1H NMR Profiling as an Approach To Differentiate Conventionally and

Article pubs.acs.org/JAFC

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H NMR Profiling as an Approach To Differentiate Conventionally and Organically Grown Tomatoes

Monika Hohmann,*,†,§ Norbert Christoph,† Helmut Wachter,† and Ulrike Holzgrabe§ †

Bavarian Health and Food Safety Authority, Luitpoldstraße 1, 97082 Würzburg, Germany Institute of Pharmacy and Food Chemistry, University of Würzburg, Am Hubland, 97074 Würzburg, Germany

§

ABSTRACT: This study describes the approach of 1H NMR profiling for the authentication of organically produced tomatoes (Solanum lycopersicum). Overall, 361 tomato samples of two different cultivars and four different producers were regularly analyzed during a 7 month period. The results of principal component analysis showed a significant trend for the separation between organically and conventionally produced tomatoes (p < 0.001 using the t test). Linear discriminant analysis demonstrated good discrimination between the growing regimens, and external validation showed 100% correctly classified tomato samples. Further validation studies, however, also disclosed unexpected differences between individual producers, which interfere with the aim of predicting the cultivation method, yet the results indicate significant differences between 1H NMR spectra of organically and conventionally grown tomatoes. KEYWORDS: 1H NMR, multivariate data analysis, tomato, Solanum lycopersicum, organic, conventional

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INTRODUCTION The demand for organically produced food has been increasing considerably; it almost tripled between 2002 and 2011, when its market share reached a worth of U.S. $62.8 billion.1 Because consumers are prepared to purchase organic food at higher prices, the risk for conventional produce fraudulently labeled as “organic” has also been increasing. To prevent and detect counterfeit, there is a strong need for analytical methods that allow the verification of organic cultivation. This study examines the differentiation between organically and conventionally grown tomatoes (Solanum lycopersicum), which are widely consumed in Europe,2 by means of multivariate data analysis of 1H NMR spectra. Within the European Union, the requirements for organic farming are stated in Council Regulation (EC) 834/2007 and Commission Regulation (EC) 889/2008. With respect to the ban of the use of synthetic pesticides and synthetic mineral fertilizer for organic farming, there are a variety of starting points for analytical differentiation. Organically produced food is often associated with statements about a better nutritional value due to allegedly increased contents of bioactive compounds and lower levels of pesticides and contaminants. Higher contents of polyphenolic compounds and higher antioxidant capacity were analyzed for tomatoes,3 tomato ketchup,4 and tomato juice.5 However, the amounts of bioactive compounds are influenced by variations due to crop year, origin,6,7 amount of fertilizer and soil nitrate,8 stage of maturity,9,10 and ripening type.11 Thus, differences in organic and conventional tomatoes were occasionally inconsistent within different varieties or crop years,12,13 and individual results are hardly comparable due to different conditions in cultivation.14 Pesticide screening of organic food is meaningful to detect the illegitimate usage of synthetic pesticides, yet residue findings can also be the result of contaminations from nearby conventional farms or anthropogenic inherited waste. Furthermore, conventional farming is not necessarily combined with the use of © 2014 American Chemical Society

pesticides for plant protection, which, especially in greenhouse cultivation, can also be performed by means of beneficial insects. Other studies show that nitrogen fertilization affects the composition of amino acids in vegetables,15 whereas these results are likewise affected by variety16 and degree of ripeness.17,18 Furthermore, variations in the pattern of mineral concentrations in organically and conventionally grown tomatoes have been described,19 which may be attributed to the metabolism of arbuscular mycorrhizal fungi in organic soils.20 Up to now, the most reliable analytical method for authentication is presented by the composition of the nitrogen isotope: the nitrogen isotope composition of the fertilizer predefines the nitrogen isotope composition of the grown crop.21−23 As the nitrogen of synthetic mineral fertilizer originates from atmospheric nitrogen with a δ15N value around 0‰ (δ15N is expressed as the relative difference from atmospheric nitrogen as standard), improper usage of conventional fertilizers can be identified by lower δ15N values than common values for organic farming.21 However, there exists an overlap between the δ15N values of organically and conventionally grown crops24 that suggests the development of further analytical methods for the verification of the growing regime. As recently described by Esslinger et al.,25 the application of spectroscopic data combined with multivariate data analysis to verify food authenticity is becoming increasingly important. Using these profiling techniques is especially reasonable in the area of food products, where attributes of authenticity represent unique quality parameters and legitimate higher prices, as is the case for organic food. Previous examples of use cover authenticity control in terms of analyzing botanical26 or geographical origin27,28 and adulteration29 of honey, geographical origin30−33 Received: Revised: Accepted: Published: 8530

May 7, 2014 July 27, 2014 July 28, 2014 July 28, 2014 dx.doi.org/10.1021/jf502113r | J. Agric. Food Chem. 2014, 62, 8530−8540

