Determination of the olive maturity index of intact fruits using image analysis.

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.

Effect of the solvent and the sample preparation on the determination of triterpene compounds in two-phase olive-mill-waste samples.

A simple and rapid extraction method has been employed to determine several value-added compounds, mainly triterpenes, in two-phase olive-mill-waste samples. The compounds were extracted with methanol or ethyl acetate, and the initial fresh samples were treated for classic techniques such as drying, drying and oil extraction, and drying and sifting of the olive stones. For the identification and quantitation of the compounds, an ultra performance liquid chromatography-mass spectrometry method was employed. The best results of the triterpenic compound content were achieved by extraction with methanol from the fresh sample for the oleanolic and ursolic acids, and erythrodiol and uvaol; and from the dried-extracted sample for the maslinic acid. Conversely, the best results for the linoleic acid content were reached by extraction with ethyl acetate from the dried-sifted sample. These are remarkable processes that make the solid wastes from the olive-oil industry reach a high added value.

Evaluation of the overall quality of olive oil using fluorescence spectroscopy.

The fluorescence spectra of some olive oils were examined in their natural and oxidised state, with wavelength range emissions of 300-800 nm and 300-400 nm used as excitation radiation. The fluorescence emissions were measured and an assessment was made of the relationship between them and the main quality parameters of olive oils, such as peroxide value, K232, K270 and acidity. These quality parameters (peroxide value, K232, K270 and acidity) are determined by laboratory methods, which though not too sophisticated, they are required solvents and materials as well as time consuming and sample preparation; there is a need for rapid analytical techniques and a low-cost technology for olive oil quality control. The oxidised oils studied had a strong fluorescence band at 430-450 nm. Extra virgin olive oil gave a different but interesting fluorescence spectrum, composed of three bands: one low intensity doublet at 440 and 455 nm; one strong band at 525 nm; and one of medium intensity at 681 nm. The band at 681 nm was identified as the chlorophyll band. The band at 525 nm was derived, at least partially, from vitamin E. The results presented demonstrate the ability of the fluorescence technique, combined with multivariate analysis, to characterise olive oils on the basis of all the quality parameters studied. Prediction models were obtained using various methods, such as partial least squares (PLS), N-way PLS (N-PLS) and external validation, in order to obtain an overall evaluation of oil quality. The best results were obtained for predicting K270 with a root mean square (RMS) prediction error of 0.08 and a correlation coefficient obtained with the external validation of 0.924. Fluorescence spectroscopy facilitates the detection of virgin olive oils obtained from defective or poorly maintained fruits (high acidity), fruits that are highly degraded in the early stages (with a high peroxide value) and oils in advanced stages of oxidation, with secondary oxidation compounds (high K232 and K270). The results indicate the potential of a spectrofluorimetric method combined with multivariate analysis to differentiate, and even quantify, the levels of oil quality. The proposed methodology could be used to accelerate analysis, is inexpensive and allows a comprehensive assessment to be made of olive oil quality.

Infrared machine vision system for the automatic detection of olive fruit quality.

External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements.

A portable Raman sensor for the rapid discrimination of olives according to fruit quality.

In the real marketplace, providing high-quality olive oil is important from the perspective of both consumers and producers. Quality control should meet all requirements in the production process, from farm to packaging. The quality of olive oil can be affected by several factors, including agricultural techniques, seasonal conditions, farming systems, maturity, method and duration of storage, and process technology. The quality of oil produced also depends largely on the quality of the olives. In an enterprise aimed at producing high-quality oils, olives with defects ('ground'; i.e., fallen to the ground) should be separated from healthy fruit ('sound'; i.e., collected directly from the tree), because a very small portion of low-quality fruit can ruin the whole batch. The fruit falls partly because of its maturation process, but also because of pest and disease attack or weather conditions (strong wind). Fruit that has fallen to the ground can suffer a rapid deterioration in quality. Currently, the separation of fruits is based mainly on visual inspection or information provided by the farmer. These are not very reliable procedures. Methods using analytical parameters to characterize the oil, such as acidity and peroxide value, can be applied, but they require a lot of time and materials. Alternative techniques are therefore needed for the rapid and inexpensive discrimination of olives as part of a quality control strategy. The work described here aims to determine the potential of low-resolution Raman spectroscopy for the discrimination of olives before the oil processing stage in order to detect whether they have been collected directly from the tree (i.e., healthy fruit) or not. Low-resolution Raman spectroscopy was applied together with multivariate procedures to achieve this aim. PCA was used to find natural clusters in the data. Supervised classification methods were then applied: Soft Independent Modeling of Class Analogy (SIMCA), PLS Discriminate Analysis (PLS-DA) and K-nearest neighbors (KNN). The best results were obtained using the KNN method, with prediction abilities of 100% for 'sound' and 97% for 'ground' in an independent validation set. These results demonstrated the potential of a portable Raman instrument for detecting good quality olives before the oil processing stage, by developing models that could be applied before this stage, thus contributing to an overall improvement in quality control.

Direct automatic determination of bitterness and total phenolic compounds in virgin olive oil using a pH-based flow-injection analysis system.

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.

Rapid and quantitative extraction method for the determination of chlorophylls and carotenoids in olive oil by high-performance liquid chromatography.

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.

Direct spectrophotometric determination of bitterness in virgin olive oil without prior isolation by pH gradient.

Bitter taste, an organoleptic characteristic of virgin olive oil, has been related to phenolic compound composition. The usual method to assess this attribute is by a sensorial panel of tasters, while in the laboratory; methods based on physicochemical properties have been assayed as K225, the most widely used one. However, a direct determination of bitterness in virgin olive oil is useful for quality-control purposes. The proposed method is supported by the observable spectral change undergone by the compounds responsible for bitterness as pH varied. This measurement was carried out directly in the oil, without prior isolation of bitter analytes. The difference of absorbance between alkaline and neutral medium showed a highly significant correlation (r = 0.988, p < 0.0001) with the conventional parameter (K225). The method was rapid, required a small sample, allowed direct determination of bitterness in virgin olive oil, and could be easily automated.

Determination of the oxidative stability of olive oil, using focused-microwave energy to accelerate the oxidation process.

A new method is proposed for rapid monitoring of the oxidative stability of virgin olive oil. Samples were irradiated with microwave energy to accelerate the oxidation process and photometric monitoring was performed at 232 and 270 nm. Conditions such as microwave irradiation power, number of irradiation cycles, and irradiation time were optimized by means of multivariate screening that showed that irradiation power is the most significant condition in the oil oxidation process. Twelve samples of extra virgin olive oil of different oxidative stability--between 19 and 130 h calculated from the induction time of the Rancimat method, usual for analysis of olive oil in routine laboratories--were analyzed by the proposed microwave-assisted method, and a decrease in the induction time between 60 and 68% was obtained. The results obtained showed that correlation between the proposed method and the Rancimat method was excellent. The correlation coefficients were 0.9953 and 0.9963 for monitoring at 232 and 270 nm, respectively.