Rapid classification of coffee bean origin is achieved simply and objectively by applying SIFT-MS to instantaneous, direct detection of emitted volatile compounds.

Aroma is an important characteristic in the acceptance of coffee by consumers, so it is important to ensure that beans with the correct aroma profile are selected for blending and roasting. Geographic origin significantly affects the aroma of coffee beans and the roasted product. Traditionally human sensory testing has been utilized for raw product screening, but this is expensive, subjective, and prone to fatigue. and prone to fatigue. An alternative is to apply instrumental analysis instead, but technologies utilizing electronic nose approaches (whether metal oxide sensors, or others) have not proven reliable replacements for sensory testing. This application note applies Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) to the detection of VOCs from green coffee beans of various geographical origins. SIFT-MS effectively distinguishes different origins rapidly, based on comprehensive profiling of the emitted aroma volatiles.

Green coffee beans from Brazil, Colombia, Ethiopia, Guatemala, and Sumatra (Indonesia) were analyzed using automated SIFT-MS. The multivariate data were then processed using the SIMCA multivariate statistical algorithm in the Infometrix® Pirouette software package. SIMCA enables the instrumental measurements and geographical origin information to be combined to build models that distinguish the origins and can subsequently be utilized to rapidly classify incoming samples using a pass/ fail approach.

Figure 1 shows the model created from the SIFT-MS data for green coffee beans of different origins. Each colored point represents a replicate measurement. The interclass distances summarized in Table 1 show that all coffee origins can be distinguished using SIFT-MS, since an interclass distance of 3 or more for the SIMCA approach indicates good separation between the different classes.


Coffee Origins Countries

Figure 1. The results of SIMCA multivariate analysis of the headspace concentration data of green coffee beans. Each color represents a different country of origin (the key is provided in Table 1). Each colored point in the class projections graph represents a replicate measurement.


This study demonstrates that SIFT-MS is readily applied as a screening tool to confirm coffee bean origin. Volatiles emitted by green beans were analyzed in less than one minute per sample using SIFT-MS. The Syft Voice200ultra SIFT-MS instrument coupled with Gerstel automation provides a robust, simple solution for rapid, objective instrumental determination of coffee origin.


The interclass distances provided by the SIMCA algorithm. A value greater than 3 indicates separability.
Interclass DistancesColombiaEthiopiaGuatemalaSumatra

Experimental Method

Green coffee beans of various origins (Brazil, Colombia, Ethiopia, Guatemala, and Sumatra) were obtained from a local importer (Altura Coffee, Auckland, New Zealand). Single beans were placed in 10-mL sample vials (seven replicates per origin) and equilibrated at room temperature (~22 °C) for 30 minutes in a standard sample vial tray on a GERSTEL Multipurpose Sampler (MPS; GERSTEL, Mülheim an der Ruhr, Germany). Analysis was carried out using a Voice200ultra SIFT-MS instrument (Syft Technologies, Christchurch, New Zealand) integrated with the GERSTEL MPS. Multivariate statistical analysis was carried out using the Pirouette software package (Infometrix, Bothel, WA, USA).

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