Journal of Agricultural and Food Chemistry

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and adulteration34 of olive oil, botanical35 or geographical origin36−40 and quality41 of wine and authenticity control of fruit juice.42 Both the multitude of recent applications and their output of results show the importance of 1H NMR profiling in terms of authenticity analysis. Previous NMR measurements of tomatoes and tomato products focused on carotenoid composition,43 degree of ripeness,44,45 quantitative NMR,4 magnetic resonance imaging (MRI) measurements,46 or diverse 2D NMR experiments.47 Here, the suitability of 1 H NMR profiling for the purpose of differentiation between organic and conventional tomatoes is discussed. Our key aim is the development of a sufficient method for an authentication of organic tomatoes against the background of food surveillance and consumer protection, independent from nutritional perspectives. Because no set definition for organic or conventional farming exists, there is ample scope for a variety of cultivation methods. For example, numerous fertilizers may be used and fruits can be grown in greenhouses as well as on open land. It is accordingly unfeasible to create an experimental design that fulfills the possible growing conditions in all facets. In this study, attention was turned on greenhouse cultivation, and authentic tomatoes were grown conventionally with mineral substrate and mineral fertilizers as well as organically with soil and organic fertilizers. An essential difference of these cultivation methods may be represented by different nitrogen availabilities. This cultivation experiment certainly does not reflect the wide range of actually existing ways of farming, but rather demonstrates a proper starting point to verify the basic suitability of 1H NMR profiling for a differentiation between organically and conventionally grown tomatoes.

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Validation Data Set Comprising CE and TF Tomato Samples. To test the suitability of statistical models, an external validation data set was created by systematically excluding both CE and TF tomato samples from calibration (validation of LDA1): overall, 81 tomato samples, organically grown at CE (harvested at weeks 23, 31 and 39), conventionally grown at CE (harvested at weeks 27, 35, and 43; excluding tomatoes planted in January), and organically grown at TF (harvested at weeks 21, 29, and 37) as well as conventionally grown at TF (harvested at weeks 24, 32 and 40), were used for validation purposes. Additional Tomato Samples for Validation. In addition, several other fruits were analyzed and used for validation (validation of LDA2): The cultivar Tica was grown under the same conditions as organically grown CE tomatoes. Fruits from six different parcels were harvested individually and as a pooled sample of these parcels at week 30 and another pooled sample of these parcels at week 35, resulting in overall eight tomato samples. Furthermore, cultivars Mecano and Tastery, grown at the CE (planted in March), were harvested as ripening (red-turning) tomatoes at week 35. Overall, 4 tomato samples were measured, one for each cultivar and type of farming. From another producer (school for horticulture in Fürth, Germany), the cultivar Mecano was grown conventionally in a greenhouse using mineral fertilizer and different substrates: cocos-plate, wood-perlite mixture, and perlite. Fruits were harvested at weeks 30 and 31, with one tomato sample for each substrate, yielding six tomato samples. Chemicals. For 1 M NaOH and 1 M HCl, NaOH pellets were purchased from VWR (Leuven, Belgium) and HCl (37%) from SigmaAldrich (St. Louis, MO, USA). TSPd4 (3-(trimethylsilyl)propionic acid-d4 sodium salt, 98 atom % D), D2O (99.9 atom % D), EDTA (ethylenediaminetetraacetic acid), and NaN3 were purchased from Merck (Darmstadt, Germany). Sample Preparation. At least 250 g of tomato was pureed and subsequently stored at −18 °C. After thawing, the samples were centrifuged for 5 min at 3528g, and the residual liquid tomato phase was used for subsequent 1H NMR measurement. For this purpose, 900 μL of liquid tomato phase was mixed with 100 μL of TSPd4 solution (containing 7 mM TSPd4, 10 mM EDTA, and 2 mM NaN3 in D2O), and the pH value of all samples was adjusted to pH 4.00 ± 0.03 using 1 M NaOH or 1 M HCl. Finally, 600 μL of the pH-adjusted solutions was filled into 5 mm NMR tubes (PP-507, purchased from RototecSpintec, Griesheim, Germany) for NMR measurement. 1 H NMR Measurement. Hardware and software equipment used for NMR measurement was completely purchased from Bruker (Bruker BioSpin, Rheinstetten, Germany) comprising a Bruker Avance 400 spectrometer with a 5 mm SEI probe with Z-gradient coils, an automatic sample changer (B-ACS-60), a BCU 05 temperature unit, and TopSpin 3.0 software. All tomato samples were measured at 301.8 ± 0.1 K, without rotation and using 4 dummy scans prior to 128 scans. Acquisition parameters have been set as follows: size of fid = 64K, spectral width = 20.5524 ppm, receiver gain = 16, acquisition time = 3.98 s, relaxation delay = 10 s, FID resolution = 0.25 Hz. Data acquisition was achieved using an experiment with a NOESY presaturation pulse sequence (Bruker 1D noesygppr1d) with water suppression via irradiation of the water frequency during the recycle and mixing time delays. The spectra were automatically phased, baseline-corrected, and calibrated to the TSP signal at 0.0 ppm. Multivariate Statistical Analysis. Data reduction was achieved by transforming NMR spectra into buckets (bundling spectral regions to one rectangular bucket, whereas the value of a bucket represents the signal intensity of the respective spectral region). Two different types of buckets (given in Table 1) were generated with Amix 3.9.12 software (Bruker Biospin GmbH). Subsequent multivariate data analysis was done on the assumption of normally distributed data. Principal component analysis (PCA) was performed with Unscrambler X version 10.0.1 (Camo Software AS, Oslo, Norway) with buckets of 1H NMR spectra scaled to unit variance. Linear discriminant analysis (LDA) of buckets was carried out with SPSS statistics 21 (IBM Corp., Armonk, NY, USA) using equal a priori probabilities for all groups and stepwise selection procedure

