Estimation of olive oil acidity using FT-IR and partial least squares regression

• Type: Article in International Scientific Journal
• Date: 2009

Authors

Nunes, A (Author) Other; Martins, J (Author) Other; Barros, AS (Author) Other; Galvis Sanchez, AC (Author) Other; Delgadillo, I (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Sensing and Instrumentation for Food Quality and Safety

Vol. 3

Pages: 187-191

ISSN: 1932-7587

Indexing

Scopus - 8 Citations

Other information

Authenticus ID: P-007-S0N

DOI: 10.1007/s11694-009-9084-2

Abstract (EN): Olive oil characteristics are directly related to olive quality. Information about olive quality is of paramount importance to olive and olive oil producers, in order to establish its price. Real-time characterization of the olives avoids mixtures of high quality with low quality fruits, and allows improvement of olive oil quality. This work describes an indirect determination of olive acidity and that allows a rapid evaluation of olive oil quality. The applied method combines chemical analysis (30 min Soxhlet olive pomace extraction) in tandem with a spectroscopic technique (FT-IR) and multivariate regression (PLS1). The most suitable calibration model found used SNV pre-processing and was built with 4 Latent Variables giving a RMSECV of 8.7% and a Q 2 of 0.97. This accurate calibration model allows the estimation of olive acidity using a FT-IR spectrum of the corresponding Soxhlet oil dry extract and therefore is a suitable method for indirect determination of FFA in olives. © 2009 Springer Science+Business Media, LLC.

Language: English

Type (Professor's evaluation): Scientific