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      Development of quantitative screen for 1550 chemicals with GC-MS

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          Abstract

          With hundreds of thousands of chemicals in the environment, effective monitoring requires high-throughput analytical techniques. This paper presents a quantitative screening method for 1550 chemicals based on statistical modeling of responses with identification and integration performed using deconvolution reporting software. The method was evaluated with representative environmental samples. We tested biological extracts, low-density polyethylene, and silicone passive sampling devices spiked with known concentrations of 196 representative chemicals. A multiple linear regression ( R 2 = 0.80) was developed with molecular weight, logP, polar surface area, and fractional ion abundance to predict chemical responses within a factor of 2.5. Linearity beyond the calibration had R 2 > 0.97 for three orders of magnitude. Median limits of quantitation were estimated to be 201 pg/μL (1.9× standard deviation). The number of detected chemicals and the accuracy of quantitation were similar for environmental samples and standard solutions. To our knowledge, this is the most precise method for the largest number of semi-volatile organic chemicals lacking authentic standards. Accessible instrumentation and software make this method cost effective in quantifying a large, customizable list of chemicals. When paired with silicone wristband passive samplers, this quantitative screen will be very useful for epidemiology where binning of concentrations is common.

          Graphical abstract

          A multiple linear regression of chemical responses measured with GC-MS allowed quantitation of 1550 chemicals in samples such as silicone wristbands.

          Electronic supplementary material

          The online version of this article (10.1007/s00216-018-0997-7) contains supplementary material, which is available to authorized users.

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          Most cited references24

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          Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

          The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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            Silicone Wristbands as Personal Passive Samplers

            Active-sampling approaches are commonly used for personal monitoring, but are limited by energy usage and data that may not represent an individual’s exposure or bioavailable concentrations. Current passive techniques often involve extensive preparation, or are developed for only a small number of targeted compounds. In this work, we present a novel application for measuring bioavailable exposure with silicone wristbands as personal passive samplers. Laboratory methodology affecting precleaning, infusion, and extraction were developed from commercially available silicone, and chromatographic background interference was reduced after solvent cleanup with good extraction efficiency (>96%). After finalizing laboratory methods, 49 compounds were sequestered during an ambient deployment which encompassed a diverse set of compounds including polycyclic aromatic hydrocarbons (PAHs), consumer products, personal care products, pesticides, phthalates, and other industrial compounds ranging in log K ow from −0.07 (caffeine) to 9.49 (tris(2-ethylhexyl) phosphate). In two hot asphalt occupational settings, silicone personal samplers sequestered 25 PAHs during 8- and 40-h exposures, as well as 2 oxygenated-PAHs (benzofluorenone and fluorenone) suggesting temporal sensitivity over a single work day or week (p < 0.05, power =0.85). Additionally, the amount of PAH sequestered differed between worksites (p < 0.05, power = 0.99), suggesting spatial sensitivity using this novel application.
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              Effect-directed analysis: a promising tool for the identification of organic toxicants in complex mixtures?

              Wastewater effluents, groundwater, surface water, sediments, soils and air particulate matter are often contaminated by a multitude of chemicals. Since often no a priori knowledge of relevant toxicants exists, chemical analysis alone is not an appropriate tool for hazard assessment. Instead, a linkage of effect data and hazardous compounds is required. For that purpose, effect-directed analysis (EDA) was developed, which is based on a combination of biotesting, fractionation procedures and chemical analytical methods. Since a controversial discussion about the prospects of success in relation to the expense exists, the current methodological state of EDA for organic toxicants in complex mixtures and important results are reviewed in this paper with the aim of establishing criteria for the successful use of this promising tool. While EDA is a powerful tool to identify specifically acting individual toxicants close to the source of emission, it is inappropriate for screening purposes and often may fail in remote areas where the concentrations of specific toxicants are too low relative to the nonspecific toxicity of the whole mixture of natural and anthropogenic compounds. The biological tools have to be carefully selected with respect to their ability to detect specific effects and their significance in hazard assessment. Sophisticated chemical tools are required to identify individual toxicants in mixtures of thousands of compounds, which are typical for contaminated environments.

                Author and article information

                Contributors
                kim.anderson@oregonstate.edu
                Journal
                Anal Bioanal Chem
                Anal Bioanal Chem
                Analytical and Bioanalytical Chemistry
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1618-2642
                1618-2650
                19 March 2018
                19 March 2018
                2018
                : 410
                : 13
                : 3101-3110
                Affiliations
                ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Environmental and Molecular Toxicology, , Oregon State University, ; 1007 Agricultural and Life Sciences Bldg., Corvallis, OR 97331 USA
                Article
                997
                10.1007/s00216-018-0997-7
                5910463
                29552732
                7a808647-c946-4ea8-b05d-f2762d29d301
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 21 September 2017
                : 13 February 2018
                : 5 March 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: T32-ES007060-32
                Categories
                Research Paper
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2018

                Analytical chemistry
                gas chromatography,multiple linear regression,chemometrics,response prediction,automated mass spectral deconvolution and identification system (amdis),passive sampling devices

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