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2024-01-12

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cc-by (c) Mara, A. et al., 2024
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/206171

Elemental fingerprinting combined with machine learning techniques as a powerful tool for geographical discrimination of honeys from nearby regions

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Recurs relacionat

https://doi.org/10.3390/foods13020243

Resum

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.

Matèries

Mel d'abelles, Taxonomia botànica, Espectrometria de masses de plasma acoblat inductivament

Matèries (anglès)

Honey, Botanical taxonomy, Inductively coupled plasma mass spectrometry

Citació

Col·leccions

Articles publicats en revistes (Enginyeria Química i Química Analítica)
Articles publicats en revistes (Institut de Recerca en Nutrició i Seguretat Alimentària (INSA·UB))

Citació

MARA, Andrea, et al. Elemental fingerprinting combined with machine learning techniques as a powerful tool for geographical discrimination of honeys from nearby regions. Foods. 2024. Vol. 13, num. 2, pags. 1-14. ISSN 2304-8158. [consulted: 19 of August of 2026]. Available at: https://hdl.handle.net/2445/206171

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