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      Determination of Ignitable Liquids in Fire Debris: Direct Analysis by Electronic Nose

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          Abstract

          Arsonists usually use an accelerant in order to start or accelerate a fire. The most widely used analytical method to determine the presence of such accelerants consists of a pre-concentration step of the ignitable liquid residues followed by chromatographic analysis. A rapid analytical method based on headspace-mass spectrometry electronic nose (E-Nose) has been developed for the analysis of Ignitable Liquid Residues (ILRs). The working conditions for the E-Nose analytical procedure were optimized by studying different fire debris samples. The optimized experimental variables were related to headspace generation, specifically, incubation temperature and incubation time. The optimal conditions were 115 °C and 10 min for these two parameters. Chemometric tools such as hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) were applied to the MS data (45–200 m/ z) to establish the most suitable spectroscopic signals for the discrimination of several ignitable liquids. The optimized method was applied to a set of fire debris samples. In order to simulate post-burn samples several ignitable liquids (gasoline, diesel, citronella, kerosene, paraffin) were used to ignite different substrates (wood, cotton, cork, paper and paperboard). A full discrimination was obtained on using discriminant analysis. This method reported here can be considered as a green technique for fire debris analyses.

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          Applications of electronic noses and tongues in food analysis

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            Electronic Nose for Microbiological Quality Control of Food Products

            Electronic noses (ENs) have recently emerged as valuable candidates in various areas of food quality control and traceability, including microbial contamination diagnosis. In this paper, the EN technology for microbiological screening of food products is reviewed. Four paradigmatic and diverse case studies are presented: (a) Alicyclobacillus spp. spoilage of fruit juices, (b) early detection of microbial contamination in processed tomatoes, (c) screening of fungal and fumonisin contamination of maize grains, and (d) fungal contamination on green coffee beans. Despite many successful results, the high intrinsic variability of food samples together with persisting limits of the sensor technology still impairs ENs trustful applications at the industrial scale. Both advantages and drawbacks of sensor technology in food quality control are discussed. Finally, recent trends and future directions are illustrated.
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              Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose.

              Analysis of 34 Sauvignon Blanc wine samples from three different countries and six regions was performed by gas chromatography-mass spectrometry (GC-MS). Linear discriminant analysis (LDA) showed that there were three distinct clusters or classes of wines with different aroma profiles. Wines from the Loire region in France and Australian wines from Tasmania and Western Australia were found to have similar aroma patterns. New Zealand wines from the Marlborough region as well as the Australian ones from Victoria were grouped together based on the volatile composition. Wines from South Australia region formed one discrete class. Seven analytes, most of them esters, were found to be the relevant chemical compounds that characterized the classes. The grouping information obtained by GC-MS, was used to train metal oxide based electronic (MOS-Enose) and mass spectrometry based electronic (MS-Enose) noses. The combined use of solid phase microextraction (SPME) and ethanol removal prior to MOS-Enose analysis, allowed an average error of prediction of the regional origins of Sauvignon Blanc wines of 6.5% compared to 24% when static headspace (SHS) was employed. For MS-Enose, the misclassification rate was higher probably due to the requirement to delimit the m/z range considered.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                13 May 2016
                May 2016
                : 16
                : 5
                : 695
                Affiliations
                [1 ]Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, Campus Universitario, 11510 Puerto Real, Spain; marta.ferreiro@ 123456uca.es (M.F.-G.); gerardo.fernandez@ 123456uca.es (G.F.B.); carmelo.garcia@ 123456uca.es (C.G.B.)
                [2 ]Department of Physical Chemistry, Faculty of Sciences, University of Cadiz, P.O. Box 40, 11510 Puerto Real, Cádiz, Spain; jesus.ayuso@ 123456uca.es (J.A.); joseangel.alvarez@ 123456uca.es (J.A.Á.)
                Author notes
                [* ]Correspondence: miguel.palma@ 123456uca.es ; Tel.: +34-956-016360; Fax: +34-956-016460
                Article
                sensors-16-00695
                10.3390/s16050695
                4883386
                27187407
                151e71c0-2c81-40a8-86d3-74b6d8c638ef
                © 2016 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/).

                History
                : 08 March 2016
                : 06 May 2016
                Categories
                Article

                Biomedical engineering
                fire accelerants,discrimination,optimization,e-nose
                Biomedical engineering
                fire accelerants, discrimination, optimization, e-nose

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