GC-MS profiling of fatty acids in green coffee (Coffea arabica L.) beans and chemometric modeling for tracing geographical origins from Ethiopia

BACKGROUND This study was aimed at the development of objective analytical method capable of verifying the production region of the coffee beans. One hundred samples of green coffee (Coffea arabica L.) beans from the major producing regions, comprising various sub-regional types, were studied for variations in their fatty acid compositions by using gas chromatography coupled with mass spectrometry. Principal component analysis (PCA) was used to visualize data trends. Linear discriminant analysis (LDA) was used to construct classification models. RESULTS Twenty-one different fatty acids were detected in all of the samples. The total fatty acid content varied from 83 to 204 g kg(-1) across the regions. Oleic, linoleic, palmitic, stearic and arachidic acids were identified as the most discriminating compounds among the production regions. The recognition and prediction abilities of the LDA model for classification at regional level were 95% and 92%, respectively, and 92% and 85%, respectively, at sub-regional level. CONCLUSION Fatty acids contain adequate information for use as descriptors of the cultivation region of coffee beans. Chemometric methods based on fatty acid composition can be used to detect fraudulently labeled coffees, with regard to the production region. These can benefit the coffee production market by providing consumers with products of the expected quality. (c) 2019 Society of Chemical Industry