O17

613.1387

481.0992

0.3

1.6

Flavonoids 17

Myricetin-pentosylhexoside a

18

Myricetin-3-O-hexoside

C27H30O15

481.0969

319.0450

-0.4

6.7

19

Quercetin-3-O-galatosidea

C21H20O12

465.0954

303.0523

1.2

9.2

C21H20O12

465.0853

303.0640

0.2

14.5

C20H18O11

435.0923

303.0521

-0.5

15.1

C21H20O11

449.1079

287.0533

0.1

6.4

C21H20O11

449.1630

287.0533

0.1

7.0

20

Quercetin-3-O-glucoside

a a

21

Quercetin-3-O-arabinoside

22

Kaempferol-3-O-galatosidea a

23

Kaempferol-3-O-glucoside

24

Kaempferol-3-O-arabinoside

C20H18O10

419.0971

287.0543

-0.3

7.5

25

Kaempferol-3-O-rutinosidea

C27H30O15

595.1638

287.0546

0.4

9.2

C21H20O11

449.1006

287.0533,

1.4

6.9

a

26

Luteolin-3-O-glucoside

27

Luteolin-3-O-galatoside

C21H20O11

449.1630

287.0533

-1.5

6.7

28

Luteolin-3-O-arabinoside

C20H18O10

419.0971

287.0543

-0.2

7.5

29

Vitexin

C21H21O10

433.1130

433.1130

-1.8

7.7

30

Laricitrin-3-O-hexoside

C22H23O13

495.1126

333.0595

-0.1

10.1

31

Procyanidin B

C30H26O12

579.1502

287.0565

0.6

9.3

32

Catechina

C15H14O6

291.0868

165.0559

0.3

6.2

C15H14O6

291.0868

165.0559

0.2

7.2

C16H18O9

355.1016

193.0554

1.1

18.5

33

a

Epicatechin

Phenolic acids 34

Chlorogenic acida

26


Page 27 of 35


35

Coumaroylgucaric acid

36

Coumaroylquinic acid

C16H18O8

339.1074

165.0546

0.3

7.2

37

Salicylic acida

C7H6O3

171.1469

139.0317

1.2

5.0

38

m-Coumaric acida

C9H8O3

165.0474

147.0438

-1.2

6.5

a

C15H16O10

357.0644

165.0560

0.8

6.7

39

Caffeic acid

C9H8O4

181.0423

117.0363

0.5

7.4

40

Ferulic acida

C10H10O4

195.0579

177.0544

0

12.9

41

a

C11H12O5

225.0685

207.0703

1.1

12.1

Sinapicacid

a

Confirmed by standard

b

The detected and theoretical masses are for the molecular ions [M+H]+

c

Retention time

27



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Table 3 Overview of characteristic marker compounds detected in examined fruit juices No

m/z（MS）

RTa（min）

Ion

1

481.0977

8.6

[M+H]+

2

355.1024

5.5

[M+H]+

3

171.0648

5.1

[M+CH3OH+H]+

4

449.1063

10.3

[M+H]+

5

481.1145

8.5

[M+H]+

6

395.0949

7.8

[M+H]+

7

319.0812

6.3

[M+H]+

8

335.0372

5.3

[M+K]+

9

385.1601

7.7

[M+NH4]+

10

133.0648

6.9

[M+H]+

11

449.1079

7.0

[M+H]+

12

273.0869

8.2

[M+H]+

13

419.0973

7.4

[M+H]+

m/z（MS/MS） 319.0429,273.0355 245.0426,153.0173 163.0385,145.0281 135.0433 139.0382,111.0446 93.0346,65.0412 317.0653 319.0429,273.0355 245.0426,153.0173 395.0930,219.0609 89.0407,149.0608 193.0490,301.0742 163.0386,185.0200 173.0048,145.0288 206.0812,188.0700 118.0654 133.0654,105.0708 103.0548,79.0560, 287.0533,449.1053 167.0557,159.0932 185.0675,200.0661 287.0536,241.0502 213.0530,185.0603

Formula

Mass error

Identification (confirmed b and tentative c)

Sample Typesd

C21H20O13

-0.1

Myricetin-3-glucoside c

B

C16H18O9

-1.6

Chlorogenic acid b

B

C7H6O3

-2.3

Salicylic acid b

B

C21H20O11

-1.6

Petunidin-arabinoside c

C

C21H20O13

-0.1

Myricetin-3-O-galactoside c

C

C18H18O10

-0.3

Unknow

C

C15H10O8

-1.0

Myricetin b

G

C10H16O10

-0.9

2,3-Di-O-carboxymethyl-D-glucose c

G

C17H21NO8

-1.1

Unknow

G

C9H8O

0.1

trans-Cinnamaldehyde b

A

C21H20O11

0.1

Kaempferol-3-O-glucoside b

A

C19H12O2

1.0

α-Naphthoflavone c

A

C20H18O10

0.3

Cyanidin-arabinoside b

B, C

28


Page 29 of 35


14 15 16 17 18

449.1075 463.0847 463.1229 465.1726 419.0971

9.7 7.2 8.7 9.1 7.5

[M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+

303.0491 301.0690 331.0808 303.0498 287.0536

C21H20O11 C22H22O11 C22H22O11 C21H20O12 C20H18O10

-0.5 -0.2 -0.8 -0.3 0.3

a

Retention time Confirmed by standard c Tentative identification d B:blueberry, C:cranberry, A:apple, G:Grape b

29


Delphinidin-rhamnoside c Peonidin-galactoside c Malvidin-arabinoside c Delphinidin-galactoside c Cyanidin-glucoside b

B, C B, C B, C B, C B, C


Figure 1

Blueberry

Cranberry

Apple

30


Grape

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Figure 2

A

B

C

D

31



Figure 3

B

A Grape Blueberry QC

Apple Cranberry

32


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Figure 4 A

B

C

D

33



Figure 5 A

B

C

D

34


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TOC Graphic

For Table of Contents Only

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