Simple and rapid instrumental characterization of sensory attributes of virgin olive oil based on the direct coupling headspace-mass spectrometry

A methodology for obtaining reliable qualitative and quantitative information about negative (fusty, muddy sediment, musty, rancid and vinegary) and positive (fruity) sensory attributes of virgin olive oils (lampante and extra) has been developed. The procedure implies the joint use of a headspace autosampler, a mass spectrometer and an adequate chemometric data treatment. For this purpose, soft independent modelling of class analogy (SIMCA) and partial least squares (PLS) regression approaches were used for attribute identification and quantification, respectively. InStep application was employed to generate a decision tree by the combination of both models in order to provide the joint prediction of the sensory attributes of a given virgin olive oil and their respective intensities by means of a single output result. The good prediction results obtained when the decision tree generated were applied to a new set of virgin olive oil samples (viz, a specificity of 100%, an average sensitivity of 86% and a RMSEP<0.8% in the quantification task) reveals its potential applicability in routine analysis.     

Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness

An electronic panel has been used to characterise the organoleptic characteristics of twenty-five extra virgin olive oils from varieties Hojiblanca, Picual and Arbequina, with different degree of bitterness. The method consists in the combination of three systems: electronic nose, electronic tongue and electronic eye. The Principal Component Analysis (PCA), where PC1, PC2 and PC3 explained 59% of the total variance between the samples, has demonstrated that the capability of discrimination of the combined system is superior to that obtained with the three instruments separately. This improvement is due to the increased information extracted from each sample. Partial Least Squares-Discriminant Analysis (PLS-DA) has allowed separation of the groups in function of olive variety with a root mean square error of prediction (RMSEP) lower than 0.099.Using PLS1 and PLS2 regression models, good correlations have been found between the signals obtained from the electronic tongue and the polyphenolic content (measured by chromatographic methods) or the bitterness index (scored by a panel of experts) with correlation coefficients higher than 0.9 in calibration and validation.These preliminary results indicate that the combination of an e-nose, an e-tongue and an e-eye can be a useful tool for the analysis of olive oil bitterness.     

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.     

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.     

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. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Direct, reagent-free determination of free fatty acid content in olive oil and olives by Fourier transform Raman spectrometry

Fourier transform (FT) Raman spectrometry in combination with partial least squares (PLS) regression was used for direct, reagent-free determination of free fatty acid (FFA) content in olive oils and olives. Oils were directly investigated in a simple flow cell. Milled olives were measured in a dedicated sample cup, which was rotated eccentrically to the horizontal laser beam during spectrum acquisition in order to compensate sample heterogeneity. Both external and internal (leave-one-out) validation were used to assess the predictive ability of the PLS calibration models for FFA content (in terms of oleic acid) in oil and olives in the range 0.20-6.14 and 0.15-3.79%, respectively. The root mean square error of prediction (RMSEP) was 0.29% for oil and 0.28% for olives. The predicted FFA contents were used to classify oils and olives in different categories according to the European Union regulations. Ninety percent of the oil samples and 80% of the olives were correctly classified. These results demonstrate that the proposed procedures can be used for screening of good quality olives before processing, as well as, for the on-line control of the produced oil.     

Standardization of SPE signals in multicomponent analysis of three benzimidazolic pesticides by spectrofluorimetry

Piecewise direct standardization (PDS) is applied to multivariate standardization of fluorescence signals using partial least squares (PLS) and principal component regression (PCR) as the calibration models. The multivariate standardization was used to transfer spectra obtained after a step of solid phase extraction (SPE) to spectra registered in pure solvent in the determination of carbendazim, fuberidazole and thiabendazole in water samples. The influential parameters, such as tolerance, window size and the number of samples of the standardization subset were optimized by means of the root mean squared error of prediction (RMSEP). Similar RMSEP values were obtained by PLS and PCR using the optimized influential parameters in the standardization. However, better predictions of the compounds were obtained in test set by the PLS model.     

Geographic origins and compositions of virgin olive oils determinated by chemometric analysis of NIR spectra

The authentication of virgin olive oil samples requires usually the use of sophisticated and time consuming analytical techniques. There is a need for fast and simple analytical techniques for the objective of a quality control methodology. Virgin olive oils present characteristic NIR spectra. Chemometric treatment of NIR spectra was assessed for the quantification of fatty acids and triacylglycerols in virgin olive oil samples (n=125) and for their classification (PLS1-DA) into five very geographically closed registered designations of origin (RDOs) of French virgin olive oils ("Aix-en-Provence", "Haute-Provence", "Nice", "Nyons" and "Vallée des Baux"). The spectroscopic interpretation of regression vectors showed that each RDO was correlated to one or two specific components of virgin olive oils according to their cultivar compositions. The results were quite satisfactory, in spite of the similarity of cultivar compositions between two denominations of origin ("Aix-en-Provence" and "Vallée des Baux"). Chemometric treatments of NIR spectra allow us to obtain similar results than those obtained by time consuming analytical techniques such as GC and HPLC, and constitute a help fast and robust for authentication of those French virgin olive oils.     