MATERIALS AND METHODS

Sample Collection. Cultivation Experiment (CE). The tomato cultivars Mecano and Tastery were grown in two different greenhouses at the Bavarian State Research Institute of Viticulture and Horticulture, both organically and conventionally (LWG, Bamberg, Germany; tomato samples are referred to as “CE organic” and “CE conv” in the following text). Thereby, conventional growing conditions were carried out as hydroponic culture using perlite substrate and mineral fertilizer, whereas organic growing conditions were carried out using soil and clover-grass silage, vinasse, Patentkali, sheep’s wool, and winter rye (previous culture for green manure) as organic fertilizer. Except for conventionally grown tomatoes of the cultivar Mecano (planted in both January and March), all plants were grown in March. For each cultivar and farming method at least three parcels of tomato plants (each consisting of 10 plants) were grown: for organic cultivation of Mecano, 4 parcels, and for Tastery, 4 parcels; for conventional cultivation of Mecano, 7 parcels (thereof 3 parcels with tomatoes planted in January and 4 parcels planted in March), and for Tastery, 5 parcels. Harvesting was performed at regular intervals of 2 weeks between weeks 21 and 45 (13 harvest times), and sampling was done by picking tomatoes systematically from different plants in the greenhouse. Tomatoes from each parcel were analyzed individually and, in addition, tomatoes from equal parcel of the same cultivation method and cultivar were pooled to one mixed sample for analysis. At this, altogether 325 CE samples were analyzed, with 52 samples from tomatoes planted in January and 273 samples from tomatoes planted in March. Trading Firms (TF). Additionally, three trading firms (referred to as “TF organic”, “TF conv 1”, and “TF conv 2” in the following text) cultivated the same cultivars (Mecano and Tastery). Conventional growing conditions were again carried out as hydroponic culture using perlite substrate and mineral fertilizer, and organic growing conditions were carried out using soil and hair meal and compost as organic fertilizer. Fruits were harvested from May to October once a month, so that in total 36 tomato samples were analyzed (3 producers × 2 cultivars × 6 months). 8531

dx.doi.org/10.1021/jf502113r | J. Agric. Food Chem. 2014, 62, 8530−8540


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As several citrate species exist in fast exchange with each other (protonated or ionic, complexed with Ca2+ and Mg2+), the observed citrate signal represents an average of the signals from those species.50 Additionally, broadened lines can arise from paramagnetic substances that lead to enlarged values of line width by shortening T2 relaxation times.51,52 To achieve high resolution and optimized line widths, small amounts of EDTA were added to each sample, because EDTA enables signal sharpening via serving as chelating agent. With regard to the 1 H NMR spectra of tomato samples, especially the line width of citric acid was clearly reduced by the addition of EDTA, whereas no major effects on chemical shifts could be detected. For the best possible conditions for multivariate data analysis, highest reproducibility has to be achieved. Therefore, all samples were pH adjusted (pH 4.00 ± 0.03) to minimize variation in chemical shifts due to protonation or deprotonation processes. As the chemical shift of citric acid is highly influenced by deviations from the requested pH value, it was used as a quality marker for adequate pH adjustment to define control limits for tolerable chemical shift deviations (deviations up to ±3.5 Hz from the left signal top of the citric acid duplet at 2.881 ppm were accepted). Principal Component Analysis. To get an overview of the given data, PCA was performed with 1H NMR spectra of tomatoes from both cultivars and types of farming (309 tomato samples are included in the evaluation, 273 from CE, excluding fruits planted in January, and 36 from TF). The scatter plot of PC1 versus PC4 (Figure 2) shows the separation of the two cultivars Mecano (circles) and Tastery (triangles) along PC1. With the aim of recognizing the effect of different cultivars, two very dissimilar cultivars were analyzed (Tastery is small fruited with an average weight of 20 g/fruit, and Mecano is normally fruited with an average weight of 100 g/fruit) and, thus, differentiation according to their cultivar is quite

Table 1. Description of the Bucketing Process and the Application of Buckets for Multivariate Data Analysis bucketing 1 spectral region exclusions

reason for exclusions bucket width application

bucketing 2

0.00−10.00 ppm

2.86−8.67 ppm

4.85−4.67 ppm 3.70−3.60 ppm 1.22−1.14 ppm

7.91−6.67 ppm 6.12−5.39 ppm 5.02−4.49 ppm 3.15−2.98 ppm elimination of irrelevant signals for a differentiation between organic and conventional tomato samples 0.007 ppm LDA2

elimination of residual water and ethanol signals (NMR tubes were reused and washed with ethanol) 0.200 ppm PCA, LDA1

(chosen method, minimization of Wilks’ lambda; selection criterion, F probabilities of 0.050 for inclusion and 0.100 for exclusion for LDA1 and 0.005 for inclusion and 0.010 for exclusion for LDA2). t tests were performed using OriginPro 9G software (OriginLab Corp., Northampton, MA, USA).

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RESULTS AND DISCUSSION H NMR Measurement and Optimization of Sample Preparation. On average, tomatoes contain 94% water and the remaining 6% of dry matter consists of 55% sugar (mainly glucose and fructose), 20% alcohol-soluble constituents (proteins, cellulose, pectin, and polysaccharides), 12% organic acids (mainly citric acid), 8% inorganic substances (minerals), and 5% of other substances, such as amino acids.48 Hence, 1 H NMR spectra of tomatoes are dominated by signals of sugar and citric acid, whereas an enlarged view also reveals diverse amino acids and other minor components (Figure 1 and Table 2). Thereby, the signals obtained are in good agreement with those reported previously.49 1

Figure 1. 1H NMR spectrum of the aqueous phase of a tomato sample with an enlarged view on the spectral range from 0.5 to 2.6 ppm (*8) and from 5.8 to 10.0 ppm (*64), measured with a 400 MHz spectrometer. Signal assignments are based on reference spectra and the literature (*Mounet et al.;45 and **Le Gall et al.53). 8532



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Table 2. Resonance Assignments of Signals in 1H NMR Spectra of the Aqueous Phase of Tomatoes with Chemical Shift and Spin−Spin Coupling,49 Using a 400 MHz NMR Spectrometer molecule

chemical shift

multiplicity

molecule

chemical shift

multiplicity

isoleucine leucine valine isoleucine valine ethyl alcohol isoleucine threonine lysine alanine isoleucine leucine lysine lysine γ-aminobutyric acid isoleucine glutamic acid glutamine glutamic acid valine γ-aminobutyric acid glutamine glutamic acid malic acid citric acid aspartic acid malic acid asparagine citric acid aspartate asparagine lysine γ-aminobutyric acid phenylalanine choline β-D-glucose histidine phenylalanine histidine β-D-glucose α-D-glucose β-D-glucose β-D-glucose α-D-glucose β-D-fructofuranose β-D-fructopyranose threonine β-D-fructofuranose valine α-D-fructofuranose ethyl alcohol

0.93 0.95 0.98 1.00 1.03 1.17 1.26 1.32 1.47 1.47 1.47 1.70 1.71 1.89 1.93 1.97 2.09 2.13 2.15 2.26 2.42 2.45 2.48 2.65 2.75 2.76 2.83 2.84 2.86 2.88 2.95 3.02 3.03 3.11 3.19 3.23 3.28 3.28 3.34 3.39 3.40 3.45 3.48 3.52 3.54 3.56 3.59 3.59 3.61 3.65 3.65

t m d d d t m d m d m m m m m m m m m m t m m dd d dd dd dd d dd dd m m dd s dd m dd m dd dd ddd t dd d d m d m

β-D-fructofuranose isoleucine α-D-fructofuranose β-D-fructopyranose α-D-glucose β-D-fructopyranose β-D-glucose α-D-glucose glutamine glutamic acid alanine alanine β-D-fructopyranose β-D-fructofuranose α-D-fructofuranose α-D-glucose α-D-glucose β-D-fructofuranose β-D-fructopyranose β-D-glucose aspartic acid β-Dfructopyranose phenylalanine α-D-fructofuranose asparagine β-D-fructopyranose histidine α-D-fructofuranose α-D-fructofuranose threonine malic acid β-D-glucose α-D-glucose glutamine asparagine tryptophan tryptophan phenylalanine phenylalanine histidine phenylalanine tryptophan glutamine asparagine tryptophan trigonelline histidine trigonelline trigonelline trigonelline

3.67 3.67 3.68 3.70 3.70 3.70 3.71 3.75 3.77 3.77 3.78 3.78 3.79 3.79 3.80 3.81 3.82 3.83 3.89 3.89 3.94 3.99 3.99 3.99 4.00 4.01 4.02 4.05 4.10 4.26 4.40 4.63 5.22 6.85 6.89 7.19 7.28 7.32 7.37 7.38 7.42 7.53 7.57 7.62 7.73 8.06 8.64 8.80 8.82 9.10

dd

dd td d dd dd t m

dd dd m m m dd dd dd ddd dd dd dd dd d dd d d

t t dd t s td d

d dd s m m s

m

1

understandable. In addition, a separation of the organically and conventionally grown groups is apparent for each cultivar, notably along PC4: both Mecano and Tastery show higher values of PC4 for the conventional than for the organic group. Given the existing overlap of organic and conventional tomato samples, values of PC4 are not capable of a reliable classification of organic fruits. Nevertheless, the findings confirm the approach that

