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Article Cite This: J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Metabolomic Approach for the Authentication of Berry Fruit Juice by Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry Coupled to Chemometrics Jiukai Zhang,† Qiuhao Yu,†,‡ Haiyan Cheng,§ Yiqiang Ge,∥ Han Liu,† Xingqian Ye,‡ and Ying Chen*,† †

Agro-Product Safety Research Center, Chinese Academy of Inspection and Quarantine, Beijing 100176, People’s Republic of China 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 § AB Sciex (China) Co., Ltd., Beijing 100102, People’s Republic of China ∥ China Rural Technology Development Center, Beijing 100045, People’s Republic of China J. Agric. Food Chem. Downloaded from pubs.acs.org by DURHAM UNIV on 07/30/18. For personal use only.

‡

S Supporting Information *

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 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 and grape juice was obtained by principal component analysis-discriminant analysis. Eighteen 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. KEYWORDS: berry fruit juice, authentication, metabolomics, chemometrics, LC-QTOF-MS (inductively coupled plasma mass spectrometry11 and isotope ratio mass spectrometry12). With regards to the targeted property of all the aforementioned methods, only a few specific adulteration practices can be successfully detected. Therefore, analytical approaches with a more global overview into fruit juice 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 of dairy products,13 alcoholic beverages,14 coffee,15 traditional Chinese medicine food,16 honey,17 olive oil,18 meat,19 etc. The advances of metabolomics have 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 levels.20,21 Multivariate data analysis, either unsupervised or supervised, is needed for

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 phenolics.1 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 fraud.2 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 juice more complicated. Currently, a number of analytical methods has been developed to discriminate genuine fruit juice and its adulterants, from sensory evaluation (electronic tongue, electronic nose5), nondestructive testing (infrared spectrometry6), DNA-based method (polymerase chain reaction7), to targeted profile detection (organic acids,8 amino acids,9 or phytochemicals10), as well as physical element analyses © XXXX American Chemical Society

Received: Revised: Accepted: Published: A

April 5, 2018 June 26, 2018 July 10, 2018 July 10, 2018 DOI: 10.1021/acs.jafc.8b01682 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry 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 23−25 23, 24 23−25, 27 23−26 23, 25 23−25 23 23, 24 25 23, 24 23−25 24, 25 23−25 23, 24 23−25

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

23, 25 23, 25 23, 25 23−25, 27 23, 25 25 25 25

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

25 25 25 25 28 27 27 28

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 concentration of 25% (v/v) for each type of juice. In total, 12 QC samples were made. All samples (50 mL) were centrifuged at 8000 rpm for 20 min at room temperature and filtered through a 0.2 μm PTFE membrane (Jinteng Corp., Tianjin, China) to remove solid components. After being 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, United States). 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-7O-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, United States). 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, United States). 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 performed as following protocol: 0−2 min, 2% B; 2−14 min, 2−95% B; 14−17

processing 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 metabolomes.20 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 selected as 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 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 (OPLSDA). 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 grape (n = 30) were collected from fruit planting base in Shandong, Liaoning, Guizhou, Jilin, and Jiangsu, China and were identified by local experts on germplasm resources. Afterward, 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 B

DOI: 10.1021/acs.jafc.8b01682 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry Table 2. Metabolites Identified by the Technology of IDA LC-QTOF-MS in Targeted Metabolomic Analysis no.

metabolites

formula

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

delphinidin-3-O-galactoside delphinidin-3-O-glucoside delphinidin-3-O-arabinoside cyanidin-3-O-galactosidea cyanidin-3-O-glucosidea 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-galactosidea malvidin-3-O-glucoside malvidin-3-O-arabinoside

C21H20O12 C21H20O12 C20H18O11 C21H20O11 C21H20O11 C20H18O10 C22H22O12 C22H22O12 C21H20O11 C21H20O11 C22H22O11 C22H22O11 C21H20O10 C23H24O12 C23H24O12 C22H22O11

17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

myricetin-pentosylhexoside myricetin-3-O-hexosidea quercetin-3-O-galatosidea quercetin-3-O-glucosidea quercetin-3-O-arabinosidea kaempferol-3-O-galatosidea kaempferol-3-O-glucosidea kaempferol-3-O-arabinoside kaempferol-3-O-rutinosidea luteolin-3-O-glucosidea luteolin-3-O-galatoside luteolin-3-O-arabinoside vitexin laricitrin-3-O-hexoside procyanidin B catechina epicatechina

