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n this paper (open access) the authors used of solid-phase microextraction (SPME)-gas chromatography-time-of-flight mass spectrometry (GC/Q-ToF-MS) combined with chemometrics to detect key differences between adulterated and non-adulterated ground roast coffee. They drilled into these differences and found two potential chemical markers for common adulterants.

They compared the aroma profiles of ground roasted coffee with some commonly used adulterants (ground roasted barley, corn and soybean). The SPME fibre collected and concentrated the headspace volatiles. Non-adulterated and adulterated samples were distinguished after applying some chemometric tools (principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA)) on the obtained chromatographic data. Two volatile compounds (1H-imidazole-4-methanol and benzene-2-(1,3-butadienyl)-1,3,5-trimethyl) were identified as potential markers for the determination of adulterants (ground roasted barley, corn or soybean) in ground roasted coffee (p-value cut-off<0.001 and fold change (FC) cut-off>10). Also, 2-furanmethanol and 2-formyl-1-methylprrrole were found as marker candidates for roasted coffee powder.

The authors tested this approach and were able to detect selected herbal adulterants (5% w/w) found in ground coffee.

In this study (open access) a non-targeted method of headspace-solid phase microextraction with gas chromatography coupled to mass spectrometry (HS-SPME-GC–MS) was developed to achieve the characterization, classification, and authentication of different coffee samples according to geographical production region, and variety (arabica/robusta). Moreover, decaffeinated and non-decaffeinated instant coffee samples were analyzed. Some samples of chicory, a potential coffee adulterant, were also been included. The GC–MS fingerprints were used to classify and characterise the analyzed coffees using principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS) regression.

185 reference samples were used.  42 were chicory (a typical coffee substitute or adulterant), 96 were coffee from three geographical production regions (Vietnam, Cambodia, and Costa Rica) and species (Arabica, Robusta, and Arabica-Robusta mixture), and 47 samples were soluble coffee (decaffeinated and non-decaffeinated). The chicory samples were purchased from Barcelona supermarkets and the coffee samples were from Vietnam, Cambodia and Costa Rica local supermarkets. Each paired PLS-DA model was built using 70 % of samples randomly selected from each group as the calibration set while the remaining 30 % of the samples were employed as the prediction set.  The authors compared models generated using two different GC columns and operating conditions.

The authors tested their model on mixtures prepared in-house from the same reference set:: Vietnamese Arabica Coffee adulterated with Vietnamese Robusta Coffee and Vietnamese Robusta Coffee adulterated with chicory.  They reported that the model could classify adulteration levels down to 15%.

(image from the paper)