Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils

The aim of this article is to study tree-based ensemble methods, new emerging modelling techniques, for authentication of samples of olive oil blends to check their suitability for classifying the samples according to the type of oil used for the blend as well as for predicting the amount of olive oil in the blend. The performance of these methods has been investigated in chromatographic fingerprint data of olive oil blends with other vegetable oils without needing either to identify or to quantify the chromatographic peaks. Different data mining methods—classification and regression trees, random forest and M5 rules—were tested for classification and prediction. In addition, these classification and regression tree approaches were also used for feature selection prior to modelling in order to reduce the number of attributes in the chromatogram. The good outcomes have shown that these methods allow one to obtain interpretable models with much more information than the traditional chemometric methods and provide valuable information for detecting which vegetable oil is mixed with olive oil and the percentage of oil used, with a single chromatogram.

The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics.

A new procedure has been developed for the classification and quantification of the adulteration of pure olive oil by soya oil, sun flower oil, corn oil, walnut oil and hazelnut oil. The study was based on a chemometric analysis of the near-infrared (NIR) spectra of olive-oil mixtures containing different adulterants. The adulteration of olive oil was carefully carried out gravimetrically in a 4 mm quartz cuvette, starting with pure olive oil in the cuvette first. NIR spectra of the 525 adulterated mixtures were measured in the region of 12,000-4000 cm(-1). The spectra were subjected batch wise to multiplicative signal correction (MSC) before calculating the principal component (PCA) models. The MSC-corrected data were subjected to Savitzky-Golay smoothing and a mean normalization procedure before developing partial least-squares calibration (PLS) models. The results revealed that the models predicted the adulterants, corn oil, sun flower oil, soya oil, walnut oil and hazelnut oil involved in olive oil with error limits +/-0.57, +/-1.32, +/-0.96, +/-0.56 and +/-0.57% weight/weight, respectively. Furthermore, the PCA developed models were able to classify unknown adulterated olive oil mixtures with almost 100% certainty. Quantification of the adulterants was carried out using their respective PLS models within the same error limits as mentioned above.     

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).     

Comparative study of electrospray and photospray ionization sources coupled to quadrupole time-of-flight mass spectrometer for olive oil authentication

The use of fast and reliable analytical procedures for olive oil authentication is a priority demand due to its wide consumption and healthy benefits. Olive oil adulteration with other cheaper vegetable oils is a common practice that has to be detected and controlled. Rapid screening methods based on high resolution tandem mass spectrometry constitute today the option of choice due to sample handling simplicity and the elimination of the chromatographic step. The selection of the ionization source is critical and the comparison of their reliability necessary. The possibilities of the direct infusion electrospray ionization (ESI) and the recently introduced atmospheric pressure photospray ionization source (APPI), coupled to quadrupole time-of-flight (QqTOF), have been critically studied and compared to control olive oil adulteration. These techniques are very rapid (approximately 1 min per sample) and have high discrimination power to elucidate key components in the edible oils studied (olive, hazelnut, sunflower and corn). Nevertheless, both sources are complementary, being APPI more sensitive for monoacyl- and diacylglycerol fragment ions and ESI for triacylglycerols. In addition, methods reproducibility's are very high, especially for APPI source. Mixtures of olive oil with the others vegetable oils can be easily discriminated which has been tested by using principal components analysis (PCA) with both ESI-MS and APPI-MS spectra. Analogously, linear discriminant analysis (LDA) confirms methods reproducibility and detection of other oils used as adulterants, in particular hazelnut oil, which is especially difficult given its chemical similarity with olive oil.     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

Direct determination of polymerized triglycerides in deep-frying olive oil by attenuated total reflectance–Fourier transform infrared spectroscopy using partial least squares regression

A partial least squares (PLS) regression model based on attenuated total reflectance–Fourier transform infrared spectra of heated olive oil samples has been developed for the determination of polymerized triacylglycerides (PTGs) generated during thermal treatment of oil. Three different approaches for selection of the spectral regions used to build the PLS model were tested and compared: (1) variable selection based on expert knowledge, (2) uninformative variable elimination PLS, and (3) interval PLS. Each of the three variable selection methods provided PLS models from heated olive oil samples with excellent performance for the prediction of PTGs in fried olive oils with comparable model statistics. However, besides a high coefficient of determination (R ² of 0.991) and low calibration, validation, and prediction errors of 1.14%, 1.21%, and 1.40% w/w, respectively, variable selection based on expert knowledge gave additionally almost identical low calibration (−0.0017% w/w) and prediction (−0.0023% w/w) bias. Furthermore, it was verified that the determination of PTGs was not influenced by the type of foodstuff fried in the olive oil.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

