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Application of targeted metabolomics to investigate optimum growing conditions to enhance bioactive content of strawberry Ikram Akhatou, Ana Sayago, Raúl González-Domínguez, and Angeles Fernández-Recamales J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b03701 • Publication Date (Web): 11 Oct 2017 Downloaded from http://pubs.acs.org on October 12, 2017

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Journal of Agricultural and Food Chemistry

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Application of Targeted Metabolomics to Investigate Optimum Growing

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Conditions to Enhance Bioactive Content of Strawberry

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Ikram Akhatou†, ‡, Ana Sayago†, ‡, Raúl González-Domínguez†,

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Fernández-Recamales†, ‡ *

‡ *

, Ángeles

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†

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21007 Huelva, Spain. ‡International Campus of Excellence CeiA3, University of

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Huelva, 21007 Huelva, Spain.

Department of Chemistry, Faculty of Experimental Sciences, University of Huelva,

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1 ACS Paragon Plus Environment


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Abstract

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A simple, sensitive and rapid assay based on liquid chromatography coupled to tandem

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mass spectrometry was designed for simultaneous quantitation of secondary metabolites

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in order to investigate the influence of variety and agronomic conditions on the

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biosynthesis of bioactive compounds in strawberry. For this purpose, strawberries

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belonging to three varieties with different sensitivity to environmental conditions

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('Camarosa', 'Festival', 'Palomar') were grown in soilless system under multiple

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agronomic conditions (electrical conductivity, substrate type and coverage). Targeted

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metabolomic analysis of polyphenolic compounds, combined with advanced

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chemometric methods based on learning machines, revealed significant differences in

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multiple bioactives, such as chlorogenic acid, ellagic acid rhamnoside, sanguiin H10,

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quercetin 3-O-glucuronide, catechin, procyanidin B2, pelargonidin 3-O-glucoside,

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cyaniding 3-O-glucoside and pelargonidin 3-O-rutinoside, which play a pivotal role in

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organoleptic properties and beneficial healthy effects of these polyphenol-rich foods.

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Keywords: Phenolic compounds, targeted metabolomics, strawberry, variety,

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agronomic practices, UHPLC-ESI-MS/MS

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Introduction

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Phenolic compounds, also called polyphenols, constitute a miscellaneous group of

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secondary metabolites widely distributed in plant kingdom. Because of their structure,

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containing aromatic rings and hydroxyl groups, and their chemical properties as

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hydrogen donors and singlet oxygen quenchers,1 polyphenols are commonly involved in

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defense mechanisms by acting as mediators of plant response against biotic and abiotic

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stresses, including those related to drought, salinity, low temperatures, heavy metal

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exposure or UV radiation. Some phenolic compounds (e.g. anthocyanins) are also

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responsible for distinct plant characteristics such as color and smell of ﬂowers and

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fruits, attractants for pollinators and seed-dispersing animals. On the other hand, several

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phenolics exhibit broad-spectrum antimicrobial activity, thus protecting plants against

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viruses, parasites and other externally damaging agents; whereas others possess

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phytoalexin properties, and their synthesis can be induced in response to wounding,

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feeding by herbivores or infection by pathogens. Thereby, it has been demonstrated that

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proanthocyanidins can contribute to defense and stress resistance, while ellagic acid and

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their derivatives play an important role by protecting plants from predators and

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regulating their growth.2 Since phenolic compounds are ubiquitous in all plants, they are

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an integral part of the human diet through the consumption of edible plants and plant-

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derived products, including fresh and cooked vegetables and fruits, legumes, spices and

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beverages such as fruit juices, wine, tea, coffee and other infusions. In recent years, the

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interest for these bioactives has largely increased due to their health beneﬁts and their

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impact on food quality. Thus, numerous efforts have been made in order to characterize

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polyphenol-rich foods and to study the influence of various factors such as variety,

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pedoclimatic conditions and geographical origin on the production of these bioactives,

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thus serving as a very valuable tool for increasing nutritional quality of plant-derived



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foods in breeding programs, as well as for assessing the authenticity and traceability of

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these foods.3,4

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Strawberries (Fragaria x ananassa Duch.) are one of the most economically important

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and widely cultivated fruit crops across the world. These summer fruits are very popular

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among consumers due to their attractive red color and highly desirable taste and flavor.

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Furthermore, epidemiological studies have revealed that consumption of berries is

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associated with the prevention and improvement of chronic-degenerative diseases.5 In

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this line, a significant number of in vivo and in vitro studies have demonstrated the

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effect of strawberry consumption on the regulation of oxidative stress,6 inflammation,7

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obesity,8 diabetes,9 cancer10 and cardiovascular diseases.11 These protective effects

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appear to be associated with the high content of bioactive compounds, including both

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micronutrients and phytochemicals.3 In particular, strawberries are a very rich source of

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anthocyanins, flavan-3-ols, flavonols, as well as ellagic acid and derivatives, which are

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well known for their antioxidant, cardioprotective and anti-inflammatory properties.12

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However, the concentration of polyphenols in strawberry significantly vary between

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genotypes,13-17 and many other factors related to growth conditions and agricultural

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practices may contribute to this phenolic profile. Moreover, their synthesis can also

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change in response to biotic and abiotic stress.18-20 Therefore, breeding programs might

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be used to develop strawberry cultivars with better organoleptic characteristics and

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enhanced production of health-related metabolites.

