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Colour is an organoleptic characteristic of virgin olive oil and an important attribute that affects the consumer perception of quality. Chlorophylls and carotenoids are the main pigments responsible for the colour of virgin olive oil. A simple analytical method for the quantitative determination of chlorophylls and carotenoids in virgin olive oils has been developed. The pigments were isolated from small samples of oil (1.0 g) by solid-phase extraction using diol-phase cartridges (diol-SPE), and the extract was analysed by reverse-phase HPLC with diode-array UV detection. Chromatographic peak resolution, reproducibility (coefficient of variation (C.V.) <4.5%) and recovery (>98.4%) for each component were satisfactory. A comparative study of the proposed method was performed versus classical liquid-liquid extraction (LLE) with N,N'-dimethylformamide and solid-phase extraction using a C18 column (C18-SPE). While 96.4% of the pigments were recovered by LLE, only 51.3% were isolated by C18-SPE in comparison to diol-SPE. Likewise, a higher alteration of pigment composition was observed when such LLE and C18-SPE procedures were used. In this sense, a higher ratio of pheophytin in comparison to that isolated by the diol-SPE procedure was achieved with both extraction procedures, indicating a greater extent of the pheophytinization reaction. Therefore, quantification of pigments from virgin olive oil by diol-SPE followed by RP-HPLC was found to be rapid, simple, required only a small amount of sample, consumed only small amounts of organic solvents, and provided high recoveries, accuracy and precision.

In this work, the maturity index of different samples of olives was objectively assessed by image analysis obtained through machine vision, in which algorithms of color-based segmentation and operators to detect edges were used. This method allows a fast, automatic and objective prediction of olive maturity index. This prediction value was compared to maturity index (MI), generally used by olive oil industry, based on the subjective visual determination of color of fruit skin and flesh. Machine vision was also applied to the automatic estimation of size and weight of olive fruits. The proposed system was tested to obtain a good performance in the classification of the fruit in batches. When applied to several olive samples, the maturity index predicted by machine vision was in close agreement with the maturity index of fruits visually estimated, values that are currently used as standards. The evaluation of weight of fruit also provided good results (R 2 =0.91). These results obtained by image analysis can be used as a useful method for the classification of olives at the reception in olive mill, allowing a better quality control of the production process.

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