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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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Flavor and taste are sensorial attributes of virgin olive oil (VOO) highly appreciated by consumers. Among the organoleptic properties of VOO, bitterness is related to the natural phenolic compounds present in the oil. Sensorial analysis is the official method to evaluate VOO flavor and bitterness, which requires highly specialized experts. Alternatively, methods based on physicochemical determinations could be useful for the industry. The present work presents a flow-injection analysis system for the direct automatic determination of bitterness and total phenolic compounds in VOO without prior isolation, based on the spectral shift undergone by phenolic compounds upon pH variation. This system enables a complete automation of the process, including dilution of the sample and its sequential injection into buffer solutions of acidic and alkaline pH. The variation of the absorbance at 274 nm showed a high correlation with bitterness and the total phenolic content of VOO, due to the close relationship between these two parameters. Thus, the proposed method determines the bitterness and phenolic compounds, with results similar to those from reference methods (relative errors ranging from 1% to 8% for bitterness and from 2% and 7% for phenolic compounds). The precision evaluated at two levels of both parameters ranged between 0.6% and 1.5% for bitterness and between 0.7% and 2.6% for phenolic compounds.

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