1H NMR Spectroscopy for Determination of the Geographical Origin of

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Omics Technologies Applied to Agriculture and Food 1

H-NMR-Spectroscopy for Determination of the Geographical Origin of Hazelnuts

René Bachmann, Sven Klockmann, Johanna Härdter, Markus Fischer, and Thomas Hackl J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b03724 • Publication Date (Web): 12 Oct 2018 Downloaded from http://pubs.acs.org on October 14, 2018

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Journal of Agricultural and Food Chemistry

1H

NMR-Spectroscopy for Determination of the Geographical Origin of

Hazelnuts

René Bachmann1, Sven Klockmann2, Johanna Haerdter1, Markus Fischer2 and Thomas Hackl1,2*

1Institute

of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146

Hamburg,

Germany

*Corresponding

author:

Tel.:

+49-40-428382804

E-Mail:

[email protected]

2HAMBURG

SCHOOL OF FOOD SCIENCE - Institute of Food Chemistry, University of

Hamburg, Grindelallee 117, 20146 Hamburg, Germany

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ABSTRACT

2

262 authentic samples were analyzed by

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discrimination of hazelnuts (Corylus avellana L.) covering samples from five countries (Germany,

4

France, Georgia, Italy and Turkey) and the harvest years 2013 - 2016. This publication describes

5

method development starting with an extraction protocol suitable for separation of polar and non-

6

polar metabolites in addition to reduction of macromolecular components. Using the polar fraction

7

for data analysis principle component analysis (PCA) was applied and used to monitor sample

8

preparation and measurement. Several machine learning algorithms were tested to build a

9

classification model. The best results were obtained by a linear discrimination analysis applying a

10

random subspace algorithm. The division of the samples in a trainings set and a test set yielded a

11

cross validation accuracy of 91% for the trainings set and an accuracy of 96% for the test set. The

12

identification of key features was carried out by Kruskal-Wallis-test and t-test. A feature assigned

13

to Betaine exhibits a significant level for the classification of all five countries and is considered a

14

possible candidate for the development of targeted approaches. Further, the results were compared

15

to a previously published study based on LC-MS analysis of non-polar metabolites. In summary,

16

this study shows the robustness and high accuracy of a discrimination model based on NMR

17

analysis of polar metabolites.

1H-NMR-spectroscopy

for the geographical

18 19

KEYWORDS

20

Metabolomics, 1H-NMR, Hazelnut, Corylus avellana, Geographical origin, multivariate statistics

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INTRODUCTION

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Hazelnut (Corylus avellana) is an ancient crop which has been known to human kind since at least

23

the Mesolithic.1 Even today, hazelnut is an important commodity in chocolate, confectionary and

24

bakery industry in their shelled and roasted form. Hazelnut is the third most commonly grown nut

25

after almond and walnut, whereat Turkey is the leading producer (64 %) followed by Italy (13 %),

26

United States, Georgia, Azerbaijan, Spain, France, Iran and China (less than 5 %).2 Hazelnuts

27

currently available on the market exhibit different qualities attributed to cultivation conditions in

28

their countries of origin. Hence, determination of geographical origin is relevant for consumers and

29

processing industries likewise. In 2014 the highest selling price was obtained for Italian hazelnuts

30

with 5.207 USD/t, while Turkey's hazelnuts afforded an 18% lower price, followed by the USA

31

(24%), Georgia (31%) and Azerbaijan (49%).2 This price discrepancy and the willingness of

32

consumers to consume more regional food is also reflected in the increasing number of products

33

registered under the EU scheme Protected Designation of Origin (PDO) or Protected

34

Geographical Indication (PGI) as well as other protected designations for products made and sold

35

outside of EU.3-5 There are currently three PGI (two for Italy, one for France) and two PDOs (Italy

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and Spain) registered. Another Spanish PDO applied for registration.6 More than twenty-five

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economic important varieties exist for C. avellana each with own characteristics and geographical

38

distributions according to climatic environments, geographical characteristics and manufacturing

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conditions.7 Relevant varieties include the Tonda Gentile Trilobata (Piedmont, Italy), Tombul

40

(Giresund, Turkey), Ata Baba (Zaqatala, Azerbaijan) or Barcelona (Oregon, USA; France).7-9

