Influence of Cultivar and Harvest Year on the Volatile Profiles of

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Influence of Cultivar and Harvest Year on the Volatile Profiles of Leaves and Roots of Carrots (Daucus carota spp. sativus Hoffm.) Detlef Ulrich, Thomas Nothnagel, and Hartwig Schulz J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b00704 • Publication Date (Web): 21 Mar 2015 Downloaded from http://pubs.acs.org on March 30, 2015

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Journal of Agricultural and Food Chemistry

Influence of Cultivar and Harvest Year on the Volatile Profiles of Leaves and Roots of Carrots (Daucus carota spp. sativus Hoffm.)

Detlef Ulrich1, Thomas Nothnagel2 and Hartwig Schulz3

Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, 1,3

2

Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection

Institute for Breeding Research on Horticultural Crops

Erwin-Baur-Strasse 27, D-06484 Quedlinburg, Germany

Corresponding Author: 1

(D.U.) Phone: (49) 3946-47231. Fax: (49) 3946-47300. Email: [email protected].

1 ACS Paragon Plus Environment


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ABSTRACT

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The focus of the present work is laid on the diversity of volatile patterns of carrots. In total

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fifteen main volatiles were semi-quantified in leaves and roots using isolation by headspace

4

solid phase microextraction followed by gas chromatography with FID and MS detection.

5

Significant differences in the main number of compounds were detected between the cultivars

6

as well as the years. Genotype-environment interactions (GxE) are discussed. The most

7

abundant metabolites β-myrcene (leaves) and terpinolene (roots) differ in the sum of all

8

interactions (cultivar x harvest year) by a factor of 22 and 62, respectively. A statistical test

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indicates significant metabolic differences between cultivars for nine volatiles in leaves and

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ten in roots. In contrast to others the volatiles α-pinene, γ-terpinene, limonene and myristicine

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in leaves as well as β-pinene, humulene and bornyl acetate in roots are relatively stable over

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years. A correlation analysis shows no strict clustering regarding root color. While the

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biosynthesis in leaves and roots is independent between these two organs for nine of the

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fifteen volatiles a significant correlation of the myristicine content between leaves and roots

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was determined which suggests the use of this compound as bitter marker in carrot breeding.

16 17 18

Keywords: volatiles, GC-MS, SPME, Genotype-environment interactions, GxE, root color,

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carrot breeding

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INTRODUCTION

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Throughout the world carrots are among the top ten of the vegetables.1 The world production

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amounted in 2008 to about 32.9 and 2010 to 33.7 million tons showing the enormous

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horticultural and economic importance of this crop.2 The three countries China, Russia and

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the United States produce the main part of 34 % of global production.

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Like other plants carrot leaves and roots contain thousands of secondary metabolites.3 In

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particular, the group of plant derived volatile metabolites (VOCs) possesses an almost

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incalculable wealth of important properties like aroma active compounds in food4,5 and

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markers for essential nutrients.6 Other important biological functions of VOCs consist in the

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defense against herbivores and microorganisms.7 Recent works also investigated the

32

relationship between stressors and VOCs which can be responsible for a so-called stress

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imprint.8,9

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Studies of the qualitative and quantitative composition of terpene patterns in carrot roots by

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GC have been reported since the 1960s in a huge number of publications.10 Duan11 postulated

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that more than 90 VOCs have been identified so far. The majority of the VOCs that could be

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extracted and identified from roots are mono-and sesquiterpenes. These compounds cover up

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to 97 % of the total content of the volatiles.12 Especially correlations between terpene patterns

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and sensory quality have been the subject of a number of publications. Of particular interest

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here were the influences of genotype, soil and climatic effects as well as of the growing

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conditions, storage and processing on carrot flavor.13,14 By comparison of sensory parameters

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with instrumental measurements for VOCs Simon15 pointed out the following relations: i) a

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high acceptance correlates with high levels of terpinolene, (E)- and (Z)-bisabolene, γ-

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terpinene and γ-terpinen-4-ol and ii) the negative sensory sensation 'harsh flavor' (off-flavor)

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is positively correlated with terpinolene, (E)-bisabolene and sabinene. The conclusion that

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both positive and negative sensory perceptions are positively correlated with the same

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compounds (terpinolene and (E)-bisabolene) may be explained by the fact that regarding 3 ACS Paragon Plus Environment


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sensory perception an optimal terpene content exists in carrots. This optimal content was

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postulated with 35-40 ppm.16 If the total terpene content exceeds this value it is perceived by

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the human senses as negative sensation (harsh flavor). The perception of the harsh flavor note

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can be masked in carrots by a high sugar content.17

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The determination of 'character impact compounds' using gas chromatography-olfactometry

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led to the identification of a total of 18 substances characterized by flavor dilution factors

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greater than 1.18 The substances belong to the chemical class of terpenes. The four compounds

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having the highest flavor dilution factors are β-myrcene, terpinolene, β-caryophyllene and

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(E)-γ-bisabolene. As an effective screening method for volatiles in carrot roots, a method

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consisting of headspace-solid phase microextraction and gas chromatography with flame

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ionization detection and mass spectrometry (HS-SPME-GC-FID and MS) was used for

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measurements in cultivars and a segregating F2 population with 200 individual plants.19-20

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The measurement of the terpene patterns of a total of 40 cultivars in combination with sensory

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evaluations pointed out that sensory sensation like sweet and non-bitter taste, sweetish,

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flowery and nutty odor impressions correlate with a high popularity of the cultivars. As a

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marker for a low popularity, the substances β-myrcene, β-caryophyllene and humulene as

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well as a high content of total terpenoids were determined.21

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In contrast to the roots, the terpene patterns of carrot leaves have hardly been studied.