C26H28O17 C27H30O15 C21H20O12 C21H20O12 C20H18O11 C21H20O11 C21H20O11 C20H18O10 C27H30O15 C21H20O11 C21H20O11 C20H18O10 C21H21O10 C22H23O13 C30H26O12 C15H14O6 C15H14O6

34 35 36 37 38 39 40 41

chlorogenic acida coumaroylgucaric acid coumaroylquinic acid salicylic acida m-coumaric acida caffeic acida ferulic acida sinapicacida

C16H18O9 C15H16O10 C16H18O8 C7H6O3 C9H8O3 C9H8O4 C10H10O4 C11H12O5

m/z (MS)b anthocyanin 465.1031 465.1726 435.0914 449.1063 449.1066 419.0971 479.1168 479.1195 449.1063 449.1071 463.1238 463.0847 433.1078 493.1341 493.1997 463.1229 flavonoids 613.1387 481.0969 465.0954 465.0853 435.0923 449.1079 449.1630 419.0971 595.1638 449.1006 449.1630 419.0971 433.1130 495.1126 579.1502 291.0868 291.0868 phenolic acids 355.1016 357.0644 339.1074 171.1469 165.0474 181.0423 195.0579 225.0685

m/z (MS/MS)

mass error (ppm)

RTc (min)

303.0484 303.0498 303.0493 287.0561 287.0545 287.0543 317.0645 317.0661 317.0653 317.0656 301.0690 301.0690 317.0647 331.0803 331.0823 331.0808

−1.2 −0.3 1.0 2.1 0.6 0.5 1.6 −0.2 −1.6 −1.0 −0.2 −1.3 1.2 0.9 −0.7 −0.8

9.1 9.2 9.6 7.1 9.5 9.7 7.3 9.8 10.3 9.9 7.2 7.6 9.5 6.7 7.8 8.7

481.0992 319.0450 303.0523 303.0640 303.0521 287.0533 287.0533 287.0543 287.0546 287.0533, 287.0533 287.0543 433.1130 333.0595 287.0565 165.0559 165.0559

0.3 −0.4 1.2 0.2 −0.5 0.1 0.1 −0.3 0.4 1.4 −1.5 −0.2 −1.8 −0.1 0.6 0.3 0.2

1.6 6.7 9.2 14.5 15.1 6.4 7.0 7.5 9.2 6.9 6.7 7.5 7.7 10.1 9.3 6.2 7.2

193.0554 165.0560 165.0546 139.0317 147.0438 117.0363 177.0544 207.0703

1.1 0.8 0.3 1.2 −1.2 0.5 0 1.1

18.5 6.7 7.2 5.0 6.5 7.4 12.9 12.1

a

Confirmed by standard. bThe detected and theoretical masses are for the molecular ions [M + H]+. cRetention time.

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: + 5500 V, declustering potential (DP): 60 V, source temperature: 550 °C. The 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-QTOFMS 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 study.16 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 the loading plot, and characteristic ions present within only one group were selected as potential biomarkers.16 The validation of the obtained PCA-DA model was performed by QC samples 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 C


Article

Journal of Agricultural and Food Chemistry

Figure 1. Heat 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. set consisted of 20 adulterated samples. The quality of the models was validated by recognition ability (R2) and prediction ability (Q2). R2 value is the percentage 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 step.22 2.5. Targeted 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. In total, 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) online databases. Entries without experimental MS/MS spectra were excluded.30 In total, 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. 2.6. Untargeted Biomarker Identification. The biomarker 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 prediction were 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 likely candidate metabolites were chosen from the above databases, according to the consistency with literature and probability in the samples under study. In addition, the fragmentation information on 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. 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 relevant ions 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 three D


Article


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

Figure 3. Results of PCA-DA of fruit juices (blueberry, cranberry, apple, and grape): (A) Scores plot, positive ionization data; (B) loadings plot, positive ionization data.

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 E


Article


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 8.2 min; (B) m/z 441.1, RT 6.6 min; (C) m/z 577.1, RT 8.3 min; (D) m/z 335.0, RT 5.3 min.

