Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions.

Autor: Mara A; Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy., Migliorini M; Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy., Ciulu M; Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy., Chignola R; Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy., Egido C; Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain., Núñez O; Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain.; Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, 08921 Barcelona, Spain.; Serra Húnter Fellow, Departament de Recerca i Universitats, Generalitat de Catalunya, Via Laietana 2, 08003 Barcelona, Spain., Sentellas S; Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain.; Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, 08921 Barcelona, Spain.; Serra Húnter Fellow, Departament de Recerca i Universitats, Generalitat de Catalunya, Via Laietana 2, 08003 Barcelona, Spain., Saurina J; Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain.; Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, 08921 Barcelona, Spain., Caredda M; Department of Animal Science, AGRIS Sardegna, Loc. Bonassai, 07100 Sassari, Italy., Deroma MA; Department of Agriculture, University of Sassari, Viale Italia, 39A, 07100 Sassari, Italy., Deidda S; Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy., Langasco I; Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy., Pilo MI; Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy., Spano N; Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy., Sanna G; Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.
Jazyk: angličtina
Zdroj: Foods (Basel, Switzerland) [Foods] 2024 Jan 12; Vol. 13 (2). Date of Electronic Publication: 2024 Jan 12.
DOI: 10.3390/foods13020243
Abstrakt: Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.
Databáze: MEDLINE
Externí odkaz: