Differences in chemical composition among commercially important

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Food and Beverage Chemistry/Biochemistry

Differences in chemical composition among commercially important cultivars of Genus Camellia Yijun Wang, Zhipeng Kan, Dongxu Wang, Liang Zhang, Xiaochun Wan, John N. McGinley, and Henry J. Thompson J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b06164 • Publication Date (Web): 21 Dec 2018 Downloaded from http://pubs.acs.org on January 7, 2019

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Journal of Agricultural and Food Chemistry

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Differences in chemical composition among commercially important cultivars of

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Genus Camellia

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Yijun Wang†, Zhipeng Kan†, Dongxu Wang†, Liang Zhang†, Xiaochun Wan†, John N.

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McGinley‡, and Henry J. Thompson*‡

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International Joint Laboratory on Tea Chemistry and Health Effects †State

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Key Laboratory of Tea Plant Biology and Utilization,

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Anhui Agricultural University, Hefei, PRC

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and

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‡ Cancer

Prevention Laboratory, Colorado State University, Fort Collins, CO, USA

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*Corresponding

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Henry J. Thompson

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Cancer Prevention Laboratory

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Colorado State University

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1173 cultivarpus Delivery

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Fort Collins, CO 80523

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970-377-3262

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[email protected]

author

ACS Paragon Plus Environment


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ABSTRACT

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Leaves from plants of the genus Camellia are used to make beverages and food

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products; however, there is limited data that compares chemical composition of the

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unprocessed leaves of cultivars traditionally used to make these products. Plucked,

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fresh leaves from fourteen commercially important cultivars were analyzed by

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UPLC-Q-TOF-MS/MS. Based on assessment of 61 compounds that are known to be

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affected by post-harvest tea processing methods, significant variation among

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unprocessed cultivar leaves was observed for compounds in 5 chemical classes:

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amino acids, catechins, flavonoids and flavone glycosides, phenolic acids, and

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alkaloids. These chemical differences were of sufficient magnitude to render two

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distinct chemically defined clusters of Camellia cultivars that did not reflect the

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traditional grouping of these cultivars based by species variant, tea type, or production

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region. Advanced statistical techniques identified candidate biomarkers for each

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chemical class to guide the development of comprehensive targeted analyses for

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constituents of biosynthetic pathways in which marked expression plasticity was

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observed. Targeted analyses of this type have the potential to identify Camellia

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species/cultivars that will facilitate the formulation of new beverages and designer

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foods with improved organoleptic characteristic and enhanced prebiotic or

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nutraceutical activity.

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Keywords: Camellia, tea bioactives, catechins, chemical composition, metabolomics


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INTRODUCTION

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The species sinensis within the genus Camellia is a primary source of the hot water

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infusion referred to as tea and is also used in the production of other food products.

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The leaves of C. sinensis are subjected to specific post-harvest processing techniques

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that give the aroma and taste characteristics of each tea type based on the chemical

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changes induced in the tea leaf, primarily the nonvolatile component, before it is

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infused.1, 2 As such, the tea science field has focused on processing-induced changes

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in the types of catechins present in the leaf since they compromise over 20% of its dry

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weight.3 The tea catechins include: catechin (C), gallocatechin (GC), epicatechin

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(EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate

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(EGCG), the most abundant secondary metabolites in the fresh leaves of C. sinenesis.

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In most tea science research, the chemical changes induced by all six typical

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processing methods are viewed through the lens of how those processes either prevent

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or allow catechins to be oxidized by endogenous polyphenol oxidase.4-6 Although

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many other chemicals, e.g., theanine, flavonoids, and caffeine have been discovered

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in tea leaves in the past decades, the comprehensive chemical profiling of Camellia

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cultivars, especially is still limited.7-10

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contributions of the unprocessed cultivar leaf used to make a tea or food product in

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comparing the chemical differences that exist in Camellia derived beverages and

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foods.

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The objective of the experiments reported herein was to use high throughput

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chromatographic analyses to extend the evaluation of differences in chemical

In fact, most work has ignored the potential



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composition among commonly used Camellia cultivars beyond catechins to other

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classes of chemicals that are known to be affected during the processing of leaves to

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the six tea types commonly produced.11 Significant variation was observed among

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cultivars in each of the chemical classes examined and these differences were

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exploited to identify candidate biomarkers to direct future development of targeted

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chemical analyses as a basis for formulation of beverages and foods with improved

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functionality.

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MATERIALS AND METHODS

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Chemicals

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Deionized water was produced using a Milli-Q water purification system (Millipore,

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Billerica, Massachusetts). Methanol and acetonitrile of LC–MS grade were purchased

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from Thermo Fisher (Thermo Scientific, USA). C, GC, EC, ECG, EGC, EGCG, gallic

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acid, caffeine, theophylline, theobromine and theanine were obtained from Yuanye

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Bio-Technology Co., Ltd. (Shanghai, China).

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Plant material

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In order to satisfy market demand for various tea types, cultivars with leaves

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exhibiting characteristic color, fragrance, shape, and taste have been produced using

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conventional breeding techniques. Leaves of a specified cultivar tend to be

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manufactured into a featured tea type either for geographical or historical reasons. In

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the present study, we selected 14 cultivars from different regions of China which


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covered major production areas of Camellia (Table 1). Moreover, the cultivars

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selected were representative sources of the six typical types of tea.