Discriminating olive and non-olive oils using HPLC-CAD and chemometrics

This work presents a method for an efficient differentiation of olive oil and several types of vegetable oils using chemometric tools. Triacylglycerides (TAGs) profiles of 126 samples of different categories and varieties of olive oils, and types of edible oils, including corn, sunflower, peanut, soybean, rapeseed, canola, seed, sesame, grape seed, and some mixed oils, have been analyzed. High-performance liquid chromatography coupled to a charged aerosol detector was used to characterize TAGs. The complete chromatograms were evaluated by PCA, PLS-DA, and MCR in combination with suitable preprocessing. The chromatographic data show two clusters; one for olive oil samples and another for the non-olive oils. Commercial oil blends are located between the groups, depending on the concentration of olive oil in the sample. As a result, a good classification among olive oils and non-olive oils and a chemical justification of such classification was achieved.     

Cetane number and thermal properties of vegetable oil,biodiesel, 1-butanol and diesel blends

Vegetable oil derived fuels for diesel engines are becoming important as alternative to petroleum diesel fuels due to their environmental friendliness and availability. Ignition quality in compression ignition (CI) engines is influenced by thermal characteristics and fuel properties. In this study, the effects of vegetable oil transesterification and vegetable oil–1-butanol-diesel blends on fuel properties, cetane number (CN) and thermal characteristics were experimentally investigated. Methyl esters (biodiesel) and 10% vegetable oil–10% 1-butanol–80% diesel blends were prepared from croton oil (CRO), coconut oil (COO) and jatropha oil (JAO). CN was measured in a CFR F-5 engine, and a thermogravimetric analysis (TG), as well as the determination of fuel properties of vegetable oils, biodiesels and blends was carried out. It can be observed for vegetable oils that they possess low volatility characteristics, low CN and high viscosity different from those of biodiesels, blends and diesel fuel. It was observed that biodiesels and blends exhibit similarities with diesel in the fuel characteristics, CN and TG curves.     

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

Application of Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics for authentication of cod-liver oil

Some vegetable oils such as canola (CaO), corn (CO), soybean (SO), and walnut (WO) oils have similar color with cod liver oil (CLO), therefore, the presence of these oils was difficult to detect using naked eye. For this reason, Fourier transform infrared (FTIR) spectroscopy using horizontal attenuated total reflectance (HATR) as sampling accessory and in the combination with chemometrics was developed for detection and quantification of these vegetable oils as adulterants in CLO. The quantification of vegetable oils was carried out by using multivariate calibrations of partial least squares (PLS) and principle component regression (PCR), while the classification between pure CLO and CLOs adulterated with CaO, CO, SO, and WO was performed using discriminant analysis (DA). PLS with FTIR normal spectra was more suitable compared with PCR for quantification purposes with coefficient of determination (R²) higher than 0.99 and root mean square error of calibration (RMSEC) in the range of 0.04-0.82% (v/v). The PLS model was further used to predict the levels of these vegetable oils in independent samples for validation/prediction purpose. The root mean square error of prediction (RMSEP) values obtained were of 1.75% (v/v) (CaO), 1.39% (v/v) (CO), 1.35% (v/v) (SO), and 1.37% (v/v) (WO), respectively. The classification using DA revealed that the developed method can classify CLO and that mixed with these vegetable oils using 9 principal components.     

Fluorescence spectra measurement of olive oil and other vegetable oils

Fluorescence spectra of some common vegetable oils, including olive oil, olive residue oil, refined olive oil, corn oil, soybean oil, sunflower oil, and cotton oil, were examined in their natural state, with a wavelength of 360 nm used as excitation radiation. All oils studied, except extra virgin olive oil, exhibited a strong fluorescence band at 430-450 nm. Extra virgin olive oil gave a different by interesting fluorescence spectrum, composed of 3 bands: one low intensity doublet at 440 and 455 nm, one strong 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 at least partly derived from vitamin E. The low intensity doublet at 440 and 455 nm correlated with the absorption intensity at 232 and 270 nm of olive oil. The measurements of these fluorescence spectra were quick (about 5 min) and easy and could possibly be used for authentification of virgin olive oil.     