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Based on the above-reported considerations, this study was aimed to investigate the

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polyphenolic profile of three strawberry cultivars with different sensitivity to

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environmental conditions, which were grown in soilless systems under different

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agronomic conditions (electrical conductivity, substrate type and coverage). A targeted

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metabolomic approach based on ultra-high performance liquid chromatography coupled


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to tandem mass spectrometry (UHPLC-MS/MS) was applied to identify and quantitate

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the main polyphenol compounds in strawberry fruits. Then, partial least squares

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discriminant analysis and random forest methods were employed to explore the

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differences in metabolite profiles between strawberry cultivars, and to assess the

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influence of agronomic practices on polyphenol production.

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Materials and Methods

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Chemicals and standards

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Acetonitrile, methanol and glacial acetic acid (LC/MS grade) were purchased from

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Fisher Scientific (Leicestershire, UK), while the ultrapure water was obtained from a

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Milli-Q water purification system (Millipore, Bedford, MA). Pure standards of gallic

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acid, protocatechuic acid, sinapic acid, (-) epicatechin gallate, ellagic acid, chlorogenic

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acid, (-) epicatechin, ferulic acid and quercetin were acquired from Sigma-Aldrich

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(Steinheim, Germany). Vanillic acid, vanillin, caffeic acid, p-hydroxyphenylacetic acid,

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cinnamic acid, p-hydroxybenzoic acid, m-coumaric acid, p-coumaric acid and tyrosol

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were from Fluka (Steinheim, Germany). Apigenin, luteolin, pelargonidin 3-O-glucoside,

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cyaniding 3-O-glucoside, (+) catechin, procyanidin B1, procyanidin B2, quercetin 3-O-

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glucoside,

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hesperidin and hyperoside were purchased from Extrasynthèse (Genay, France).

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Experimental design and sampling

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Strawberries (Fragaria x ananassa Duch.) were cultivated in soilless systems, in

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plantations managed by the University of Huelva (southwest Spain, latitude 37° 14ʹ N,

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longitude 6° 53ʹ W, altitude 23 m). In this study, three cultivars were investigated

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('Palomar', 'Festival' and 'Camarosa'), which were grown under different electrical

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conductivities (1, 2 and 3 dS/m), two macrotunnel types (covered and uncovered) and

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three substrates (coconut fiber, perlite and rockwool). Detailed description about the

kaempferol

3-O-glucoside,

isorhamnetin

3-O-glucoside,


quercetrin,


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experimental design can be found in our previous works.4,17 For each study group,

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several fruits were collected and pooled, and then washed and homogenized using a

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kitchen mixer. These samples were stored for up to 2 months at -21 °C until further

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analysis.

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Sample preparation

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Homogenized fruits (5.0 g) were mixed with 10 mL of methanol, then submitted to

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sonication for 15 min and finally centrifuged at 10000 rpm during 10 min at 4 °C.

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Supernatants were taken to dryness at 40 °C by using a rotary evaporator, and residues

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were redissolved in 3 mL of 50% methanol (v/v). The concentrated extracts were

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filtered through 0.20 µm nylon filter prior to injection.

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Targeted metabolomic analysis

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An Agilent 1200 series ultra-high performance liquid chromatography system (Agilent

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Technologies, Santa Clara, CA), equipped with binary pump system, vacuum degasser,

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cooled autosampler and thermostated column compartment, was employed for targeted

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metabolomic analysis of strawberry extracts. The column used was a 50 mm × 2.1 mm

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i.d., 1.8 µm, Zorbax SB-C18 (Agilent Technologies, Santa Clara, CA), thermostated at

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30 °C. Optimum separation was achieved using a binary gradient delivered at 0.4

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mL/min flow rate, consisting of 0.2% acetic acid in water (v/v) at pH 3.10 (solvent A),

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and pure acetonitrile (solvent B). The gradient elution program was: 0-3 min, 5% B; 3-

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15 min, 5-40% B; 15-15.5 min, 40-100% B; 15.5-17 min, 100% B isocratic; and finally

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returning to initial conditions in 6 min. The injection volume was 5 µL, and the

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autosampler was maintained at 4 °C. The eluent from the column was directly

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introduced into a model 6410 triple quadrupole mass spectrometer equipped with

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electrospray ionization source (ESI), and controlled by MassHunter Workstation

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Software (Agilent Technologies, Santa Clara, CA). Source working conditions were:


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capillary voltage 4000 V, gas flow rate 10 L/min, gas temperature 300 °C and nebulizer

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pressure 35 psi. Phenolic acids, flavonoids, ellagitannins and ellagic acid derivates were

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detected in the negative ion mode, while anthocyanins were analyzed under positive ion

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mode. Detection was performed in dynamic multiple reaction monitoring (dMRM)

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mode using retention times and detection windows, which improves chromatographic

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peaks and provides increased reproducibility and accuracy of quantitation. For

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maximum sensitivity and specificity, instrumental parameters were empirically

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determined to optimize MRM transitions (two for each analyte). The most sensitive

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transition was used for quantitation purposes, and the other one as qualifier transition.

