Abstract：
:Being a product with a high market value, olive oil undergoes adulterations. Therefore, studies that make the verification of the authenticity of olive oil more efficient are necessary. The aim of this study was to develop a robust model using FT-NIR and PLS-DA to discriminate extra virgin olive oil samples and build individual models to differentiate adulterated extra virgin olive oil samples. The best PLS-DA-OPS classification model for olive oils showed specificity (Spe) and accuracy (Acc) values higher than 99.7% and 99.9%. For the classification of adulterants, PLS-DA-OPS models presented values of Spe at 96.0% and Acc above 95.5% for varieties. For the blend, the best PLS-DA-GA model presented Acc and Spe values greater than 98.2% and 98.8%. Reliable and robust models have been built, allowing differentiation from seven adulterants to genuine extra virgin olive oils.
journal_name
Food Chemjournal_title
Food chemistryauthors
Vieira LS,Assis C,de Queiroz MELR,Neves AA,de Oliveira AFdoi
10.1016/j.foodchem.2020.128866subject
Has Abstractpub_date
2020-12-13 00:00:00pages
128866eissn
0308-8146issn
1873-7072pii
S0308-8146(20)32728-Xjournal_volume
345pub_type
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