<?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%">Brian W Smith</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chemical Profiling to Differentiating Geographic Growing Origin of Coffee</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%">Africa, Eastern</style></keyword><keyword><style  face="normal" font="default" size="100%">Central America</style></keyword><keyword><style  face="normal" font="default" size="100%">Coffee</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Elements</style></keyword><keyword><style  face="normal" font="default" size="100%">Indonesia</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks (Computer)</style></keyword><keyword><style  face="normal" font="default" size="100%">South America</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectrum Analysis</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%">03/2002</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">2068-75</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 demonstrate the feasibility of this method to differentiate the geographical growing regions of coffee beans. Elemental analysis (K, Mg, Ca, Na, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, S, Cd, Pb, and P) of coffee bean samples was performed using ICPAES. There were 160 coffee samples analyzed from the three major coffee-growing regions: Indonesia, East Africa, and Central/South America. A computational evaluation of the data sets was carried out using statistical pattern recognition methods including principal component analysis, discriminant function analysis, and neural network modeling. This paper reports the development of a method combining elemental analysis and classification techniques that may be widely applied to the determination of the geographical origin of foods.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/11902958?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>