<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Norman D Forsberg</style></author><author><style face="normal" font="default" size="100%">Glenn R Wilson</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Determination of parent and substituted polycyclic aromatic hydrocarbons in high-fat salmon using a modified QuEChERS extraction, dispersive SPE and GC-MS.</style></title><secondary-title><style face="normal" font="default" size="100%">J Agric Food Chem</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Agric. Food Chem.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Fats</style></keyword><keyword><style  face="normal" font="default" size="100%">Food Contamination</style></keyword><keyword><style  face="normal" font="default" size="100%">Gas Chromatography-Mass Spectrometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Polycyclic Hydrocarbons, Aromatic</style></keyword><keyword><style  face="normal" font="default" size="100%">Salmon</style></keyword><keyword><style  face="normal" font="default" size="100%">Seafood</style></keyword><keyword><style  face="normal" font="default" size="100%">Solid Phase Extraction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">59</style></volume><pages><style face="normal" font="default" size="100%">8108-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A fast and easy modified QuEChERS (quick, easy, cheap, rugged and safe) extraction method has been developed and validated for determination of 33 parent and substituted polycyclic aromatic hydrocarbons (PAHs) in high-fat smoked salmon that greatly enhances analyte recovery compared to traditional QuEChERS procedures. Sample processing includes extraction of PAHs into a solution of ethyl acetate, acetone and isooctane followed by cleanup with dispersive SPE and analysis by GC-MS in SIM mode. Method performance was assessed in spike recovery experiments (500 μg/g wet weight) in three commercially available smoked salmon with 3-11% fat. Recoveries of some 2-, 3- and 5-ring PAHs were improved 50-200% over traditional methods, while average recovery across all PAHs was improved 67%. Method precision was good with replicate extractions typically yielding relative standard deviations &amp;lt;10%, and detection limits were in the low ng/g range. With this method, a single analyst could extract and clean up ≥60 samples for PAH analysis in an 8 h work day.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">15</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21732651?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Kevin A Hobbie</style></author><author><style face="normal" font="default" size="100%">Brian W Smith</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chemical profiling with modeling differentiates wild and farm-raised salmon.</style></title><secondary-title><style face="normal" font="default" size="100%">J Agric Food Chem</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Agric. Food Chem.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Fisheries</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Salmon</style></keyword><keyword><style  face="normal" font="default" size="100%">Seafood</style></keyword><keyword><style  face="normal" font="default" size="100%">Trace Elements</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2010</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">11768-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Classifications of fish production methods, wild or farm-raised salmon, by elemental profiles or C and N stable isotope ratios combined with various modeling approaches were determined. Elemental analysis (As, Ba, Be, Ca, Co, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, and Zn) of wild and farm-raised salmon samples was performed using an inductively coupled plasma atomic emission spectroscopy. Isotopic and compositional analyses of carbon and nitrogen were performed using mass spectrometry as an alternative fingerprinting technique. Each salmon (king salmon, Oncorhynchus tshawytscha ; coho salmon, Oncorhynchus kisutch ; Atlantic salmon, Salmo salar ) was analyzed from two food production practices, wild and farm raised. Principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for data exploration and visualization. Five classification modeling approaches were investigated: linear discriminate function, quadratic discriminant function, neural network, probabilistic neural network, and neural network bagging. Methods for evaluating model reliability included four strategies: resubstitution, cross-validation, and two very different test set scenarios. Generally speaking, the models performed well, with the percentage of samples classified correctly depending on the particular choice of model and evaluation method used.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">22</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/20973481?dopt=Abstract</style></custom1></record></records></xml>