Application Note - Magnetic Resonance

Verifying the Geographical Origin of Tomatoes

Introduction

In an age of global markets, the geographical origin of foods is becoming increasingly important. In most cases, products, such as coffee and bananas, will be labeled with the country where the crop was grown. Some products, however, are deemed especially desirable if they are from a specific region within a country, such as wine and honey. 

With the price a producer can receive for a product dependent on the region of origin, geographical certification is a matter of great importance among producers since it provides a tool to differentiate their products on the global market and to maximize their income. Similarly, consumers often develop a preference for the characteristic features or taste of produce from a particular region. 

Geographical certification can thus act as an indicator of quality and provide reassurance based on the good reputation of specific areas, in addition to confirming from whence a product originated. As such, geographical certification is increasingly recognized by the World Trade Organization as a valuable marketing tool in the global economy. 

Producers are consequently understandably protective of their geographical certification and are keen to ensure that inferior products are not incorrectly marketed, posing a risk to their reputation and market share.

Characteristic soil components and climatic conditions of different regions are reflected in the chemical composition of produce. Analysis of the specific chemical content of produce can thus be used to verify geographical origin. There is now a wide variety of different analytical techniques available for the determination of the geographical origin of food products using metabolomic analysis. These include isotopic methods, Fourier transform infrared spectroscopy and Raman spectroscopy, and mass spectrometry combined with either gas chromatography or liquid chromatography.1,2  

Tomato Categorization

Tomatoes are one of the most widely grown crops in the world, promoted for their nutritional and antioxidant properties. Indeed, the tomato contains numerous health-promoting compounds, including carotenoids, tocopherols, and phytosterols. It is a popular ingredient in many culinary dishes across the globe.

In Europe, tomatoes account for around a quarter of the total output of fresh vegetables. Italy is a key producer of tomatoes, having around 92,000 hectares dedicated to tomato production. The long history of tomato cultivation in Italy has allowed for the evolution of a wide diversity of traditional varieties. This includes several cherry tomatoes that are cultivated in greenhouses in many different regions of Italy. 

A simple separation analysis successfully distinguished between Sicilian cherry tomatoes of different provenance3. However, a multifactorial approach was needed to differentiate the tomatoes according to cultivar, seasonality, and year of production4

The variations in climatic conditions during different years are reflected in the final composition of the tomato. For example, the lycopene content of a tomato is determined by the prevailing temperatures5. However, previous characterization studies of the PGI Pachino cherry tomato-based on the lipophilic metabolic profile determined by nuclear magnetic resonance (NMR) spectroscopy found no significant differences for three consecutive years3.

NMR has now been applied to the determination of the geographical origin of cherry tomatoes. 

NMR Metabolomic Analysis of Tomatoes

Nuclear magnetic resonance (NMR) spectroscopy has been applied for a wide range of food analyses to facilitate quality assurance, structure characterization and adulteration detection6,7. It has also proved to be a valuable tool in the verification of geographic origin8,9.

NMR has already been shown to help with the determination of tomato provenance, differentiating between organic and conventional tomatoes, and identifying genetically modified tomatoes 10,11.

NMR most recently has been used for the geographical characterization of cv Shiren cherry tomatoes grown in Pachino (Sicily) and Sabaudia (Latium)12. The 1H NMR profiles of the lipophilic components of the different cherry tomato harvests were obtained using a Bruker AVANCE 400 MHz spectrometer and compared using three steps of multivariate statistical analysis. Cultivar and seasonality were not factors in the analysis as all the tomatoes were harvested in the summer. 

In addition, images of the internal morphological organization of vegetal tissues were obtained by magnetic resonance imaging using a Bruker Avance 300 MHz spectrometer equipped with a 5 mm broadband probe head,  operating at room temperature.

The NMR spectra revealed significant differences between tomatoes from different production years12. The Sabaudia cherry tomatoes from 2004 had higher levels of phospholipids but fewer polyunsaturated acids and lycopene than those of 2005. MRI data also detected differences in the pericarp of tomatoes from the two harvesting years; this was thought to be a consequence of reduced water flow in the outer layer of tomato tissue during 2004.

However, the overall characteristic NMR spectra for the different geographical origins were maintained, making it possible to differentiate Sabaudia cherry tomatoes from Pachino cherry tomatoes. Using the nearest neighbor algorithm, 84% to 87% of the Pachino tomatoes were correctly classified, as were approximately 77% of the Sabaudia tomatoes12. The recognition ability varied from 82% to 84.4% and the prediction ability from 76% to 95%. 

PCA analysis also showed a high level of separation of Pachino and Sabaudia cherry tomatoes but had better recognition and prediction abilities; these were close to 100%. Phytosterols and differences in aroma components were the compounds that most facilitated discrimination.

References

  1. Junior ACM, Maione C, Barbosa RM, et al. Journal of Chemometrics. 2018;32(8): 1–10.
  2. Danezis GP, Tsagkaris AS, Camin F, et al. Trends Anal Chem. 2016;85:123–132. 
  3. Masetti O, Ciampa A, Nisini L, et al. Food Res Int. 2017;100(1):623–630.
  4. Masetti O, Ciampa A, Nisini L, et al. Food Chem. 2014;162:215–222.
  5. Leyva R, Constán-Aguilar C, Basco B, et al. J Sci Food Agric. 2014;94(1):63–70.
  6. Marcone MF, Wang S, Albabish W, et al. Food Res Int. 2013;51:729–747.
  7. Dowlatabadi R, Farshidfar F, Zare Z, et al. Metabolomics. 2017;13(2):1‐11.
  8. Consonni R, Cagliani LR, Cogliati C. Talanta. 2012;88:420–426.
  9. Girelli GR, Del Coco L, Fanizzi FP. Eur.J.Lipid.Sci.Technol. 2016;118:1380–1388.
  10. Hohmann M, Norbert C, Wachter H, Holzgrabe U. J Agric Food Chem. 2014;62(33):8530–8540.
  11. Le Gall G, Colquhoun IJ, Davis AL, et al. J Agric Food Chem. 2003;51(9):2447–2456.
  12. Masetti O, et al. Journal of Chemometrics. 2020;34:e3191.