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).     

Multi-capillary column-ion mobility spectrometry: a potential screening system to differentiate virgin olive oils

The potential of a headspace device coupled to multi-capillary column-ion mobility spectrometry has been studied as a screening system to differentiate virgin olive oils (“lampante,” “virgin,” and “extra virgin” olive oil). The last two types are virgin olive oil samples of very similar characteristics, which were very difficult to distinguish with the existing analytical method. The procedure involves the direct introduction of the virgin olive oil sample into a vial, headspace generation, and automatic injection of the volatiles into a gas chromatograph-ion mobility spectrometer. The data obtained after the analysis by duplicate of 98 samples of three different categories of virgin olive oils, were preprocessed and submitted to a detailed chemometric treatment to classify the virgin olive oil samples according to their sensory quality. The same virgin olive oil samples were also analyzed by an expert’s panel to establish their category and use these data as reference values to check the potential of this new screening system. This comparison confirms the potential of the results presented here. The model was able to classify 97% of virgin olive oil samples in their corresponding group. Finally, the chemometric method was validated obtaining a percentage of prediction of 87%. These results provide promising perspectives for the use of ion mobility spectrometry to differentiate virgin olive oil samples according to their quality instead of using the classical analytical procedure.     

Sequential (step-by-step) detection,identification and quantitation of extra virgin olive oil adulteration by chemometric treatment of chromatographic profiles

An analytical method for the sequential detection, identification and quantitation of extra virgin olive oil adulteration with four edible vegetable oils--sunflower, corn, peanut and coconut oils--is proposed. The only data required for this method are the results obtained from an analysis of the lipid fraction by gas chromatography-mass spectrometry. A total number of 566 samples (pure oils and samples of adulterated olive oil) were used to develop the chemometric models, which were designed to accomplish, step-by-step, the three aims of the method: to detect whether an olive oil sample is adulterated, to identify the type of adulterant used in the fraud, and to determine how much aldulterant is in the sample. Qualitative analysis was carried out via two chemometric approaches--soft independent modelling of class analogy (SIMCA) and K nearest neighbours (KNN)--both approaches exhibited prediction abilities that were always higher than 91% for adulterant detection and 88% for type of adulterant identification. Quantitative analysis was based on partial least squares regression (PLSR), which yielded R2 values of >0.90 for calibration and validation sets and thus made it possible to determine adulteration with excellent precision according to the Shenk criteria.     

Determination of soil properties using Fourier transform mid-infrared photoacoustic spectroscopy

Photoacoustic spectroscopy (PAS) is based on the absorption of electromagnetic radiation by analyte molecules, and this technique has emerged as a valuable tool for the study of materials like biological, chemical and geological samples. In this paper, Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) was used in the prediction of soil properties. Air-dried soil samples (n = 56) from Fengqiu Ecology Experimental Station Chinese Academy of Sciences were involved in this experiment, and FTIR-PAS spectra of these soil samples were recorded. These FTIR-PAS spectra indicated abundant soil information, but overlapping of absorption made it difficult to make direct measurement of soil properties. Partial least squares (PLS) models based on soil FTIR-PAS spectra was developed to predict available nitrogen (N), phosphorus (P), potassium (K) and organic matter content of soil. 42 soil samples were firstly used in leave-one-out cross-validation, and calibration error, calibration coefficient, validation error and ratio of standard deviation to prediction error (RPD) were obtained to optimize the PLS factor number; then based on the optimized PLS models the soil properties of the other 14 soil samples were predicted. The calibration statistics showed that the PLS model was suitable to use in the prediction of available N, P, K and organic matter content of soil. This prediction technique was non-destructive, and no sample pre-treatment was needed, which made FTIR-PAS a very promising method for fast evaluation of soil properties as well as soil quality.     

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.     