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Habegger and Schnitzler22-23 and Hampel et al. 24-25 studied the biosynthesis of VOCs in

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relation of the leaves and roots. No correlation of metabolite patterns between leaves and

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roots was found.

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As monoterpenes are related to plant defense, Ibrahim26 et al. studied the influence of

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temperature and exogenous limonene treatment on the headspace volatiles emitted from

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leaves. There are also only a few publications on the effects of VOCs of the leaves in the

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process of plant-herbivore interaction. For example Seljasen27 determined an influence of 4 ACS Paragon Plus Environment

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Psyllid attack on the terpene pattern of the plant besides effects on the bitter compounds

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falcarindiol and 6-methoxymellein.

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Regardless of the immense number of publications dealing with flavor compounds of carrots

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no consensus about typical carrot volatile patterns exists. The lack of agreement in defining

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the essential chemical compounds of carrot aroma is depicted by analyzing twelve

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publications which give compilations of volatiles.1,11,12,15,18,26,30-35 Altogether 124 different

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VOCs were reported. However not a single compound out of the compilation of 124 is

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mentioned in all of the twelve papers coincidently. Consensus exists only in eleven out of

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twelve studies for five terpenoid compounds: sabinene, limonene, terpinolene, β-

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caryophyllene and α-humulene. In addition frequently mentioned volatiles are the following:

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β-pinene (10x), α-pinene, γ-terpinene and p-cymene (each 9x).

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In summary it can be stated that the knowledge about VOC patterns of roots is present in

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particular in relation to the sensory quality, while leaves have been relatively unexamined.

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Especially the aspect of metabolic diversity in species is yet little-known. For carrot breeding

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approaches aiming to obtain good sensory properties in combination with high resistance to

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pests or diseases, knowledge of the usable biodiversity and the genotype-environment

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interactions (GxE) of VOCs are an important prerequisite.

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In the present study for the first time ten carrot cultivars, representing the main root colors

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white, yellow, red, orange and purple and growing in a three-year field experiment were

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analyzed with regard to the qualitative and quantitative composition of the VOCs of both

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roots and leaves. The influence of cultivar, harvest year, correlation between the VOCs and

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the comparison between leaf and root metabolite patterns were examined using multivariate

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statistical methods.

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MATERIAL AND METHODS

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Chemicals. The reference substances hexanal, hexanol, limonene, γ-terpinene, humulene, and

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myristicine were purchased from Sigma-Aldrich Germany, Steinheim, Germany. The

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substances α-pinene, β-pinene, sabinene, β-myrcene, terpinolene, β-caryophyllene were from

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Carl Roth Co KG, Karlsruhe, Germany. (E)-2-hexenal was from Merck KGaA, Darmstadt,

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Germany and bornyl acetate was delivered from CHEMOS GmbH, Regenstauf, Germany.

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Plant material. The leaves and roots of ten carrot cultivars (Daucus carota L.) of different

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origin and color from a triennial field trial study at the experimental station field in

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Quedlinburg were included in the analysis. Cultivar specifications are given in Table 1. The

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seeds were sown (100 seeds/m) by a seed drill as plots in flat beds with two rows, each of

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them 3 m long and with a row distance of 45 cm by a seed drill. Plots were arranged in a

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randomized block design with four agronomical replications (biological replications).

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Chemical analyses were performed using 10 healthy and untouched marketable roots. Washed

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carrot leaves without the petioles as well as roots were sliced, immediately frozen with liquid

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nitrogen, and stored until analysis at -80 °C.

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Volatile analysis by headspace SPME-gas chromatography. After thawing the plant

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material, samples were homogenized for 1 min in a 20 % NaCl solution (leaves:NaCl solution

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= 1:10, w/v and roots:NaCl solution = 1:1.5) in a Waring Blendor. The homogenate was

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filtered using gauze. For each sample, four 20 mL-headspace vials each containing 4 g solid

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NaCl for saturation were filled with a 10 mL aliquot of the supernatant and sealed with a

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magnetic crimp cap including a septum. For automated headspace SPME-GC, a 100-µm-

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polydimethylsiloxane fibre (Supelco, Bellefonte, PA, USA) and a MPS2-autosampler

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(Gerstel, Mühlheim, Germany) were used. After an equilibration time of 10 min at 35 °C

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using the shaker (300 rpm) the fiber was exposed to the headspace for 15 min at 35 °C. 6 ACS Paragon Plus Environment

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Desorption was performed within 2 min in splitless mode and 3 min with split at 250 °C. An

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Agilent Technologies 6890 GC equipped with a HP-5ms column (0.25 mm ID, 30 m length

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and 0.5 µm film thickness) and FID were used for separation and detection. Carrier gas was

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hydrogen using a flow rate of 1.1 mL min-1. The temperature program was the following: 45

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°C (5 min), from 45 to 210 °C at 3 °C min-1 and held 25.5 min at 210 °C. The volatiles were

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identified by parallel runs of selected samples on an identically equipped GC-MS by library

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search (NIST and MassFinder), retention indices and co-elution of authentic samples (except

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for germacrene). All samples were run with four agronomical replications.

