Laser-based classification of olive oils assisted by machine learning.
Acidity
Chemometrics
LDA, SVM and RFC algorithmic models
Laser-induced breakdown spectroscopy (LIBS)
Olive oil
Journal
Food chemistry
ISSN: 1873-7072
Titre abrégé: Food Chem
Pays: England
ID NLM: 7702639
Informations de publication
Date de publication:
01 Jan 2020
01 Jan 2020
Historique:
received:
23
04
2019
revised:
18
07
2019
accepted:
04
08
2019
pubmed:
14
8
2019
medline:
13
11
2019
entrez:
13
8
2019
Statut:
ppublish
Résumé
Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidity and its authentication in terms of PDO (Protected Designation of Origin) and PGI (Protected Geographical Indications) characterizations are nowadays necessary and of great importance for the market of olive oil and the related economic activities. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used assisted by machine learning algorithms for retrieving of the information contained in the LIBS spectra to provide a simple, reliable, and ultrafast methodology for olive oils classification in terms of the degree of acidity and geographical origin. The combination of LIBS technique with machine learning statistical analysis approaches constitute a very powerful tool for the fast, in-situ and remote quality control of olive oil.
Identifiants
pubmed: 31404874
pii: S0308-8146(19)31441-4
doi: 10.1016/j.foodchem.2019.125329
pii:
doi:
Substances chimiques
Olive Oil
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
125329Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.