傅里叶变换红外光声光谱法测定土壤中有效磷

以中国科学院封丘生态实验站长期定位实验区的土样为材料(68样),利用傅里叶转换红外光声光谱测定土壤有效磷:以Olsen-P为因变量,通过傅里转换红外光声光谱构建偏最小二乘法和人工神经网络模型,利用模型进行预测。结果表明,偏最小二乘法模型的相关系数(R2)为0.96,校正标准偏差为1.79mg/kg,验证标准偏差为5.25mg/kg;人工神经网络模型的校正系数为0.84,校正标准偏差为2.40mg/kg,验证标准偏差为5.43mg/kg。两种模型均可以用于土壤有效磷的预测,且偏最小二乘模型优于人工神经网络模型。该方法的特点是无需样品前处理,且测定对样品无破坏,为土壤有效磷的快速测定提供新的手段。     

Determination of edible oil parameters by near infrared spectrometry

A chemometric method has been developed for the determination of acidity and peroxide index in edible oils of different types and origins by using near infrared spectroscopy (NIR) measurements. Different methods for selecting the calibration set, after an hierarchical cluster analysis, were applied. After discrimination of olive oils from maize, seed and sunflower, the prediction capabilities of partial least squares (PLS) multivariate calibration of NIR data were evaluated. Several preprocessing alternatives (first derivative, multiplicative scatter correction, vector normalization, constant offset elimination, mean centering and standard normal variate) were investigated by using the root mean square error of validation (RMSEV) and prediction (RMSEP), as control parameters. Under the best conditions studied, the validation set provides RMSEP values of 0.034 and 0.037% (w/w) for acidity in (I) olive oil group and (II) sunflower, seed and maize oils group. RMSEP values for peroxide in both sample groups, expressed as mequiv. O₂ kg⁻¹, were, respectively 1.87 and 0.79. The limit of detection of the methodology developed was 0.03% for acidity in both groups of edible oils (I and II), and 0.9 and 0.8 mequiv. O₂ kg⁻¹ for peroxide in the olive oil and other edible oils groups, respectively. In fact, the methodology developed is proposed for direct acidity quantification and for the screening of peroxide index in edible oils, requiring less than 30 s per sample without any previous treatment.     

偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究

利用偏最小二乘将瘦肉的近红外光谱数据分别与其棕榈酸、棕榈油酸、硬脂酸、油酸、亚油酸含量建立校正模型,并用交互校验和外部检验来考查模型的可靠性.各脂肪酸模型的校正相关系数分别为0.9998、0.9844、0.9963、0.9754、0.9969,均方估计残差(RMSEC)分别为0.0231、0.0485、0.111、0.373、0.311,交互校验均方残差(RMSECV)分别为0.509、0.115、0.225、0.848、0.649.应用所建立的各脂肪酸近红外模型对瘦肉脂肪酸组成进行预测,并对各脂肪酸的预测值与气相色谱法测定值进行配对t-检验,结果表明两者差异均不显著(p>0.05).     

拉曼光谱结合背景扣除化学计量学方法用于汽油中MTBE含量的快速测定研究

应用便携式拉曼光谱仪测量了汽油样本的拉曼光谱,以自适应迭代惩罚最小二乘方法(airPLS)对光谱进行了背景扣除和平滑处理,并选取特征峰区间利用偏最小二乘方法(PLS)建立了预测甲基叔丁基醚(MT-BE)的校正模型。以训练集相关系数和拟合误差及测试集相关系数和预测误差作为判定依据,确定了最佳建模条件。最终训练集相关系数为0.996 0,拟合误差为0.316 1,测试集相关系数为0.996 6,预测误差为0.490 1。结果表明采用便携式拉曼光谱结合化学计量学方法处理,可以满足对汽油中MTBE含量快速检测的要求。     

Kinetic spectrophotometric determination of trace amounts of palladium by whole kinetic curve and a fixed time method using resazurine sulfide reaction

The univariate and multivariate calibration methods were applied for the determination of trace amounts of palladium based on the catalytic effect on the reaction between resazurine and sulfide. The decrease in absorbance of resazurine at 602 nm over a fixed time is proportional to the concentration of palladium over the range of 10.0-160.0 ng mL(-1). The calibration matrix for partial least squares (PLS) regression was designed with 14 samples. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration without loss of prediction ability using spectrophotometric method. The root mean square error of prediction (RMSEP) for palladium determination with fixed-time, PLS and OSC-PLS were 3.71, 2.84 and 0.68, respectively. This procedure allows the determination of palladium in synthetic and real samples with good reliability of the determination.     

Preliminary study on application of mid infrared spectroscopy for the evaluation of the virgin olive oil "freshness"

The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique.Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA).The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils.