The sheer complexity of coffee aromas is illustrated by the broad spectrum of notes, the myriad ways of influencing the flavor profile, and the subtle interplay between the sensory organs. This complexity is compounded by a problem that seems almost trivial in comparison – namely, the challenges of communicating about aromas internationally: »For example, let’s say we want a fruity coffee and that we’d like it to have a blackcurrant note. The grower country we’re communicating with may have a different understanding of what a blackcurrant tastes like. In the worst-case scenario, the fruit might not even exist there. Conversely, we may or may not be familiar with fruit varieties that they use in the description. Technical analysis could bring greater clarity and standardization to the international coffee trade.« Although there are currently no precise means of analyzing coffee aromas, the Digital Sensory Perception working group at Fraunhofer IIS has succeeded in developing a coffee classifier that can distinguish between coffees of different types – and from different producers – based on their gas profile.
Robusta or arabica? The coffee classifier knows!
The coffee classifier uses an AI-based system for gas analysis, which relies on a gas sensor. From the various sensor types available, the researchers chose a metal oxide (MOX) sensor for testing purposes in this case. Teresa Scholz, senior scientist in the Digital Sensory Perception working group, explains: »We first have to collect data about the gases from various coffee types and then use the algorithm to say, ‘That’s precisely this brand, this variety,’ and so on.« If this classification is to be carried out automatically in the future, with the lowest possible error probability, it will require large quantities of data rather than just a few examples. This also illustrates why it would be difficult to develop an accurate gas analysis system for each individual facet of an aroma. Given that large quantities of data are already needed just for the four selected producers and types, one can easily imagine the huge quantities of data needed to reliably analyze the full spectrum of aromas.
»The question is always: Can gas sensor technology provide results of sufficient quality for the intended application, and does gas analysis pay off compared with conventional methods when it comes to price?«
– group manager Sebastian Hettenkofer
From quality control of coffee to smoke alarms – applications of gas sensor technology
One realistic use case of AI-based gas analysis is in quality control – for example, to determine whether a shipment actually contains the type of coffee ordered. There are also numerous applications beyond the world of coffee. In the PINOT project, gas analysis is used to help winegrowers and cellar masters with the quality assurance of wine aroma, taste, and texture. The technology could potentially also be used in special smoke alarms. It’s clear that the applications of AI-based gas analysis are as varied as the world of coffee aromas itself.