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      Comparison of the Novel Thin Film-Solid Phase Microextraction and Sorptive Extraction Methods for Picual and Hojiblanca Olive Oil Volatile Fraction Analysis in Headspace

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          Abstract

          For first time, the new device named thin film solid phase microextraction (TF-SPME) has been used to determine the volatile profile of the Picual and Hojiblanca varieties of extra virgin olive oils. To this end, different traditional sampling methods such as headspace sorptive extraction (HSSE) with polydimethylsiloxane (PDMS) and polyethyleneglycol-modified silicone (EG/Silicone) Twisters ® have been compared with the TF-SPME devices coated with different extraction polymeric phases. PARADISe software was used as a non-targeting method to process all data. The best results were obtained by HSSE-PDMS and 2TF-SPME. Moreover, the 2TF-SPME extraction method achieved the most adequate results of linearity for most compounds, according to F-values, while the intermediate precision results were similar for both 2TF-SPME and HSSE-PDMS sampling methods. Different sensitivity was observed between both sampling methods depending on the volatile compound, without being clearly influenced by the polarity of them. Although both sampling methods enabled the main active aroma of olive oil to be determined and for them to be differentiated according to olive variety, the 2TF-SPME method appears to be the most suitable for this goal.

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          Most cited references 28

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          Stir bar sorptive extraction for trace analysis.

          Stir bar sorptive extraction (SBSE) was introduced in 1999 as a solventless sample preparation method for the extraction and enrichment of organic compounds from aqueous matrices. The method is based on sorptive extraction, whereby the solutes are extracted into a polymer coating on a magnetic stirring rod. The extraction is controlled by the partitioning coefficient of the solutes between the polymer coating and the sample matrix and by the phase ratio between the polymer coating and the sample volume. For a polydimethylsiloxane coating and aqueous samples, this partitioning coefficient resembles the octanol-water partitioning coefficient. In comparison to solid phase micro-extraction, a larger amount of sorptive extraction phase is used and consequently extremely high sensitivities can be obtained as illustrated by several successful applications in trace analysis in environmental, food and biomedical fields. Initially SBSE was mostly used for the extraction of compounds from aqueous matrices. The technique has also been applied in headspace mode for liquid and solid samples and in passive air sampling mode. In this review article, the principles of stir bar sorptive extraction are described and an overview of SBSE applications is given.
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            Characterisation of 39 varietal virgin olive oils by their volatile compositions

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              Gas chromatography - mass spectrometry data processing made easy.

              Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples.
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                Author and article information

                Journal
                Foods
                Foods
                foods
                Foods
                MDPI
                2304-8158
                05 June 2020
                June 2020
                : 9
                : 6
                Affiliations
                Área de Nutrición y Bromatología, Dpto. de Nutrición y Bromatología, Toxicología y Medicina Legal, Facultad de Farmacia, Universidad de Sevilla, C/P, García González n°2, E-41012 Sevilla, Spain; msegura2@ 123456us.es (M.P.S.-B.); rrios5@ 123456us.es (R.R.-R.); c_ubeda@ 123456us.es (C.U.); mlmorales@ 123456us.es (M.L.M.)
                Author notes
                [* ]Correspondence: rcallejon@ 123456us.es
                Article
                foods-09-00748
                10.3390/foods9060748
                7353552
                32517060
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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