In the last decade, the consumption trend of organic food has increased dramatically worldwide. However, the lack of reliable chemical markers to discriminate between organic and conventional products makes this market susceptible to food fraud in products labeled as “organic”.
Journal of Chromatography A, 1546 (2018) 66–76 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma A non-targeted metabolomic approach to identify food markers to support discrimination between organic and conventional tomato crops María Jesús Martínez Bueno, Francisco José Díaz-Galiano, Łukasz Rajski, Víctor Cutillas, Amadeo R Fernández-Alba ∗ University of Almería, Department of Physics and Chemistry, Agrifood Campus of International Excellence (ceiA3), Ctra Sacramento s/n, La Ca˜ nada de San Urbano, 04120, Almería, Spain a r t i c l e i n f o Article history: Received November 2017 Received in revised form 26 February 2018 Accepted March 2018 Available online March 2018 Keywords: Fingerprint Authenticity Natural food components Pesticides HRAMS IRMS a b s t r a c t In the last decade, the consumption trend of organic food has increased dramatically worldwide However, the lack of reliable chemical markers to discriminate between organic and conventional products makes this market susceptible to food fraud in products labeled as “organic” Metabolomic fingerprinting approach has been demonstrated as the best option for a full characterization of metabolome occurring in plants, since their pattern may reflect the impact of both endogenous and exogenous factors In the present study, advanced technologies based on high performance liquid chromatography-highresolution accurate mass spectrometry (HPLC-HRAMS) has been used for marker search in organic and conventional tomatoes grown in greenhouse under controlled agronomic conditions The screening of unknown compounds comprised the retrospective analysis of all tomato samples throughout the studied period and data processing using databases (mzCloud, ChemSpider and PubChem) In addition, stable nitrogen isotope analysis (␦15 N) was assessed as a possible indicator to support discrimination between both production systems using crop/fertilizer correlations Pesticide residue analyses were also applied as a well-established way to evaluate the organic production Finally, the evaluation by combined chemometric analysis of high-resolution accurate mass spectrometry (HRAMS) and ␦15 N data provided a robust classification model in accordance with the agricultural practices Principal component analysis (PCA) showed a sample clustering according to farming systems and significant differences in the sample profile was observed for six bioactive components (L-tyrosyl-L-isoleucyl-L-threonyl-L-threonine, trilobatin, phloridzin, tomatine, phloretin and echinenone) © 2018 The Authors Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Introduction With a worldwide harvest of over 162 million tons annually, tomato (Solanum lycopersicum L.) is the second most important vegetable crop in the world next to potato (FAOSTAT Database) [1] Spain cultivates 25% of the tomatoes produced in Europe (approx million tons), making the country the second largest producer in the European Union (EU), behind Italy, according to data released by the EU statistics office, Eurostat (EUROSTAT Database) [2] In the last decade, the production and consumption of organic food has increased dramatically worldwide The EU organic food market is the second largest in the world behind the US´ı In Spain, the total number of hectares dedicated to organic tomato production in ∗ Corresponding author E-mail address: amadeo@ual.es (A.R Fernández-Alba) Andalusia reached 350.55 in 2014, 66.3% more than the previous year Organic farming in the EU is supported by EU law, Regulations (EC) No 834/2007 [3] and 889/2008/EC [4], with detailed rules on production, labelling and control via an organic action plan [5] Organic farming does not permit the use of synthetic chemicals, including pesticides and fertilizers Nonetheless, the presence of synthetic pesticides in organic food may arise from environmental pollution (i.e from neighbouring farms, contaminated soils, etc.) Up to now, little data have been available in the scientific literature on pesticide residues in organic foods [6] Thus, several issues within the regulatory framework need to be improved, as recognised by the Commission [7] The lack of knowledge and the lack of reliable markers to discriminate between organic and conventional products make this market susceptible to foods labeled as “organic” that have, in fact, been produced conventionally Certainly, instances of fraud published about the sale of https://doi.org/10.1016/j.chroma.2018.03.002 0021-9673/© 2018 The Authors Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) M.J Martínez Bueno et al / J Chromatogr A 1546 (2018) 66–76 organic products are few and far between although the International Organic Accreditation Service has expressed doubts about whether this type of fraud is indeed small or whether the low incidence of fraud detection is due to authorities, or competent agencies, not having sufficient capability to perform adequate inspections [6] In the area of analytical chemistry, diode array detector tandem mass spectrometry (DAD-MS/MS), liquid chromatography tandem mass spectrometry (LC–MS/MS), inductively coupled plasma mass spectrometry (ICP-MS), mid-infrared spectroscopy (MIR); nuclear magnetic resonance (NMR) spectroscopy and isotope ratio mass spectrometry (IRMS) have been the most common analytical detection technologies investigated as tools for organic tomatoes authentication [8–10] With regard to IRMS, the analysis of elements such as nitrogen (N) has been considered as a potential indicator for food authentication control to support the discrimination between conventionally and organically grown produce using crop/fertilizer correlations However, the disadvantages of this approach are that many factors are known to affect the nutrient content of crops (e.g fertilizer use, the vegetables’ growing time, the use of leguminous plants for enhancing the nitrogen fertility of soil, water type used for irrigation, etc.) [11,12] During the last years, non-targeted methods have been the basis of the discoveries within “omics”, such as metabolomics or foodomics, all centred on obtaining mass spectra (MS) for a whole range of masses and the subsequent identification and characterization of molecules responsible for a certain attribute [13] The MS profile of a food sample can be regarded as an analytical signature of the food product and thus can help in discriminating between different production practices, reflecting the impact of both endogenous and exogenous factors as well as food properties Primary metabolites are generic components, while secondary metabolites are food specific components and potentially reliable markers Some works have reported a higher content of these compounds, such as polyphenols, in organic food [9,14–16] The different content of bioactive compounds in organic and conventional agricultural practices has been attributed to the absence of synthetic pesticides in organic farming It leads plants to synthesize more secondary metabolites to protect themselves against phytopathogens than tomato plants grown under conventional conditions [17] But also, some authors have reported that the type of fertilizers and the availability of inorganic nitrogen can modulate the plant’s biosynthetic pathways and therefore the levels of the natural food components [17,18] High-resolution accurate mass spectrometry (HRAMS) systems seem to be the best candidates for MS-profiling studies, mainly due to the last advances of using the full-scan acquisition mode with high sensitivity, along with high-resolving power (>50,000 FWHM) and accurate mass measurement (1–5 ppm) [17,19,20] Nevertheless, the potential of these tools to achieve a reliable authentication strategy applicable in routine practice has not been extensively explored for application to date On the other hand, given the large quantity of signals detected in HRAMS, the data sets are extremely complex so, regardless of whether the goal is group establishment or marker selection, it is necessary to use statistical tools to extract information To date, among the most common approaches are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Partial Least Squares-Discriminant Analysis (PLS-DA) [21,22] Several comparative research studies have been conducted to compare the composition of various crops originated from conventional and organic farms, but most had a poor-designed experimental study [10,17,23,24,25], obtaining variable results that make it impossible to reach a definitive conclusion on finding significant differences between crops Because of this, the aim of this study was to investigate the potential of advanced technologies such as HRAMS to identify suit- 67 able markers capable of distinguishing between organically and conventionally grown tomatoes under controlled agronomic conditions, in a climatic region of leading EU production such as the Mediterranean Moreover, another innovative aspect of the present work concerns the data combination of IRMS and LC-HRAMS analysis by chemometric methods as a tool to get robust classification models to discriminate between different practices Finally, the last objective was to carry out a pesticide residue analysis to evaluate the influence of synthetic pesticides present in the secondary metabolites content Experimental section 2.1 Reagents Water used for LC–MS analysis was obtained from a Milli-Q water purification system (Direct-QTM Ultrapure Water System Millipore, Bedford, MA, USA), which provided a specific resistance of 18.2 M cm Methanol (MeOH) and acetonitrile (AcN) HPLC–MS grade were supplied from Merck (Darmstadt, Germany) Anhydrous magnesium sulphate was supplied by Panreac (Barcelona, Spain) Formic acid (98% purity) was purchased from Fluka (Buchs, Switzerland) The standards dimethoate-d6 (CAS 1219794-81-6), dichlorvosd6 (CAS 203645-53-8), tomatine hydrate (CAS 17406-45-0), tomatidine hydrochloride (CAS 6192-62-7), phloridzin (CAS 6081-1), phloretin (CAS 60-82-2), (±)-catechin (CAS 7295-85-4), acetanilide (CAS 103-84-4), IAEA-N-1 (CAS 7783-20-2) and IAEAN-2 (CAS 7783-20-2) were purchased from Sigma-Aldrich Quimica S.A (Madrid, Spain) All were of analytical quality Individual stock standard solutions were prepared at about mg/mL in acetonitrile, and stored at −20 ◦ C Individual standard solutions of dimethoated6 and dichlorvos-d6 prepared in methanol were used as injection internal standards for LC analysis in order to ensure quality measurements 2.2 Sample crops and preparation Cocktail tomatoes (Solanum lycopersicum var cerasiforme) were produced in greenhouses under controlled agronomic conditions in two different farms, located in Almeria (Southeast Spain), using organic and conventional cultivation methods Organic samples were provided by a local company, expert in crops cultivated in