41

Differentiation of the geographical origin or the variety may be based on morphological

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characteristics or chemical targeted analysis.10-13 Various analytical methods have been applied for

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non-targeted chemical profiling including NMR, ICP-MS, NIR, GC-MS or LC-MS.8, 10, 14-19 In 3 ACS Paragon Plus Environment


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most cases subject of these studies was either a small/restricted geographical region and/or a typical

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variety of a geographical region. For example, 1H NMR was applied in a metabolomics based

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approach for the differentiation of Italian hazelnut varieties.20, 21 However, only the geographic

47

origin is a market relevant parameter because varieties usually are not declared in final products.

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Approx. 90% of the worldwide annual yield of hazelnut is used by processing industries. They are

49

dependent on a steady supply of high quality commodity. Supply downtimes caused e.g. by crop

50

failure has a high impact on market rates as occurred in 2014 in Turkey after March onset in winter.

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While from a traditional point of view most cultivation countries are all located around the 45th

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latitude, other cultivation countries such as Chile, South Africa and Australia, become more and

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more important in response to changing climatic conditions and the demand for stable annual

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yield.22, 23-25 These changes in market situation may increase future demand for comprehensive

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protocols for geographic authentication of hazelnuts in quality control including the major crop

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countries.

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The basic concept of a metabolomics-based approach for authenticity control of food is the

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assumption that the metabolites of raw materials with different location factors and cultivation

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conditions differ significantly and reproducibly in the concentration (quantity) or presence (quality)

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of certain metabolites and metabolite patterns.26 This work addresses for the first time the non-

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targeted NMR spectroscopic analysis of a large quantity of hazelnut samples from five different

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Eurasian countries. The data analysis generates a model that predicts the origin of samples from a

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test set with a high accuracy.

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MATERIALS AND METHODS

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Reagents and chemicals 4 ACS Paragon Plus Environment

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Deuterated chloroform (99.8%), methanol (99.8%) and Deuteriumoxide (99.9%) were purchased

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from Eurisotop (Saint-Aubin Cedex, France). Sodium azide (99.5%), potassium phosphate

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monobasic anhydrous (>99%) and potassium phosphate dibasic anhydrous (>98%) were purchased

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from Sigma Aldrich (Merck KGaA, Darmstadt, Germany).

71 72

Hazelnut samples

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Overall 262 authentic raw hazelnut samples of different varieties, origins and producers from

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harvest years 2013 (5), 2014 (76), 2015 (105) and 2016 (76) were used for analyses. Figure S1

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(supporting information, p S XVIII) gives a graphical overview of the sample distribution. The

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samples were harvested in the respective commercial relevant regions of each country, represented

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by 134 French (mainly Midi-Pyrénées and Aquitaine), 28 German (mainly in Bavaria), 44 Italian

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(Piedmont, Campania and Lazio), 41 Turkish (Ordu, Akçakoca and Samsun) and 15 Georgian

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(Guria, Samegrelo and Imereti) samples. Due to the large sample set we were dependent on

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collaborators for sample acquisition and most samples were provided by importers and distributers.

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The authenticity of all samples was declared by our collaborators. For detailed information about

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the origin, variety and supplier of all samples see supporting information (p SII, table S1). Each

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sample comprises either 1000 grams’ hazelnut kernels with skin (testa) or 1500 grams unshelled

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hazelnuts.

85 86

Sample treatment

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All hazelnut samples were handled in accordance with KLOCKMANN et al.19 100 g of the grist were

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stored for one week in a freezer (-20°C) to evaporate the dry ice. Two protocols were developed

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for sample extraction. Each sample was extracted three times, to avoid the influence of outliers. 5 ACS Paragon Plus Environment


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Extraction protocol A: of the polar metabolites 500 mg of the resulting lyophilisate was mixed with

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1.5 mL extraction solvent (chloroform-d/methanol-d4/200 mM deuterated phosphate buffer 2/1/2)

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and two steel balls followed by ball milling for 3 min at 3.1 m/s using a Bead Ruptor 24 equipped

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with a 1.5 mL microtube carriage kit (Biolabproducts, Bebensee, Germany). The remaining

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suspension was centrifuged for 15 min at 14,000 x g and 4 °C. 600 µL of the supernatant were

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taken and transferred into a 5 mm NMR tube (Deutero, Katellaun).

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Extraction protocol B: The two-phase extraction was equally performed as in protocol A. After

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centrifuging 200 µL of the supernatant was taken and 400 µL Methanol-d4 was added. The mixture

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was vortexed for 2 seconds and centrifuged again for 2 min at 14,000 x g and 4 °C. 200 µL of the

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resulting supernatant was mixed with 500 µL potassium phosphate buffer (200 mM) and

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transferred into a 5 mm NMR tube (Deutero, Katellaun, Germany).

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NMR data acquisition

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All spectra were acquired on a Bruker Avance III 400MHz spectrometer (Bruker Biospin,

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Rheinstetten, Germany) operating at 400.13MHz. The noesygppr1d pulse sequence was used for

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acquisition of water suppressed 1H NMR-spectra applying the digitization mode baseopt. Each

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spectrum was recorded at 300 K, with 64 scans, 65536 complex data points, a spectral width of

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8417.5 Hz. The RG was set to 64 and the transmitter frequency offset was set to 1924.6 Hz. For

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data processing the FIDs were Fourier transformed with a line broadening factor of 0.3, baseline

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corrected and phased with Topspin 3.2 (Bruker Biospins, Rheinstetten, Germany).

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2D TOCSY-spectra were acquired using the sequence dipsi2esgpph at 300K, applying 32 scans,

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256 (F1) and 2048 (F2) complex data points, a spectral width of 4085.0 Hz and with states-TPPI

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FnMODE. The RG was set to 256 and the transmitter frequency offset was set to 1881.8 Hz. The 6 ACS Paragon Plus Environment

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spectra were processed with a sine bell shift (SSB) of 2 in F1 and F2 and applying 32 linear

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prediction coefficients for a complex linear foreward prediction in F1. 2D-JRES-spectra were

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acquired applying the jresgpprqf pulse sequence at 300K, using 1 Scan, 40 (F1) and 8192 (F2)

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complex data points and a spectral width of 6684.5 Hz. The RG was set to 114 and the transmitter

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frequency offset was set to 1882.0 Hz. The spectra were processed with a sine bell shift of 0,

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magnitude calculation in F1 and executing tilt and symj command for spectrum symmetrization. A

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two-dimensional baseline correction was applied to all 2D spectra.

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Statistical analysis

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The spectra were transferred to AMIX 3.9.14 (Bruker Biospins, Rheinstetten, Germany) and

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calibrated to the TMSP signal. Various methods like automated bucketing with variable bucket size

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as well as manually bucketing were compared to each other. The residual solvent signal of

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methanol-d4 (3.303-3.338 ppm) and the region used for water suppression (4.645-4.971 ppm) were

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excluded from the bucket table. For automated bucketing the best results were obtained for a bucket

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size of 0.03 ppm. The best results regarding principle component analysis (PCA) were obtained

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for manually defined buckets with variable size. 222 buckets were manually defined as listed in

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the supporting information (p. SXIV, S2). The buckets were scaled to the total intensity (rows).

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This data was used to carry out a principle component analysis (PCA).132 Columns were scaled to

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unit variance and the number of principal components was set to a minimum explained variance of

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95%. For each sample group a confidence level of 95% was defined. The analysis of significant

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buckets was carried out using a Kruskal-Wallis-test and a confidence level of 95% (supporting

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information, p. SXIV, S3). The p-score was Bonferroni corrected. A classification analysis was set

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up using Classification Learner (Matlab R2015b, MathWorks, Inc.). The 262 samples were split 7 ACS Paragon Plus Environment


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into a training set consisting of 172 samples (Germany 19, France 88, Georgia 10, Italy 28 and

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Turkey 27) and a test set of 90 samples (Germany 9, France 46, Georgia 5, Italy 16 and Turkey

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14). For statistical analysis the normalized bucket table was exported from AMIX to Matlab. A

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manually classifier training with fivefold cross validation was used to compare different classifiers,

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including decision trees, support vector machines and nearest neighbor classifiers (supporting

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information, p. SXVIII, table S4). All features were selected because a feature selection to buckets

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that were significant in kruskal-wallis-test did not improve the accuracy of the classifier. The best

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results were obtained for a subspace discriminant classifier with 14 subspace dimensions and 30

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learners.