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Members of the State Key Laboratory of Tea Plant Biology and Utilization, Anhui

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Agricultural University, Hefei, PRC guided the selection and collection of plant

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material. This study was double-blinded in that the individuals at Anhui Agricultural

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University providing Camellia cultivars were blinded to the intent of the analyses and

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the analytical team at Colorado State University was blinded to the botanical

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classification of the leaves that were evaluated.

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Specimen collection and initial processing

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One kg fresh leaves of each cultivar, harvested using the one bud and two leaves

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criterion that defines high quality young leaves, were obtained from a uniform

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location on each Camellia tree during one growing season. The collected leaves were

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fixed by quick heating in a microwave oven for 2 minutes at nominal power of 700W

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and baked at 60℃ for 240 min until dry without any additional type of post-harvest

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processing. All samples were ground into fine powder and stored at -80℃. Each

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cultivar was randomly assigned an identification number with no knowledge of

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species, region grown, or the type of tea into which it is manufactured.

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Metabolite extraction

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Metabolite extraction was carried out using a modified Bligh and Dyer method

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published by Sana et al.12 To account for differential solubility of various compounds

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and to extract the largest number of compounds possible from the lyophilized



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powders, extractions were carried out at pH 2, 7, and 9.13 The aqueous methanolic

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fractions were pooled (the chloroform fraction was not analyzed), dried under

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nitrogen and stored at -80°C. These residues were weighed and reconstituted to the

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same weight/volume immediately prior to injection on the UPLC-MS.

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LC-MS Analysis by UPLC-Q-TOF-MS/MS

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One μL injections were performed on a Waters Acquity UPLC system (Waters

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Corp.). Separation was performed on a Waters Acquity UPLC C8 column (1.8 µM,

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1.0 x 100 mm), using a gradient from solvent A (95% water, 5% methanol, 0.1%

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formic acid) to solvent B (95% methanol, 5% water, 0.1% formic acid). Injections

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were made in 100% A, which was held for 0.1 min, ramped to 100% B in 13 min and

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held for 3 min, returned to starting conditions over 0.1 min, and allowed to equilibrate

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for 4.9 min. Flow rate was constant at 140 µL/min for the duration of the run. The

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column was held at 50°C, samples were held at 10°C.

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Column eluent was infused into a Waters Xevo G2 Q-TOF MS fitted with an

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electrospray source. Data were collected in positive or negative ion mode, scanning

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from 50-1200 at a rate of 5 scans per second. Alternate scans were collected in MSe

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mode, where all ions were fragmented using a collision energy ramp of 15-30 V.

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Calibration was performed prior to sample analysis via infusion of sodium formate

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solution, with mass accuracy within 1 ppm. The capillary voltage was held at 2200V,

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the source temp at 150°C, and the desolvation temperature at 350°C at a nitrogen

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desolvation gas flow rate of 800 L/hr. The collision energy was held at 6 volts for MS

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mode. Samples were evaluated in the positive ion mode.


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Peak detection, deconvolution, filtering, and scaling

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Waters raw data files were converted to .cdf format using Databridge software

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(Waters Corp.), and feature detection and alignment was performed using XCMS.

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Candidate ion annotations

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The tandem mass spectrometry (MS/MS) of these features were collected by Data

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Depend MS/MS model and subjected to in silico analysis that combined manually

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matching with MS2 fragments against online databases (Metlin, HMDB, Mass Bank,

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Mzcloud).14-17 The screened features were further filtered by database TMDB

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(http://pcsb.ahau.edu.cn:8080/TCDB/f),18 a specific tea database enrolled all the

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phytochemicals in tea that previously reported in literature, and the features were

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finally annotated. Authentic standards of C, GC, EC, ECG, EGC, EGCG, gallic acid,

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caffeine, theophylline, theobromine and theanine were used as validation.

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Statistical analysis of chemical composition data.

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Fourteen Camellia cultivars were each evaluated in triplicate (14 x 3, N=42).

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Multivariate analysis of variance (MANOVA) and supervised and unsupervised

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multivariate clustering techniques were employed to visualize and evaluate the data as

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previously reported by us.19 For MANOVA, the dependent variables (candidate

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ions) were analyzed simultaneously with the independent variable being cultivar or

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the PCA based grouping of cultivars.

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chemical class is reported.

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multivariate technique was used to determine relationships among cultivars with no

The Hoteling multivariate statistic for each

Principal components analysis (PCA), an unsupervised



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prior knowledge of class membership. Orthogonal projections to latent structures for

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discriminant analysis (OPLS-DA), a supervised, class-based method where class

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membership is assigned and used to elicit maximum data separation was applied using

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the class information to partition variation into predictive and orthogonal components

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The contribution of each component partitioned into between-class (predictive) and

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within-class (orthogonal) variance is also estimated. Score plots of the first two score

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vectors for the PCA models were drawn, along with 95% confidence ellipses based on

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Hotelling’s multivariate T2, to identify outliers that might bias the results of

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OPLS-DA. For OPLS-DA, class separation was shown in several ways. The first

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predictive score was plotted against the first orthogonal score to visualize the within-

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and between-class variability associated with the first principal component. S-plots

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were constructed to identify influential ions in the separation of species. All analyses

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were done using SIMCA-P+ v.14 (Umetrics, Umea, Sweden). Heatmap analysis was

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performed with Multi Experiment Viewer open source software v.4.8.1

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(http://mev.tm4.org). MANOVA and the significance level of the metabolite

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differences between groups was calculated by ANOVA with pairwise post hoc

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comparisons by the method of Bonferroni were computed using Systat Statistical

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Analysis software v.13 (Systat Software, Inc., San Jose, CA).