Quantification of the intensity of virgin olive oil sensory attributes by direct coupling headspace-mass spectrometry and multivariate calibration techniques

The main sensory defects of virgin olive oils (rancid, vinegary, winey, muddy sediment, musty and vegetable water) and one positive attribute (fruity) characteristic of three monovarietal extra virgin olive oils (Arbequina, Picual and Frantoio) have been quantified using the direct coupling headspace-mass spectrometry. The results obtained were compared with those provided by the panel test for the same samples. Taking into account that no chromatographic separation exists, multivariate calibration techniques (partial least squares, PLS, and principal components regression, PCR) were used to create the appropriate models. The best results, in terms of standard error of prediction and prediction residual error sum of squares were obtained by PLS and therefore it was used for the prediction of a new set of samples with the above-mentioned positive and negative attributes at different concentration levels. The samples were also assessed by the panel test and good correlations were obtained in all cases. In order to extend the applicability of the model with the time, a multiplicative calibration transfer was used. The benefit of this approach was found to be more marked for the negative than the positive attributes.     

Optimization and application of methods of triacylglycerol evaluation for characterization of olive oil adulteration by soybean oil with HPLC-APCI-MS-MS

Triacylglycerols (TAGs) are the main constituents of vegetable oils where they occur in complex mixtures with characteristic distributions. Mass spectrometry using an atmospheric pressure chemical ionization interface (APCI-MS) run in positive mode and an Ion Trap mass analyser were applied in the study of olive and soybean oils and their mixtures. Direct injections of soybean and olive oil solutions allowed the identification of ions derived from the main TAGs of both oils. This procedure showed to be a simple and powerful tool to evaluate mixtures or addition of soybean to olive oil. TAG separation was optimized by high performance liquid chromatography (HPLC) using an octadecylsilica LiChrospher column (250 mm × 3 mm; 5 μm) and a gradient composed of acetonitrile and 2-propanol allowed the separation of the main TAGs of the studied oils. APCI vaporization temperature was optimized and best signals were obtained at 370 °C. Multiple reaction monitoring (MRM) employing the transition of the protonated TAG molecules ([M+H]⁺) to the protonated diacylglycerol fragments ([M+H−R]⁺) improved the selectivity of TAG detection and was used in quantitative studies. Different strategies were developed to evaluate oil composition following TAG analysis by MRM. The external standard calibration and standard additions methods were compared for triolein quantification but the former showed to be biased. Further quantitative studies were based on the estimates of soybean and olive oil proportions in mixtures by comparison of TAG areas found in mixtures of known and unknown composition of both oils. Good agreement with expected or labeled values was found for a commercial blend containing 15% (w/w) of olive oil in soybean oil and to a 1:1 mixture of both oils, showing the potential of this method in characterizing oil mixtures and estimating oil proportions. Olive oils of different origins were also evaluated by mass spectra data obtained after direct injections of oil solutions and principal component analysis (PCA). Argentinean olive oils were clustered in a different area of the principal components plot (PC2 × PC1) in comparison with European olive oils. The commercial blend containing 15% (w/w) of olive oil in soybean oil appeared in a completely different area of the graphic, showing the potential of this method to screen out for olive oil adulterations.     

Determination of oil and water content in olive pomace using near infrared and Raman spectrometry. A comparative study

Near infrared (NIR) reflectance and Raman spectrometry were compared for determination of the oil and water content of olive pomace, a by-product in olive oil production. To enable comparison of the spectral techniques the same sample sets were used for calibration (1.74–3.93% oil, 48.3–67.0% water) and for validation (1.77–3.74% oil, 50.0–64.5% water). Several partial least squares (PLS) regression models were optimized by cross-validation with cancellation groups, including different spectral pretreatments for each technique. Best models were achieved with first-derivative spectra for both oil and water content. Prediction results for an independent validation set were similar for both techniques. The values of root mean square error of prediction (RMSEP) were 0.19 and 0.20–0.21 for oil content and 2.0 and 1.8 for water content, using Raman and NIR, respectively. The possibility of improving these results by combining the information of both techniques was also tested. The best models constructed using the appended spectra resulted in slightly better performance for oil content (RMSEP 0.17) but no improvement for water content.     

