Publications

Authors:

Mara, Andrea; Migliorini, Matteo; Ciulu, Marco; Chignola, Roberto; Egido, Carla; Núñez, Oscar; Sentellas, Sònia; Saurina, Javier; Caredda, Marco; Deroma, Mario A.; Deidda, Sara; Langasco, Ilaria; Pilo, Maria I.; Spano, Nadia; Sanna, Gavino

Title:

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

Year:

2024

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Referee:

No

Name of journal:

FOODS

ISSN of journal:

2304-8158

Page numbers:

1-14

Keyword:

honey; geographical classification; botanical classification; elements; ICP-MS

Short description of contents:

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 machinelearning 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

Product ID:

137156

Handle IRIS:

11562/1117126

Last Modified:

September 25, 2024

Bibliographic citation:

Mara, Andrea; Migliorini, Matteo; Ciulu, Marco; Chignola, Roberto; Egido, Carla; Núñez, Oscar; Sentellas, Sònia; Saurina, Javier; Caredda, Marco; Deroma, Mario A.; Deidda, Sara; Langasco, Ilaria; Pilo, Maria I.; Spano, Nadia; Sanna, Gavino, Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions «FOODS» , 2024 , pp. 1-14

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