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Data processing and statistics. The commercial software ChromStat2.6 by (Analyt,

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Müllheim, Germany) was used for raw data processing.28,29 Data inputs for ChromStat 2.6

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were raw data from the percentage reports (retention time/peak area data pairs) performed

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with the software package Chemstation (version Rev.B.02.01.-SR1 [260]) by Agilent. Using

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ChromStat2.6, the chromatograms were divided in up to 200 time intervals, each of which

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representing a peak (substance) occurring in at least one chromatogram of the analysis set.

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The peak detection threshold was set on the 10-fold value of noise. The values are given as

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raw data (peak area in counts) which also can be described as relative concentration because

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of the normalized sample preparation. The semiquantitation by the software ChromStat 2.6

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was focused on fifteen VOCs summarized in Table 2.

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A primary data analysis of normality gave a Non-Gaussian distribution for most of the VOCs

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(data not shown). Therefore, a non-parametric Kruskal-Wallis procedure using the software

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SYSTAT13 (Systat Software, Inc., Chicago, IL, USA) was used to compare the VOC data of

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different cultivars for the single harvest years and additionally over the three years in

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summary. The Pearson correlation was performed with STATISTICA7.1. (StatSoft Europe

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GmbH, Hamburg, Germany). The heat map was constructed with the open source software

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Multi Experiment Viewer (MEV; http://www.tm4.org). 7 ACS Paragon Plus Environment


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Results and discussion

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Selection of metabolite targets.

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In this research the quantities of fifteen compounds identified in both leaves and roots were

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used for further processing. The reasons to focus on the substances listed in Table 2 are the

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following. First, the selected VOCs are of high abundance in most of the samples which is a

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result of the chosen volatile extraction method (HS-SPME). Secondly, these substances are

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identified by mass spectrometry (library search) and additional co-elution of authentic

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reference substances. The only exception is germacrene, for which no reference was available.

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Thirdly, the selection includes nine aroma compounds which are described in the literature as

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so-called ‘character impact compounds’ (CIC) in carrot roots.18 Additionally the compilation

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includes all of the mutual compounds published for carrots in the literature (from 11 studies)

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mentioned above. Finally, the substances listed in Table 2 are known to be involved in various

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biological activities which are of interest in plant genetics and breeding activities.

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Cultivar and harvest year affect the contents of volatile metabolites. The data set of VOC

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patterns containing ten cultivars, three harvest years and four agronomical replications each is

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visualized by a heat map in Figure 1. Both qualitative and quantitative differences in VOC

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pattern were observed between cultivars as well as the harvest years. Qualitative differences

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are defined as values below the detection threshold of the SPME-GC-FID method used (7

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counts) and are depicted as black spots in the heat map. For example, bornyl acetate (1BA)

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has never been detected in leaves over the three years in the cultivars ‘White Satin’,

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‘Yellowstone’, ‘Nutrired’, ‘Santa Cruz’, ‘Deep Purple’, ‘Pusa Kesar’ and ‘Anthonina’, while

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in the remaining three cultivars low to medium levels were found. In roots hexanal (2H) is

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only detected in some samples of four cultivars at low concentrations (‘Nutrired’, ‘Pusa 8 ACS Paragon Plus Environment

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Kesar’, ‘Santa Cruz’, ‘Nerac’). The compounds (E)-2-hexenal and 1-hexanol are exclusively

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present in in leaves only.

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In general, the variability of metabolites seems to be determined by the cultivar especially in

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roots (Figure 1, Tables 3 and 4). Particularly the content of the monoterpenes α-pinene (2aP),

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sabinene (2Sa), β-pinene (2bP), β-myrcene (2bM), limonene (2L) and terpinene (2Te)

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decreases in the order white, yellow, red, orange and purple. In contrast, the monoterpene

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terpinolene (2Te) show high concentrations in roots of all cultivars. The most abundant VOC

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in leaves is β-myrcene (1bM) with a mean of 10,883 counts and a maximum of 30,884 counts

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(sample 9.1.3). In roots the highest concentration was found for the monoterpene terpinolene

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(2Te) with a mean of 15,331 counts and a maximum of 82,569 counts (sample 3.3.1). In

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general, the concentrations of VOCs were found in a similar concentration range in leaves and

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roots although the sample preparation procedure for leaves works with a higher relation

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between tissue and NaCl solution by a factor of 6.67.

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The statistical comparison of VOC patterns of the genotypes for the single harvest years and

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over the three years are summarized for both leaves and roots in Table 3 and 4, respectively.

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-Leaves. The ten cultivars show significant differences for eleven of the fifteen leaf volatiles

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during the first year in difference to the compounds (E)-2-hexenal (1E2H), limonene (1L), β-

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caryophyllene (1bC) and humulene (1Hu) (Table 3). In the second harvest year the two VOCs

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β-caryophyllene (1bC) and humulene (1Hu) are not significantly different between the

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cultivars whereas the third cultivation year was characterized by significant cultivar

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differences for all of the 15 VOCs. The statistical analysis for three years in summary shows

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nine significant metabolites.