13C nuclear magnetic resonance spectroscopy to determine olive oil grades

¹³C nuclear magnetic resonance spectroscopy was used in a first attempt to differentiate olive oil samples by grades. High resolution ¹³C NMR Distortionless Enhancement by Polarization Transfer (DEPT) spectra of 137 olive oil samples from the four grades, extra virgin olive oils, olive oils, olive pomace oils and lampante olive oils, were measured. The data relative to the resonance intensities (variables) of the unsaturated carbons of oleate (C-9 and C-10) and linoleate (L-9, L-10 and L-12) chains attached at the 1,3- and 2-positions of triacylglycerols were analyzed by linear discriminant analysis. The 1,3- and 2- carbons of the glycerol moiety of triacylglycerols along with the C-2, C-16 and C-18 resonance intensities of saturated, oleate and linoleate chains were also analyzed by linear discriminant analysis. The three discriminanting functions, which were calculated by using a stepwise variable selection algorithm, classified in the true group by cross-validation procedure, respectively, 76.9, 70.0, 94.4 and 100% of the extra virgin, olive oil, olive pomace oil and lampante olive oil grades.     

Usefulness of the direct coupling headspace-mass spectrometry for sensory quality characterization of virgin olive oil samples

The applicability of the headspace coupled to mass spectrometry for evaluation of the sensory quality of virgin olive oil samples is presented. The volatiles of the oil are directly transferred from the sample vial to the detector without chromatographic separation. The mass spectrum obtained can be considered as a fingerprint of the oil sample and can be used for classification purposes. After a training step with samples previously qualified following the official method, a classification model was created using the supervised technique soft independent modeling of class analogy (SIMCA). Eight negative (rancid, winey-vinegary, muddy sediment, hay-wood, vegetable water, earthy, fusty and musty-humidity) and three principal positive attributes (fruity, bitter and pungent) have been included in this study. With them, a classification model consisting of two main classes (extra- and lampante-virgin olive oil) was constructed. In addition, the unsupervised technique cluster analysis permited the discrimination among oils with different negative attributes. The proposed procedure has been applied to the classification of commercial samples (as extra- or lampante-virgin olive oils) and the results were compared with those provided by the expert's panel with acceptable correlation.     

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties

The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were of four different categories (“extra-virgin olive oil”, “virgin olive oil”, “olive oil” and “olive-pomace” oil), and for the “extra-virgin” category, six different well-identified olive oil varieties (“hojiblanca”, “manzanilla”, “picual”, “cornicabra”, “arbequina” and “frantoio”) and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric (to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering, it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded in classifying the samples. Correct classification rates were assessed by cross-validation.     

Synchronous fluorescence spectroscopy for quantitative determination of virgin olive oil adulteration with sunflower oil

Adulteration of extra virgin olive oil with sunflower oil is a major issue for the olive oil industry. In this paper, the potential of total synchronous fluorescence (TSyF) spectra to differentiate virgin olive oil from sunflower oil and synchronous fluorescence (SyF) spectra combined with multivariate analysis to assess the adulteration of virgin olive oil are demonstrated. TSyF spectra were acquired by varying the excitation wavelength in the region 270–720 nm and the wavelength interval (Δλ) in the region from 20 to 120 nm. TSyF contour plots for sunflower, in contrast to virgin olive oil, show a fluorescence region in the excitation wavelength range 325–385 nm. Fifteen different virgin olive oil samples were adulterated with sunflower oil at varying levels (0.5–95%) resulting in one hundred and thirty six mixtures. The partial least-squares regression model was used for quantification of the adulteration using wavelength intervals of 20 and 80 nm. This technique is useful for detection of sunflower oil in virgin olive oil at levels down to 3.4% (w/v) in just two and a half minutes using an 80-nm wavelength interval.     

Absorption and photoluminescence of Buriti oil/polystyrene and Buriti oil/poly(methyl methacrylate) blends

This work reports the preparation and characterization of Buriti (Mauritia flexuosa L.) oil/polystyrene (PS) and Buriti oil/poly(methyl methacrylate) blends. The Buriti is an abundant palm tree of the Amazon region. This oil was used because of its chemical composition (high concentrations of oleic acid, tocopherols and carotenoids, especially β-carotene) and interesting optical properties, such as absorption and photoluminescence. The incorporation of the vegetable oil in the PS and PMMA matrices renders orange-colored blends, which were verified to absorb UV-Vis radiation and emit light in the green region. The intensity of these properties is proportional to the oil content in the samples. Micrographs of the blends showed that the oil is located in cavities distributed in the polymeric matrices. This work shows that it is possible to employ the Buriti oil, a cheaper and abundant natural resource, to improve absorption and light emission properties of PS and PMMA polymers.