Multielement composition and strontium isotope ratio used as provenance indicators for apples from different growing areas in Northern Italy Agnese Aguzzoni1*, Michele Bassi2, Anastassiya Tchaikovsky3, Peter Robatscher2, Werner Tirler4, Francesca Scandellari1, Massimo Tagliavini1, Thomas Prohaska5 1 Free University of Bolzano, Bolzano, Italy; 2 Laimburg Research Centre, Ora, Italy; 3 FFoQSI - Austrian Competence Centre for Feed and Food Quality, Safety & Innovation, Tulln, Austria; 4 Eco-Research, Bolzano, Italy; 5 University of Natural Resources and Life Sciences Vienna, Department of Chemistry, VIRIS Laboratory, Tulln, Austria. *Corresponding author – E-mail:
[email protected]
introduction
results – multielement composition
The analysis of the multielement fingerprint and Sr isotope composition revealed to be an effective approach to determine agrifood origin.1,2 This chemical information provides regional fingerprints reflecting mainly the local soil composition.3,4
The aim of this work is: • to evaluate the discrimination power of soilderived traceability markers; • to classify apples based on their provenance.
results – linear discriminant analysis
Fig. 2 Result of the principal component analysis (PCA) for the first three principal components. • • • • •
Emilia Romagna Veneto Trentino Alto Adige – Val Venosta Trentino Alto Adige – Bassa Atesina Trentino Alto Adige – Val di Non
https://www.biorfarm.com/mela-golden-delicious-la-preferita/
sampling campaign Apples (Malus x domestica Borkh., cv. Golden Delicious, n=80) from orchards in: • • • • •
2
Emilia Romagna Veneto Trentino Alto Adige – Val Venosta Trentino Alto Adige – Bassa Atesina Trentino Alto Adige – Val di Non
Fig. 1 Geological map of North Italy. Main features: Alluvial deposits (Holocene) Limestones (Palaegene-Upper cretaceous) Dolomites (Middle Triassic) Mediumgrade pre-alpine metamorphite Dolomites (Lower Triassic) Ryolites (Permo-carboniferous cycle) Glacial deposits (Pleistocene)
Fig. 4 Result of the linear discriminant analysis combining variables from the multielement composition and
• multivariate data analysis (PCA) allows a better visualization of the results; • partial separation of the samples according to origin visible, especially for samples from Emilia Romagna; • variables with the greatest impact on the PCs: Ba, Co, K, Mg, Na, Sr.
cutting, freeze-drying, homogenization, microwave assisted acid digestion with HNO3 (Anton Paar Multiwave 3000, Rotor 16MF100)
• through the linear discriminant analysis it is possible to develop a classification model based on sample origin: Accuracy in prediction = 92.6%; Means misclassification value = 7.4% ; • slight overlap especially between samples from two regions: Bassa Atesina and Veneto. outputs
results – strontium isotope ratio
This study shows that combining multielement composition and 87Sr/86Sr ratio good results in samples discrimination based on their geographical provenance can be reached. This was possible despite the limited extension of the studied area and common geological features among different sampling sites. Since not all the regions are yet separated with the same high accuracy, additional variables, such as supplemental isotopic ratios (δD, δ18O, δ11B), can be included in further studies.
E m ilia R om agna
materials and methods
V e n e to
T r e n tin o A lto A d ig e V a l V e n o s ta
T r e n tin o A lto A d ig e
Ph. Aguzzoni
B a s s a A te s in a
elemental analysis ICP-QMS (NexIon 2000, PerkinElmer)
T r e n tin o A lto A d ig e Val di Non
Ph. Aguzzoni
0 .7 0 5
0 .7 1 0
0 .7 1 5 87
Sr/matrix-separation using a Sr specific resin
Ph. Aguzzoni
Sr-isotope measurements MC ICP-SFMS (NEPTUNE™ Plus, Thermo Scientific) (external intra-element calibration for instrumental isotope fractionation using NIST SRM 987)
87Sr/86Sr.
Sr/
86
0 .7 2 0
0 .7 2 5
Sr
Fig. 3 Results of the 87Sr/86Sr analysis sorted per origin. Mean ± standard deviation is provided for each orchard.
• the whole distribution of data for samples collected in orchards from several regions falls utmost within the same range of values (0.7080-0.7100). • this is in agreement with the geological features characterizing the sampling sites, since many orchards were located in alluvial plains;5 • it is not possible to distinguish all the apple geographical origins based solely on the 87Sr/86Sr .
References – 1 Bong et al., Food Control, 2013, 30, 626–630; 2 Brunner et al., Eur. Food Res. Technol., 2010, 231, 623–634; 3 Drivelos and Georgiou, TrAC - Trends Anal. Chem., 2012, 40, 38–51; 4 Kelly et al., Trends Food Sci. Technol., 2005, 16, 555–567; 5 Voerkelius et al., Food Chem., 2010, 118, 933–940. Acknowledgements – The Autonomous Province of Bolzano, Department of Innovation, Research and University (Decision n. 1472, 07.10.2013) is gratefully acknowledged for financial support. The competence centre FFoQSI is funded by the Austrian ministries BMVIT, BMWFW and the Austrian provinces Niederoesterreich, Upper Austria and Vienna within the scope of COMET - Competence Centers for Excellent Technologies.
