**6. Quality evaluation of the olives at the oil extraction plant**

#### **6.1 Determination of oil and water content in olives and olive pomace**

Information about olive quality is very important for the olive and olive oil producers as fruits with larger amounts of oil are highly priced. In addition to water content, fat content is also an important parameter for the optimization of the extraction procedures. Olive pomace can be re-extracted in the same industrial facilities or dried and sold in the form of dried olive pomace (O´Brien 2004).

Conventional oil and water analytical determinations could be replaced by real-time methods that avoid mixtures of high quality with low quality fruits. Muick and coworkers (2004) applied NIR and Raman spectroscopy to the determination of oil and water content in olive pomace. Later on, Bendini et al. (2007) were able to determine fat content, moisture and acid value directly in olives, using a Fourier Transform near-infrared (FT-NIR) instrument located in an industrial mill.

Here, the application of NIR in tandem with a multivariate regression method for the quantification of oil and water directly in fresh hammer milled olive and olive pomace samples is discussed (Barros et al. 2009). A total of 159 olive and olive pomace samples were used to build the calibration set. In order to validate the built models (for oil and water), 108 olive and olive pomace samples were used as independent set. In order to build the calibration models for the quantification of oil and water in hammer milled olive and olive

The coefficient of determination higher than 0.99, and the low root mean squared error of prediction (RMSEP) suggest a good predictive power. PLS1 regression based calibration models were used to predict the percentage of adulterant in the independent data sets. Results presented in Table 3 suggest that NIR spectroscopy in tandem with PLS1 regression is suitable to detect and quantify adulteration with other edible oils (sunflower, soya, maize

> **Model 2 (Maize)**

**P1** 0.3 0.1 0.1 0.3 0.2 0.0 0.2 0.4 0.4 **P2** 2.0 1.9 0.1 1.8 0.3 1.0 0.1 1.7 1.9 **P3** 8.0 7.8 0.1 7.7 0.1 7.5 0.1 10.0 2.2 **P4** 16.0 16.1 0.1 15.6 0.1 15.2 0.1 14.9 0.7 **P5** 37.0 36.9 0.2 34.5 0.1 36.9 0.3 36.1 2.9 **P6** 58.0 57.9 0.2 56.1 0.1 57.7 0.1 56.9 0.6 **P7** 71.0 71.1 0.1 69.0 0.1 70.6 0.2 71.2 0.8 **P8** 87.0 86.9 0.1 84.9 0.2 83.3 0.1 82.6 1.9

Information about olive quality is very important for the olive and olive oil producers as fruits with larger amounts of oil are highly priced. In addition to water content, fat content is also an important parameter for the optimization of the extraction procedures. Olive pomace can be re-extracted in the same industrial facilities or dried and sold in the form of

Conventional oil and water analytical determinations could be replaced by real-time methods that avoid mixtures of high quality with low quality fruits. Muick and coworkers (2004) applied NIR and Raman spectroscopy to the determination of oil and water content in olive pomace. Later on, Bendini et al. (2007) were able to determine fat content, moisture and acid value directly in olives, using a Fourier Transform near-infrared (FT-NIR)

Here, the application of NIR in tandem with a multivariate regression method for the quantification of oil and water directly in fresh hammer milled olive and olive pomace samples is discussed (Barros et al. 2009). A total of 159 olive and olive pomace samples were used to build the calibration set. In order to validate the built models (for oil and water), 108 olive and olive pomace samples were used as independent set. In order to build the calibration models for the quantification of oil and water in hammer milled olive and olive

**Predicted Oil Content in Virgin Olive Oil (%)** 

> **Model 3 (Soya)**

**Model 4 (Refined Olive Oil)** 

refined olive oil) in extra virgin olive oil up to 2% (w/w).

**(Sunflower)**

Table 3. Predicted values using the calibration model built for each adulterant.

**6. Quality evaluation of the olives at the oil extraction plant 6.1 Determination of oil and water content in olives and olive pomace** 

**Observed Oil Content in Virgin Olive Oil (%)**

dried olive pomace (O´Brien 2004).

instrument located in an industrial mill.

 **Model 1**

**Prediction Sample nº**  pomace using NIR and PLS1 regression, a Monte Carlo Cross-Validation (MCCV) (Xu & Liang 2001) framework was used. This approach was needed for building robust calibration models for real-time industrial application.

The model for water content estimation was built by a preliminary spectrum pre-treatment by computing the 1st derivative, in order to minimize the baseline effect, followed by Standard Normal Deviate (SNV). A PLS1 regression model with 3 LVs (Latent Variables) was needed to obtain predictive power with a Q2 of 0.96 and a relative RMSECV of 1.1%. The **b** vector plot for the water calibration models and the relationship between measured and predicted water values using a PLS1 are presented, respectively, in Figure 4a and Figure 4b.

Fig. 4. (a) b vector plot for the water calibration and (b) relationship between measured and predicted water values using a PLS1 model with 3 Latent Variables (Reproduced with permission from Barros et al. 2009 Springer 2009).

Quality Evaluation of Olives, Olive Pomace and Olive Oil by Infrared Spectroscopy 141

The models showed a good predictive power considering the nature (heterogeneity) of the milled olive fruits and olive pomace samples. NIR technique in tandem with PLS1 regression was found suitable for the quantification of these two important parameters. At industrial scale, the results show that NIR can be used for an extensive screening process of the olive fruits and olive pomace. In fact, when compared to classical approaches of analysis, this methodology is faster, allows larger number of samples in real-time and is

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

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

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

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

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

environmental sustainable.

quality olive oil.

and skin.

et al. 2001).

olive oil acidity.

control.

**6.2 Acid value quantification in olives** 

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 5a and Figure 5b.

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).

The models showed a good predictive power considering the nature (heterogeneity) of the milled olive fruits and olive pomace samples. NIR technique in tandem with PLS1 regression was found suitable for the quantification of these two important parameters. At industrial scale, the results show that NIR can be used for an extensive screening process of the olive fruits and olive pomace. In fact, when compared to classical approaches of analysis, this methodology is faster, allows larger number of samples in real-time and is environmental sustainable.