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RESULTS

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Extracts of three replicate samples of all cultivars were evaluated via

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UPLC-Q-TOF-MS/MS. Our analyses focused on 61compounds known to be altered

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during typical tea processing procedures; identity was validated using authentic

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standards and/or advanced in silico procedures.11 The differences among cultivars in

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each analyte are shown graphically (Figure 1). Clear differences among cultivars are

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apparent in each of the five chemical classes into which the annotated chemicals were

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classified and this was confirmed by multivariate analysis of variance (MANOVA)

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using signal intensity for all compounds as the dependent variable and cultivar as the

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independent variable. In the initial analysis, the effect of cultivar was highly

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significant (Hotelling multivariate p-value 100-fold existed among the 14 cultivars in some compounds. This

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demonstrates remarkable plasticity in the biosynthetic activity occurring in leaves

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among the 14 cultivars evaluated. While the growing location for the samples

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investigated was not standardized, i.e. they came from a number of regions within

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China, and therefore we cannot comment on the contribution of genetics versus

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environmental factors to the differences that were observed, our data shows a level of

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variation demonstrating that specific chemical pathways are enhanced or diminished

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in commercially produced cultivars. Moreover, our data indicate that these pathways

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can be intentionally manipulated by either conventional breeding approaches or the

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use of genetic engineering tools in order to develop cultivars with chemically defined

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traits.

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Figure 2 is the output from an unsupervised cluster analysis of the data displayed in



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the heat map shown in Figure 1. The 14 cultivars separated into two groups.

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Examination of available information about these cultivars (Tables 1 and 2) failed to

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identify agronomic or tea production traits that explain these groupings. Thus, it can

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be argued that the chemical analyses provided unique insight about cultivar

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characteristics. Moreover, further analysis using an advanced statistical approach

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(OPLS-DA) indicated that the chemicals that had the greatest influence on separating

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the cultivars into these groups were from the five chemical classes evaluated, i.e., no

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one chemical class dominated the discriminant analysis (Figure 3B and Supplemental

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Table 2).

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separated into groupings by unsupervised analysis when the data source was limited

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to candidate compounds within a specific chemical class. The manner in which

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cultivars were grouped differed depending on the class of chemicals that was

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considered (Figure 4). Using this approach it was possible to identify cultivars high in

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theanine and low in caffeine and visa versa, a set of relationships already considered

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in marketing tea products that either promote sleep or enhance alertness, respectively.

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On the other hand, these data also identified cultivars that differ with respect to

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catechin and flavonoid content.

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be the use of the ground leaves of a high flavonoid-low catechin cultivar to enriched

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food products in order to provide a prebiotic effect without creating concern for

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catechin mediated oxidative liver toxicity. 20-22

Because of this observation, we speculated that cultivars could be

As mentioned above, one application of this could

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Our data indicate that differences in chemical content extend beyond widely


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investigated tea catechins and that high throughput chemical analysis can be used to

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elucidate new chemistries that have the potential to contribute to new product

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development. To our knowledge, this data analysis approach, particularly PCA by

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chemical class, has not previously been reported in research on Camellia, although

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several recent studies applied either targeted or untargeted metabolomics approaches

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to investigate the seasonal, geographical or genetic impact on chemical composition

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of the tea plant leaf.23-25

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A barrier to using LC-MS for detailed characterization of tea leaf chemistry has been

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the limited ability to detect chemical signatures of less abundant ions in the face of the

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high concentrations of catechins in leaves. However, recently developed signal

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collection and processing work flows make it possible to accurately detect and

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quantify the presence of lower abundance ions.26,

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development of pathway specific targeted LC-MS analyses for each of the five

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chemical classes listed and perhaps for additional pathways based on the “Other

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category” reported in Figure 1.

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use the data reported herein to focus the analytical platform on the implicated

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pathways and all metabolites known to be synthesized within that pathway(s) so that

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comprehensive quantitative data can be derived to better characterize the metabolite

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composition of Camellia species/cultivars of interest and more broadly to conduct

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chemotaxonomic analyses among genera in the family Theaceae28.

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Development of these targeted approaches should

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Our findings support the

Strengths and limitations



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The strengths of this investigation include the collection of Camellia cultivars

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selected for evaluation, the double blinded nature of the study design, and the rapid

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treatment of plant material to assure that chemical composition was minimally

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modified in the leaves once they were harvested. New work would benefit from an

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equal number of cultivars for each tea type and collection region.

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Conclusion

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The work reported herein underscores the fact that the fresh leaves of commercially

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important cultivars of Camellia vary markedly not only in tea catechins but also less

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prominent classes of phytochemicals: amino acids, flavonoids and flavone glycosides,

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phenolic acids, and alkaloids. The feasibility of developing pathway based targeted

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LC-MS analyses using emerging analytical approaches creates an opportunity to

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identify and/or develop Camellia cultivars with chemical properties that could serve

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many purposes including: development of new beverages and food products,

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decreased need for exogenous chemicals to control pests in tea gardens, enhanced

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prebiotic or nutraceutical activity, and/or decreased potential for toxicity during

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chronic exposure.