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Identification and quantitation of phenolic compounds was performed using commercial

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standards, when possible. On the contrary, all phenolic derivatives for which

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commercial standards were not available were identified according to previously

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published literature.21 Briefly, ellagic acid derivatives presented characteristic fragments

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at m/z 301 and 207 Da; MS/MS spectra of quercetin derivatives were dominated by

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daughter ions of m/z 301, 179 and 151 Da; while fragmentation of kaempferol and

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pelargonidin derivatives yielded intense signals at m/z 285 and 271 Da, respectively. In

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accordance, the estimated concentrations of ellagic acid derivatives were expressed as

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ellagic acid equivalents, quercetin derivatives as quercetin equivalents, kaempferol

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derivatives as kaempferol 3-O-glucoside equivalents, and pelargonidin derivatives as

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pelargondin 3-O-glucoside equivalents.

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Analytical quality control

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The proposed methodology was validated according to UNE 82009-1:1998 normative.

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The within-laboratory repeatability and reproducibility were assessed by analyzing an

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extract six times within the same day and over a period of 1 month in duplicate,

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respectively. Calibration curves were built at seven concentration levels (0.5-40 µg/mL



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for anthocyanins; 0.05-8.0 µg/mL for the rest of phenolic compounds) by diluting the

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stock solutions (1000 µg/mL in methanol). Recoveries were determined in pooled

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samples by spiking with pure standards at three concentration levels, and three

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replicates from each sample were analyzed.

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Statistical Analysis

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Metabolomic data were subjected to supervised classification methods in order to

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investigate the sample classification in predefined groups and to find the variables with

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the highest discriminant power. As a final step, the Kruskal-Wallis test was performed

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on previously selected metabolites in order to validate these results. Statistical treatment

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of data was performed by using Statistica 8.0 (StatSoft, Tulsa, UK), SIMCA-P v11.5

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(UMETRICS, Umeå, Sweden) and R statistical software 2009 (R Foundation for

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Statistical Computing, Vienna, Austria) packages. Two data mining methods, partial

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least square discriminant analysis (PLS-DA) and random forest (RF), were applied for

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building predictive models, which were subsequently compared for evaluating their

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classification and prediction abilities during cross-validation procedure. In addition,

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sensitivity and specificity of these models were also evaluated using confusion matrices.

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Results and Discussion

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Metabolite Profiling Analysis

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Phenolic profiles of three strawberry cultivars, grown using multiple agronomic

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practices, were evaluated by liquid chromatography-mass spectrometry in MRM

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scanning mode. Twenty-nine different compounds were detected and quantitated, which

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can be classified into different groups of polyphenols: a) phenolic acids (gallic, vanillic,

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protocatechuic, p-hydroxybenzoic, chlorogenic, cinnamic, caffeic, m-coumaric and p-

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coumaric acids); b) ellagic acid and derivatives (HHDP galloyl glucose, ellagic acid

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pentoside, ellagic acid rhamnoside, and sanguiin H10); c) flavonols (quercetin,


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quercetin 3-O-glucoside, quercetin 3-O-galactoside, quercetin 3-O-glucuronide,

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kaempferol 3-O-glucoside and kaempferol 3-O-acetylhexoside); d) flavones (apigenin);

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e) flavan-3-ols (catechin, epicatechin, epicatechin gallate, procyanidins B1 and B2); f)

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anthocyanins (pelargonidin 3-O-glucoside, cyaniding 3-O-glucoside, pelargonidin 3-O-

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rutinoside). The structures of major phenolic compounds detected in strawberries are

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shown in Figure 1. Relative standard deviation obtained for the within-laboratory

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repeatability and reproducibility of the proposed methodology were below 7.3% and

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10.4%, respectively. The higher RSD for the repeatability study corresponded to

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chlorogenic acid, whereas the worst reproducibility was showed by quercetin 3-O-

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glucoside. The method here developed presented a good linearity in the studied

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concentration range (0.5-40 µg/mL for anthocyanins; 0.05-8.0 µg/mL for the rest of

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phenolic compounds), with correlation coefficients higher than 0.99 for all the

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compounds. The limits of detection and quantitation were calculated as the

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concentration necessary to produce a chromatographic signal three and ten times higher

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than the baseline noise, respectively. Results obtained were satisfactory to quantitate

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target compounds at the concentrations found in strawberry samples. Finally, recoveries

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ranged from 54-121% for phenolic acids, from 59-106% for flavonols, from 67-128%

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for flavan-3-ols, and from 75-95% for anthocyanins. Recovery for apigenin was 75%.