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The volatile bornyl acetate (1Ba) was detected in low to medium amounts in three out of the

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ten cultivars (Figure 1). In all three years this compound was semiquantified only in the

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cultivar ‘Nerac’, while cv. ‘Blanche’, cv. ‘BL 710015’ and cv. ‘Pusa Kesar’ showed bornyl 9 ACS Paragon Plus Environment


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acetate only in the first year and not in all replicas. All other cultivars expressed no bornyl

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acetate. Thus this compound seems to be a characteristic metabolite for discrimination of the

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mentioned cultivars. The partial expression in the cultivars ‘Blanche’, ‘BL710015’ and ‘Pusa

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Kesar’ suggests strong influences of the harvest year. Two of the compounds, hexanol (1Hol)

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and β-pinene (1bP), were not detectable in year two, suggesting also an influence of the

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climatic conditions. The calculation over the whole period of three years gives no

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significances for six compounds (1aP, 1Sa, 1bM, 1L,1gT and 1Te), which suggests a stable

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expression independently from the environmental influences. In conclusion the VOC selection

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shows significant differences between the tested cultivars. Partially strong variation between

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the years suggests additional influences of the climatic conditions. On the other hand six

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VOCs (1aP, 1Sa, 1bM, 1L,1gT and 1Te) were expressed in equal relations between the

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cultivars over the three years. It appears plausible that these six VOCs and bornyl acetate

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seem to be suitable for discrimination of carrot cultivars on the leaf metabolite level.

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-Roots. In contrast to leaves the VOCs (E)-2-hexenal and hexanol were not detected in root

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samples (Table 4). The root volatile hexanal (2H) showed a unique behavior because it was

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detected only in year one and exclusively for the cultivars ‘Nutrired’, ‘Pusar Kesar’ and

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‘Nerac’. The compound hexanal is discussed in literature not only in association with plant

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defense (36) but also with stress responses.36,37 The presence in the first year on the one hand

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and the absence in the years two and three on the other hand suggests an association to the

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dryer and warmer climatic conditions in cultivation year one.

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Considering a year by year comparison, all of the thirteen detected VOCs are significant

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between the tested cultivars. However, a statistical analysis of root volatiles based on the

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average of the three years showed differences between the cultivars for ten of the thirteen

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VOCs (Table 4). The high variation between the years with even qualitative differences for

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hexanal suggests influences of the environmental conditions similar to the discussion for leaf

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volatiles. The calculation for root VOCs over three cultivation years shows an equal relation 10 ACS Paragon Plus Environment

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in expression for 2Sa, 2BA and 2bC, which can be interpreted as independency from the

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environmental conditions for these compounds. This seems to be suitable for discrimination

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of carrot cultivars on the basis of the root metabolom. The occurrence of hexanal as stress

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indicator should be tested in further experiments.

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Correlation between metabolite patterns and root color. The used cultivars are

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characterized by five different root colors: white, yellow, red, orange and purple. Because

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especially the white and yellow cultivars ‘White Satin’, ‘Blanche’, ’Yellowstone’, ‘BL71005’

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show a distinct increased level of the root compounds sabinene (2Sa), β-myrcene (2bM),

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limonene (2L) and γ-terpinene (2gT) (Figure 1), a correlation analysis was performed using

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the values for every cultivar as mean of yearly and agronomical replications.

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The cluster analysis of the leaf volatiles (Figure 2) results in two main clusters with the

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cultivars ‘Nerac’ and ‘Nutrired’ in one and the remaining cultivars in a second cluster. The

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second cluster again is subdivided into two sub-clusters with the cultivars ‘Blanche’,

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‘Yellowstone’ and ‘White Satin’ as one and the five others as a second group, respectively.

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For the root volatiles (Figure 2) also two main clusters were specified but with different

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cultivars than in leaves. Cluster one contains the cultivars ‘Santa Cruz’ and ‘Nerac’, both with

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orange roots. The second cluster comprises the remaining eight cultivars. Similar to the leaf

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volatiles the second cluster is subdivided into two sub-clusters, one exactly the same as for

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leaves with the cultivars ‘Blanche’, ‘Yellowstone’ and ‘White Satin’. The VOC patterns of

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roots partly correlate with the root color for orange, red and purple carrots, whereas in leaves

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no clear correlation of VOC pattern and root color occurs. It is noteworthy that the two white

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cultivars ‘White Satin’ and ‘Blanche’ carry totally different patterns regarding the terpene

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acetate (2BA), the sesquiterpenes (2bC, 2Hu) and the phenylpropanoid myristicine in replica

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3 (2My).

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Correlations among the volatile compounds. A Pearson correlation analysis shows that 48

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coefficients (out of 105 possible single correlations) with significant values exist between the

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individual compounds in leaves and 46 (out of 78) in roots, respectively (Table 5).

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In leaves the three compounds hexanal (1H), (E)-2-hexenal (1E2H) and hexanol (1Hol)

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correlate among each other positively with high to medium values. High correlation

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coefficients occur for the pairs sabinene (1Sa) and γ-terpinene (1gT) (0.95), sabinene (1Sa)

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and terpinolene (1Te) (0.84) as well as between terpinolene (1Te) and γ-terpinene (1gT) with a

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value of 0.82. The concentration of the phenylpropene myristicine (1My) correlates with ten

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VOCs with significant positive values. The highest coefficient (0.85) occurs between

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myristicine (1My) and the sesquiterpene germacrene (1Ge) (Table5).