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ABBREVIATIONS USED C CAF cultivar GA EC ECG EGC GCG EGCG OPLS-DA PCA VIP

catechin caffeine cultivarellia gallic acid epicatechin epicatechin gallate epigallocatechin gallocatechin gallate epigallocatechin gallate orthogonal projections to latent structures for discriminant analysis principal component analysis variable importance projection

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ACKNOWLEDGEMENTS

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The authors thank Dr. Zhengzhu Zhang for providing the leaf samples, and Weiqin

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Jiang, Zhongjian Zhu, and Corey Brocekling for their excellent technical assistance.

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FUNDING

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This work was supported by the Key research and development projects of Anhui

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province (1804b06020367), High-End Foreign Experts Recruitment Program

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(GDT20143400024), the Earmarked fund for China Agriculture Research System

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(CARS-19), Anhui Major Demonstration Project for Leading Talent Team on Tea

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Chemistry and Health (1306c083018), and the Colorado State University Office of

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International Programs.

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(28) Wang, Y.; Yang, Y.; Wei, C.; Wan, X.; Thompson, H. J. Principles of Biomedical Agriculture Applied to the Plant Family Theaceae To Identify Novel Interventions for Cancer Prevention and Control. J Agric. Food Chem. 2016, 64 (14), 2809-2814.

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436


Table 1.

Camellia cultivars investigated

IDa

Species

Region

Tea name

Tea type

Anhui

Houkui

green

Yunnan

Puer

dark

1

C. sinensis cv. Shidacha

2

C. assamica cv. Purui

3

C. sinensis cv. Wancha 91

Anhui

Maofeng

green

4

C. sinensis cv. Longjing 43

Zhejiang

Longjing

green

5

C. assamica cv. Yunkang 10

Yunnan

Puer

dark

6

C. sinensis cv. Fudingdabaicha

Fujian

Fuding

white

7

C. assamica cv. Zijuan

Yunnan

Puer

dark

8

C. sinensis cv. Keemenzhong

Anhui

Qihong

black

9

C. sinensis cv. Jinguanyin

Fujian

Tieguanyin

oolong

10

C. sinensis cv. Caoxi 1

Anhui

Maofeng

green

11

C. sinensis cv. Shuchazao

Anhui

Huangya

yellow

12

C. sinensis cv. Huangshanzhong

Anhui

Maofeng

green

13

C. sinensis cv. Huangguanyin

Fujian

Tieguanyin

oolong

14

C. sinensis cv. Baiye 1

Zhejiang

Baicha

green

437

aFresh

438

fourteen cultivars from different regions of China which covered major production

439

areas of Camellia. The cultivars selected were representative sources of six typical

440

types of tea.

leaves (two leaves and a bud stage of development) were harvested from

441 442 443



444 445

Table 2. Categorization of Camellia cultivars by PCA defined chemical grouping PCA Group A

Cultivar

ID Brief Description

Fuding Dabaicha

6

A

Jinguanyin

9

A

Baiye 1

14

A

Caoxi 1

10

A

Huangguanyin

13

B

Shidacha

1

B

Longjing 43

4

B

Wancha 91

3

B

Shuchazao

11

B

Purui

2

B

Zijuan

7

B

Huangshanzhong 12

B

Yunkang 10

5

Recognized as a national-level improved cultivar in 1985. It belongs to clones, small trees, middle leaves, early-born seeds, and suitable for white tea and green tea. An asexual propagation cultivar of the clonal bred by the hybrid breeding method. It has large and tidy germination density, strong tenderness, and is suitable for oolong tea. A variant cultivar selected from Zhejiang local population. It is shrub type and middle-leaf clones. In early spring, young buds are jade-white and they are the best varieties for making Anji-baicha green tea. An asexual propagation cultivar selected from the Huangshanzhong population. The buds have strong fertility and tenderness, both are suitable for Huangshanmaofeng green tea. An asexual propagation cultivar selected from artificial hybrid progeny. It is a small arbor type, early-born, with strong fertility, strong germination and strong tenderness and suitable for oolong tea, black tea and green tea. A sexual propagation cultivar with large leaves and medium buds, specifically to make Taipinghoukui green tea which is one of top ten famous teas in China A national-grade cultivar with strong germination ability, high germination density and is suitable for West Lake Longjing green tea. Selected by systematic breeding. It has strong growth potential and good tenderness, and is suitable for green tea. An asexual propagation cultivar which is a shrub, a middle leaf, and an early-growing species. The cultivar has strong growth potential, good tenderness, high yield and is suitable for green tea. cultivated from 1973 by single-plant breeding. It is clonal, arbor-type, large-leaf, early-growing, strong fertility of bud leaves, suitable for black tea and dark tea. A variant of Yunnan large leaf population with purple stem, purple leaf and purple bud. Buds have strong fertility and tenderness. It is suitable for Huangshanmaofeng green tea. A cultivar that was systematically selected from the natural population in 1954. It is a arbor type with fast growth of


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B

Keemenzhong

8

new shoots and strong ability to grow buds and is suitable for black tea and dark tea. a sexual propagation cultivar with shrub type, middle leaf, yellow and green leaves, and high yield, produced in Qimen County, Anhui Province, and was recognized by the state in 1985. It is suitable for black tea and green tea.

