Determining the geographic origin of potatoes with trace metal analysis using statistical and neural network classifiers.
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.
Anderson
K A
KA
Department of Food Science and Toxicology and Analytical Sciences Laboratory, Holm Research Center, University of Idaho, Moscow 83844-2201, USA.
Magnuson
B A
BA
Tschirgi
M L
ML
Smith
B
B
D000704:Analysis of Variance; D016002:Discriminant Analysis; D005843:Geography; D007061:Idaho; D008670:Metals / Q000032:analysis*; D016571:Neural Networks, Computer; D011786:Quality Control; D011198:Solanum tuberosum / Q000737:chemistry / Q000145:classification*; D014131:Trace Elements / Q000032:analysis*
D008670:Metals; D014131:Trace Elements