Quality Control Of Olive Oil Research Articles

Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil

One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K 270 and K 232 , and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality. • We present the application of 1D-CNN to single fluorescence spectra without pre- and post- processing of the data. • We discuss a method for the application of 1D-CNN to small datasets. • The 1D-CNN determines the physicochemical parameters within the instrument errors. • The proposed solution has the potential to substitute chemical analyses to assess the quality of olive oil.

Laser-based classification of olive oils assisted by machine learning

Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidity and its authentication in terms of PDO (Protected Designation of Origin) and PGI (Protected Geographical Indications) characterizations are nowadays necessary and of great importance for the market of olive oil and the related economic activities. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used assisted by machine learning algorithms for retrieving of the information contained in the LIBS spectra to provide a simple, reliable, and ultrafast methodology for olive oils classification in terms of the degree of acidity and geographical origin. The combination of LIBS technique with machine learning statistical analysis approaches constitute a very powerful tool for the fast, in-situ and remote quality control of olive oil.

Intelligent modelling to monitor the evolution of quality of extra virgin olive oil in simulated distribution conditions

One of the main attributes of extra virgin olive oil (EVOO) is that it possesses multiple advantages for human health, which makes quality control of this functional food an attractive action. In the present research, samples of two different EVOOs (Almazara del Ebro and As Pontis) were held under three different temperature conditions (3 °C, 40 °C, and room temperature (∼23 °C)). These different temperatures, as well as time, led to an alteration in the properties of the samples that were studied with visible spectroscopy and multilayer perceptrons (MLPs), which are non-linear mathematical tools. The absorption peaks representing the chlorophylls and carotenoids present in EVOO decrease with time and temperature. Generally, the results show that higher temperatures contribute more to the degradation of EVOO when compared to lower ones. The obtained information was used to design and optimise two MLPs. These tools were able to properly distinguish the time and temperature conditions that EVOO samples underwent. This technique is fast, user-friendly, inexpensive, and non-destructive, so it could be of great use for real-time quality control of olive oils during, for example, their storage and distribution, as ideal conditions could be potentially found.

Quality Control of Olive Oils Using Machine Learning and Electronic Nose

The adulteration of olive oils can be detected with chemical test. This is very expensive and takes very long time. Thus, this study is focused on reducing both time and cost. For this purpose, the raw data has been collected from olive oils by using an e-nose from different regions in Balikesir in Turkey. This study presents two methods to analyze quality control of olive oils. In the first method, 32 inputs are applied to the classifiers directly. In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naïve Bayesian, K-Nearest Neighbors (k-NN), Linear Discriminate Analysis (LDA), Decision Tree, Artificial Neural Networks (ANN), and Support Vector Machine (SVM) were used. Then performances of these classifiers were compared according to their accuracies.

Alkyl esters content and other quality parameters in oil mill: A response surface methodology study

The quality of the olives oil can be altered during their processing. This paper studies the influence that the washing of olive oils has on their alkyl esters content and on other quality parameters. A Central Composite Rotatable Design (CCRD) with five center points has been used to modify the factors, rotational frequency of the motor of the vertical centrifuge (between 34 and 47 Hz) and percentage of added water (between 6 and 34%). Results show that the content in alkyl esters of olive oils increases by the flow from the decanter to the vertical centrifuge. The use of water in the vertical centrifuge decreases the content in ethyl and methyl esters 0.8 and 0.6 mg/kg, respectively, per percentage unit of added water. The acidity, peroxide index, K232, chlorophyll, and carotenoid contents are slightly altered by the oil flow from the decanter to vertical centrifuge, but not by operating factors. K270, total polyphenols and orthodiphenols change with the speed of the vertical centrifuge, but water does not affect them. For the normal operation frequency of the centrifuge (50 Hz), K270 is increased about 14%, while the total polyphenols and orthodiphenols decrease by 27 and 62%, respectively.Practical applications: Quality control of olive oils (virgin and extra virgin).Olive‐oil production process: differences in the content of EE and TP between the vertical centrifuge way out and the decanter way out.

New analytical methods for the quality control and authentication of olive oil

The sensory, nutritional and pharmacological value of olive oil (OO) is scientifically documented and highly acknowledged [1, 2]. The beneficial effects of OO are attributed mainly to monounsaturated fatty acids but also to biophenols. Indeed, in 2011, the European Food Safety Authority (EFSA) published a scientific opinion approving the correlation of certain OO polyphenols (hydroxytyrosol and its derivatives) and protection of LDL from oxidative damage [3]. However, OO comprises a versatile and mutable mixture since its composition is strongly associated to multiple factors such as production procedure, olive origin, variety, collection season, etc. Moreover, many constituents are still unknown and most of them commercially unavailable. Thus, the main goal of the current study is the development of efficient LC-PDA and LC-HRMSn methodologies for authentication and quality control of OO [4]. Several constituents were isolated and identified while special attention was given to the chromatographic and spectrometric behavior of secoiridoids such as oleocanthal and oleacein which are major compounds in OO but highly unstable. Different platforms such as UPLC, PR-HPLC, NP-HPLC, SFC-UV and stationary phases (RP, NP, chiral) as well as diverse sample preparation procedures have been employed for the development of specific and reproducible methods for the qualitative and quantitative analysis of OO.

Electrospray ionization mass spectrometry and partial least squares discriminant analysis applied to the quality control of olive oil

Direct infusion electrospray ionization mass spectrometry in the positive ion mode [ESI(+)-MS] is used to obtain fingerprints of aqueous-methanolic extracts of two types of olive oils, extra virgin (EV) and ordinary (OR), as well as of samples of EV olive oil adulterated by the addition of OR olive oil and other edible oils: corn (CO), sunflower (SF), soybean (SO) and canola (CA). The MS data is treated by the partial least squares discriminant analysis (PLS-DA) protocol aiming at discriminating the above-mentioned classes formed by the genuine olive oils, EV (1) and OR (2), as well as the EV adulterated samples, i.e. EV/SO (3), EV/CO (4), EV/SF (5), EV/CA (6) and EV/OR (7). The PLS-DA model employed is built with 190 and 70 samples for the training and test sets, respectively. For all classes (1-7), EV and OR olive oils as well as the adulterated samples (in a proportion varying from 0.5 to 20.0% w/w) are properly classified. The developed methodology required no ions identification and demonstrated to be fast, as each measurement lasted about 3 min including the extraction step and MS analysis, and reliable, because high sensitivities (rate of true positives) and specificities (rate of true negatives) were achieved. Finally, it can be envisaged that this approach has potential to be applied in quality control of EV olive oils.

Using a Visible Vision System for On-Line Determination of Quality Parameters of Olive Fruits

The increased expectations for food products of high quality and safety standards and the need for accurate, fast and objective quality determination of these characteristics in food products continue to grow. In this situation, new techniques are necessary to enable on-line control of quality parameters. Computer vision provides one alternative for an automated, non-destructive and cost-effective technique in order to accomplish these requirements. The maturity index and sanitary conditions were objectively assessed on-line by image analysis obtained through machine vision, in which algorithms of colour-based segmentation, as well as the main operators to detect edges were used. The proposed methodology is able to estimate the maturity index and the percentage of defects in olives. In addition, this system can be potentially used on-line in the classification of olives, which means that it could help to improve the quality control of olive oil in factories.

Determination of betaines in vegetable oils by capillary electrophoresis tandem mass spectrometry – application to the detection of olive oil adulteration with seed oils

A CE-tandem mass spectrometry (MS²) methodology enabling the simultaneous determination of betaines (glycine betaine, trigonelline, proline betaine and total content of carnitines) in vegetable oils was developed. Betaines were derivatized with butanol previous to their baseline separation in 10 min using a 0.1 M formic acid buffer at pH 2.0. Ion trap conditions were optimized in order to maximize the selectivity and sensitivity. Analytical characteristics of the proposed method were established by evaluating its selectivity, linearity, precision (RSDs ranged from 4.8 to 10.7% for corrected peak areas) and accuracy by means of recovery studies (from 80 to 99%) and LODs and LOQs at 0.1 ppb level. The method was applied for the determination of the selected betaines in seed oils and extra virgin olive oils. MS² experiments provided the fingerprint fragmentation for the betaines identified in vegetable oils. In extra virgin olive oils, carnitines were not detected, making it possible to propose them as a feasible novel marker for the detection of adulterations of olive oils. Application of the developed method for the analysis of different mixtures of extra virgin olive oil with seed oil (between 2 and 10%) enabled the detection and quantitation of the total content of carnitines. The results obtained show the high potential of the developed method for the authentication and quality control of olive oils.