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RESULTS AND DISSCUSION

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Dried hazelnuts contain about 60% of lipids and less than 5% of water. Nevertheless, we focused

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on the polar fraction due to the larger dispersion of NMR signals. Previous work showed that 1H

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NMR-spectra of non-polar hazelnut extracts contain less information than the spectra of polar

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extracts.20,28 Indeed the non-polar extract of hazelnuts shows only a small chemical variation

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between the major abundant metabolites (supporting information, p. SXIV, figure S2). The

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spectrum of the polar fraction of a French hazelnut sample is shown in figure 1. In total, 16

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metabolites belonging to the class of amino acids, carbohydrates and organic acids were identified

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and are annotated in the spectrum. The identification of metabolites was carried out by evaluation

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of chemical shifts and coupling constants, database search (HMDB, BMRB) and acquisition of 2D

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TOCSY and JRES spectra. The identity of metabolites was further tested by spike-in experiments

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with reference samples. Table 1 summarizes signals that were used for assignment.

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The first analyses were carried out with a standard two phase extraction protocol using chloroform-

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d1/methanol-d4/deuterated phosphate buffer 2/1/2.29-31 Two phase extraction was necessary to

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remove lipids effectively from the polar extract. After phase separation the polar extracts were

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filled directly in NMR-tubes and 1D-NOESY spectra were acquired (extraction protocol A). These

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spectra showed in addition to the expected signals of small molecule metabolites very broad signals

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with their highest intensities from 4.7 to 0.8 ppm. These signals were assigned to not precipitated

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proteins, exhibited a high variance in intensities from sample to sample and reasonable integration

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of metabolite signals was not possible. Therefore, the extraction protocol was optimized regarding

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quantitative protein precipitation. 400 µL methanol-d4 was added to 200 µL of the previously

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isolated polar fraction (methanol-d4 deuterated phosphate buffer 1/2) from two-phase extraction

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for protein precipitation. The solution was vortexed, centrifuged and 500 µL of deuterated

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phosphate buffer was added. 600 µL of the resulting solution was used for analysis (extraction

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protocol B). This procedure of additional protein precipitation significantly reduced the protein

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content of the NMR samples and was selected for measurement of all samples (figure 2).

172 173

Statistical Analysis

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Each hazelnut sample was extracted three times and each extract measured by 1H-NMR using the

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1D NOESY sequence for water suppression. PCA was used for visualizing the data variance and

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for a quick and effective identification of outliers. Single outliers of the triple measurement were

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removed. The result of the PCA is shown in Figure 2 with the 3D Plot of PC1 vs. PC2 vs. PC3

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(2A).19, 27 The first ten principal components account for 80% of the total variance. The PCA shows

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a clustering for all sample groups, as well as a separation for German hazelnuts compared to

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Georgian, Italian and Turkish ones. The variance of a single sample group is larger than the distance 9 ACS Paragon Plus Environment


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between sample groups. In particular, the overlapping is high between French and Italian samples.

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Hence, there is no explicit separation in the PCA, as indicated by the overlapping confidence

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intervals (α = 0.05).

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Further statistical methods, based on supervised machine learning algorithms, were evaluated for

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an efficient classification of the experimental data. The normalized bucket table was exported from

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AMIX to Matlab. Using the Classification Learner App, different classifiers were compared,

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including decision trees, support vector machines and nearest neighbor classifiers, but the best

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results were achieved applying the subspace discriminant classifier using a random subspace

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algorithm (Table S4, supporting information). The subspace discriminant classifier is picking a

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subset of random features before applying the training algorithm (here linear discriminant analysis,

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LDA). Afterwards the results of the models are combined. 2/3 (overall 172) of all samples were

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used for training the classifier using all 222 buckets in the calculation. This classifier was applied

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to the training set and validated by a fivefold cross validation. The resultant confusion matrix is

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shown in figure 4. The cross validation yielded an accuracy of 91% indicating a strong and robust

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model. This is also supported by the high accuracy (> 80%) that was achieved by other

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classification algorithms, in particular decision tree and support vector machine (supporting

197

information, p. SXVIII, S4). The best result in terms of country classification were obtained for

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Georgian samples, without misclassification. The samples from Turkey and France show

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equivalent results with 96% and 94% true positive rate (TPR), respectively. The lowest accuracy

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with 78% was obtained for the assignment of Italian samples. The highest false positive rate (FPR)

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occurs for Italian samples that were misclassified as Georgian and for German samples that were

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misclassified as French. Because the overall performance of the classifier on internal cross

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validation was promising, a test set was built with the remaining 1/3 (overall 90) of all samples for 10 ACS Paragon Plus Environment

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external validation. 96% of the samples from the validation set were predicted correctly. The

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corresponding confusion matrix is shown in figure 5. Most misclassifications were obtained again

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for Italian samples, where two samples were classified as French. Again, no misclassification was

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obtained for Georgian samples and the overall assignment accuracy is comparable to the accuracy

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obtained from the cross validation. The robust accuracies of the test and training sets are an

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excellent basis for the distinction of hazelnuts in terms of food authentication.

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A further validation of the NMR/classification model allows the comparison to a previously

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published LC-MS study on a subset of the sample set presented here (196/262) from the same five

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countries. The LC-MS analysis achieved an accuracy of 100% for the training set and 80% for the

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prediction set applying a support vector machine/SIMCA classifier by a non-targeted approach.19

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The non-targeted MS analysis was further developed to a targeted LC-QqQ-MS/MS method that

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yielded an accuracy above 98%. Remarkably, in the LC-MS study not only a different analytical

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technique was applied, but also the non-polar fraction was investigated. Compounds identified are

217

e.g. di- and triacylglycerols, phosphatidylcholines and phosphatidylethanolamines. Remarkably,

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both approaches, NMR on polar and MS on non-polar metabolites, exhibit a similar clustering with

219

comparable sample distribution along the principle components (figure 3A and B). The internal

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variances of clusters from NMR measurement is slightly higher compared to the MS analysis. The

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largest differences in the data is for separation of German samples from Georgian, Italian and

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Turkish samples. For both data sets the separation is along PC1. On the other hand, the best

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separation between the Georgian, Italian and Turkish is along PC2 and PC3 in both studies. The

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data exhibits a remarkable comparability although two different sets of metabolites, polar and non-

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polar, were analyzed. The internal variance in metabolite levels is similar for both fractions



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(polarities) of natural products. The variances, neither within the polar nor within the non-polar

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metabolites, is sufficient for unequivocal sample assignment by PCA.

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Metabolites are the end products of cellular regulatory processes, and their concentrations can be

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regarded as the ultimate response of biological systems to genetic or environmental changes. The

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metabolome is likewise influenced by genomic differences attributed to varieties and exogenous

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factors attributed to local growth condition. Since identical varieties are only cultivated in limited

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regions it is not possible to conclude which of these factors have the largest impact on the individual

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composition of metabolites. However, in this study heterogeneous sample groups composed by

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many varieties show similarities that allows us the classification of samples due to their

235

geographical origin with a minimal influence of varieties in this model.

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Relevant Metabolites

237

The identification of metabolites permits evaluation of key metabolites, the metabolic pathways

238

involved and/or the development of a strategy for targeted analysis. Buckets that are significant for

239

each sample group have to be identified following a spectroscopic analysis for structure

240

determination and identification of the corresponding metabolites. In a first step the significance

241

of each bucket was analyzed by kruskal-wallis-test, comparing the median of each bucket for a

242

single sample group with the medians of the remaining samples. A bucket is considered significant

243

if the Bonferroni corrected p-value is below 0.000225. The Bonferroni correction is used to avoid

244

the problem of multiple comparison. Therefore, the significance level (p < 0.05) is divided by the

245

number of observations (here 222 buckets). The full bucket list with their corresponding p-values

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is shown in the supporting information (p. XVIII, S3). Overall 196 of 222 buckets exhibit a

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significant p-value for at least one sample group. No improvement in the classifier model was 12 ACS Paragon Plus Environment

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achieved when the remaining 26 non-relevant buckets were removed from the model. The