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. 3.2. Untargeted Metabolomics Analysis. To obtain comprehensive information on the metabolites of 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 were achieved in 20 min. A UPLC system with gradient elution mode was employed to ensure the maximized chromatographic performance. 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 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 ionization 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

map. Both apple and grape juice lacked delphinidin-3-Oglucoside, 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 important constituents of many berry fruits.31,32 Several members of these compound groups were identified as sensory-active features in those studies.33 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 compound 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 analysis,23,29,34 our present study reported for the first comprehensive characterization of the F


Article

Journal of Agricultural and Food Chemistry Table 3. Overview of Characteristic Marker Compounds Detected in Examined Fruit Juices no.

m/z (MS)

RTa (min)

1

481.0977

8.6

[M + H]+

2 3

355.1024 171.0648

5.5 5.1

4 5

449.1063 481.1145

10.3 8.5

[M + H]+ [M + CH3OH + H]+ [M + H]+ [M + H]+

6 7

395.0949 319.0812

7.8 6.3

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

8

335.0372

5.3

[M + K]+

9 10

385.1601 133.0648

7.7 6.9

[M + NH4]+ [M + H]+

11 12

449.1079 273.0869

7.0 8.2

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

13

419.0973

7.4

[M + H]+

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

ion

[M [M [M [M [M

+ + + + +

H]+ H]+ H]+ H]+ 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 303.0491 301.0690 331.0808 303.0498 287.0536

identification (confirmedb and tentativec)

formula

mass error

C21H20O13

−0.1

myricetin-3-glucosidec

B

C16H18O9 C7H6O3

−1.6 −2.3

chlorogenic acidb salicylic acidb

B B

C21H20O11 C21H20O13

−1.6 −0.1

petunidin-arabinosidec myricetin-3-O-galactosidec

C C

C18H18O10 C15H10O8

−0.3 −1.0

unknown myricetinb

C G

C10H16O10

−0.9

G

C17H21NO8 C9H8O

−1.1 0.1

2,3-di-O-carboxymethyl-Dglucosec unknown trans-cinnamaldehydeb

G A

C21H20O11 C19H12O2

0.1 1.0

kaempferol-3-O-glucosideb α-naphthoflavonec

A A

C20H18O10

0.3

cyanidin-arabinosideb

B, C

C21H20O11 C22H22O11 C22H22O11 C21H20O12 C20H18O10

−0.5 −0.2 −0.8 −0.3 0.3

c

delphinidin-rhamnoside peonidin-galactosidec malvidin-arabinosidec delphinidin-galactosidec cyanidin-glucosideb

sample typesd

B, B, B, B, B,

C C C C C

a

Retention time bConfirmed by standard. cTentative identification. dB: blueberry, C: cranberry, A: apple, G: grape.

3.3. Identification of Marker Compounds. The identification of discriminating marker compounds was accomplished by QTOF hybrid mass spectrometer due to its high resolution, mass accuracy, and full-spectrum acquisition capabilities. 2 The main procedure included elemental composition identification, molecular formula prediction, structural formula identification, isomer confirmation, etc. Using the above procedure, 16 of 18 markers 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 C18 column was as follows: galactoside, glucoside, and 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 three isomers hits in the database: gossypin, myricetin-3-glucoside, and myricetin-3galactoside. However, only myricetin glycosides might be present in fruit juices, and this was supported by the result in targeted metabolomics. Further, 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

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, 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 origin.35 These results clearly confirmed the stability and reproducibility of the analytical method. As 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), and 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 6.6 min), 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 possibility for them to 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. G


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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 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.

− C6H12O5]+) by the extra loss of one hexose. According to retention time order on the C-18 column of the glycosides, biomarkers no. 1 and no. 5 were concluded to be myricetin-3glucoside and myricetin-3-galactoside, respectively. 3.4. Analytical Method Validation. 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 biomarkers in adulterated juice samples 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 profiles for selected biomarkers across the sample set (data not shown). In the final analytical procedure, a supervised pattern recognition method, the 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 by blueberry 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, analyses of currently known juice compounds such as flavonoids, anthocyanins, and 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 transcinnamaldehyde, α-naphthoflavone, 2,3-di-O-carboxymethyl-Dglucose, etc. These biomarkers can be adopted, alone or H