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In roots the two lipoxygenase (LOX) products (E)-2-hexenal (2E2H) and hexanol (2Hol) were

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not detected while hexanal (2H) was found sporadically in low concentrations only in three

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cultivars (‘Nutrired’, ‘Pusa Kesar’ and ‘Nerac’)(Figure 1). The highest correlation coefficient

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(0.94) occurs between the compounds limonene (2L) and terpinolene (2Te) (Table 5). Also in

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roots the compound myristicine (2My) is correlating positively with seven other volatiles.

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Differences in volatile patterns between leaf and root. Earlier findings by Habegger and

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Schnitzler22,24 and Hampel et al.25 proved that the biosynthesis in leaves and roots maybe

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independent from each other. To check the relation between the organs a correlation was

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performed between the set of leaf and root VOCs, separately for every harvest year and also

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as the mean over three years. In contrast, the results in our study show that six out of the

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thirteen VOCs correlate between leaves and roots over all years (Table 6).

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Also Figure 1 indicates differences between the two plant organs. The most distinct difference

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was found for the three C6 compounds (green leaf compounds, LOX) hexanal (H), (E)-2-

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hexenal (E2H) and hexanol (Hol). While these compounds are present in leaves with highly

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abundant peaks, hexanal (2H) occurs in roots only in three cultivars (’Nutrired’, ‘Pusa Kesar’, 12 ACS Paragon Plus Environment

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‘Nerac’) in the first cultivation year. Positive correlations were found for the compounds α-

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pinene (aP), sabinene (Sa), γ-terpinene (gT) and terpinolene (T) for both the individual years

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and three years average (Table 6). Correlations for either individual years or the mean of three

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years were evidenced for β-myrcene (bM), β-caryophyllene (bC), humulene (Hu), germacrene

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(Ge) and myristicine (My). Myristicine (My) generally is negatively correlated between the

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plant organs for three years and the single years 2 and 3. The overall level as well the

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variability is much higher in roots than in leaves for this compound (Figure 1). The closest

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correlation between leaves and roots occurs for the volatile sabinene (Sa) with high

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correlation coefficients in all years and the mean of three years.

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Consequences for metabolic studies and cultivar breeding. The diversity of volatile

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patterns shows more noticeable differences in roots than in leaves (Figure 1). Between the

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cultivars both qualitative and quantitative differences in VOC pattern exist. A genotype-

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environment (GxE) interaction was evidenced for the VOC patterns. On the one hand for a

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number of VOCs the analyses showed a similar expression over the years (e.g. 1gT, 1aP, 1My,

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1L, 2Hu, 2bP, 2Ba) and seems to indicate a discrimination of the cultivars. On the other hand

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a group of analyzed VOCs (e.g. 1bP, 1Hol, 2My, 2Hu) are highly influenced by environmental

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factors such as soil temperature and water availability determined as ‘harvest year’.

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The ten analyzed cultivars belong to five different root color types. The root color depends on

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the proportion of the non-volatile metabolites such as lycopene, carotene, lutene and

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anthocyanins. Due to noticeable differences in root color, also an influence on metabolic

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patterns may be expected. Even if there are some quantitative differences between

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monoterpenes especially in roots (Figure 1), a correlation analysis based on 30 VOCs in

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leaves and roots indicates only a partial clustering regarding root color (Figure 2). In

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particular, the two white cultivars ‘White Satin’ and ‘Blanche’ present completely different 13 ACS Paragon Plus Environment


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patterns regarding terpene acetate (2Ba), the sesquiterpenes (2bC, 2Hu, 2Ge) and the

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phenylpropanoid myristicine (2My). But these differences may be influenced by other factors

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like breeding status because the very old cultivar ‘Blanche’ belongs to the so-called open

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pollinated cultivars, whereas ‘White Satin’ represents a modern F1 hybrid.

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The fifteen quantified VOCs are derived from three different biosynthetic pathways. The

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compounds hexanal (H), (E)-2-hexenal (E2H) and hexanol (Hol) are generated by the LOX

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pathway, whereas myristicin (My) is synthesized according to the phenylpropanoid pathway.

311

The remaining VOCs are terpenoids (mono- and sesquiterpenes as well as one terpene

312

acetate). In leaves the three LOX derived compounds (1H, 1E2H, 1Hol) are highly positively

313

correlated with each other. In roots this type of metabolites is not present or only sporadically

314

detected, which may be a hint on the different ecological requirements of the two different

315

plant organs regarding plant defense. As an example, the three mentioned compounds are

316

known for an inhibiting effect on fungi like Botrytis cinerea.38,39 Some high correlation

317

coefficients between monoterpenes are caused by the synthesis at the same pathway. For

318

example the cyclic monoterpenes sabinene (1Sa), γ-terpinene (1gT) and terpinolene (1Te)

319

which correlate highly in leaves are synthesized via the α-terpinyl cation.40 The same

320

relationship exists between limonene (2L) and terpinolene (2Te), which correlate with a

321

coefficient of 0.94 in roots. Interestingly, numerous correlations of the phenylpropanoid

322

myristicine in leaves and roots to mono- and sesquiterpenes exist. In recent literature no

323

connection of these two biosynthetic pathways has been established yet.

