TitleChemical Profiling to Differentiating Geographic Growing Origin of Coffee
Publication TypeJournal Article
Year of Publication2002
AuthorsAnderson KA, Smith BW
JournalJ Agric Food Chem
Date Published03/2002
Africa, Eastern, Central America, Coffee, Discriminant Analysis, Elements, Indonesia, Neural Networks (Computer), South America, Spectrum Analysis

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.

Alternate JournalJ. Agric. Food Chem.
PubMed ID11902958
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