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Advisor(s)
Abstract(s)
This work aims at characterizing the mineral profile of a Portuguese variety of pear, Rocha pear, and study the
relationship between the nutritional profile and geographical origin. Multielement analysis of twenty-four elements (Al, As, B, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Se, Sn, Sr, Tl, Zn) was performed using an ICP-MS and ICP-OES, on 50 samples originating from two regions, one with Protected Designation of Origin (PDO), known as “Pera Rocha do Oeste”, and the other without (nPDO). Correlation analysis was performed to check for potential relationships between elements. Results from PDO and nPDO regions were compared using chemometric tools, namely Principal Component Analysis, Hierarquical Cluster Analysis and Linear Discriminant Analysis (LDA). The predictive model, built and validated, classified samples according to their regional origin with 100% accuracy. The present study shows that multielement analysis combined with the appropriate statistical tools can be a valuable contribution from the identification of the geographical provenance of Rocha Pears. It provides important nutritional information regarding the mineral composition of Rocha pears, not available until know, promoting the linkage between the nutritional profile and geographical origin, filling the gap of absent values in FCDBs.
Highlights: Mineral characterization of Rocha pear was done by ICP-MS; Chemometric techniques (PCA, HCA and LDA) were applied to the data; Geographical origins were discriminated based on mineral content; Tl, Mn, Al, Ni and Na were the elements with highest variability between regions.
Highlights: Mineral characterization of Rocha pear was done by ICP-MS; Chemometric techniques (PCA, HCA and LDA) were applied to the data; Geographical origins were discriminated based on mineral content; Tl, Mn, Al, Ni and Na were the elements with highest variability between regions.
Description
The authors would like to thank Instituto Politécnico de Castelo Branco for their collaboration in collecting samples.
Keywords
Food Analysis Food Composition Authenticity Rocha Pear Classification Traceability Inorganic Markers Fruits (agro-product) Multielement Analysis Chemometrics Composição dos Alimentos
Pedagogical Context
Citation
J Food Compost Anal. 2019;77:1-8. doi:10.1016/j.jfca.2018.12.005
Publisher
Elsevier
