Characterization of olive oil classes using a Chemsensor and pattern recognition techniques

Comprehensive evaluation of direct injection mass spectrometry for the quantitative profiling of volatiles in food samples

Although qualitative strategies based on direct injection mass spectrometry (DIMS) have recently emerged as an alternative for the rapid classification of food samples, the potential of these approaches in quantitative tasks has scarcely been addressed to date. In this paper, the applicability of different multivariate regression procedures to data collected by DIMS from simulated mixtures has been evaluated. The most relevant factors affecting quantitation, such as random noise, the number of calibration samples, type of validation, mixture complexity and similarity of mass spectra, were also considered and comprehensively discussed. Based on the conclusions drawn from simulated data, and as an example of application, experimental mass spectral fingerprints collected by direct thermal desorption coupled to mass spectrometry were used for the quantitation of major volatiles in Thymus zygis subsp. zygis chemotypes. The results obtained, validated with the direct thermal desorption coupled to gas chromatography-mass spectrometry method here used as a reference, show the potential of DIMS approaches for the fast and precise quantitative profiling of volatiles in foods.This article is part of the themed issue 'Quantitative mass spectrometry'.

Direct analysis of volatile methylsiloxanes in gaseous matrixes using atmospheric pressure chemical ionization-tandem mass spectrometry

Atmospheric pressure chemical ionization-tandem mass spectrometry (APCI-MS/MS) was applied for the first time to the direct analysis of octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) in gaseous matrixes without extraction or prior chromatographic separation. Mass spectrometric characteristics of both compounds under APCI conditions and their fragmentation behavior in MS/MS were investigated. Unlike the classical gas chromatography/mass spectrometry (GC/MS), which involves solvent extraction before gas chromatography, the proposed approach prevents contamination from GC system components and provides unambiguous structural assignments. The method performs well achieving good linearity (R(2) > 0.997), low limits of detection (4-6 microg/m(3)), good precision (RSD < 10%) and accuracy (>93%), and a wide dynamic range. Its applicability to real-world samples was evaluated through measurements of D4 and D5 concentrations in air and biogas samples. The high sensitivity, selectivity, and reliability of this method render our approach a good alternative to the commonly used GC/MS method.

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.

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 permitted 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.

Data reduction in headspace analysis of blood and urine samples for robust bacterial identification

This paper demonstrates the application of chemical headspace analysis to the problem of classifying the presence of bacteria in biomedical samples by using computational tools. Blood and urine samples of disparate forms were analysed using a Cyrano Sciences C320 electronic nose together with an Agilent 4440 Chemosensor. The high dimensional data sets resulting from these devices present computational problems for parameter estimation of discriminant models. A variety of data reduction and pattern recognition techniques were employed in an attempt to optimise the classification process. A 100% successful classification rate for the blood data from the Agilent 4440 was achieved by combining a Sammon mapping with a radial basis function neural network. In comparison a successful classification rate of 80% was achieved for the urine data from the C320 which were analysed using a novel nonlinear time series model.

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.

Discrimination of olives according to fruit quality using Fourier transform Raman spectroscopy and pattern recognition techniques

Fourier transform Raman spectroscopy combined with pattern recognition has been used to discriminate olives of different qualities. They included samples of sound olives, olives with frostbite, olives that have been collected from the ground, fermented olives, and olive samples with diseases. Milled olives were measured in a dedicated sample cup, which was rotated during spectrum acquisition. A preliminary study of the data set structure was performed using hierarchical cluster analysis and principal component analysis. Two supervised pattern recognition techniques, K-nearest neighbors and soft independent modeling of class analogy (SIMCA), were tested using a "leave-a-fourth-out" cross-validation procedure. SIMCA provided the best results, with prediction abilities of 95% for sound, 93% for frostbite, 96% for ground, and 92% for fermented olives. The olive samples with diseases (too few to define a class) were included in the validation and recognized as not belonging to any class. None of the damaged olive samples was wrongly predicted to the class of sound olives. With this approach a selection of sound olives for the production of high-quality virgin olive oil can be achieved.

Combining headspace gas chromatography with mass spectrometry detection for confirmation of hydrocarbon residues in virgin olive oil following automatic screening

Hydrocarbon residues can be present in virgin olive oils either naturally or as contaminants. Presence of benzene, toluene, ethylbenzene, xylene isomers and styrene (BTEXS) in virgin olive oil, demanded the establishment of a cut-off level to discriminate oil samples containing these residues at normal levels from those at high levels caused by contamination. By introducing volatile components present in the headspace (HS) of the oil samples (without prior chromatographic separation) into the ionization source of a mass spectrometer, samples were classified as containing normal or high levels of BTEXS (recommendable or non-recommendable forhuman consumption). Confirmation and quantification of contaminated virgin olive oils were achieved by combining HS sampling with gas chromatography-mass spectrometry (GC-MS). No sample pretreatment was necessary for the chromatographic method, but the addition of 600 microl of ethyl acetate as chemical modifier for 10 ml of virgin olive oil. Detection limits ranged between 3 and 9 ng/ml. Oil analysis showed the presence of toluene in all samples, as well as the absence of benzene, ethylbenzene and o-xylene.

Direct screening and confirmation of benzene, toluene, ethylbenzene and xylenes in water

A novel, straightforward sample screening method for benzene, toluene, ethylbenzene and xylene isomers (BTEX) in water is proposed. The method is based on the direct coupling of a headspace (HS) sampler with a mass spectrometer by using a chromatographic column heated to 200 degrees C as an interface. Samples are acidified and subjected to the headspace generation process, the resulting volatile fraction being directly introduced into the source of the mass spectrometer. The large number of samples to be analyzed and the wide range of m/z ratios scanned (75-110) suggest the use of chemometric approaches based on pattern recognition techniques (PRT). For sample classification purposes, the detection limit of the method (overall response 4.0 ng/ml BTEX) was selected as the cut-off level. The method proved highly reliable as no false negatives were obtained at the legally established concentration levels. Positive water samples were confirmed by using the same instrumental setup as in the screening method, but by heating the chromatographic column at 40-200 degrees C to separate the analytes.