line with EU organic production legislation (EC 834/2007 and 889/2008) Ecological manure was used for their production without the usage of synthetic fertilizers nor pesticides Plant protection against pests was based on herbal extracts Conventional samples were provided by a farmer of the same area In this crop system, mineral fertilizers containing soluble inorganic nitrogen and other mineral contents (K, Ca, Na, Mg, Fe, Zn) were applied In this case, commercial pesticides were employed for protection against pests The tomatoes were produced from September-2016 to May-2017, which corresponds to a full harvest cycle To obtain representative samples, and thus to compensate for possible variability in their composition, samples were taken from different areas of the field, over a full week, monthly, and then pooled (approx kg) Samples were harvested in full maturity Additionally, 11 tomato samples were purchased from different local markets (labeled as organic or non-organic) in order to check the classification capability of the developed model A total of 25 tomato samples were analyzed (14 produced under controlled agronomic conditions and 11 from local markets) Detailed information on samples, crop system, farms, fertilisation and plant protection is shown in Table Tomatoes (skin, flesh, and pips) were blended to obtain a tomato paste, freeze-dried and ground to a fine powder of an average parti- 68 M.J Martínez Bueno et al / J Chromatogr A 1546 (2018) 66–76 Table Detailed information on the tomato samples produced under controlled agronomic conditions, crop system, farms, fertilisation and plant protection carried out in this study Crop system Manure Fertilizer Pest control # samples Name Organic Conventional Ecological Conventional – Mineral Herbal extracts Pesticides 7 O C cle size of about mm Dried tomato powder samples (0.5 ± 0.01 g) were weighed in a 50 mL polypropylene centrifuge tube and then L of a 10 mg/L methanolic internal standard solution was added (dichlorvos-d6) Next, the tomatoes were rehydrated by adding 0.5 mL of ultrapure water, and the mixture was vortexed for 30 s The tubes were then automatically shaken for min, after the addition mL of methanol After that, 0.5 g Mg2 SO4 (anhydrous) plus 0.25 g NaCl were added directly to each tube and the mixture was shaken again (automatically) for more Finally, a centrifugation step (3500 rpm/1730 × g, min) was performed, and 0.5 mL of the supernatant was diluted with 0.5 mL of ultrapure water Samples were stored at −20 ◦ C until analysis by LC-Q-Orbitrap-MS 2.3 HRAMS analysis To evaluate the differences between organic and conventional production systems, a non-targeted analysis was applied using an LC-ESI-Q-Orbitrap For the LC separation, UHPLC DionexTM Ultimate 3000 (Thermo ScientificTM , San Jose, USA) was used Mobile phase A was 98% water and 2% methanol whereas mobile phase B was 98% methanol and 2% water; both mobile phases contained mM of ammonium formate and 0.1% formic acid Separation was carried out on a Phenomenex Luna C8 column (mobile phase flow: 350 L/min) The length, diameter and particle size were 100 mm, 2.0 mm and m, respectively The column was thermostated at 30 ◦ C The mobile phase gradient started form 100% of mobile phase A and maintained for min, from to min, the amount of mobile phase B increased to 30%, from to to 50%, from to 11 to 100% 100% of B was maintained until 14 Following this, the mobile phase was changed to 100% A and maintained over for re-equilibration The injection volume was 10 L The autosampler was thermostated at 10 ◦ C A Q-Orbitrap (Thermo Scientific, Bremen, Germany) mass spectrometer was equipped with Heated Electrospray Ionization Source (HESI II) The HESI parameters in positive polarity were as follows: sheath gas flow rate: 40; auxiliary gas flow rate: 5; sweep gas flow rate: 1; spray voltage: 3.00 kV; capillary temperature: 280 ◦ C; S–lens RF level: 55.0; heater temperature: 350 ◦ C The instrument was operated in full scan/all ion fragmentation MS2 (AIF) AIF is an acquisition mode in which all precursor ions are fragmented without a preselection in the quadrupole Fragmentation is obtained with the higher energy collision-induced dissociation (HCD) cell, located at the far side of the quadrupole ion trap “C-trap” During filling of the HCD collision cell, the energy can be set to step between values at specified percent values around the chosen middle energy regardless of the ion’s characteristics The parameters of full scan analysis were as follows: scan range 74−1100 m/z, resolution 70,000 FWHM and automatic gain control (AGC) target × 106 AGC is used to regulate the total number if ions collected in the C-trap before being injected into the orbitrap for analysis The maximum ion injection time (max IT) was set to ‘AUTO’, Parameters of “All Ion Fragmentation MS2 mode” were as follows: resolution 70 000 FWHM, collision energy CE 30 V, AGC target × 106 , max IT auto, scan range 74–1100 m/z An external mass calibration and quadrupole calibration were carried out daily, using a mixture of n−butylamine, caffeine, Ultramark 1621 and Met-Arg-Phe-Ala (MRFA) 2.4 IRMS analysis Dried tomato powder samples (2 ± 0.1 g) were weighed into tin capsules Nitrogen isotope composition was determined using a Flash EA1112 elemental analyzer coupled to a Finnigan Deltaplus isotope ratio mass spectrometer (Thermo Scientific, Bremen, Germany) All samples were analyzed in duplicate, and for the majority of samples the absolute difference between duplicate measurements was