Continued from yesterday’s post
It’s an interesting idea, that sensor testing might be able to do what both of those profiling systems just drew on, with the input an objective, measured assessment. One problem: related sensor based aspect testing just isn’t there yet. It complicates things that when we taste foods we’re really sensing two different things (aside from feel aspects like astringency, and noticing aftertaste, which is something else). We pick up basic flavors through our tongue (sweetnees, salt, sourness, bitterness, and umami, or savory taste), and also detect aromatic compounds through our nasal passages, a version of the sense of smell.
Electronic tongues and noses can replicate some of both, but there are gaps in getting them to cover our range as effectively, and even broader gaps when it comes to interpretation. It’s all early in the research phase. If there was a strong commercial driver for machines to taste things as effectively as we do they probably would be a lot more advanced, even though most of the current simpler forms of sensing equipment, testing process, and analysis (AI function) have only been developed in the last 20 years or so.
Even the description of the research into that subject is a bit much:
Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach
In this paper, we have (analyzed using a metal oxide sensor (MOS)-based electronic nose (EN)) five tea samples with different qualities… The flavour of tea is determined mainly by its taste and smell, which are determined by hundreds of volatile organic compounds (VOC) and non-volatile organic compounds present in tea. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human organoleptic profiling panels… The methods are also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and thus to explore the possibility of replacing existing analytical and profiling panel methods…
I’d love to say more, and I did include a lot more detailed research findings citations in that recent post, but that long summary was still only a limited sample of the range of looking into the potential. You can kind of see where it’s going, and a bit about how it works, but it’s all very complicated, technical, and at present very limited in function. I’ve talked to the Teapasar vendor about it and there’s more to pass on about what they’re doing, but filling that in will require more research.
Images provided by author