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Table 1 shows the mean, minimum and maximum concentration values for the

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polyphenolic compounds detected in the three strawberry cultivars. As can be observed,

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each cultivar showed a characteristic phenolic profile, 'Camarosa' being the strawberry

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variety with greatest total phenolic content mainly due to the high amounts of ellagic

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acid and its derivatives, as well as anthocyanins, which accounted for 44.5% and 35.7%

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of the total polyphenols, respectively. It should be noted that these results agreed with

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our previous study findings3 and with results reported by other authors.22 In this context,



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pelargonidin 3-O-glucoside was the major anthocyanin found in the three cultivars, in

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agreement with previously published results.23 On the other hand, ellagic acid and

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derivatives were the most concentrated phenolic acids in strawberries, in consonance

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with other author’s findings.24-25 Particularly, conjugated ellagic acid rhamnoside was

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the most important metabolite in terms of quantity in the three cultivars (141.43, 123.40

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and 107.87 µg/g of fresh fruit, for 'Camarosa', 'Festival' and 'Palomar', respectively).

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Flavonols and flavan-3-ols were also abundant in strawberries, and accounted for 9.0

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and 8.0% of the total phenolic content, respectively. In absolute terms, the higher levels

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of flavonols were found in cv. 'Camarosa' (46.5 µg/g of fresh fruit,), followed by

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'Palomar' (38.9 µg/g of fresh fruit,) and 'Festival' (37.4 µg/g of fresh fruit,). Quercetin 3-

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O-glucuronide was the most concentrated flavonol in the three surveyed strawberry

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cultivars, with a mean value of 37.7 µg/g. In this line, glucosides and glucuronides of

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quercetin and kaempferol have previously been identified as the major flavonols in

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strawberry.25-26 Regarding the family of flavanols, the higher levels were found in cv.

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'Festival' (10.23 µg/g of fresh fruit,) and the lower in cv. 'Palomar' (5.8 µg/g of fresh

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fruit,). Procyanidin B1 was the predominant flavanol in the three studied strawberry

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cultivars (mean value 22.6 µg/g), followed by catechin (14.03 µg/g), results that agree

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with those reported by other authors.27-28 Finally, it is noteworthy that the total content

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of phenolic acids in the three strawberry cultivars was very similar, except for

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chlorogenic acid, ranging from 2.2 µg/g (cv. 'Festival') to 3.2 µg/g of fresh fruit (cv.

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'Palomar'). On the contrary, the concentration of chlorogenic acid was highly variable,

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'Festival' being the cultivar with greater content (12.7 µg/g) followed by 'Palomar' (5.8

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µg/g) and 'Camarosa' (3.5 µg/g). In this sense, previous studies conducted on

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strawberries have also reported variable composition and contents of phenolic acids.28-31


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To conclude, the flavone apigenin was detected in the three strawberry varieties, with a

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mean content of 0.065 µg/g.

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Chemometric discrimination of strawberry cultivars based on PLS-DA

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Partial least square discriminant analysis (PLS-DA) was applied to identify potential

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markers for cultivar differentiation. This supervised multivariate statistical tool is based

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on the search of latent variables (or components), able to discriminate between

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previously defined categories. In order to optimize the number of these components data

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are usually subjected to cross-validation, so that the statistical performance of the model

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can be defined by the following parameters: RY2, the proportion of variance of the

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response variable explained by the model; RX2, the proportion of variance in the data

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explained by the model; and Q2, the predictive ability of the model.

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Before performing statistical analyses, data were submitted to Pareto scaling to reduce

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the relative importance of larger values. Then, three binary classification models were

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built (model A, 'Camarosa' vs. 'Festival'; model B, 'Camarosa' vs. 'Palomar'; model C,

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'Festival' vs. 'Palomar'), and their quality was evaluated using seven-fold cross

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validation. In all cases, a two-component model was obtained with good quality of fit

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(R2X = 0.645, 0.702 and 0.659, for model A, B and C, respectively), validity (R2Y =

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0.655, 0.751 and 0.733, for model A, B and C, respectively) and prediction (Q2 = 0.527,

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0.663 and 0.664, for model A, B and C, respectively), which resulted in a satisfactory

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separation of sample classes in the corresponding scores plots (Figure 2A-C). Then,

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discriminant metabolites were selected according to the variable importance for the

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projection parameter (VIP), which indicates the importance of the variable in the model.

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For this, phenolic metabolites with VIP values higher than 1.0 were picked and

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compared by using the Kruskal-Wallis test. These results are summarized in box-plots

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represented in Figure 3. As can be observed, 'Camarosa' presented significantly higher



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levels of anthocyanins, ellagic acid rhamnoside, quercetin 3-O-glucuronide and

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procyanidin B2, while cv. 'Festival' was richer in catechin and chlorogenic acid, and cv.

