APPLICATION OF LIGHT STABLE ISOTOPE RATIOS AND ELEMENTAL PROFILE IN THE ORGANIC VEGETABLES

CHARACTERIZATION

I. FEHER, D.A. MAGDAS, A. DEHELEAN, G. CRISTEA, C. VOICA

National Institute for Research and Development of Isotopic and Molecular Technologies, 67–103 Donat Street, P.O. Box 700, 400293, Cluj-Napoca, Romania

E-mail: alina.magdas@itim-cj.ro

Received September 24, 2016

Abstract. This work proposed marker associations for the differentiation of some organic vegetable types (salads, tomatoes, potatoes, cabbages, garlic and leek) from vegetables obtained through conventional agricultural practices. For this purpose, a number of 30 organic and 15 conventional vegetables samples were analyzed from isotopic and elemental point of view. Thus, the best differentiation markers, for these two categories, were found for each vegetable type. Another aim of this study was the differentiation of vegetables grown in greenhouses from those produced in field. For this purpose, the best differentiation elements prove to be: δ13C, Ni, Mo, Cd and Pb.

Key words: organic products, isotopic ratios, multielemental profile, discriminant analysis.

1. INTRODUCTION

Organic products are considered by some consumers to be healthier, more natural and environmentally friendlier than that from conventional production [1–3]. Increasing the consumer demand for organic food, introduction of certification and premium prices have created a financial enticement to mislabel and try to pass off cheaper conventionally grown products as organic. In order to differentiate these two categories, the association of robust analytical methods able to distinguish organically and conventionally produced foods is needed for authentication purposes.

Previous studies have demonstrated that the stable isotope ratio analysis of nitrogen may be exploited to discriminate between products obtained using conventional agriculture system and those obtained under organic requirements [3–7]. Beside isotopic markers, other useful markers for this purpose are given by the major and trace element profiling [3, 8–10].

Romanian Journal of Physics 62, 803 (2017)

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Analytical techniques, such Isotope Ratios Mass Spectrometry (IRMS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), usually provide large amount of experimental data, which might become difficult to interpret or to extract the most meaningful information [11]. Food authentication area strongly depends upon multivariate statistical techniques, because it can provide mathematical models for correct of classification of sample with unknown origin [12] or can reduce the variable to some representative markers for a certain purpose (i.e. geographical origin) [13].

The aim of this work was to establish the most suitable markers, that are able to differentiate organic from conventional vegetables, or which might differentiate greenhouse grown vegetables from those grown in the field. These classifications were established using chemometric tools applied upon isotopic and multielemental experimental data.

2. MATERIALS AND METHODS

A total number of 45 samples, consisting of 10 salad, 10 tomato, 10 potato samples, 8 cabbage samples, 5 garlic and 2 leek samples, were collected and analyzed from isotopic and multielemental point of view. Among chosen samples, 30 samples were organic and 15 were conventional, while 8 samples were greenhouse grown and 37 were grown in the field. All analyzed samples were cultivated in Transylvania area, Romania.

2.1. STABLE ISOTOPES MEASUREMENTS

For 13C/12C and 15N/14N determination, the analyses were performed using an isotopic ratio mass spectrometer (IRMS, Delta V Advantage, Thermo Fisher Scientific) coupled with an elemental analyzer (EA, FlashEA 1112 HT, Thermo Fisher Scientific). Variations in stable isotope ratios were reported as parts per thousand (‰) deviations from international standards: δ (‰) = [(Rsample/Rstandard) – –1] × 103, where R is the ratio between the heavy and light isotopes, Rsample is the isotopic ratio of the sample and Rstandard is that of the standard. Each sample was analyzed in triplicate and the average was calculated; for each run one laboratory standard (urea for carbon and nitrogen) was analyzed to check the accuracy of the analysis. The limit of uncertainty of the isotopic analysis was ± 0.2 ‰ for δ13C and ± 0.5 ‰ for δ15N. The δ18O and δ2H values in extracted water from vegetable samples were determined using a liquid-water isotope analyzer (DLT – 100, Los Gatos Research) [14]. The limit of uncertainty of the isotopic analysis was ± 0.2 ‰ for δ18O and ± 0.6 ‰ for δ2H. Each sample was analyzed six times, but only the

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last three injections were taking into consideration for the calculations. A set of five internal standards was used, having δ18O = –19.57 ‰, –15.55 ‰, –11.54 ‰, –7.14 ‰ and –2.96 ‰, and δD = –154.1 ‰, –117.0 ‰, –79.0 ‰, –43.6 ‰ and –9.8 ‰, respectively.

