LC-MS Untargeted Metabolomics To Explain the Signal Metabolites

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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

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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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1 ACS Paragon Plus Environment


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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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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

LC-MS Untargeted Metabolomics To Explain the Signal Metabolites

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