Review of the use of phytosterols as a detection tool for adulteration of olive oil with hazelnut oil

Adulteration of virgin olive oil with less expensive oils such as hazelnut oil is a serious problem for quality control of olive oil. Detection of the presence of hazelnut oil in olive oil at low percentages (<20%) is limited with current official standard methods. In this review, various classes of phytosterols in these two oils are assessed as possible markers to detect adulterated olive oil. The composition of 4-desmethyl- and 4-monomethylsterols is similar in both oils, but the 4,4′-dimethylsterols differ. Lupeol and an unknown (lupane skeleton) compound from 4,4′-dimethylsterols are exclusively present in hazelnut oil and can be used as markers via GC–MS monitoring to detect adulteration at levels as low as 2%. The phytosterol classes need to be separated and enriched by a preparative method prior to analysis by GC or GC/MS; these SPE and TLC methods are also described in this review.

Direct olive oil authentication: Detection of adulteration of olive oil with hazelnut oil by direct coupling of headspace and mass spectrometry, and multivariate regression techniques

Control of adulteration of olive oil, together with authentication and contamination, is one of the main aspects in the quality control of olive oil. Adulteration with hazelnut oil is one of the most difficult to detect due to the similar composition of hazelnut and olive oils; both virgin olive oil and olive oil are subjected to that kind of adulteration. The main objective of this work was to develop an analytical method able to detect adulteration of virgin olive oils and olive oils with hazelnut oil by means of its analysis by a headspace autosampler directly coupled to a mass spectrometer used as detector (ChemSensor). As no chromatographic separation of the individual components of the samples exists, a global signal of the sample is obtained and employed for its characterization by means of chemometric techniques. Four different crude hazelnut oils from Turkey were employed for the development of the method. Multivariate regression techniques (partial least squares and principal components analysis) were applied to generate adequate regression models. Good values were obtained in both techniques for the parameters employed (standard errors of prediction (SEP) and prediction residual error sum of squares (PRESS)) to evaluate its goodness. With the proposed method, minimum adulteration levels of 7 and 15% can be detected in refined and virgin olive oils, respectively. Once validated, the method was applied to the detection of such adulteration in commercial olive oil and virgin olive oil samples.

Advanced automation of a flow injection analysis system for quality control of olive oil through the use of a distributed expert system

A new methodology—based on the combination of flow injection analysis and a distributed expert system—is proposed for the on-line chemical quality control of olive oil. This knowledge-based system is in charge of carrying out the flow injection determination of total acidity, peroxide value, and UV spectrophotometric measurements ( K 232 and K 270), according to EU legislation. On the other hand, the expert system, apart from supervising the correct functioning of the system (devices, clogging, analysis frequency, and so on), performs the definite classification of the analyzed oil by evaluating the oil quality from the values yielded, according to previously established specifications. Satisfactory results have been obtained in the application of this approach to different samples of Spanish olive oil along the storage process. The distributed expert system also allows for the remote control of the analysis process owing to the interconnection of the different nodes by means of a communication network.

Niveles de calcio, magnesio, manganeso, zinc, selenio y cromo en aceites de oliva comercializados en Andalucía

We determined the presence of Ca, Mg, Mn, Zn, Se and Cr in 64 samples of olive virgin oil, olive oil and marc oil produced in Andalusia, southern Spain. Flame atomization atomic absorption spectroscopy was used for Ca and Mg, and the electrothermal atomization mode was used for Mn, Zn, Se and Cr. Samples were previously ashed with HNO 3 and V 2 O 5 . Analytical conditions were optimized to control for interference and to obtain appropriate accuracy and precision factors. The method is potentially useful for quality control of olive oils. The concentrations ranged from 0.055 to 2.725 mg/kg for Ca; 0.300 to 5.300 mg/kg for Mg, 13.213 to 296.356 m g/kg for Mn, 8.758 to 36.575 m g/kg for Zn, undetectable to 178.515 m g/kg for Se, and undetectable to 41.510 m g/kg for Cr. No statistically significant differences were found between the three types of oil in the concentrations of these elements. There were significant correlations between the concentrations of the pairs Se-Cr, Mn-Cr, Mg-Zn, Ca-Zn and Ca-Cr. Because of the high intake of olive oil in our setting, this food can be considered a major source of essential elemental nutrients.