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'Palomar' in sanguiin H10.

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Numerous evidences emphasize the genotype as a pivotal player in the compositional

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variability in primary and secondary metabolites. Their effects on the nutritional and

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sensorial quality of plant derived foods are well known,13-17 and several breeding and

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biotechnological programs are focused on them.32 In this context, various authors have

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previously studied the influence of variety in the content of phytochemicals in

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strawberry.33,34 Other works focused on investigating metabolic changes in strawberry

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during ripening, as well as the differences between organic crops, conventional crops,

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and breeding lines with wild genotypes.35,36 The differential accumulation pattern of

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bioactive componds during fruit development is another important issue, as previously

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reported by Fait et al.37 They found that metabolism in strawberry plant during

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development is organ specific, with a shift towards the conversion of proanthocyanidins

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into anthocyanins during maturation in the receptacle, and the accumulation of

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ellagitannins and flavonoids in the achene during early and late development. In the

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present work, targeted metabolomic profiling by UHPLC-MS/MS revealed significant

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changes in phenolic compounds between three strawberry cultivars with different

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sensitivity to the environment (i.e. 'Camarosa', 'Palomar' and 'Festival'), including

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anthocyanins, ellagic acids and ellagitannis, flavonols, flavan-3-ols and phenolic acids.

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Phenolic profiles showed that the most resistant cultivar (cv. 'Camarosa') presents

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higher levels of anthocyanins, ellagic acid rhamnoside, quercetin 3-O-glucuronide and

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procyanidin B2 compared with more sensitive strawberries, which might indicate an up-

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regulation of the shikimate and malonate pathway. In this sense, it is well known that

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anthocyanins, flavonol glycosides and proanthocyanidins are synthesized in response to


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environmental and developmental signals.38 Furthermore, it has been previously

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described that increased content of flavonoids could be associated with tolerance

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mechanisms against abiotic stress, and that tolerance depends on the species, genotype

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and stage of development of the plant. Thus, it has been demonstrated that strawberry

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production of polyphenols is highly influenced by drought stress,25,39-40 salt stress,19,20

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availability of nutrients in growing medium,41,42 extreme temperatures43,44 and

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ultraviolet radiation.45,46

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Predictive modeling of strawberry cultivars using Random Forest

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Complementarily, random forest (RF) analysis was also applied in order to compare its

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statistical performance with that provided by PLS-DA models, since this statistical tool

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has demonstrated excellent efficiency during the last decade for the analysis of complex

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data sets in many research areas, including food science. RF is a non-parametric and

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non-linear classification and regression algorithm based on ensemble learning, which

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operates by generating multiple decision trees on bootstrap samples taken from original

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data and aggregating their results. Trees are split to many nodes using different subsets

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of randomly selected input variables (m). Thus, the most important parameters to

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optimized RF analysis are the value of m (attributes number made available at each

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node during the growing of trees) and the number of decision trees. Moreover, the

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model quality can be evaluated by the out-of-bag (OOB) error, which is the error

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calculated by predicting data not used for growing the trees. In practice, other

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measurements can also be used for validation purposes, such as Gini index (a measure

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of node purity). Furthermore, the Gini index is also used to evaluate the potential

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discriminant ability of each feature, i.e. the importance of each variable in the model.

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Thus, the mean decrease of the Gini index (MDGI) can be used to select the most

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important metabolites for inter-class discrimination.



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In the case of our three-class strawberry dataset, samples were perfectly classified using

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1000 trees and two predictors for each node. During tree construction, one third of the

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training samples were taken as the test set and the out-of-bag (OOB) data were then

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used to estimate the classification error. Thereby, the statistical performance of the built

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model was 97.4% accuracy rate for training set, 93.3% accuracy rate for testing set, and

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2.56% error rate for OOB. Finally, the RF model was also used for feature selection,

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since variables can be ranked according to their importance in the obtained

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classification. The results indicated that the content of chlorogenic acid, ellagic acid

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rhamnoside, cyaniding 3-O-glucoside, quercetin 3-O-glucoside, epicatechin, sanguiin

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H10, pelargonidin 3-O-rutinoside and hyperoside were by far the eight most

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discriminant variables, in agreement with results provided by PLS-DA. Finally, we

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compared the statistical performance of the two pattern recognition tools here employed

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by computing the sensitivity (SENS) and specificity (SPEC) parameters from the

338

obtained confusion matrixes. Sensitivity and specificity mean values for each cultivar,

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as well as for the overall dataset, are listed in Table 2. The results show that the three

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cultivars are predicted with low SENS (values range from 66-88.8%) when PLS-DA

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modeling was applied, while the SPEC values were close to 100%. On the other hand,

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RF modeling significantly improved SENS values for the three groups, especially for

343

'Camarosa' (100%), maintaining similar specificity values. Therefore, taking into

344

account the overall results, it could be concluded that RF provide higher sensitivity

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compared with the PLS-DA model, by increasing about 24% (models A and B) and

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15.6% (model C), but slightly decreasing the specificity.

