Bernardo Moreno Cordero

Detection of adulterants in olive oil by headspace-mass spectrometry.

In the present work, we propose the use of direct coupling of a headspace sampler to a mass spectrometer for the detection of adulterants in olive oil. Samples of olive oils were mixed with different proportions of sunflower oil and olive-pomace oil, respectively, and patterns of the volatile compounds in the original and mixed samples were generated. Application of the linear discriminant analysis technique to the data from the signals was sufficient to differentiate the adulterated from the non-adulterated oils and to discriminate the type of adulteration. The results obtained revealed 100% success in classification and close to 100% in prediction. The main advantages of the proposed methodology are the speed of analysis (since no prior sample preparation steps are required), low cost, and the simplicity of the measuring process.

Cloud point preconcentration and flame atomic absorption spectrometry : application to the determination of cadmium

Cloud point methodology has been successfully used for the preconcentration of trace amounts of cadmium as a prior step to its determination by flame atomic absorption spectrometry. A procedure based on the formation of a complex with 1-(2-pyridylazo)-2-naphthol (PAN) is used for the preconcentration of cadmium in a surfactant-rich phase of Triton X-114. The chemical variables affecting the preconcentration step and the viscosity of the solution affecting the detection process have been optimized. Under the optimum conditions, a precision of 3.0% was achieved. The preconcentration of only 15 ml of sample with 0.05% Triton X-114 permits the detection of <0.4 ppb of cadmium with a concentration factor of 120.

Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils

Abstract An “electronic nose” has been used for the detection of adulterations of virgin olive oil. The system, comprising 12 metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, feature selection techniques were employed to choose a set of optimally discriminant variables. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and artificial neural networks (ANN) were applied. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils and it was even possible to identify the type of oil used in the adulteration. Promising results were also obtained as regards quantification of the percentages of adulteration.

Micelle-mediated methodology for the preconcentration of uranium prior to its determination by flow injection

Cloud point extraction has been used for the preconcentration of uranium, prior to its determination by flow injection. The non-ionic surfactant employed was Triton X-114 and the reagent chosen to form a hydrophobic chelate of uranium was 1-(2-pyridylazo)-2-naphthol. The optimum conditions for the preconcentration and determination of uranium have been studied. This methodology has been applied to the determination of trace amounts of uranium in tap and river waters from Salamanca.

Cloud Point Methodology : A New Approach for Preconcentration and Separation in Hydrodynamic Systems of Analysis

The analytical potential shown by the cloud point phenomenon for the separation and preconcentration of different analytes as an alternative method to other separation techniques is studied. We offer and discuss several examples that can be applied in flow injection analysis and high performance liquid chromatography with both optical (UV and fluorescence) and electrochemical detection.

Classification of vegetable oils by linear discriminant analysis of Electronic Nose data

The purpose of this work was to attempt to classify edible vegetable oils by chemometric treatment of the data obtained from an array of gas sensors. A commercial Electronic Nose (FOX 2000) comprising six metal oxide semiconductor sensors was used to generate a pattern of the volatile compounds present in the samples. Linear discriminant analysis (LDA) was applied to the patterns generated to achieve several classification tasks. The procedure for obtaining the signals and the chemometric treatment are rapid and simple, and provide classification and prediction capabilities higher than 95%.

Simplified QuEChERS approach for the extraction of chlorinated compounds from soil samples.

A simplified version of the QuEChERS method for the extraction of chlorinated pollutant compounds from soil samples is proposed. The procedure involves simple liquid extraction of the soil sample with ethyl acetate, followed by the addition of anhydrous MgSO(4). Gas chromatography/electron capture detection (ECD) is then used to analyse the extracts without any other sample pretreatment. This new QuEChERS version includes, therefore, fewer treatment stages of the sample, which makes the final procedure simpler, faster, and cheaper and minimizes the creation of errors associated with this step. Three chlorinated compounds (chloroform, 1,2-dichlorobenzene, and hexachlorobenzene) of different volatility and polarity have been selected as target compounds and two different solvents (acetonitrile and ethyl acetate) have been evaluated in order to prove the suitability of the proposed approach for the extraction of these compounds from different soil samples. The suitability of the acetonitrile and ethyl acetate for PTV-GC analysis has also been evaluated. Recoveries between 62 and 93% and reproducibilities between 3.5 and 7.6% have been achieved.

Electronic nose based on metal oxide semiconductor sensors and pattern recognition techniques: characterisation of vegetable oils

Different supervised pattern recognition treatments were applied to the signals generated by an electronic nose for the classification of vegetable oils. The system, comprising six metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Feature selection techniques were employed to choose a set of optimally discriminant variables. The K-nearest neighbours (KNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), soft independent modelling of class analogy (SIMCA) and artificial neural networks (ANN) were applied to model the different classes. The results obtained indicated good classification and prediction capabilities, the neural networks being those that afforded the best results.

Analytical applications of membrane extraction in chromatography and electrophoresis.

An overview of the analytical applications of membrane-based systems for sample enrichment in chromatography and capillary electrophoresis is presented. A brief introduction to the different types of membranes and the main forces related to the transport through them is also given.

Determination of trihalomethanes in water samples: A review

This article reviews the most recent literature addressing the analytical methods applied for trihalomethanes (THMs) determination in water samples. This analysis is usually performed with gas chromatography (GC) combined with a preconcentration step. The detectors most widely used in this type of analyses are mass spectrometers (MS) and electron capture detectors (ECD). Here, we review the analytical characteristics, the time required for analysis, and the simplicity of the optimised methods. The main difference between these methods lies in the sample pretreatment step; therefore, special emphasis is placed on this aspect. The techniques covered are direct aqueous injection (DAI), liquid-liquid extraction (LLE), headspace (HS), and membrane-based techniques. We also review the main chromatographic columns employed and consider novel aspects of chromatographic analysis, such as the use of fast gas chromatography (FGC). Concerning the detection step, besides the common techniques, the use of uncommon detectors such as fluorescence detector, pulsed discharge photoionization detector (PDPID), dry electrolytic conductivity detector (DELCD), atomic emission detector (AED) and inductively coupled plasma-mass spectrometry (ICP-MS) for this type of analysis is described.