H NMR spectra show significant differences between the cultivation methods (p < 0.001 using t test of PC4 values). Comparability of all other impact parameters has to be ensured to reason that differences between organic and conventional tomato samples can be attributed to the kind of growing regimen. As immoderate heating of greenhouses is contradictory to the concepts of environmental acceptability for organic cultivation, 8533



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both cultivars, observed along PC3 for Mecano and along PC1 for Tastery (Figure 3). Because PC3 explains only 4.6% of the data variance for the PCA of Mecano in comparison to PC1 with 55.8% explained variance for the PCA of Tastery, variations of 1 H NMR spectra according to the type of farming seem to have a higher impact on the total variance in 1H NMR spectra for Tastery than for Mecano. Linear Discriminant Analysis. Just as PCA, LDA forms linear combinations of variables to best explain the given data. The main difference between PCA and LDA is the fact that LDA is a supervised method. At first, each sample is assigned to a group subject to the sample’s properties. Second, discriminant variables are examined by weighing the sample’s variables, so that the differentiation between the predefined groups is maximized and differences of samples within one group are minimized. Once a discriminant analysis has been carried out, the resulting model can serve for an assignment of unknown samples to one of the predefined groups. At first, LDA was performed with the two groups of organically or conventionally produced tomatoes (LDA1; altogether 309 NMR spectra were used for examination, 273 for CE, with tomatoes planted in March, and 36 for TF). As can be seen in Figure 4, the groups are successfully separated without any region of overlap. Moreover, the unwanted differentiation of the cultivars Mecano and Tastery does not occur because variables for cultivar separation are consciously weighted low in the computation of LDA. The major disadvantage of supervised multivariate data analysis techniques such as LDA is the risk for building overfitted models. A possibly biased focus on model samples can hamper the application of group prediction for other samples. Hence, representative model samples are a key prerequisite for capable LDA models. In this context, validation of LDA1 was performed to evaluate the suitability for a prediction of the group membership. The data set was separated into a calibration data set and a validation data set in two different ways. First, the calibration data set was created by systematically excluding both tomato samples

Figure 2. Scatter plot of PC1 versus PC4: triangles, Tastery; circles, Mecano; dark gray data points, conventional tomatoes; light gray data points, organic tomatoes. The group of Mecano conventionally planted in January is projected into the PCA plot and marked by crosses.

the planting is commonly performed later than in case of conventional farming using intensively heated greenhouses. To determine whether potential differences in organic and conventional tomatoes are irrespective of the planting date, Mecano cultivar was planted conventionally in both January and March, and 1H NMR spectra were subsequently checked for differences. When the group of Mecano cultivar planted in January was projected into the previously mentioned PCA model, no data separation from the group of conventionally grown Mecano cultivar planted in March is recognizable (Figure 2). Hence, the planting date evidently did not affect the composition of tomato ingredients. Next, PCA was performed with each cultivar individually with overall 148 NMR spectra for Mecano cultivar (130 for CE planted in March and 18 for TF) and 161 NMR spectra for Tastery cultivar (143 for CE and 18 for TF). PCA scatter plots show a significant separation (p < 0.001 using t test) between the groups of organically and conventionally grown tomatoes for

Figure 3. Scatter plots of PC1 versus PC3 of PCA for Mecano samples (circles; left side) and PC1 versus PC2 for Tastery samples (triangles; right side): dark gray data points, conventional tomatoes; light gray data points, organic tomatoes. 8534



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At this point the question arises whether it is possible to build a sufficient database of 1H NMR spectra from authentic tomatoes that fulfills the requirement to reflect the actually given variety of variable organically and conventionally grown tomatoes. Given the limitation of the present data set, this question cannot be answered yet, but the results so far suggest an extension of the experimental design with additional tomato varieties, producers, and farming conditions. Moreover, it is reasonable to further examine the unwanted differences between greenhouses of the same growing regimen. For this, another LDA (LDA2) was performed with CE and TF tomato samples from each greenhouse classified with a single group, resulting in overall five groups (CE organic, CE conv, TF conv 1, TF conv 2, TF organic). Best separation results were achieved when a selection of spectral regions was used instead of the whole range from 0 to 10 ppm (Table 1). The selection was based on spectral regions with apparent differences between NMR spectra of organically and conventionally grown tomatoes, defined by a visual comparison when overlaying spectra with different colors according to organic and conventional cultivation. This rather represents a preselection, because another stepwise selection (using critical F values) is performed when buckets are undergoing LDA. The results of LDA2 can be seen at Figure 5. As the two groups of conventional TF tomato samples are hardly separated, it can be assumed that no relevant differences exist between them, yet all other groups are separated well. The unwanted differentiation between CE groups (circles) and TF groups (triangles and squares) appears along discriminant variable 2, showing higher values for CE groups than for TF groups. Besides, the key information on LDA2 is the separation achieved by discriminant variable 1: the conventional groups show higher values of discriminant variable 1 than the organic groups (Figure 5). Thus, the type of farming obviously represents a latent factor herein, and it was tested if LDA2 enables a prediction of the growing regimen based on the value of discriminant variable 1. For this purpose, first validation steps with additional tomato samples were performed. Basically, two cases of wrong