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Metabolomic alterations associated with agronomic practices

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The nutritional value of berries is also significantly affected by agronomic and

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environmental conditions in which plants are cultivated, including soil type, climate and


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critical temperatures.33,47-49 For this reason, metabolomic data were re-analyzed by PLS-

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DA considering the different agronomic conditions as categorical factors with the aim to

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elucidate the effect of cultivation practices on the bioactive content of strawberries.

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Each cultivar was statistically treated separately, and the performance of models

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obtained was evaluated by using the parameters Rx2, Ry2 and Q2, listed in Table 3.

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Furthermore, discriminant metabolites were selected following the procedure previously

356

described (Table 4).

357

Considering the macrotunnel type as the categorical factor (i.e. covered and uncovered),

358

PLS-DA models were able to explain 67.2, 68.1 and 64.9% of the variance for cv.

359

'Camarosa', 'Festival' and 'Palomar', respectively. The VIP scores indicated that

360

anthocyanins (pelargonidin 3-O-glucoside, cyaniding 3-O-glucoside and pelargonidin 3-

361

O-rutinoside) and ellagic acid derivatives (pentoside and rhamnoside) were the most

362

important variables to discriminate between the two groups. For the three cultivars,

363

anthocyanins showed higher concentrations in strawberries from covered macrotunel.

364

Particularly, higher content of pelargonidin 3-O-glucoside, cyaniding 3-O-glucoside,

365

pelargonidin 3-O-rutinoside, ellagic acid pentoside and ellagic acid rhamnoside was

366

found in 'Camarosa' strawberries grown in covered macrotunnels. Similarly,

367

concentrations of anthocyanins, procyanidin B2, catechin and quercetin 3-O-

368

glucuronide were higher in cv. 'Festival'; and pelargonidin 3-O-glucoside, pelargonidin

369

3-O-rutinoside, ellagic acid, ellagic acid rhamnoside, sanguiin H10, procyanidin B2 and

370

catechin for cv. 'Palomar'. These differences could be allocated to the controlled

371

climatic conditions that strawberries experiment when are grown in covered

372

macrotunnel (reduced light intensity, higher temperature, controlled moisture). Under

373

these conditions, previous studies have demonstrated an accumulation of anthocyanins

374

in strawberries.3,45 Josuttis et al.45 also found a higher content of flavonols and a lower



375

total concentration of anthocyanins in open-field strawberry fruits. On the other hand,

376

Wang and Zheng43 observed that when strawberry fruits were submitted to relatively

377

high temperatures (22-30 °C), they increased the antioxidant concentration in the fruits

378

with respect to the plants that grew at lower temperatures (12-18 °C).

379

In relation to the electrical conductivity, a poor separation was observed in PLS-DA

380

models between samples, yielding low predictability (Q2 = -0.21, -0.08 and 0.179, for

381

'Camarosa', 'Festival' and 'Palomar', respectively). In that case, the most discriminant

382

variables were pelargonidin 3-O-glucoside, ellagic acid, ellagic acid rhamnoside,

383

quercetin 3-O-glucuronide, procyanidin B2 and catechin for cv. 'Camarosa';

384

pelargonidin 3-O-glucoside, pelargonidin 3-O-rutinoside, ellagic acid rhamnoside,

385

quercetin 3-O-glucuronide, procyanidin B2,

386

'Festival'; and pelargonidin 3-O-glucoside, pelargonidin 3-O-rutinoside, ellagic acid

387

rhamnoside, procyanidin B2 and catechin for cv. 'Palomar'. For the three cultivars,

388

increased pelargonidin 3-O-glucoside content was observed for higher irrigation

389

conductivity. However, only significant changes of pelargonidin 3-O-glucoside (33%)

390

and ellagic acid rhamnoside (12%) were found for cv. 'Palomar'. These results are in

391

accordance with those published by Keutgen and Pawelzik,19,20 who found that

392

moderate salt stress significantly increased the antioxidant capacity, total phenolic

393

compounds and anthocyanins in cv. 'Korona'.

394

Finally, PLS-DA models according to the growing substrate explained 65.4, 64.0 and

395

59.8% of the variance for 'Camarosa', 'Festival' and 'Palomar', but low predictability

396

values were observed (Table 3). In this case, discriminant compounds between study

397

groups were pelargonidin 3-O-glucoside, pelargonidin 3-O-rutinoside, cyaniding 3-O-

398

glucoside, ellagic acid pentoside, ellagic acid rhamnoside, quercetin 3-O-glucuronide,

399

procyanidin B2 and catechin for cv. 'Camarosa'; pelargonidin 3-O-glucoside,

catechin and chlorogenic acid for cv.