324

Habegger and Schnitzler22,23 analyzed the essential oils extracted from leaves and roots

325

separately to study correlations between the two plant organs and make predictions from the

326

patterns of the leaf VOCs for the composition of the roots for plant breeding. The authors

327

draw the conclusion that the metabolic patterns in both plant organs develop independently.

328

The present study gives a more detailed picture. At least seven of the VOCs correlate between

329

leaves and roots (over three years), six positively and one negatively (myristicine). It is 14 ACS Paragon Plus Environment

Page 15 of 29


330

known that this compound (My) is involved in the formation of bitter taste in carrots.41

331

Therefore, the development of a new method to predict the bitter taste in carrot roots based on

332

the analytical data of the leaves may be an important approach for early selection in breeding

333

programs. The results obtained in this study give basic information for research and breeding

334

regarding quality including flavor, resistance against diseases as well as availability of VOCs

335

in nutrition. Recently, our research activities are directed to reveal the genetic background of

336

the described VOCs including candidate gene approaches, marker development and genetic

337

mapping.

338 339

Abbreviations Used

340

CIC – character impact compound; FID – flame ionization detector; GC-MS – gas

341

chromatography/mass spectrometry; MS – mass spectrometry; LOX – lipoxygenase; HS-

342

SPME – headspace solid phase microextraction; VOCs – volatile organic compounds

343 344 345

Acknowledgment

346

The authors wish to thank Kirsten Weiß, Ines Kasten, Martina Hoppe and Barbara Sell for

347

excellent technical assistance. The project was funded by DFG Schu 566/10-1.

348 349

ASSOCIATED CONTENT

350

Supporting Information

351

The supporting material describes the common climatic and experimental conditions at the

352

experimental field in Quedlinburg as well as the specific conditions over the three harvest

353

years. This material is available free of charge via the Internet at http://pubs.acs.org.

354 355 15 ACS Paragon Plus Environment


356

Page 16 of 29

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496



497

Page 20 of 29

Figure captions

498 499

Figure 1: Heat map for fifteen VOCs in leaves and roots separately, estimated for ten

500

cultivars, three harvest years and four agronomical replications each. Color code for relative

501

VOC concentrations in counts of peak area (color bar on bottom position): black = 0; red =

502

2500; grey = missing value. Sample code: ‘1.1.1-4’ corresponds to ‘cultivar 1 (White Satin).

503

harvest year 1. agronomical replications1-4’. Root color: wh – white; ye – yellow; re – red; or –

504

orange pu – purple.

505 506

Figure 2: Hierarchical Clustering (Pearson Correlation Coefficient, Average Linkage Method)

507

separately for leaves (left) and roots (right), on the basis of fifteen VOCs as means of year and

508

replicas (N = 3). Root color code: wh – white; ye – yellow; or – orange; re – red; pu – purple.

509


Page 21 of 29


Tables

Table 1: Origin and relevant characters of carrot cultivars used in the study Abbr Cultivar

Source1

Status2 Root color Root type

1

White Satin Bejo

F1

white

Flakkeer

2

Blanche*

OP

white

Danvers/Flakkeer

3

Yellowstone Bejo

OP

yellow

Flakkeer

4

BL710015

Seminis

BL

yellow

Nantes

5

Nutrired

Seminis

OP

red

Nantes/Flakkeer

6

Pusa Kesar

WGRU 6755 LR

red

Flakkeer

7

Santa Cruz

Seminis

OP

orange

Chantenay

8

Nerac

Bejo

F1

orange

Nantaise

9

Deep Purple Bejo

F1

purple

Flakkeer

10

Anthonina

OP

purple

Flakkeer

INH 126

Seminis

*Blanche ½ long des Voges; 1Bejo Zaden B.V. (NL), Seminis (USA), INH-Institute National Horticulture, Angers (F), WGRU-Wellesbourne Genetic Resources Unit , University of Warwick, (UK); 2OP-open pollinated cultivar, F1- hybrid cultivar, BL – breeding line, LR – land race.



Page 22 of 29

Table 2: Carrot VOCs and known properties

1

hexanal

Abbreviation leaf/root 1H / 2H

2

(E)-2-hexenal

1E2H / 2E2H

22, 23*

SC, fu

3

hexanol

1Hol / 2Hol

SC

4

α-pinene

1aP / 2aP

5

sabinene

1Sa

6

β-pinene

1bP / 2bP

7

β-myrcene

1bM / 2bM

8

limonene

1L

9

γ-terpinene

1gT / 2gT

10

terpinolene

1Te

11

bornyl acetate

1Ba / 2Ba

12

β-caryophyllene

1bC / 2bC

13

humulene

1Hu / 2Hu

14

germacrene

1Ge / 2Ge

10, 16, 18, 22*, 23*, 26*,31, 32, 34, 42, 43, 45, 46 10, 16, 18, 22*, 23*, 26*, 30, 32, 34, 42, 43, 45, 46, 10, 16, 18, 26*, 30, 31, 32, 34, 42, 43, 45, 46 10, 16, 18, 22*, 23*, 26*, 30, 31, 32, 34, 42, 43, 45, 46 10, 16, 18, 22*, 23*, 26*, 30, 31, 32, 34, 42, 43, 45, 46, , 10, 16, 18, 26*, 30, 31, 32, 34, 42, 43, 45, 46 10, 16, 18, 26*, 30, 31, 32, 34, 42, 43, 45, 46 10, 16, 26*, 30, 31, 32, 42, 43, 45, 46 10, 16, 18, 22*, 23*,26*, 30, 31, 32, 34, 42, 43, 45, 46, , 18, 26*, 30, 31, 32, ,34, 44, 46, 22*,23*

