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

Sensing and Instrumentation for Food Quality and Safety 01/2009; 3(3):187-191. DOI:10.1007/s11694-009-9084-2

ABSTRACT 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 (30min
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 Q2 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.

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    ABSTRACT: Fourier transform Raman spectroscopy combined with pattern recognition has been used to discriminate olives of different qualities. They included samples of sound olives, olives with frostbite, olives that have been collected from the ground, fermented olives, and olive samples with diseases. Milled olives were measured in a dedicated sample cup, which was rotated during spectrum acquisition. A preliminary study of the data set structure was performed using hierarchical cluster analysis and principal component analysis. Two supervised pattern recognition techniques, K-nearest neighbors and soft independent modeling of class analogy (SIMCA), were tested using a "leave-a-fourth-out" cross-validation procedure. SIMCA provided the best results, with prediction abilities of 95% for sound, 93% for frostbite, 96% for ground, and 92% for fermented olives. The olive samples with diseases (too few to define a class) were included in the validation and recognized as not belonging to any class. None of the damaged olive samples was wrongly predicted to the class of sound olives. With this approach a selection of sound olives for the production of high-quality virgin olive oil can be achieved.
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    ABSTRACT: A chemometric method has been applied for the determination of the free fatty acid (FFA) concentration in commercial olive oil samples of different types an origins by using Fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements. Different methods for selecting the training set, including hierarchical cluster analysis, were applied and compared. The prediction capabilities of partial least squares (PLS) multivariate calibration methods, net analyte signal (NAS) preprocessing followed by PLS or classical least squares (CLS) regression method of ATR–FTIR data were evaluated. Several aspects, like spectral range to be considered, different preprocessing alternatives (mean centering, multiplicative scattering correction, standard normal variate (SNV)), together with a critical evaluation of the calibration set were made on using the mean square error of cross-validation and prediction, as control parameters. Using a calibration set of 16 samples the properties of 28 samples were predicted with relative precision of triplicates of 0.017 wt.%. The mean difference between predicted and actual values and the standard deviation of mean differences were −0.001 and 0.037 wt.%, respectively.The limit of detection (LOD), sensitivity and selectivity of the methodology developed were evaluated in terms of the net analyte signal, being found a limit of detection of 0.072 wt.%, a sensitivity value of 0.077 in terms of analytical signal per unit of concentration, being expressed that in wt.%, and a linear relationship (R2=0.9963) between selectivity and FFA concentration (equivalent to 0.24% for a sample containing 1 wt.% of FFA).
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