Page 16 of 35

Page 17 of 35


400

pelargonidin 3-O-rutinoside, ellagic acid rhamnoside, ellagic acid pentoside and

401

chlorogenic acid for cv. 'Festival'; and pelargonidin 3-O-glucoside, ellagic acid, ellagic

402

acid pentoside, ellagic acid rhamnoside, sanguiin H10, procyanidin B2 and catechin for

403

cv. 'Palomar'. The higher content of pelargonidin 3-O-glucoside was observed in

404

strawberries grown in perlite, regardless the cultivar. For 'Camarosa' cultivar, the use of

405

coconut fiber yielded increased pelargonidin 3-O-rutinoside, ellagic acid rhamnoside

406

and quercetin 3-O-glucuronide, while strawberries cultivated in perlite were richer in

407

procyanidin B2 and catechin. For 'Festival' cultivar, ellagic acid rhamnoside was

408

increased in strawberries cultivated in coconut fiber, while perlite induced the

409

accumulation of pelargonidin 3-O-rutinoside and ellagic acid pentoside. Finally,

410

increased levels of ellagic acid and procyanidin B2 were observed in 'Palomar'

411

strawberries grown in coconut fiber, while those grown in perlite were richer in

412

catechin, ellagic acid pentoside and sanguiin H10. It should be noted that these results

413

agree with previous findings that point to coconut fiber as the best soilless substrate for

414

strawberry cultivation.48

415

Therefore, it could be concluded that the application of targeted metabolomics and

416

subsequent multivariate statistics by means of PLS-DA and RF allows the identification

417

of changes in levels of strawberry polyphenols associated with variety and the

418

application of various agronomic practices, including different electrical conductivities

419

of irrigation, macrotunnel type and substrate. The most discriminant bioactive

420

compounds were anthocyanins (pelargonidin 3-O-glucoside, pelargonidin 3-O-

421

rutinoside, cyaniding 3-O-glucoside), ellagic acid derivatives (ellagic acid rhamnoside,

422

sanguiin H10), flavan-3-ols (catechin, procyanidin B2), chlorogenic acid, and quercetin

423

3-O-glucuronide. Thus, it should be noted that the understanding the influence of



424

genotype and agronomic conditions on the biosynthesis of strawberry antioxidants

425

might be crucial for the promotion of strawberry fruits as a nutraceutical food.

426 427

Abbreviations. MRM, multiple reaction monitoring; PLS-DA, partial least squares

428

discriminant analysis; RF, random forest; SENS, sensitivity; SPEC, specificity; VIP,

429

variable importance for the projection.

430

Supporting Information. This material is available free of charge via the Internet at

431

http://pubs.acs.org.

432

Table S1. Retention time and MRM parameters of phenolic compounds profiled by

433

UHPLC-MS/MS analysis.

434

Figure S1. Discriminant variables obtained from Random Forest analysis ranked

435

according to the Gini index.

436 437

Author Information

438

*Corresponding authors (Tel: +34 959219975, Fax: +34 959219942; E-mail:

439

[email protected]), (Tel: +34 959219958, Fax: +34 959219942; E-mail:

440

[email protected])

441 442

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(47) Nestby, R.; Martinussen, I.; Krogstad, T.; Uleberg, E. Effect of fertilization, tiller

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598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614



615

FIGURE CAPTIONS

616

Figure 1. Major phenolic compounds detected in strawberry fruits.

617

Figure 2. Scores plots of PLS-DA models for two-class comparisons. (A) 'Festival' vs.

618

'Camarosa', (B) 'Palomar' vs. 'Camarosa', (C) 'Palomar' vs. 'Festival'.

619

Figure 3. Box plots with whiskers for discriminant polyphenols for differentiation of

620

strawberry cultivars.

621


Page 26 of 35

Page 27 of 35


Table 1.

Mean, Minimum and Maximum Concentration Values for Phenolic

Metabolites Detected in the Three Strawberry Varieties. 'Camarosa'

'Festival'

'Palomar'

(n=18)

(n=18)

(n=18)

Compounds Mean

Min

Max

Mean

Min

(µg/g)

(µg/g)

(µg/g)

(µg/g)

(µg/g) (µg/g) (µg/g) (µg/g) (µg/g)