Figure 4. Frequency histogram of the values of discriminant variable 1 obtained by LDA1: dark gray, conventional tomato samples; light gray, organic tomato samples.

from CE and TF for validation. Overall, 81 tomato samples formed the validation data set, which is 26.21% of all 309 tomato samples (see Materials and Methods). In this case, validation showed excellent results with 100% correctly predicted group identities. Second, fruits from CE were used as calibration data set and fruits from TF as validation data set. Thus, merely 72.2% of group identities were predicted correctly. When the calibration model is based only on fruits grown at CE, it is rather differentiating between the two specific CE greenhouses than between organic and conventional tomatoes in general, which makes it inapplicable for a group prediction of tomatoes from TF. In this context, the promising validation results (when both CE and TF tomato samples are included in the calibration data set) need to be questioned critically in terms of a basic suitability for an authentication of organic tomatoes from other producers. However, this validation illustrates the potential for differentiation if representative cultivation conditions are taken into consideration for LDA.

Figure 5. 3D scatter plot of discriminant variables 1, 2, and 3, with sphere symbols for CE samples, tetrahedral symbols for TF organic and TF conv 1, and cube symbols for TF conv 2: dark gray data points, conventional tomaotes; light gray data points, organic tomatoes (left side). Frequency histogram of the values of discriminant variable 1 obtained by LDA2: dark gray, conventional samples; light gray, organic samples (the auxiliary line shows the 5% quantile of the values of discriminant variable 1 for conventional tomato samples; right side). 8535



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Table 3. Results for the Prediction of the Growing Regimen Based on the Value of Discriminant Variable 1 (Obtained by LDA2) for Several Additional Tomato Samples (See Materials and Methods) dv1 (LDA2)

sample type and no. of samples (n) (i) tomatoes of cultivar Tica organically grown at CE (n = 8)

Tica Tica Tica Tica Tica Tica Tica Tica

(ii) tomatoes (cultivar Mecano) of another producer (Fürth, Germany) organically grown using different substrates, (n = 6)

wood-perlite wood-perlite cocos cocos perlite perlite

(iii) unripe (red-turning) tomatoes (cultivars Mecano and Tastery) organically/conventionally grown at CE (n = 4)

Mecano Tastery Mecano Tastery

−1.56 −3.85 −2.73 −5.19 −1.79 −5.84 −5.10 −3.76

growing regimen

prediction

organic organic organic organic organic organic organic organic

organic organic organic organic organic organic organic organic

1.51 4.19 2.93 5.38 2.87 3.68

conventional conventional conventional conventional conventional conventional

organic conventional conventional conventional conventional conventional

−3.20 −4.04 2.41 4.83

organic organic conventional conventional

organic organic conventional conventional

Figure 6. Projection of the prediction results for (i) Tica samples (left), (ii) Mecano samples conventionally grown on different substrates (middle), and (iii) unripe CE samples of Mecano and Tastery (right) into the 3D scatter plot of LDA2 (top; see Figure 5) and into the frequency histogram of the values of discriminant variable 1 obtained by LDA2 (bottom; see Figure 5).

predictions can occur: an organic fruit is falsely determined as conventional or a conventional fruit is falsely determined as organic. With the aim of preventing unjust allegations against

reliable organic producers, the risk for conventionally grown tomatoes being falsely assigned as organic is rather accepted than the risk for organically grown tomatoes being falsely identified as 8536



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Figure 7. Box plot of relative bucket intensities for the cultivars Mecano and Tastery (top) and organically and conventionally grown tomatoes for Mecano (middle) and Tastery (bottom). Each box is determined by the 25th and 75th percentiles and each whisker by the 5th and 95th percentiles. Values higher than 75th percentile +1.5*interquartile range and lower than 25th percentile −1.5*interquartile range are marked by a cross as outliers. Significantly different bucket intensities are colored white/gray, and nonsignificantly different bucket intensities are colored white/red.

