Taste is an incredibly difficult thing to measure and understand; my startup[1] builds machine learning based tools for flavor profiling and quality control in

Taste is an incredibly difficult thing to measure and understand; my startup[1] builds machine learning based tools for flavor profiling and quality control in the craft beverage industry - I'm speaking from experience! Before you can even approach understanding what a specific product tastes like, and what individuals and populations like or dislike about the product, you need to: 1. Develop a consistent system for collecting flavor profiles - while eliminating exogenous factors such as unattributed variables, halo and masking effect, environmental effects, and cognitive effects. 2. Collect thousands of reviews. Either from an IID sample of the population (sure...), or include the collection of demographic and personal identifiers within the data. 3. Understand the effects of age, race, sex, socio-economic status, and past tasting experiences. 4. Build basic flavor models, test that your system isn't leaving out important information that causes unseen variance within the product reviews, and continue collecting data. After all of that, the real fun begins! At this point, we can determine most of the chemistry of a product, its risk of flaws taints and contaminations, its production process, and its optimal target demographic just from a few full-sensory reviews. But we don't rely on standard sensory science to do it - that's stuck trying to figure out how Craft Foods can add less of a cheaper sugar substitute before people notice. [1] www.gastrograph.com