LC-MS Untargeted Metabolomics To Explain the Signal Metabolites

May 16, 2017 - LC-MS Untargeted Metabolomics To Explain the Signal Metabolites. Inducing Browning in Fresh-Cut Lettuce. Carlos J. García, Rocío ...
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LC-MS untargeted metabolomics to explain the signal metabolites inducing browning in fresh-cut lettuce Carlos J. Garcia, Rocio Garcia-Villalba, Maria I. Gil, and Francisco A. Tomas-Barberan J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 16 May 2017 Downloaded from http://pubs.acs.org on May 16, 2017

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Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Journal of Agricultural and Food Chemistry

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LC-MS untargeted metabolomics to explain the signal metabolites inducing

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browning in fresh-cut lettuce

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Carlos J. García, Rocío García-Villalba, María I. Gil, Francisco A. Tomas-Barberan*

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Affiliation: Research group on Quality, Safety, and Bioactivity of Plant Foods. CEBAS

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(CSIC), P.O. Box 164, Espinardo, Murcia 30100, Spain

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Revista: Journal of Agricultural and Food Chemistry.

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Section: Omics applied to Food and Agriculture.

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*Corresponding author: [email protected]

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ABSTRACT: Enzymatic browning is one of the main causes of quality loss in lettuce

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as a prepared and ready to eat cut salad. An untargeted metabolomics approach using

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UPLC-ESI-QTOF-MS was performed to explain the wound response of lettuce after

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cutting and to identify the metabolites responsible of browning. Two cultivars of

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Romaine lettuce with different browning susceptibility were studied at short time

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intervals after cutting. From the total 5975 entities obtained from the raw data after

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alignment, filtration reduced the number of features to 2959, and the statistical analysis

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found that only 1132 entities were significantly different. Principal component analysis

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(PCA) clearly showed that these samples grouped according to cultivar and time after

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cutting. From those, only fifteen metabolites belonging to lysophospholipids,

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oxylipin/jasmonate metabolites, and phenolic compounds were able to explain the

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browning process. These selected metabolites showed different trends after cutting;

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some decreased rapidly, others increased but decreased then after, whereas others

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increased during the whole period of storage. In general, the fast-browning cultivar

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showed a faster wound response and a higher raw intensity of some key metabolites

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than the slow-browning one. Just after cutting the fast-browning cultivar contained

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eleven out of the fifteen browning-associated metabolites, while the slow-browning

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cultivar only had five of them. These metabolites could be used as biomarkers in

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breeding programs for the selection of lettuce cultivars with lower browning potential

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for fresh-cut.

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KEYWORDS: wound-signal, UPLC-ESI-QTOF-MS, PCA, phenolic metabolism,

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minimal processing, lettuce

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INTRODUCTION

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Lettuce (Lactuca sativa) is a major fresh vegetable crop of great economic importance

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for the fresh-cut processing industry which demands products with longer shelf life.

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Enzymatic browning is an important biochemical disorder that leads to significant

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quality loss after lettuce is cut and is promoted during storage, causing product

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rejection. Polyphenol-oxidase (PPO) is the main enzyme involved in the oxidative

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browning reaction of cut lettuce. PPO is found in young plants in the thylakoid

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membrane of chloroplasts and is released through membrane disruption .1 Wounding

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induces activation from latent to fully active PPO, although the total lettuce PPO levels

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(latent plus active) are not affected .2,3 Wound stress of plant tissues triggers an increase

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in the levels of phenolic compounds, particularly phenylpropanoids. This wound-

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induced phenolic biosynthesis is mediated through an increase in phenylalanine

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ammonia-lyase (PAL) activity. PAL, the first enzyme in the biosynthesis of phenolic

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PPO substrates via the phenylpropanoid pathway, increases immediately after lettuce

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cutting and has been found to be closely related to browning development.4.5 The fresh-

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cut process induces an abiotic stress response that triggers phenylpropanoid

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metabolism.6 To date, the most investigated and widely accepted hypothesis of