The results for additional tomato samples grown on different substrates are also satisfying: although no further tomato samples of this specific producer are part of the LDA2 model, correct predictions were achieved for five of six tomato samples, and the falsely predicted one has a value very close to the decision limit of the 5% quantile. Hence, the sort of substrate did not interfere with a prediction of conventional farming. The prediction for unripe fruits of Mecano and Tastery cultivar was right for all samples and, consequently, the degree of ripeness did not hamper an assignment of the cultivation method. Designation of Substances That Are Relevant for Differentiation. In view of the differentiation between organically and conventionally grown tomatoes based on 1H NMR spectra, it is especially interesting to detect spectral regions and the corresponding substances that are responsible for data separation. Given the promising validation results for an assignment of the growing regimen based on discriminant value 1 of LDA2, the

conventional. Hence, the decision rule for an assignment was built on the consideration that no more than 5% of conventional tomato samples are accepted to be falsely predicted as organically grown fruits. The value of discriminant variable 1 within LDA2 served as predictive indicator for the validation data set: values lower than the 5% quantile for discriminant variable 1 of conventional tomato samples (discriminant variable < 1.88) were assigned to organic farming and higher values to conventional farming (Figure 5). In this way prediction was applied for several additional fruits that vary in cultivar, type of substrate for conventional farming, and degree of ripeness (see Materials and Methods). Results are displayed in Table 3 and Figure 6. The results for the cultivar Tica are very promising, especially when considering that this cultivar was not taken into account within the LDA2 model. Because the cultivar Tica is close to Mecano, it can be assumed that once a cultivar type is part of the model, adequate prediction for similar cultivars can be performed. 8537



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Figure 8. Box plot of relative bucket intensities, scaled to total signal intensities of the respective NMR spectra, for the cultivars Mecano and Tastery (top) and organically and conventionally grown tomatoes for Mecano (middle) and Tastery (bottom). Each box is determined by the 25th and 75th percentiles, and each whisker by the 5th and 95th percentiles. Values higher than 75th percentile +1.5*interquartile range and lower than 25th percentile −1.5*interquartile range are marked by a cross as outliers. Significantly different bucket intensities are colored white/gray, and nonsignificantly different bucket intensities are colored white/red.

influenced by the tomato cultivar and consistently higher for the group of Tastery than for the group of Mecano. Additionally, within both cultivars, the signal intensities show on average higher values for conventionally grown than for organically grown tomatoes. In this connection, the differences are more distinct for Tastery than for Mecano. Because the bucket intensities are continuously increased for the respective conventional group, this may be attributed to increased dry matter levels in conventionally grown fruits2 instead of compositional differences. To reduce the influence of dry matter, the bucket intensities were referred to the total signal intensity of the spectra and again compared within cultivars and cultivation methods (Figure 8; the total intensity was defined as signal intensity from 0 to 10 ppm minus the regions of water and ethanol signals from 4.85−4.67, 3.70−3.60 and 1.22−1.14 ppm).

overall 66 buckets used for its calculation were analyzed in more detail. Thirty-five buckets could be assigned to the reference signals of malic acid (A), asparagine (B), aspartic acid (C), fructose (D), glucose (E), histidine (F), choline (G), threonine (H), trigonelline (I), and adenosine monophosphate (J; assignment according to the literature53). These buckets were checked for significant differences between the cultivars Mecano and Tastery as well as for differences between organically and conventionally grown tomatoes between each cultivar. Figures 7 and 8 show a comparison of the relative bucket intensities within cultivars and growing regimens in a box-plot diagram, whereby nonsignificantly different bucket intensities are highlighted white/red (t test: p > 0.05) and significantly different bucket intensities are colored white/gray (t test: p < 0.05). Figure 7 illustrates that the bucket intensities are mainly 8538