2.2. ICP-MS MEASUREMENTS

Samples for multi-element analysis were prepared in batches of 6. Approximately 0.1 g of dried vegetables sample was weighed out into a Teflon receptacle. 2.5 mL of ultrapure nitric acid was added and the whole system was put in an oven at 180 °C for 12 hours after which the solutions were colourless and residue free. Ultrapure water was added up to 50 mL. For each sample analysis three replicates were measured in order to assure the control quality of measurements.

High-purity inductively coupled plasma multielement standard solutions from PerkinElmer Life and Analytical Sciences, were used for the calibration curve in the quantitative analysis, 10 mg·L-1: multi-element Calibration Standard 3 (Ag, Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, U, V, Zn); multi-element Calibration Standard 2 (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pt, Sm, Sc, Tb, Th, Tm, Y, Yb) and multi-element Calibration Standard 4 (Au, Hf, Ir, Pd, Pt, Rh, Ru, Sb, Sn, Te).

High purity HNO3 (65 % w/w, Merck) was used as received. All solutions were prepared using ultrapure water (18.2 MΩ cm-1) obtained with a Milli-Q ultrapure water system (Milipore) water purification system.

The ICP-MS analysis was carried out under the following conditions: nebulizer gas flow rates, 0.92 L·min-1; auxiliary gas flow, 1.2 L·min-1; plasma gas flow, 15 L·min-1; lens voltage, 7.25 V; radiofrequency power, 1100 W; CeO/Ce = 0.021; Ba++/Ba+ = 0.016. The instrument was tuned daily with an Elan 6100 Setup/Stab/Masscal Solution following the manufacturer’s specifications.

2.3. CHEMOMETRIC PROCESSING

All chemometric processing were performed using specific software. Experimental obtained data were interpreted using Linear Discriminant Analysis (LDA). In order to select the most powerful parameter and to exclude the redundant variables from the model, a stepwise method was employed. Based on existing samples, called training set, a model was created and an initial classification was made. The suitability of obtained model was realized, by testing one sample from training set, as unknown one. This process is called “leave-one out” cross validation. The success of obtained discriminant model is verified by the correct classified samples in cross-validation procedure [15].

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3. RESULTS AND DISCUSSION

The first purpose of this study was the identification of specific particularities of each vegetable from isotopic and elemental point of view. For this aim, in order to find the best markers that could differentiate among different vegetables types, LDA statistic treatment was applied to all investigated samples. Since six groups (formed by salads, tomatoes, potatoes, cabbages, garlic and leek samples) were compared, each representing one type of vegetable, five discriminant functions were obtained. All five had statistical significance (p < 0.05), the first two explaining 92.2 % from total variance of dataset (Wilks lambda was 0.009 and 0.077). The sample distribution is presented in Figure 1. The initial classification was 93.3 %, while the cross-validation procedure managed to reclassify a percent of 88.9 % from cases. This classification was made based on the strongest predictors, P, Sr, δ18O, δ15N and δ13C. It was observed that these differences among vegetables are mainly related to the plant physiological factors and given by the isotopic ratios of oxygen (18O/16O), nitrogen (15N/14N) and carbon (13C/12C). This could be explained through the fact that, the isotopic enrichment degree depends on the plant species, even for the species grown in the same geographic area [16].

Fig. 1 – Sample distribution according to variety using the first two statistical discriminant functions.

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Strontium is chemically similar to calcium, and its biogeochemical cycles are comparable. Poor Sr immobilization by soils leads to large availability for plants [17]. Sr is normally considered a useful variable in the determination of geographical origin since levels in soil/plants are related to the underlying geology [18]. Previous studies reported a scientific technique for discriminating origin of onion [19], tomato [20], garlic [21], potato [22] using elemental composition, finding a significant difference in the concentrations of elements (i.e. Sr).