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variability in the concentration of the individual metabolites is large and some signals like lactic

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acid do not appear in every spectrum. Metabolites only have been classified as significant for a

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sample group/country, if all their signals show a significant p-value. Betaine, in contrast to the

252

other metabolites identified so far, exhibits significant differences in medians with relevant p-

253

values for all sample groups. Thus, betaine could be considered a candidate for the development

254

of rapid tests based on targeted approaches. In the classification of Turkish samples, nearly all

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small organic acids, e.g. acetate, malate, citrate or fumarate, exhibited a significant p-value in the

256

kruskal-wallis-test. However, there is no explicit correlation in their concentrations. While malate

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has a rather low concentration, the levels of citrate, formate and fumarate are higher in Turkish

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samples than in other countries (except for Georgia). Boxplots and Spidercharts for selected

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metabolites are shown in Error! Reference source not found.. Most of the relevant metabolites

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not identified so far are minor-components and are often coincided with signals from major

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components. More effort will be necessary for a reliable identification of these in future studies.

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Our work demonstrates that geographical origin determination of hazelnuts with NMR is possible

263

with a comparable accuracy to LC-MS methodology although focusing on a different part of the

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metabolome. Compared to the results of preceding studies, this study exhibits an equal accuracy

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for the determination of the geographical origin, indicating a very robust and applicable model.19,

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27

267

to identification of more relevant metabolites for an even more accurate distinction. It becomes

268

apparent that the developed model reliably recognizes a large number of important countries of

269

origin, with a minimized influence of their varieties and is suitable for determining the geographical

270

origin.

The combination of two different analyzing techniques and matrices (polar and non-polar) leads



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ABBREVIATIONS USED

273

DE, Germany; EU, European Union; FID, free induction decay; FNR, false negative rate; FP, false

274

positive; FR, France; GE, Georgia; IT, Italy; JRES, J-resolved; NMR, nuclear magnetic resonance;

275

LC-MS, liquid chromatography/mass spectrometry; PC, principal component; PCA, principle

276

component analysis; PDO, protected designation of origin; PGI, protected geographic indication;

277

RG, receiver gain; TOCSY, total correlation spectroscopy; TPR, true positive rate; TR, Turkey

278 279

ACKNOWLEDGEMENT

280

The authors are very grateful to SCA Unicoque, Erzeugerorganisation Deutscher Haselnussanbauer

281

UG,

282

Fürth/Sortenversuchsanstalt Gonnersdorf, Stelma SRL Unipersonale, AgroTeamConsulting,

283

Institute of Biotechnology and Microbiology, University of Hamburg, August Storck KG,

284

Seeberger GmbH, Crisol de Frutos Secos, Azienda Agricola Cascina Valcrosa, Basaran Entegre

285

Gıda san. ve Tic. A.Ş, Alta Langa Azienda Agricola, Corilu Societa Cooperativa Agricola, Coselva

286

SCCL, Eganut LLC and Franken Genuss UG & Co.KG, Ferrero OHG mbH, Heinrich Brüning

287

GmbH, August Töpfer & Co. (GmbH & Co.) KG, Lübecker Marzipan-Fabrik v. Minden & Bruhns

288

GmbH & Co. KG, Carl Wilhelm Clasen GmbH, Horst Walberg Trockenfrucht Import GmbH,

289

Fratelli Caffa s.a.s. and Rapunzel Naturkost for providing us with authentic hazelnut samples. The

290

authors thank Vera Priegnitz and Claudia Wontorra for their support in sample measurement.

Schlüter&Maack

GmbH,

Amt

für

Ernährung,

Landwirtschaft

und

Forsten

291 292

SUPPORTING INFORMATION


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Detailed list of all used hazelnut samples with suppliers, provenance and cultivar information and

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the bucket list with detailed limits of the buckets are shown in the supporting information.



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380

FIGURE CAPTIONS

381

Figure 1: 400 MHz 1H-NMR-spectrum of a polar extract from a French hazelnut sample. Peaks

382

that were used for metabolite identification are marked in the spectrum.