446

Pedigree information is available from the Chinese Crop Germplasm Information

447

System: http://www.cgris.net/cgris_english.html.

448



Figure 1. Heat Map of cultivar by 61 annotated compounds determined by LC-MS/MS


Page 24 of 36

Page 25 of 36


Figure 2.

PCA score plot (42 samples, 14 x 3)



Figure 3. A. OPLS-DA score plot of the 61 annotated compounds. B. VIP values of the 61 compounds in the OPLS-DA (Red, VIP≥1; green, VIP8 )-GC Epicatechin Theaflavin Hyperoside Vitexin-2''-O-

0.48b 3.29± 0.33b 1.58± 0.19a 1.58± 0.09b 3.86± 0.60b 2.06± 0.37a 4.65± 0.26a 2.32± 0.12a 3.16± 0.36ab 1.00± 0.15b 1.88± 0.35a 6.1±1. 38bc 2.46± 0.2def 62.99

42cd 1.43± 0.21c 1.00± 0.38a 1.56± 0.16b 1.00± 0.21b 1.65± 0.49a 1.38± 0.33b 1.38± 0.06b 1.00± 0.19b 3.47± 0.38ab 2.46± 0.57a 5.73± 1.22bc 16.53 ±1.67a 1.00±

.22d 2.36±0 .29bc 1.57±0 .18a 4.4±0. 38a 5.17±0 .11b 1.92±0 .38a 1.97±0 .75ab 1.82±0 .1ab 1.54±0 .3b 1.48±0 .25b 1.78±0 .20a 7.59±1 .15bc 5.64±0 .44bcd 27.28±

.5c 2.99±0 .33bc 1.63±0 .06a 2.92±0 .6ab 3.69±0 .79b 1.72±0 .39a 2.84±1 .01ab 1.83±0 .09ab 2.54±0 .16ab 2.30±0 .48b 1.09±0 .14a 5.09±0 .97bc 4.62±0 .50cd 65.64±

.35bc 2.11±0 .27bc 1.12±0 .11a 2.74±0 .12ab 2.11±0 .13b 2.16±0 .45a 1.00±0 .06b 1.89±0 .37ab 1.63±0 .33b 1.51±0 .07b 1.52±0 .26a 4.16±0 .67bc 8.77±0 .69b 1.98±0

.14d 5.48±0 .53a 1.98±0 .65a 2.45±0 .48b 3.13±0 .58b 2.05±0 .44a 2.83±1 .28ab 1.56±0 .11ab 4.11±0 .52ab 2.26±0 .33b 1.61±0 .27a 1.00±0 .15c 7.53±0 .76bc 166.4±

.08d 1.00±0 .15c 1.17±0 .06a 1.13±0 .17b 20.71± 6.05a 2.09±0 .47a 1.68±0 .53ab 1.59±0 .29ab 1.4±0. 09b 4.04±0 .16a 1.37±0 .13a 14.31± 1.95ab 7.24±0 .58bc 11.03±

0.42bc 4.73± 0.43ab 1.58± 0.16a 1.71± 0.07b 4.66± 0.35b 1.77± 0.33a 1.35± 0.12b 2.15± 0.13ab 2.24± 0.13b 1.78± 0.3b 1.97± 0.25a 3.55± 0.59bc 1.00± 0.11f 47.13


0.22bc 2.15± 0.13bc 1.41± 0.06a 3.97± 0.49ab 1.58± 0.25b 1.64± 0.28a 1.35± 0.09b 1.79± 0.07ab 5.4±1. 34a 1.89± 0.12b 1.73± 0.18a 3.02± 0.34bc 3.18± 0.21def 75.7±

5bc 5.31±0. 64a 1.47±0. 18a 3.46±0. 26ab 2.88±0. 5b 2.25±0. 58a 1.63±0. 1ab 1.66±0. 12ab 3.39±0. 97ab 1.97±0. 32b 1.57±0. 38a 1.67±0. 23bc 3.54±0. 42def 15.34±

.74a 1.96±0 .29bc 1.52±0 .17a 3.22±0 .39ab 3.35±0 .3b 1.80±0 .3a 1.86±0 .12ab 2.18±0 .11ab 5.20±0 .48a 2.90±0 .52ab 1.77±0 .48a 7.87±0 .88b 2.41±0 .23def 40.09±

.47c 1.98±0 .22bc 1.44±0 .15a 1.00±0 .14b 3.19±0 .38b 2.07±0 .32a 1.16±0 .11b 2.1±0. 16ab 2.14±0 .59b 1.08±0 .14b 2.12±0 .35a 14.42± 2.72ab 1.22±0 .17ef 20.43±

.15c 1.65±0 .10bc 1.57±0 .04a 2.54±0 .30b 2.35±0 .10b 1.00±0 .07a 1.53±0 .08ab 1.81±0 .04ab 2.15±0 .11b 1.47±0 .27b 1.72±0 .09a 18.79± 1.26a 4.38±0 .19cde 43.15±

