Agro-Climatic Information to Enhance the Machine-Learning Classification of Olive Oils from Near-Infrared Spectra.

ABSTRACT: The integrity of extra virgin olive oil (EVOO) quality markers can be compromised owing to deceptive marketing practices, such as misleading geographical origin claims or counterfeit certification labels, i.e., protected designations of origin (PDO). Therefore, it is imperative to introduce ecofriendly, rapid, and economical analytical methods for authenticating EVOO, such as near-infrared (NIR) spectroscopy. Unlike traditional techniques such as chromatography, NIR spectra contain unresolved bands; hence, chemometric tools such as principal component analysis (PCA) are essential for extracting valuable information from them. Herein, PCA was employed to reduce the high dimensionality of the NIR spectra. The PCA factors were then integrated as explanatory variables in machine-learning class

SUBMITTER: Sanchez-Rodriguez MI 

PROVIDER: S-EPMC11578292 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature