Chemical Profiling To Differentiate Geographic Growing Origins of Coffee

Columbus

American Chemical Society

2002

S. 2068-2075

Bandaufführung

171049

Journal of agricultural and food chemistry : a publication of the American Chemical Society, S. 2068-2075

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.
KEYWORDS: Neural network; geographic authenticity; canonical discriminant analysis; discriminant function analysis; principal component analysis; elemental analysis; trace element analysis; coffee beans; geographic origin; bioavailable