15

myristicine

1My / 2My

10, 16, 32, 42,43

No

1

Compound

/ 2Sa

/ 1L

/ 2Te

Properties1

Reference 34

SC

SC, CIC SC, CIC SC, CIC SC, CIC, av, ab SC, CIC, av, ab SC, CIC, av, ab SC, CIC, av SC SC, CIC SC, SIC, ab ab

SC: semiochemical after www.pherobase.com; CIC: aroma character impact compound in

carrot roots; av: antiviral; ab: antibacterial; fu: fungicide. *: identified in leaves.


Page 23 of 29


Table 3: Comparison of fifteen VOCs in ten carrot cultivars over three years semi-quantified in leaves with the Kruskal-Wallis test for significance VOC

1H

N

119

Relative concentration in counts

Comparison of cultivars with the Kruskal-Wallis Test1

Mean

Year1

78.64

Min

Max

0.00

436.10

Year2

KW

p

29.92

0.00 a

Year3

Three years

KW

p

KW

p

KW

p

35.52

0.00

25.32

0.00

52.87

0.00

25.26

0.00

22.69

0.01

65.00

0.00

1E2H

119

293.02

0.00

1148.68

16.84

0.05

1Hol

117

19.72

0.00

122.74

29.76

0.00

n.d.

-

28.91

0.00

48.52

0.00

1aP

120

3091.50

170.28

16698.30

23.62

0.00

23.67

0.00

23.57

0.01

3.44

0.18a

1Sa

120

2847.22

42.45

23520.00

33.05

0.00

35.32

0.00

34.76

0.00

4.22

0.18a

1bP

120

146.91

0.00

779.97

38.68

0.00

n.d.

-

25.51

0.00

72.34

0.00

1bM

120

10882.67

1386.72

30883.60

32.13

0.00

29.23

0.00

28.29

0.00

5.05

0.08a

1L

120

2544.55

505.88

7546.98

12.66

0.18a

20.95

0.01

26.43

0.00

1.85

0.40a

1gT

120

355.61

0.00

1946.50

29.93

0.00

35.35

0.00

32.45

0.00

1.82

0.40a

1Te

120

347.24

12.13

2200.45

28.16

0.00

29.27

0.00

29.94

0.00

2.26

0.32a

1BA

116

5.45

0.00

129.54

34.18

0.00

37.86

0.00

1bC

120

2748.58

731.49

7367.16

14.94

0.09

a a

18.46

0.03

6.33

0.04

0.08

a

24.14

0.00

6.71

0.04

14.44

0.11

a

29.14

0.00

20.21

0.00

15.5

1Hu

120

583.55

127.72

1458.86

11.69

0.23

1Ge

120

2307.55

159.27

8162.69

24.14

0.00

26.69

0.00

29.25

0.00

13.57

0.00

1My

120

109.85

14.59

366.68

17.32

0.04

25.54

0.00

30.85

0.00

21.17

0.00

1

KW – Kruskal-Wallis test statistics; n.d. – not detected; significance differences at p < 0.05;

‘a’ indicates non-significant differences between the harvest years.



Page 24 of 29

Table 4: Comparison of fifteen VOCs in ten carrot cultivars over three years expressed in the roots with the Kruskal-Wallis test for significance

VOC

N

Relative concentration in counts

Comparison of cultivars with the Kruskal-Wallis Test1

Mean

Years1

Min

Max

Year2

Year3

Three years

KW

p

KW

p

KW

p

KW

p

2H

119

1.22

0.00

32.49

32.71

0.00

n.d.

-

n.d.

-

17.42

0.00

2aP

120

1275.54

42.49

6863.78

30.74

0.00

35.24

0.00

34.19

0.00

14.52

0.00

2Sa

120

1954.64

0.00

11910.60

36.71

0.00

37.52

0.00

37.35

0.00

3.47

0.18a

2bP

120

851.18

34.01

4420.85

35.08

0.00

33.98

0.00

32.78

0.00

11.04

0.00

2bM

120

1364.68

63.95

6751.40

36.01

0.00

33.56

0.00

34.11

0.00

6.74

0.03

2L

120

1442.71

133.96

5968.89

33.21

0.00

35.41

0.00

35.41

0.00

16.36

0.00

2gT

120

1507.68

207.77

4667.84

32.52

0.00

34.54

0.00

32.22

0.00

12.46

0.00

2Te

120

15331.34

1336.56

82568.70

34.76

0.00

34.57

0.00

35.55

0.00

14.75

0.00

2BA

115

389.59

0.00

1582.50

35.06

0.00

31.93

0.00

32.09

0.00

0.47

0.79a

2bC

120

1624.62

82.46

8636.64

33.61

0.00

32.67

0.00

32.07

0.00

4.57

0.10a

2Hu

120

339.50

41.70

1786.43

32.15

0.00

32.31

0.00

32.05

0.00

31.98

0.00

2Ge

120

72.41

0.00

236.68

34.78

0.00

34.32

0.00

32.06

0.00

6.71

0.04

2My

119

243.56

6.31

3117.78

33.21

0.00

31.96

0.00

34.01

0.00

36.57

0.00

1

KW – Kruskal-Wallis test statistics; n.d. – not detected; significance differences at p < 0.05;

‘a’ indicates non-significant differences between the harvest years.