gallic acid

0.12

0.09

0.16

0.12

0.10

0.19

0.15

0.10

0.22

protocatechuic acid

0.06

0.05

0.08

0.06

0.05

0.09

0.06

0.06

0.07

p-hydroxybenzoic acid

0.24

0.18

0.44

0.21

0.15

0.36

0.22

0.15

0.39

vanillic acid

0.02

0.01

0.04

0.04

0.02

0.10

0.03

0.01

0.07

cinnamic acid

0.71

0.36

1.25

0.64

0.24

2.53

0.84

0.40

2.00

caffeic acid

0.62

0.31

1.08

0.46

0.31

1.03

0.54

0.28

1.31

p-coumaric acid

1.22

0.48

2.47

0.67

0.30

1.97

1.35

0.47

3.97

m-coumaric acid

0.01

0.01

0.02

0.01

0.01

0.02

0.02

0.01

0.09

chlorogenic acid

3.50

2.64

4.79

12.73

3.51

16.60

5.80

3.56

8.00

(-) epicatechin

0.01

0.00

0.05

0.01

0.00

0.04

0.06

0.01

0.11

(+) catechin

14.00

4.63

24.66

16.57

1.33

24.65

11.52

4.69

17.06

(-) epicatechin gallate

0.06

0.05

0.07

0.06

0.05

0.07

0.06

0.05

0.07

procyanidin B2

0.15

0.12

0.21

0.18

0.11

0.92

0.18

0.14

0.23

procyanidin B1

23.13

6.15

50.54

30.03

4.14

49.32

14.71

2.95

29.07

quercetin 3-O-glucoside

0.30

0.20

0.39

0.19

0.12

0.31

0.29

0.13

0.39

quercetin 3-O-galactoside

0.13

0.05

0.20

0.05

0.01

0.14

0.12

0.03

0.20

quercetin

0.10

0.02

0.39

0.07

0.03

0.28

0.13

0.02

0.68

quercetin 3-O-glucuronide

43.07

23.43

59.09

34.57

24.96

47.44

35.59

28.76 43.29


Max

Mean

Min

Max


kaempferol 3-O-glucoside

Page 28 of 35

1.06

0.29

1.73

1.05

0.51

1.52

1.15

0.56

1.62

hexoside

1.89

0.39

3.13

1.45

0.50

1.87

1.60

0.82

2.33

apigenin

0.09

0.00

0.27

0.05

0.00

0.16

0.05

0.00

0.22

ellagic acid pentoside

25.51

18.88

33.10

22.33

17.15

28.58

22.24

17.09 26.99

HHDP galloyl hexose

9.23

7.87

17.33

9.57

7.50

13.60

9.15

7.65

11.45

sanguiin H10

2.37

1.54

5.23

1.74

1.14

3.23

3.52

1.65

4.89

ellagic acid rhamnoside

141.43 116.97 170.49 123.40

103.82 147.38 107.87 94.49 126.54

ellagic acid

25.40

18.37

44.78

24.80

18.39

30.38

22.28

17.02 30.66

cyanidin 3-O-glucoside

12.53

7.68

21.17

10.06

6.69

16.27

7.29

5.70

pelargonidin 3-O-glucoside

121.29 56.88

213.00 105.20

5.78

185.25 93.99

44.38 136.39

52.87

7.45

41.51

7.43

kaempferol

3-O-acetyl-

pelargonidin 3-O-rutinoside 29.56

17.72

18.86


15.69

8.57

34.66

Page 29 of 35


Table 2. Comparison of the Statistical Performance for the Two Classification Methods: Partial Least Squares Discriminant Analysis (PLS-DA) and Random Forest (RF). 'Camarosa'

'Festival'

'Palomar'

Overall

SENS

SPEC

SENS

SPEC

SENS

SENS

SPEC

Cam-Fes

66.6

94.4

88.8

100

77.7

97.2

Cam-Pal

72.2

100

Model SPEC

PLS83.3

100

77.7

100

DA Fes-Pal RF

100

94

77.7

100

88.8

94.4

83.3

97.2

94.4

100

94.4

100

96.3

96.3



Table 3. Statistical Parameters of PLS-DA Models for Classification of Strawberries According to Growing Conditions (Type of Coverage, Electrical Conductivity and Substrate). Cultivar

Factor

R2 X

R2Y

Q2

Coverage

0.672

0.512

0.241

0.648

0.157

-0.21

Substrate

0.654

0.183

-0.21

Coverage

0.681

0.399

-0.21

EC

0.629

0.308

-0.08

Substrate

0.640

0.284

-0.05

Coverage

0.649

0.504

0.084

EC

0.665

0.438

0.179

Substrate

0.598

0.124

-0.21

'Camarosa' EC

'Festival'

'Palomar'


Page 30 of 35

Page 31 of 35


Table 4. Discriminant Polyphenolic Compounds Associated with Different Growing Conditions of Strawberries Cultivated in Soilless Systems (p-values in brackets). Coverage Compound

CAM

EC

Substrate

FES

PAL

CAM

FES

NS

NS

NS

NS

PAL

CAM

FES

PAL

NS

NS

NS

Pelargonidin 37% 3-O-

33%

(0.0405)

(0.0244)

glucoside Pelargonidin 61%

69%

3-O-

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

NS

(0.0003) (0.0189) rutinoside Cyanidin 3- 43% O-glucoside

38%

(0.0037) (0.0127)

Ellagic acid

12% NS

NS

rhamnoside

-12% NS

(0.0178)

(0.0222)

Ellagic acid

-17% NS

NS

NS

NS

NS

NS

NS

NS

pentoside

(0.0082)

Sanguiin

*

33% NS

H10

NS *

NS

NS

NS

NS

(0.0153) * perlite vs. rockwool; N.S. non significant


NS

NS

NS


FIGURE 1


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FIGURE 2



FIGURE 3


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