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(4) Muntean, E. Simultaneous Carbohydrate Chromatography and Unsuppressed Ion Chromatography in Detecting Fruit Juices Adulteration. Chromatographia 2010, 71, 69−74. (5) Baietto, M.; Wilson, A. D. Electronic-nose applications for fruit identification, ripeness and quality grading. Sensors 2015, 15, 899− 931. (6) Downey, G.; Kelly, J. D. Detection and Quantification of Apple Adulteration in Diluted and Sulfited Strawberry and Raspberry Purées Using Visible and Near-Infrared Spectroscopy. J. Agric. Food Chem. 2004, 52, 204−209. (7) Palmieri, L.; Bozza, E.; Giongo, L. Soft Fruit Traceability in Food Matrices using Real-Time PCR. Nutrients 2009, 1, 316. (8) Ehling, S.; Cole, S. Analysis of Organic Acids in Fruit Juices by Liquid Chromatography−Mass Spectrometry: An Enhanced Tool for Authenticity Testing. J. Agric. Food Chem. 2011, 59, 2229−2234. (9) Gómez-Ariza, J. L.; Villegas-Portero, M. J.; Bernal-Daza, V. Characterization and analysis of amino acids in orange juice by HPLC−MS/MS for authenticity assessment. Anal. Chim. Acta 2005, 540, 221−230. (10) Dragovićuzelac, V.; Pospišil, J.; Levaj, B.; Delonga, K. The Study of Phenolic Profiles of Apricot and Apple Purees by HPLC for the Evaluation of Apricot Juices and Jams Authenticity. Food Chem. 2005, 91, 373−383. (11) Schwartz, R. S.; Hecking, L. T. Determination of geographic origin of agricultural products by multivariate analysis of trace element composition. J. Anal. At. Spectrom. 1991, 6, 637−642. (12) Rossmann, A. Determination of stable isotope rations in food analysis. Food Rev. Int. 2001, 17, 347−381. (13) Santos, P. M.; Pereira-Filho, E. R.; Rodriguez-Saona, L. E. Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis. Food Chem. 2013, 138, 19−24. (14) Vaclavik, L.; Lacina, O.; Hajslova, J.; Zweigenbaum, J. The use of high performance liquid chromatography−quadrupole time-offlight mass spectrometry coupled to advanced data mining and chemometric tools for discrimination and classification of red wines according to their variety. Anal. Chim. Acta 2011, 685, 45−51. (15) Arana, V. A.; Medina, J.; Alarcon, R.; Moreno, E.; Heintz, L.; Schäfer, H.; Wist, J. Coffee’s country of origin determined by NMR: The Colombian case. Food Chem. 2015, 175, 500−506. (16) Zhang, J.; Wang, P.; Wei, X.; Li, L.; Cheng, H.; Wu, Y.; Zeng, W.; Yu, H.; Chen, Y. A metabolomics approach for authentication of Ophiocordyceps sinensis by liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Food Res. Int. 2015, 76, 489−497. (17) Donarski, J. A.; Jones, S. A.; Charlton, A. J. Application of Cryoprobe 1H Nuclear Magnetic Resonance Spectroscopy and Multivariate Analysis for the Verification of Corsican Honey. J. Agric. Food Chem. 2008, 56, 5451−5456. (18) Vaclavik, L.; Cajka, T.; Hrbek, V.; Hajslova, J. Ambient mass spectrometry employing direct analysis in real time (DART) ion source for olive oil quality and authenticity assessment. Anal. Chim. Acta 2009, 645, 56−63. (19) Mannina, L.; Sobolev, A. P.; Capitani, D.; Iaffaldano, N.; Rosato, M. P.; Ragni, P.; Reale, A.; Sorrentino, E.; D’Amico, I.; Coppola, R. NMR metabolic profiling of organic and aqueous sea bass extracts: Implications in the discrimination of wild and cultured sea bass. Talanta 2008, 77, 433−444. (20) Wishart, D. S. Metabolomics: applications to food science and nutrition research. Trends Food Sci. Technol. 2008, 19, 482−493. (21) Ellis, D. I.; Brewster, V. L.; Dunn, W. B.; Allwood, J. W.; Golovanov, A. P.; Goodacre, R. Fingerprinting food: current technologies for the detection of food adulteration and contamination. Chem. Soc. Rev. 2012, 41, 5706−5727. (22) Guijarro-Diez, M.; Nozal, L.; Marina, M. L.; Crego, A. L. Metabolomic fingerprinting of saffron by LC/MS: novel authenticity markers. Anal. Bioanal. 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

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 analyses would 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 metabolites.36 In conclusion, in the present study, a LC-QTOF-MS-based metabolomic approach was developed as 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 comprehensive evaluation of berry fruit juice authenticity. Furthermore, 18 characteristic biomarkers were identified using elemental formula calculation and online database searches. On the basis of 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. The results demonstrated that metabolomic coupled to chemometric tools and global databases has potential as a reliable analytical method for food authentication.

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.8b01682.

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OPLS-DA score plot and information on juice samples (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; Tel.: +86 10 53897910; Fax: +86 10 53897910. ORCID

Ying Chen: 0000-0002-8433-3341 Funding

This work was supported by the National Key R&D Program of China (Grant 2016YFD0401104) and the Fundamental Research Funds for the Public Research Institutes of Chinese Academy of Inspection and Quarantine (Grant 2016JK005). Notes

The authors declare no competing financial interest.

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REFERENCES

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