<?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%">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><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%">Perez, Angela L</style></author><author><style face="normal" font="default" size="100%">Brian W Smith</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%">Stable isotope and trace element profiling combined with classification models to differentiate geographic growing origin for three fruits: effects of subregion and variety.</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%">Argentina</style></keyword><keyword><style  face="normal" font="default" size="100%">Blueberry Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Chile</style></keyword><keyword><style  face="normal" font="default" size="100%">Environment</style></keyword><keyword><style  face="normal" font="default" size="100%">Fragaria</style></keyword><keyword><style  face="normal" font="default" size="100%">Fruit</style></keyword><keyword><style  face="normal" font="default" size="100%">Isotopes</style></keyword><keyword><style  face="normal" font="default" size="100%">Mexico</style></keyword><keyword><style  face="normal" font="default" size="100%">Oregon</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyrus</style></keyword><keyword><style  face="normal" font="default" size="100%">Trace Elements</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2006</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">54</style></volume><pages><style face="normal" font="default" size="100%">4506-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Classifications of geographic growing origin of three fresh fruits combining elemental profiles with various modeling approaches were determined. Elemental analysis (Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, V, and Zn) of strawberry, blueberry, and pear samples was performed using inductively coupled plasma argon atomic emission spectrometer. Bulk stable carbon and nitrogen isotope analyses in pear were performed using mass spectrometry as an alternative fingerprinting technique. Each fruit, strawberry (Fragaria x ananassa), blueberry (Vaccinium caesariense/corymbosum), and pear (Pyrus communis), was analyzed from two growing regions: Oregon vs Mexico, Chile, and Argentina, respectively. Principal component analysis and canonical discriminant analysis were used for data visualization. The data were modeled using linear discriminant function, quadratic discriminant function, neural network, genetic neural network, and hierarchical tree models with successful classification ranging from 70 to 100% depending on commodity and model. Effects of Oregon subregional and variety classification were investigated with similar success rates.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">13</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/16786991?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%">Padilla, Kimberly L</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%">Trace element concentration in tree-rings biomonitoring centuries of environmental change.</style></title><secondary-title><style face="normal" font="default" size="100%">Chemosphere</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Chemosphere</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acid Rain</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Mass Spectrometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Pinus</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Trace Elements</style></keyword><keyword><style  face="normal" font="default" size="100%">Trees</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2002</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">575-85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Inductively coupled plasma mass spectrometry (ICP-MS) was used to examine trace element concentration in tree-rings over three and half centuries to assess macro-trends of environmental change. Tree-rings of a 350+ year old mammoth ponderosa pine (Pinus ponderosa) were analyzed for element concentration and evaluated versus local and global historical events. The ponderosa pine was located 100 miles south of the Canada/USA border and 180 miles east of the Pacific Ocean, and grew near apple orchards, a public road, and Swakane Creek in western Washington, USA. The elements tested did not all display the same time versus concentration patterns. Copper and chromium displayed cyclic concentration patterns over the last 350+ years, which appear to be associated with local events. Strontium, barium, zinc and cadmium were found to be relatively constant between the mid 1600s and the early 1800s. Strontium, barium, zinc, and cadmium then increased beginning in the early 1800s for approximately 50 years then decreased to present day 2000. Significantly, similar changes seen in Ca, Mg, and Zn in other studies have been attributed to acid rain, whereas, in our study area there is no history of anthropogenic acid rain. Most importantly, our data goes back to the mid-1600s several hundred years further back than most other studies of this nature. This additional time data provides for a better context of trend data not previously available.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/12430645?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%">Magnuson, B A</style></author><author><style face="normal" font="default" size="100%">Tschirgi, M L</style></author><author><style face="normal" font="default" size="100%">Smith, B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Determining the geographic origin of potatoes with trace metal analysis using statistical and neural network classifiers.</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%">Analysis of Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Geography</style></keyword><keyword><style  face="normal" font="default" size="100%">Idaho</style></keyword><keyword><style  face="normal" font="default" size="100%">Metals</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks, Computer</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality Control</style></keyword><keyword><style  face="normal" font="default" size="100%">Solanum tuberosum</style></keyword><keyword><style  face="normal" font="default" size="100%">Trace Elements</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1999 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1568-75</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The objective of this research was to develop a method to confirm the geographical authenticity of Idaho-labeled potatoes as Idaho-grown potatoes. Elemental analysis (K, Mg, Ca, Sr, Ba, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, S, Cd, Pb, and P) of potato samples was performed using ICPAES. Six hundred eight potato samples were collected from known geographic growing sites in the U.S. and Canada. An exhaustive computational evaluation of the 608 x 18 data sets was carried out using statistical (PCA, CDA, discriminant function analysis, and k-nearest neighbors) and neural network techniques. The neural network classification of the samples into two geographic regions (defined as Idaho and non-Idaho) using a bagging technique had the highest percentage of correct classifications, with a nearly 100% degree of accuracy. We report the development of a method combining elemental analysis and neural network classification that may be widely applied to the determination of the geographical origin of unprocessed, fresh commodities.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record></records></xml>