title: Chemical profiling to differentiate geographic growing origins of coffee.
abstract: 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.
authors.affiliation: Department of Environmental and Molecular Toxicology, Food Safety and Environmental Stewardship Program, Oregon State University, Corvallis, Oregon 97331, USA. kim.anderson@orst.edu
authors.forename: Kim A
authors.initials: KA
authors.lastname: Anderson
authors.forename: Brian W
authors.initials: BW
authors.lastname: Smith
mesh_terms: D000351:Africa, Eastern; D002489:Central America; D003069:Coffee / Q000737:chemistry* / Q000145:classification*; D016002:Discriminant Analysis; D004602:Elements; D007214:Indonesia; D016571:Neural Networks, Computer; D013020:South America; D013057:Spectrum Analysis / Q000379:methods
chemical_list: D003069:Coffee; D004602:Elements