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browning development in lettuce is related to the presence of PAL and the constitutive

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composition of phenolic compounds .7 Several authors have correlated the extent of

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browning in cut lettuce with the content of phenolic compounds.8,9 Other secondary

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metabolites, such as sesquiterpene lactones, have also been associated with lettuce

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discoloration, although following a different pathway.10 There is a consensus in the

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mechanism of wound-induced tissue browning that starts with a signal produced from

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the damaged membranes that induces phenolic biosynthesis enzymes, particularly PAL,

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mediated by jasmonic acid and ethylene.11,12 However, the changes at a metabolic level

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Journal of Agricultural and Food Chemistry

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during the browning process have not been studied systematically using a metabolomics

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approach.

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In this context, metabolomics offers a powerful tool to study global changes in

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the entire metabolite set using advanced analytical technologies combined with

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multivariate statistical analysis13,14 and has been applied to understand the composition

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and origin of plant-derived foods.15,16 The potential of metabolomics has been applied

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recently to better characterize the lettuce metabolome through NMR17, GC-TOF MS18,

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and UPLC-QTOF.19 These techniques have already been used in several studies

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associated with the exposure of lettuce to arsenic-contaminated irrigation water20, and

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pesticides21, and the effect of lettuce transgenic modification.22A protocol for studying

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the metabolome of cut lettuce has recently been reported.23 However, to the best of our

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knowledge, untargeted metabolomics has not been explored to understand the browning

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process so far.

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The aim of this study was to get insight into the global metabolic changes

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occurring after lettuce cutting to identify the responsible metabolites related to the

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wound response and the subsequent browning process. To this end, an untargeted

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metabolomics approach using UPLC-ESI-QTOF was applied to evaluate two Romaine

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lettuce cultivars with different browning susceptibility after cutting.

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MATERIALS AND METHODS

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Chemicals and Reagents. Methanol and acetonitrile were from J.T Baker

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(Deventer, The Netherlands) and formic acid was from Panreac (Barcelona, Spain). All

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other chemicals and reagents were of analytical grade. Milli-Q system (Millipore Corp.,

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Bedford, MA, USA) ultrapure water was used throughout the study. Authentic

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standards of phenolic metabolites were obtained from the CEBAS-CSIC phenolic

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standards collection.

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Plant material. Two Romaine lettuce cultivars were selected, FB (fast

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browning) and SB (slow browning), after an intensive screening of lettuce genotypes

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with different browning susceptibility.24 Lettuces were harvested in Torrepacheco

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(Murcia, Spain) in March 2014 when heads reached the commercial maturity stage

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based on head weight and core length.25

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Processing and storage conditions. Lettuces were harvested the day before

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processing and stored for 24 h at 7 ºC and 80% RH. Lettuce processing was as

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previously described.23 Six leaves from the middle part of the head were selected.

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Midribs were carefully excised and cut in pieces of 1.5 cm length in less than 20 min to

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reduce as much as possible the induction of the wound response. Samples of 30 g were

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packaged in plastic bags (120mm x 200 mm) without sealing to maintain an air

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atmosphere and high humidity avoiding water loss. Midrib samples were stored for 2, 6,

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24, 48, 72, 96 and 120 h after cutting at 7 ºC. Plant material was either frozen

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immediately using liquid nitrogen (samples at 0 h) or after the different time intervals,

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then freeze-dried and finally milled to a fine powder. Four replicates (corresponding to

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four heads) per cultivar and time after cutting were processed.