0.06d 1.58± 0.05bc 2.32± 0.44a 2.05± 0.37b 2.73± 0.32b 1.49± 0.14a 3.21± 0.92ab 1.00± 0.02b 2.68± 0.15ab 1.00± 0.21b 1.00± 0.09a 4.87± 0.52bc 7.63± 0.15bc 68.50


rhamnoside

±5.43b

0.22f

3.16def

7.67bc

.34f

13.77a

1.20f

c

Procyanidin

2.58± 0.21b

±4.59c

4.58b

Page 32 of 36

2.30ef

3.81cde

2.52def

1.85cde

d

3.14± 0.39ab

±3.28b c

2.87±0 2.32±0 2.61±0 2.43±0 4.01±0 2.03± 1.00± .39ab .21bcd .30b .21bc .14a 0.19bcd 0.06e

2.39±0. 28bcd

2.46±0 2.42±0 1.11±0 1.21± .23bc .25bc .06de 0.02cde

3.11±0. 32c 3.04±0. 41bcd

3.07±0 .25c 2.82±0 .32cde

1.18±0 .16de 2.55±0 .35cdef

3.45±0 .13bc 2.52±0 .13cdef

6.20± 0.12a 4.71± 0.13ab

1.26±0 .16d 1.12±0 .19c 2.82±0 .47c 3.41±0 .57ab 1.55±0 .25b 2.46±0 .25bcde

1.28±0 .03d 1.04±0 .08c 1.96±0 .16c 1.00±0 .10b 1.27±0 .09b 1.49±0 .09de

2.01± 0.10bcd 1.49± 0.08bc 2.10± 0.16c 3.35± 0.26ab 1.00± 0.04b 3.33± 0.16abc

e

Myricetin 3'-glucoside Eriodictyol Vicenin 2 Naringenin Isovitexin Quercetin Herbacetin Myricetin

1.93± 0.17cde 4.76± 0.42a 1.00± 0.16d 2.02± 0.3abc 3.08± 0.39bc 4.60± 0.70a 2.25± 0.34b 2.31± 0.26bcd

1.00± 0.11e 1.00± 0.17f 3.39± 0.41a 2.95± 0.50a 2.92± 0.50c 2.41± 0.43ab 2.64± 0.49b 1.00± 0.09e

e

Kaempferol Rutin

1.84± 0.22de 41.17

4.70±0 .37ab 3.92±0 .3abc 1.64±0 .21bcd 1.67±0 .17abc 3.43±0 .46bc 2.97±0 .32ab 2.09±0 .32b 3.43±0 .39ab

5.56±0 .58a 3.78±0 .54abcd 1.34±0 .34cd 2.84±0 .43ab 3.29±0 .68bc 3.60±0 .72ab 3.20±0 .40b 4.49±0 .69a

2.68±0 .23cd 2.09±0 .21def 2.65±0 .11ab 2.18±0 .25abc 5.51±0 .64ab 3.77±0 .51ab 1.26±0 .16b 1.54±0 .12de

5.51±0 .52a 2.52±0 .33cdef 2.40±0 .09abc 1.15±0 .16c 6.13±0 .93a 4.35±0 .72a 2.74±0 .94b 2.32±0 .28bcde

3.14±0 .23bc 1.14±0 .14ef 1.84±0 .19bcd 2.13±0 .28abc 1.00±0 .15c 1.83±0 .18ab 1.66±0 .21b 1.54±0 .10de

1.39± 0.13de 2.59± 0.31cde

2.23± 0.11cde 3.22± 0.27abc

f

d

1.15± 0.11d 1.21± 0.14c 1.81± 0.15c 3.94± 0.55a 1.63± 0.16b 2.96± 0.29abc

1.85± 0.08bcd 1.44± 0.08c 1.89± 0.24c 3.51± 0.49ab 8.72± 1.17a 1.81± 0.2cde

1.33±0. 17cd 1.00±0. 20c 2.54±0. 44c 3.22±0. 72ab 1.71±0. 34b 2.9±0.3 7abcd

1.61±0 .22bcd 1.65±0 .2abc 2.17±0 .28c 3.15±0 .50ab 1.97±0 .19b 2.68±0 .19bcd

1.00± 0.09e 8.6±1.

1.85±0. 29de 60.71±

4.12±0 3.82±0 1.09±0 1.33± .49bcde .67cde .06e 0.01e 64.19± 31.97± 19.33± 37.03

d

5.92± 0.77bc 1.86±

2.04±0 4.51±0 7.19±0 1.16±0 9.94±1 6.34± .30de .67bcd .9ab .17e .05a 0.83bc 25.41± 42.55± 1.00±0 38.01± 346.9± 21.42


Page 33 of 36


±4.07b

0.49c

4.02bc

23.5b

.19c

6.24bc

26.61a

c

Kaempferol-3 -O-glucoside Indoleacrylic acid 4-Hydroxycin namic acid 4-Hydroxybe nzoic acid Theogallin Pyroglutamic acid Kynurenic acid Caffeic acid Salicylic acid Pantothenic acid Caffeine