383

Figure 2: Comparison of the 400 MHz 1H-NMR-spectra of polar hazelnut extracts applying

384

extraction protocol A and B. In protocol B an additional precipitation step by addition of methanol-

385

d4 was used for more effective protein removal. Because the sample was further diluted the total

386

concentration of metabolites was reduced. Particularly, in the range of 4.7 to 0.8 ppm ppm, a

387

significant decrease of broad lines is accomplished

388

Figure 3: A) PCA scores plots of PC1 vs. PC2 vs PC3 of the NMR-analysis (multiple

389

measurements). Explained variance PC1=37.0%; PC2=11.1%; PC3=9.7%. B) PCA scores plot of

390

the LC-MS analysis (single measurements) from Klockmann et al. Both PCA Plots show a similar

391

distribution of the sample groups although they were extracted and analyzed by different

392

techniques.

393

Figure 4: Confidence Matrix of the training set. 0: Germany; 1: France, 2: Georgia; 3: Italy; 4:

394

Turkey. 2/3 (overall 179) of all samples were used as a training set. Applying a random subspace

395

algorithm, the classifier resulting in a fivefold cross validated accuracy of 91%.

396

Figure 5: Confidence Matrix of the test set. 0: Germany; 1: France, 2: Georgia; 3: Italy; 4: Turkey.

397

The test set was built with the 1/3 (overall 90) of all samples. 96% of the samples from the

398

validation set were predicted correctly.

399

Figure 6: A: Box-Whiskers-Plots showing the centered and scaled values for selected (identified)

400

metabolites. DE: Germany; FR: France; GE: Georgia; IT: Italy; TR: Turkey; B: absolute median

401

values for the selected metabolites. Alanine, Betaine and Malate show the biggest difference in

402

concentration between the sample groups. C: normalized (to mean value) change of the medians of 20 ACS Paragon Plus Environment

Page 21 of 28


403

selected metabolites. This plot illustrates differences between the individual sample groups.

404

Despite these different profiles, almost all buckets are significant in the t-test for the classification

405

of at least one country.



Page 22 of 28

TABLES Table 1: Identified metabolites of a polar hazelnut extract with their impact on origin discrimination Chemical shift  [ppm]

Multiplicity

Metabolite

J [Hz]

Significant for1

0.998

d

Valine

7.1

France, Turkey

1.049

d

Valine

7.0

France, Turkey

1.032

d

Isoleucine

7.1

-

1.326

d

Threonine

6.9

-

1.327

d

Lactate

6.7

-

1.482

d

Alanine

7.2

Germany, Georgia

1.909

s

Acetate

-

Turkey

2.020-2.188

m

Glutamate

-

Germany, Italy, Turkey

2.296-2.242

m

Valine

-

France, Turkey

2.350

dd

Malate

15.3, 10.1

Turkey

2.518

d

Citrate

15.2

Georgia, Turkey

2.658

d

Citrate

15.1

Georgia, Turkey

2.659

dd

Malate

15.0, 3.2

Turkey

3.209

s

Choline

-

Germany

3.271

s

Betaine

-

Germany, France, Georgia, Italy, Turkey

3.456

t

Sucrose

9.4

Italy

3.537

dd

Sucrose

10.0, 3.0

Italy

3.666

bs

Sucrose

-

Italy

3.757

t

Sucrose

9.6

Italy

3.791 -3.806

m

Sucrose

-

Italy

4.041

t

Sucrose

8.3

Italy

4.199

d

Sucrose

8.4

Italy

4.278

dd

Malate

10.1, 3.0

Turkey

4.622

d

Glucose

8.0

Turkey

5.215

d

Glucose

3.9

Turkey


Page 23 of 28


Chemical shift  [ppm]

Multiplicity

Metabolite

J [Hz]

Significant for1

5.409

d

Sucrose

3.9

Italy

6.513

s

Fumarate

-

Georgia, Turkey

6.885

pseudo-d

Tyrosine

8.5

Germany

7.188

pseudo-d

Tyrosine

8.4

Germany

8.461

s

Formate

-

Turkey

1according

to Kruskal-Wallis-test

FIGURES Figure 1



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Figure 2

Figure 3


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Figure 4



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Figure 5

Figure 6


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TABLE OF CONTENT GRAPHIC


1H NMR Spectroscopy for Determination of the Geographical Origin of

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