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!<p>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:<p>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.<p>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.<p>3. Understand the effects of age, race, sex, socio-economic status, and past tasting experiences.<p>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.<p>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.<p>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.<p>[1] www.gastrograph.com