Page 25 of 29


Table 5: Pearson correlation among VOCs for leaves and roots Leaves VOC

1H

1H

1

1E2H

0.81

1E2H 1Hol

1aP

1Sa

1bP

1bM

1L

1gT

0.56

0.59

1

1aP

0.01a

0.08a

0.13a

1

1Sa

0.18a

0.05a

0.10a

-0.13a 1

1bP

0.42

0.37

0.39

0.35

0.54

1

1bM

0.32

0.21

0.11 a

-0.31

0.29

0.21

1

1L

-0.10a

-0.05a -0.11a

0.02a

0.19a

1

0.95

0.49

0.35

0.01a 1

0.84

0.52

0.22

0.04a 0.82

1Te

0.21 0.15

a

-0.14

a

1bC

-0.08

a

1Hu

-0.24

1BA

1BA

1bC 1Hu

1Ge 1My

1

1Hol

1gT

1Te

0.08

a

0.11

a

-0.14

a

-0.12

a

-0.28 a

-0.01a 0.01 a

0.08

a

-0.13

0.12

a

a

0.03

-0.16

a

-0.14

a

a

0.21 -0.09

0.03 a

0.01a

-0.35

-0.03

a

-0.02

-0.18

0.22

-0.06

-0.11

a

a

0.37

0.04

a

0.07a

0.37

-0.10a -0.07a 0.31

-0.10a -0.25

0.11

a

1

a

0.06a

1

0.14a

0.26

1 0.53 1

0.36

0.30

0.40

0.16

0.37

0.32

0.17

0.36 0.24

1

-0.11 a 0.11 a

0.29

0.29

0.24

0.22

0.28

0.31

0.27

0.38 0.26

0.85 1

2Sa

2bP

2bM

2L

2gT

2Te

2BA

2bC 2Hu

2Ge 2My

-0.05

0.24

0.10

1My

-0.11a

0.09a

2E2H 2Hol

-0.09

a

a

a

1Ge

a

a

a

Roots 2aP

VOC

2H

2H

1

2E2H

n.d.

1

2Hol

n.d.

n.d.

1

2aP

0.11a

n.d.

n.d.

1

-0.15

a

n.d.

n.d.

-0.01a 1

2bP

-0.03

a

n.d.

n.d.

0.51

0.41

1

2bM

-0.08a

n.d.

n.d.

0.11a

0.77

0.43

1

2Sa

a

2L

0.11

n.d.

n.d.

0.43

0.72

0.71

0.74

1

2gT

-0.05a

n.d.

n.d.

0.49

0.60

0.62

0.56

0.79

1

2Te

-0.11

a

n.d.

n.d.

0.36

0.69

0.62

0.66

0.94

0.72

1

2BA

0.06 a

n.d.

n.d.

0.42

0.12a

0.34

-0.12a 0.38

0.33

0.33

2bC

-0.10 a n.d.

n.d.

0.07a

0.28

0.22

-0.02a 0.27

0.22

0.18a

a

a

0.13

a

0.11

a

-0.11

a

0.03

a

0.07

a

-0.11

1 a

0.49

1

0.32

0.69 1

2Hu

0.11

n.d.

n.d.

-0.02

2Ge

-0.16a

n.d.

n.d.

-0.04a 0.63

0.42

0.53

0.58

0.35

0.50

0.19 a 0.60 0.35

2My

0.05a

n.d.

n.d.

0.23

0.07a

0.08a

0.03a

0.33

0.36

0.34

0.45

1

0.26 -0.11 a 0.22 1

Pearson correlation coefficients are significant for values > 0.19, N = 100. a represents nonsignificant correlation coefficients for p < 0.05. n.d. – not detected.



Page 26 of 29

Table 6: Pearson correlation of individual VOCs between leaves and roots.

VOC

Three years n = 120

Year 1 n = 40

Year 2 n = 40

Year 3 n = 40

H aP Sa bP bM L gT Te BA bC Hu Ge My

0.03a 0.53 0.66 0.13a 0.23 -0.05a 0.46 0.42 0.11a 0.15a 0.19a 0.29 -0.22

0.23a 0.55 0.67 0.11a 0.19a -0.07a 0.48 0.46 0.02a 0.04a -0.11a 0.11a -0.11a

n.d. 0.48 0.76 n.d. 0.27a -0.04a 0.45 0.41 0.28a 0.38 0.38 0.36 -0.34

n.d. 0.52 0.87 0.05a 0.15a -0.14a 0.63 0.77 0.09a 0.12a 0.10a 0.32 -0.44

Pearson correlation coefficients are significant for values > 0.19, N = 120. a represents nonsignificant correlation coefficients for p < 0.05. n.d. – not detectable.


Page 27 of 29


Figure graphics

Figure 1 27 ACS Paragon Plus Environment


Page 28 of 29

Figure 2


Page 29 of 29


TOC graphic (For Table of Contents Only)


Influence of Cultivar and Harvest Year on the Volatile Profiles of

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