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(6) Hallmann, E.; Lipowski, J.; Marszalek, K.; Rembialkowska, E. The seasonal variation in bioactive compounds content in juice from organic and non-organic tomatoes. Plant Foods Hum. Nutr. 2013, 68, 171−176. (7) Aherne, S. A.; Jiwan, M. A.; Daly, T.; O’Brien, N. M. Geographical location has greater impact on carotenoid content and bioaccessibility from tomatoes than variety. Plant Foods Hum. Nutr. 2009, 64, 250−256. (8) Gravel, V.; Blok, W.; Hallmann, E.; Carmona-Torres, C.; Wang, H. Y.; Van de Peppel, A.; Còndor Golec, A. F.; Dorais, M.; Van Meeterens, U.; Heuvelink, E.; Rembialkowska, E.; Van Bruggen, A. H. C. Differences in N uptake and fruit quality between organically and conventionally grown greenhouse tomatoes. Agron. Sustain. Dev. 2010, 30, 797−806. (9) Rodríguez-Munoz, E.; Herrera-Ruiz, G.; Pedraza-Aboytes, G.; Loarca-Pina, G. Antioxidant capacity and antimutagenic activity of natural oleoresin from greenhouse grown tomatoes (Lycopersicon esculentum). Plant Foods Hum. Nutr. 2009, 64, 46−51. (10) Dalal, K. B.; Salunkhe, D. K.; Boe, A. A.; Olson, L. E. Certain physiological and biochemical changes in developing tomato fruit (Lycopersicon esculentum Mill). J. Food Sci. 1965, 30, 504−508. (11) Giovanelli, G.; Lavelli, V.; Peri, C.; Nobili, S. Variation in antioxidant components of tomato during vine and post-harvest ripening. J. Sci. Food Agric. 1999, 79, 1583−1588. (12) Migliori, C.; Di Cesare, L. F.; Lo Scalzo, R.; Campanelli, G.; Ferrari, V. Effects of organic farming and genotype on alimentary and nutraceutical parameters in tomato fruits. J. Sci. Food Agric. 2012, 92, 2833−2839. (13) Hallmann, E. The influence of organic and conventional cultivation systems on the nutritional value and content of bioactive compounds in selected tomato types. J. Sci. Food Agric. 2012, 92, 2840− 2848. (14) Nelson, L.; Giles, J.; Macilwain, C.; Gewin, V. Organic FAQs. Nature 2004, 428, 796−798. (15) Eppendorfer, W. H.; Bille, S. W. Free and total amino acid composition of edible parts of beans, kale, spinach, cauliflower and potatoes as influenced by nitrogen fertilisation and phosphorus and potassium deficiency. J. Sci. Food Agric. 1996, 71, 449−458. (16) Muttucumaru, N.; Powers, S. J.; Elmore, J. S.; Mottram, D. S.; Halford, N. G. Effects of nitrogen and sulfur fertilization on free amino acids, sugars, and acrylamide-forming potential in potato. J. Agric. Food Chem. 2013, 61, 6734−6742. (17) Boggio, S. B.; Palatnik, J. F.; Heldt, H. W.; Valle, E. M. Changes in amino acid composition and nitrogen metabolizing enzymes in ripening fruits of Lycopersicon esculentum Mill. Plant Sci. 2000, 159, 125−133. (18) Valle, E. M.; Boggio, S. B.; Heldt, H. W. Free amino acid composition of phloem sap and growing fruit of Lycopersicon esculentum. Plant Cell Physiol. 1998, 39, 458−461. (19) Kelly, S. D.; Bateman, A. S. Comparison of mineral concentrations in commercially grown organic and conventional crops − tomatoes (Lycopersicum esculentum) and lettuces (Lactuca sativa). Food Chem. 2010, 119, 738−745. (20) Gosling, P.; Hodge, A.; Goodlass, G.; Bending, G. D. Arbuscular mycorrhizal fungi and organic farming. Agric. Ecosyst. Environ. 2006, 113, 17−35. (21) Bateman, A. S.; Kelly, S. D.; Jickells, T. D. Nitrogen isotope relationships between crops and fertilizer: implications for using nitrogen isotope analysis as an indicator of agricultural regime. J. Agric. Food Chem. 2005, 53, 5760−5765. (22) Yoneyama, T.; Kouno, K.; Yazaki, J. Variation of natural 15N abundance of crops and soils in Japan with special reference to the effect of soil conditions and fertilizer applications. Soil Sci. Plant Nutr. 1990, 36, 667−675. (23) Nakano, A.; Uehara, Y.; Yamauchi, A. Effect of organic and inorganic fertigation on yields, δ15N values and δ13C values of tomato (Lycopersicon esculentum Mill. cv. Saturn). Plant Soil 2003, 255, 343− 349. (24) Bateman, A. S.; Kelly, S. D.; Woolfe, M. Nitrogen isotope composition of organically and conventionally grown crops. J. Agric. Food Chem. 2007, 55, 2664−2670.

This scaling procedure massively changes the image of bucket intensities: the separation of Mecano and Tastery as well as the separation of the organic and conventional group for Tastery deteriorated, whereas the separation of organic and conventional Mecano tomato samples improved. Moreover, higher signal intensities are averaged for the organic group compared to the conventional group, opposed to previous results for unscaled buckets. However, individual buckets show irregular distributions, although they are actually assigned to the same substance. For example, the bucket at 4.40 ppm shows significant differences between organically and conventionally grown tomatoes for Mecano cultivar, whereas the bucket at 4.38 ppm is nonsignificantly different for these growing regimens (Figure 7), despite both buckets being attributed to malic acid (A). This is presumably due to an overlap with signals from other ingredients that are not assigned yet. Thus, the present results are first estimations, and at this point classical quantitative methods are needed to confirm these results.

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AUTHOR INFORMATION

Corresponding Author

*(M.H.) Phone: +49 9131 68087159. Fax: +49 9131 68087210. E-mail: [email protected]. Funding

This research was funded by the Bavarian State Ministry of the Environment and Consumer Protection within Research Project 11-33. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS

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REFERENCES

Special thanks are due to colleagues from the Bavarian State Research Institute of Viticulture and Horticulture (LWG, Bamberg, Germany) for the helpful introduction into agricultural implementations and the realization of the cultivation experiment. Furthermore, particular thanks are given to all producers for providing authentic tomato samples.

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1H NMR Profiling as an Approach To Differentiate Conventionally and

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