For the identification of suitable markers that could differentiate greenhouse grown vegetables from those produced in field, LDA test was applied and the classification criterion was the growing condition of vegetables. The parameters obtained as being the best predictors for this criterion were: Ni, Mo, Cd, Pb, and δ13C. Also, the obtained discriminate function was statistically significant (p = 0.001) for agricultural practices discrimination (Wilks lambda 0.231) and explained 100 % of data variance. The graphical representation is in Figure 2. 100 % of the original samples were correctly classified, while a percent of 93.3 % was obtained in cross-validation. Very negative δ13C – value are indicative for greenhouse cultivation and could be also explained by the influence of depleted δ13C values of the natural gas heating source on the available CO2 for plant growing [1]. The application of some fertilizers or animal manures to agricultural soil could increase some potentially toxic elements (i.e. Cd, Pb, Ni) in soil, which may be absorbed by plants and, thus, increase the risk of contamination in agricultural products [23]. The differences in these metal concentrations could appear due to the different pesticide types that are used for greenhouse vegetable cultivation as compared with those used for field.

Fig. 2 – Discrimination of vegetables according to their growing conditions

(greenhouse and field).

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The last differentiation, which was made by using LDA, consists in the discrimination of organic grown vegetables from those cultivated under conventional practices. When all analyzed samples were taken into consideration, the two compared vegetables groups (organic and conventional) were initially classified in percent of 84.4 %. The obtained significant function (p = 0.01, Wilks lambda 0.553) was tested and the same percent as initial was obtained in cross-validation. Five samples from organic group and two samples from conventional group were misclassified, both in initial and cross validation tests. The main predictor for this purpose was δ15N. The explanation for this consists in the fact that synthetic nitrogen fertilizers have δ15N values significantly lower (from – 6 ‰ to 6 ‰) than the manures and fertilizers (from 1 ‰ to 37 ‰) permitted in organic agriculture [24] and thus, the differences between these two inputs could contribute to the differentiation of products obtained through these two agriculture practices types. The graphical separation is presented in Figure 3. The weak separation between organic and conventional grown vegetables, that was obtained, is due to the large amount of vegetable type that was compared. In this case the differences that appear, in terms of elemental and isotopic content, among vegetables types, are overlapping with the particularities of organic and conventional grown plants.

Fig. 3 – Organic and conventional growth vegetable distribution after LDA.

For reducing the plant variability, LDA was applied separately on plants with subterranean edible part (potatoes and garlic) and plants having edible part above-ground (tomatoes, salads, cabbages), taken into consideration the fact that plants having the edible part in the same environment might have comparable tendencies of accumulation/elemental translocation.

For subterranean vegetables, both initial and cross validation were 100 % correct classify. The obtained discriminate function, explaining 100 % of total

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variance and a Wilks lambda value of 0.002 was obtained (Fig. 4). This function had as variables the following parameters: Tl, Cu, Eu, δ2H, Os, Sm, Pb, In and Ru.

In this case, the higher copper concentrations found for the organic subterranean vegetables could be due to residual Bordeaux mixture. This mixture (a copper sulphate and lime solution) is used to help control fungal and bacterial diseases [25], and is permitted in organic cultivation. The mean level of Pb in the organically grown samples is higher compared to a mean value of samples for the conventionally grown samples. This could be due to the application of some organic fertilizers or animal manures to agricultural soil that increases potentially toxic elements in soil.

Fig. 4 – Organic and conventional grown potatoes and garlic sample distribution.

The obtained classification for plants having edible parts above-ground, was a little lower comparing with those subterranean, namely 92.9 %, in both classification processes. Two samples from organic group were misclassified (Fig. 5). The obtained classification models had as main parameters Zn and δ15N. The variance explained was 100 % and Wilks lambda was 0.364.

The anthropogenic sources of Zn (atmospheric deposition, organic and mineral fertilizers, pesticides, sewage sludge) can raise concentration of this element in top soils in agricultural area [17]. Mean Zn concentration is higher for organically grown over ground samples compared to those grown conventionally. Zn is common constituents of animal feed supplements and a possible explanation for the Zn as marker in the organic above-ground samples is that the application of manures, more typical of organic cultivation, may act as an additional source of Zn to the soil and the plants [26, 27]. However, it should be noted that metal bio-availability and especially plant-uptake is strongly dependent on the form of the

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metal on entering the soil, the physic-chemical properties of the soil and the crop type [28].

Fig. 5 – Organic and conventional grown salad,

tomato and cabbage sample distribution.