1.66± 0.28c 8.24± 0.93bcd

±3.12b

00c

15.19b

7.82b

7.19bc

1.62bc

c

3.25± 0.69c 9.69± 1.20abc

1.14±0 .15c 7.37±0 .7bcdef

1.13±0 .21c 7.25±1 .55bcdef

6.61±0 .82b 8.61±0 .43bcd

1.57±0 .42c 1.88±0 .35ef

11.89± 1.55a 7.84±0 .73bcde

2.40± 0.29c 15.41 ±2.47a

c

1.71± 0.23c 1.00± 0.03f

1.00±0. 22c 10.26± 1.68abc

1.03±0 .14c 9.13±1 .05abc

1.50±0 .28c 12.54± 1.61ab

1.05±0 .06c 2.64±0 .19def

e

1.89± 0.40b 10.52 ±0.78b 2.35± 0.59a 1.23± 0.09bc 6.15± 1.12ab 1.14± 0.11a 1.92± 0.19a 1.97± 0.55a 1.79± 0.08a

±2.08b 1.03± 0.06c 5.73± 0.76cde f

4.96± 1.40a 2.8±0. 59efg 3.01± 0.26a 1.19± 0.16bc 1.78± 0.26bc 1.00± 0.12a 2.08± 0.34a 1.72± 0.64a 1.36± 0.1abcd

1.56±0 .44b 1.83±0 .19fg 1.69±0 .13a 1.1±0. 04bc 6.05±0 .75ab 1.17±0 .19a 1.63±0 .18a 1.74±0 .43a 1.66±0 .11ab

1.44±0 .19b 1.35±0 .51fg 1.76±0 .28a 1.08±0 .12c 7.12±1 .74a 1.18±0 .15a 1.02±0 .16a 1.95±0 .51a 1.61±0 .08abc

2.28±0 .25ab 2.84±0 .51efg 3.05±0 .33a 1.00±0 .11c 1.51±0 .35c 1.38±0 .27a 1.91±0 .15a 1.47±0 .28a 1.61±0 .11abc

1.72±0 .53b 1.35±0 .24fg 1.11±0 .21a 2.11±0 .25a 2.6±0. 54bc 1.24±0 .19a 1.57±0 .23a 2.33±0 .59a 1.00±0 .06d

2.3±0. 61ab 8.13±0 .79bc 5.13±2 .55a 1.01±0 .10c 1.18±0 .21c 1.25±0 .22a 1.47±0 .03a 1.65±0 .11a 1.33±0 .04bcd

1.45± 0.35b 2.26± 0.28fg 4.59± 1.39a 1.41± 0.14abc 4.12± 0.68abc 1.21± 0.07a 1.00± 0.09a 1.00± 0.16b 1.54± 0.1abc

1.39± 0.19b 8.49± 0.56bc 1.00± 0.11a 1.88± 0.14ab 1.00± 0.03c 1.03± 0.13a 1.80± 0.17a 1.92± 0.19a 1.15± 0.03cd

1.55±0. 26b 6.67±1. 01cd 2.83±0. 27a 1.65±0. 17abc 4.31±1. 11abc 1.30±0. 32a 1.39±0. 39a 2.12±0. 42a 1.32±0. 10bcd

2.39±0 .39ab 4.10±0 .35def 1.92±0 .42a 1.15±0 .07bc 3.86±0 .47abc 1.00±0 .27a 1.74±0 .16a 1.57±0 .05a 1.56±0 .1abc

1.00±0 .15b 5.73±0 .68cde 2.53±0 .29a 1.79±0 .19abc 3.76±0 .77abc 1.16±0 .20a 1.44±0 .21a 2.14±0 .16a 1.67±0 .12ab

1.55±0 .40b 13.92± 0.28a 1.50±0 .01a 1.25±0 .01bc 1.00±0 .04c 1.03±0 .001a 1.54±0 .24a 1.83±0 .34a 1.25±0 .02bcd

1.70± 0.01b 1.00± 0.03g 1.51± 0.83a 1.45± 0.21abc 5.18± 1.23abc 1.3±0. 33a 1.14± 0.11a 3.38± 0.71a 1.42± 0.03abc d



Theobromine Theophylline

3.46± 0.25cd 4.12± 0.44bcd

3.35±0 .41cd 2.48±0 .32de

11.71± 1.47a 8.26±0 .46ab

2.20±0 .28d 1.77±0 .39e

11.48± 1.3a 8.74±1 .32a

12.17 ±1.95a 9.09± 1.73a

1.86± 0.16d 1.69± 0.21e

Page 34 of 36

10.11 ±1.32a 8.50± 1.29a

7.44±0 .66abc 6.28±0 .65abcd

2.79±0. 27cd 2.62±0. 39de

4.59± 0.8def

11.29± 13.81± 2.06±0 9.42±1 1.00±0 5.35± 6.28± 8.84±1. 1.64abc 2.05a .24ef .48abcd .19f 0.58cde 0.64cde 57abcd

5.29±0 .36bcd 3.61±0 .35cde

8.28±0 .41ab 7.53±1 .00abc

1.95±0 .09d 1.65±0 .06e

1.00± 0.20d 1.00± 0.07e

e

Eugenol Coumarin PC(18:2/0:0)

