**6.2 Acid value quantification in olives**

140 Olive Oil – Constituents, Quality, Health Properties and Bioconversions

For oil calibration model the spectra pre-treatment was the same as for the water calibration model and, in this case, 3 LVs were needed to obtain predictive power with a Q2 of 0.88 and a relative RMSECV of 2.64%. The **b** vector plot for the oil calibration models and the relationship between measured and predicted oil values are shown, respectively, in Figure

(a)

(b) Fig. 5. (a) b vector plot for the oil calibration and (b) relationship between measured and predicted oil values using a PLS1 model with 3 Latent Variables (Reproduced with

permission from Barros et al. 2009 Springer 2009).

5a and Figure 5b.

Several factors may affect the olive characteristics and consequently its quality (Muick et al. 2004) specially the increase of free fatty acids (FFA) due to the action of lipases (Morelló et al. 2003). Consequently, the classification of olive oils based on their FFA content prior to processing is an important measure to improve and guarantee the production of good quality olive oil.

Previous works (Muick et al. 2003) report the application of Raman spectroscopy to the direct determination of FFA in milled olives. It is not possible to predict FFA content directly in the milled olive by NIR, probably because of the complexity of the matrix: kernel, pulp and skin.

The authors proposed a method for a rapid determination of free fatty acids in olive (Nunes et al. 2009). This procedure combines Soxhlet extraction for 30 minutes with MID-IR spectroscopy coupled to a multivariate regression method (PLS1). The oil extracted from olives (crushed with a hammer mill) was used for infrared analysis and for free fatty acids determination according to UNE 55030 (AENOR 55030:1961) protocol. MID-IR spectra were acquired by ATR in a Golden Gate accessory (one reflection), in the range of 4000 to 600 cm-1. The data set comprising a total of 210 spectra (42 x 5) was imported into an in-house developed procedure for performing PLS1 (Helland 2001; Martens 2001; Wold et al. 2001).

Figure 6a, shows a linear correlation between the actual olive oil acid values and those estimated by the PLS1 model within the considered values range. The corresponding b vector profile shown in Figure 6b clearly identifies the band located at 1710 cm-1 (carboxylic n-C=O) as the most important one, related to the quantified olive oil acid value. Moreover, the band located at 1743 cm-1 (assigned to triacylglycerols n-C=O ester group), although weaker than the previous one, also contributes to the modeling of the olive oil acidity.

The PLS1 calibration model with a Monte Carlo Cross-Validation approach was built in the spectroscopic region of 1850-1600 cm-1 (with SNV pre-treatment, 4 LVs, a RMSECV(%) of 8.7 and a Q2 of 0.97). It represents an optimized method for calibration of FFA in extracted olive oil and a proposal for an indirect but quick acid value determination in olives and consequently fruit quality. The short extraction time and the spectroscopic determination of FFA from the MID-IR spectra instead of the titration step (with the consequent decreasing use of reagents and analysis time), provide more reliable results and permit a tight sampling control.

Quality Evaluation of Olives, Olive Pomace and Olive Oil by Infrared Spectroscopy 143

Infrared spectroscopic techniques have a potential in assisting and simplifying olive oil characterization. NIR spectroscopy in tandem with multivariate calibration models could provide a comprehensive chemical characterization of an olive oil sample for waxes, total

NIR infrared may also contribute to the identification and qualification of adulterants in virgin olive oil (additions of refined olive oil, sunflower oil, maize oil and soya oil with "as

Moreover, NIR and MID-IR spectroscopy as tool the advantage that it can be used to quantify oil and water content directly in olive and olive pomace and also to measure FFA directly in olives, allowing a quick quality evaluation that may reduce the processing time and cost.

The spectral profiles extracted from infrared spectra using chemometric methods could in many cases be a substitute for chromatographic and wet chemistry analysis, for olive oil overall characterization. Therefore, spectrometers of this type can be an important tool in modern olive oil analytical laboratory since they have so many advantages such as sensitivity, versatility (several type of analysis with only one equipment), real-time/in-line measurements,

Authors wish to acknowledge the support from QOPNA unit (project PEst-C/QUI/UI0062/2011). Alexandra Nunes thanks Fundação para a Ciência e a Tecnologia for

Aranda F., Gómez-Alonso S., Rivera del Álamo R.M., Salvador M.D. & Fregapane G. (2004).

Asociación Española de Normalización y Racionalización (AENOR), Norma UNE 55030

Baeten V., Aparicio R., Marigheto N.A. & Wilson R.H. (1999). Olive oil analysis by infrared

Barros A., Nunes A., Martins J. & Delgadillo I. (2009). Determination of oil and water in

Bendini A., Cerretani L., Virgilio F.D., Belloni P., Lercker G. & Toschi T.G. (2007). In-process

Bertran E., Blanco M., Coello J., Iturriaga H., Maspoch S. & Montolin I. (2000). Near infrared

Triglyceride, total and 2-position fatty acid composition of Cornicabra virgin olive oil: Comparison with other Spanish cultivars. *Food Chem.* 86, (August 2004), pp.

and Raman spectroscopy: methodologies and applications. In *Handbook of Olive Oil - Analysis and Properties*, J. L. Harwood & R. Aparicio (eds.). Aspen Publication,

olive and olive pomace by NIR and multivariate analysis. *Sens. & Instrumen. Food* 

monitoring in industrial olive mill by means of FT-NIR. *Eur. J. Lipid Sci. Technol.*

spectrometry and pattern recognition as screening methods for the authentication of virgin olive oils of very close geographical origins. *J. Near Infrared Spectrosc.* 8(1),

minimal sample preparation, relatively low cost implementation and high throughput.

sterols, sterol composition and free fatty acids composition.

low as 2% (w/w)).

**8. Acknowledgment** 

her Post-PhD grant.

485-492, ISSN 0308-8146

(AENOR, Madrid, 1961)

ISBN 0-8342-1633-7, London

*Qual.* 3, (July 2009), pp. 180–186

pp. 45-52

109, (May 2007), pp. 498-504, ISSN 1438-9312

**9. References** 

Fig. 6. (a) PLS1 regression relationship between actual and predicted value of olive oil acidity from the application of acidity calibration model and (b) the corresponding b vector plot (Reproduced with permission from Nunes et al. 2009 Springer 2009).

#### **7. Conclusions**

The high sensitivity and reproducibility provided by the modern spectrometers allow indepth studies of food systems, like olives and olive oil. The complexity of these matrices requires chemometric tools to extract both qualitative and quantitative information.

Infrared spectroscopic techniques have a potential in assisting and simplifying olive oil characterization. NIR spectroscopy in tandem with multivariate calibration models could provide a comprehensive chemical characterization of an olive oil sample for waxes, total sterols, sterol composition and free fatty acids composition.

NIR infrared may also contribute to the identification and qualification of adulterants in virgin olive oil (additions of refined olive oil, sunflower oil, maize oil and soya oil with "as low as 2% (w/w)).

Moreover, NIR and MID-IR spectroscopy as tool the advantage that it can be used to quantify oil and water content directly in olive and olive pomace and also to measure FFA directly in olives, allowing a quick quality evaluation that may reduce the processing time and cost.

The spectral profiles extracted from infrared spectra using chemometric methods could in many cases be a substitute for chromatographic and wet chemistry analysis, for olive oil overall characterization. Therefore, spectrometers of this type can be an important tool in modern olive oil analytical laboratory since they have so many advantages such as sensitivity, versatility (several type of analysis with only one equipment), real-time/in-line measurements, minimal sample preparation, relatively low cost implementation and high throughput.