The obtained smaller separation of the elements from this group, as compared with subterranean vegetables, could be due to the fact, that in this case, different plant types were compared. To underline the differences that occur among these three categories of vegetables, a new classification was performed. The discrimination of these three plants groups was achieved through the next 5 variables: Ir, Cu, Mo, Cd and Ti. The obtained cross validation percent was in this case 89.3 %, due to overlapping between tomatoes and cabbages samples but nevertheless, the salad samples were very well separated from the rest (Fig. 6). Since three types of vegetables were compared, two discriminate function were obtained and both were statistically significant (p = 0.001). First function (Wilks lambda 0.026) had a variance percent of 88.6 % and had as main parameters Ir, Cd and Ti. The second function (Wilks lambda 0.026) explained a variance of 11.6 % and main parameters were Mo and Cu.

For a better understanding of the differences that occur between the elemental accumulation of slow-growing plants (growing period over 80 days) and fast growing plant (under 80 days), a comparison between this two vegetables categories was performed. The initial and cross validation percents obtained were improved as compared with the last case, and thus, a separation of 100 % was reached (Fig. 7). Besides the previously obtained markers (Ir, Cd and Ti), the isotope ratios of carbon and nitrogen were found as predictors for this differentiation.

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Fig. 6 – Vegetables growing above ground discrimination.

Fig. 7 – Vegetables discrimination using isotopic (δ15N and δ13C) and elemental

(Ir, Cd and Ti) predictors.

It was observed that Ir, Cd and Ti concentrations were higher in salad samples. Up to now, there are not many published papers related to the presence of these elements in food matrices and further studies in this direction should be made.

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For underlining the specific markers that can be used for each plant species, for the differentiation of organic vegetables from those produced under conventional agriculture system, LDA was applied individually on studied vegetables types. It was taken into account only the vegetables groups for which a sufficient number of samples and also an equilibrated distribution between number of samples was available (cabbages, potatoes, salads). For instance, in tomatoes case, a disadvantage in the application of statistical approach is the number of samples that could be compared (9 organic samples an one conventional). Usually, the sample groups that are compared should consist in more or less equal number of samples in order to obtain optimal results.

For cabbages samples, the discrimination is illustrated in Figure 8. The initial and cross validation was made based on P, Cr, Tl and Eu, and were 100 % percent in both cases. Regarding P content, a number of comparative studies [29–31] showed higher phosphorus content in organically grown crops as compared to conventionally samples, depending on the crops type. Under organic management colonization by mycorhizal fungi are greatly increased, which are very important for feeding orchard plants. This increases the utilizable root space and enhances P uptake by plant [32].

Fig. 8 – Cabbages samples representation according to growing conditions.

The potatoes samples were 100 % initial correct classify (Fig. 9) and possible markers could be P, Sr, Rh and δ13C. The cross validation procedure was also 100%.

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Fig. 9 – Potatoes samples representation according to growing conditions.

Similar to other vegetables, the initial classification for salad samples was 100 % (Fig. 10) and, based on Mo and δ15N the cross validation gave 100 % of correct classified samples.

Fig. 10 – Salads samples representation according to growing conditions.

4. CONCLUSIONS

In this work, a number of 45 vegetables (10 salads, 10 tomatoes, 10 potatoes, 8 cabbages, 5 garlic and 2 leek samples), were collected and analyzed from

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isotopic and multielemental point of view. The purposes of this work was the identification of the best markers that could distinguish the greenhouse produce vegetables from those grown in field as well as the differentiator elements between organic and conventional obtained vegetables.

The identified best markers, for the differentiation of organic from conventional grown vegetables, proved to be: Ni, Mo, Cd, Pb, and δ13C. By taking into account these variables, an initial classification of 100 % for the original samples was obtained whereas a percent of 93.3 % was obtained in cross-validation.

For the differentiation of organic vegetables from those grown under conventional practices, LDA data processing was applied using all experimental data obtained for entire investigated vegetables set. The two groups were initially correct classified in percent of 84.4 % followed by a similar cross-validation percent. In order to a better distinguishing of the two product types (organic vs. conventional), a splitting of the samples in two groups was made, accordingly vegetables types: subterranean and above-ground plants. In this case, a differentiation of organic samples in a percent of 100 % for subterranean vegetables was obtained while, for aboveground plants a separation of 92.9 % was archived. It was observed that the best differentiation percent between organic and conventional vegetables was obtained when statistical treatment was applied inside the same plant categories (i.e. salads, potatoes, cabbages).

Acknowledgements. This work was supported by Executive Unit for Financing Education Higher R&D and Innovation, UEFISCDI PN-II-RU-TE-159/01.10.2015.

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