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Digital image analysis. Images of midribs were captured using a digital camera

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(Nikon D7100) in a darkness room. The set up conditions for the camera were: lens

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(Nikon Advanced Multi-CAM 3500DX), light provided by a flash (Metz Mecablitz)

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with a speed of 1/125s, color temperature of 5600 K, focal opening 22 (f) and length 35

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mm. In each image, 20 midrib pieces were placed together facing up the cut edge. At

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each sampling time, an image of a grey card was captured, in the same conditions for

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the balance of the image colors. Four images, one per replicate, were captured per

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cultivar at 0 h and 120 h after storage and saved as JPEG file. Images were analyzed

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using the software ImageJ version 1.48v (NIH Image, National Institute of Health,

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Bethesda, USA) as reported previously.23 The total cut edge area was selected by

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contrast and considered as the study area. The extension of tissue browning was

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evaluated as: % Browning =

Browned pixels Total pixels area

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The RGB color model was used to identify the browned pixels in the range of

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hue (20-35), saturation (40-195) and brightness (0-225). Values of hue, saturation, and

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brightness were measured following the HSB color model. One-way analyses of

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variance (ANOVA) with a significant level of p < 0.05 were performed. Bilateral

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correlations were determined by Pearson’s correlation with a confidence interval

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established at 95% using PASW Statistics 20 for Windows (SPSS Inc., Chicago, IL,

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USA).

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Untargeted metabolomics analysis. Compounds were extracted according to

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the method described by Llorach et al (2008)26 with minor modifications. Freeze dried

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samples (0.3 g) were mixed with 10 mL of methanol:water (80:20). The extraction

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mixture was sonicated at 30 °C for 30 min, centrifuged at 5,000 g for 15 min and

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directly filtered through 0.22 µm PVDF filter to be analyzed by UPLC-ESI-QTOF.

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Chromatographic separation was performed on an UPLC system (Agilent 1290

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Infinity, Agilent Technologies, Waldbronn, Germany) coupled to Mass QTOF (6550

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Accurate) with electrospray ionization via Jet Stream Technology and a C18 column

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(Poroshell 120, 3 x 100 mm, 2.7 µm pore size). Chromatographic and mass

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spectrometry conditions were the same as described previously.23 Briefly, 3 µL of

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methanolic extracts were injected and separated on the column with a flow rate of 0.4

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mL/min and mobile phases of water + 0.1% formic acid (A) and acetonitrile (ACN) + 7 ACS Paragon Plus Environment

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0.1% formic acid (B). Nitrogen was used as nebulizer (35 psi, 9 L/min) and drying gas

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(280 ºC, 9 L/min, sheath gas temperature 400 ºC and sheath gas flow 12 L/min). Spectra

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were acquired in the range 100-1100 m/z in negative mode, with a fragmentor voltage of

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100 V and acquisition rate 1.5 spectra/s. Samples were also analyzed in the positive

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mode, but in this case the differences between cultivars were less significant and

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therefore were not further studied. Targeted MS/MS analyses were developed to add

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confidence to compound identification. MS/MS product ion spectra were collected at an

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m/z range of 50–800 using a retention time window of min, an isolation window of 4.0

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amu, a collision energy of 30 V and an acquisition rate of 4 spectra/s.

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The main parameters for data extraction and pre-processing were previously

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optimized23 (Figure 1). Briefly, Mass Hunter Qualitative Analysis software (version

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B.06.00 Agilent Technologies) was used for feature (ion) extraction applying molecular

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feature extraction algorithm (MFE). Parameters were set as minimal intensity 5000

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counts, m/z range 100-1100, charge state and the lowest number of ions in the isotopic

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distribution 2 with a peak spacing tolerance of 0.0025 m/z plus 7 ppm. Once ions were

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extracted, files were transformed to .cef format and exported to Mass Profiler

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Professional (MPP) software (version B.12.01, Agilent Technologies, Santa Clara, CA,

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USA). Samples were classified into different experimental groups (named conditions)

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considering two variables: time after cutting (0, 2, 6, 24, 48, 72, 96 and 120 h) and

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cultivars (FB and SB). Compounds from different samples were aligned with a retention

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time window of 0.15 min and a mass window of 5 ppm + 2 mDa. Then filter by flag

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was applied considering only the molecular features that were present in at least 100%

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of replicates in one condition. Data were log transformed and mean centered before

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statistical analysis.

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Two-way ANOVA analysis with variables time after cutting and cultivar was

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applied to the pretreated samples considering p