Pubblicazioni

Autori:

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

Titolo:

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

Anno:

2024

Tipologia prodotto:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Lingua:

Inglese

Referee:

No

Nome rivista:

FOODS

ISSN Rivista:

2304-8158

Intervallo pagine:

1-14

Parole chiave:

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

Breve descrizione dei contenuti:

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

Id prodotto:

137156

Handle IRIS:

11562/1117126

ultima modifica:

25 settembre 2024

Citazione bibliografica:

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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