5.23± 0.66def 4.48± 0.3bcd 7.72± 1.1bc

1.77± 0.32fg 7.53± 1.27bcd

1.00±0 .10g 7.52±1 .08bcd

2.55±0 .29cdefg 5.05±0 .89bcde

2.32±0 .23efg 6.70±0 .83bcd

1.29±0 .17fg 1.30±0 .21e

2.54±1 .19cdefg 2.77±0 .43cde

12.78± 7.27±1 6.07±0 7.04± 1.31ab .16bcde .35cdef 0.21bcd

f

f

e

7.20± 0.59a 3.74± 0.46bcd

1.95± 0.16efg 1.00± 0.08e

3.46±0. 40bcdef 3.49±0. 64bcde

1.86±0 .15efg 5.99±0 .69bcde

5.02±0 .50ab 13.65± 2.39a

4.19±0 .08bcde 2.35±0 .23de

4.86± 0.1abc 8.61± 0.61ab

2.11± 0.24gh 1.00± 0.2b 1.00± 0.11c 1.02± 0.1c 1.00± 0.03e

2.61±0. 38fgh 12.68± 2.05a 2.22±0. 34bc 2.19±0. 3b 78.35± 10.97bc

13.35± 0.9b 1.39±0 .24b 3.56±0 .46bc 2.11±0 .18bc 60.56± 9.87cde

9.44±0 .73c 16.14± 2.57a 6.23±1 .11a 3.35±0 .45a 20.29± 4.5de

6.89±0 .08cd 5.54±0 .28b 1.92±0 .17bc 1.88±0 .08bc 2.15±0 .06e

19.02 ±0.68a 5.21± 0.18b 3.95± 0.17ab 1.70± 0.06ab 1.79± 0.15e

e

Adenosine 4-Hydroxyco umarin PC(16:0/0:0) Phthalic anhydride Biotin

5.82± 0.57de 13.37 ±1.66a 4.14± 0.66ab 2.09± 0.25bc 98.09 ±5.64b

3.8±0. 22efg 1.98± 0.25b 2.69± 0.51bc 1.36± 0.19bc 250±3 5.27a

13.73± 0.42b 3.42±0 .41b 2.99±0 .47bc 1.26±0 .12bc 3.41±0 .55e

6.21±0 .85de 1.95±0 .31b 2.46±0 .46bc 1.82±0 .22bc 4.74±1 .19e

5.23±0 .27def 2.05±0 .19b 3.17±0 .43bc 1.7±0. 18bc 131.7± 10.73b

1.00±0 .18h 5.42±0 .82b 1.16±0 .25c 1.00±0 .13c 4.08±0 .77e

3.95±0 .34efg 2.49±0 .35b 1.68±0 .23c 1.56±0 .16bc 221.9± 24.02a

2.82± 0.30fgh 15.14 ±1.76a 2.32± 0.33bc 1.84± 0.18bc 4.82± 0.75e

c

Piperitone

1.52± 0.2d

d

1±0.3 2d

5.45±0 4.91±0 6.3±0. .71bc .75bc 78b

1.31±0 5.46±0 1.03± .22d .51bc 0.13d


2.88± 0.27cd

6.32±0. 86b

1.25±0 5.44±0 3.44±0 11.18 .07d .66bc .07cd ±0.29a

Page 35 of 36


Pheophorbide A Phaeophorbid eB Umbelliferon e Salicylaldehy de

4.02± 0.56ef 4.84± 1.19e 6.18± 0.93bc 5.18± 0.52bcd

3.69± 0.53ef 2.95± 0.48e 1.00± 0.24e 10.75 ±1.76a

35.4±4 .09c 29.35± 4.96bc 1.67±0 .27e 3.48±0 .29cde

4.95±0 .59ef 3.86±0 .89e 1.5±0. 23e 5.16±0 .88bcd

1.35±0 .23ef 1.89±0 .14e 1.66±0 .22e 4.42±0 .44cde

1.25±0 .34f 1.00±0 .24e 5.72±0 .85bcd 1.23±0 .22de

2.09±0 .28ef 3.32±0 .52e 1.38±0 .44e 6.99±0 .6abc

1.00± 0.18f 1.09± 0.04e 6.23± 0.86bc 4.68± 0.71bcd

8.36± 0.76ef 5.2±1. 09e 2.55± 0.19cde 2.77± 0.24de

9.14±1. 79ef 5.62±1. 78e 11.68± 1.64a 2.87±0. 47de

24.93± 2.19cd 24.33± 3.55cd 1.56±0 .33e 4.59±0 .83bcde

15.22± 2.81de 11.58± 3.02de 7.50±1 .23b 4.76±0 .86bcde

124.4± 4.57a 86.52± 7.6a 1.73±0 .17e 8.52±0 .43ab

78.87 ±5.86b 41.16 ±2.09b 2.12± 0.14de 1.00± 0.12e

e

a-h:

Values in the same row that are labeled with different superscript letters differ significantly (P < 0.05). Statistical analysis was ANOVA with pairwise post hoc comparisons by the method of Bonferroni.



Supplemental Table 2.

Page 36 of 36

Student t-test analysis for compounds with VIP values ≥1

Name Caffeine Theanine Theophylline Theobromine Indoleacrylic acid 1-deoxy-1-L-theanino-D-fructose Myricetin 3'-glucoside Epigallocatechin Gallate

VIP values 4.29 3.05 3.02 2.90 1.69 1.54 1.08 1.05


P-value

Differences in chemical composition among commercially important

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