Production managers, sensory experts, and quality control professionals use the Gastrograph system to find and detect flaws or batch variations in their products. Though all of our clients are serious about the flavor profile and consistency of their products, and review with a critical palate, not all reviews are of equal trust and weight. Any single Gastrograph review is not solely dependent on the product; the reviewer’s experience, preferences, environment, and health all play a role in their response to flavor.

Trust is important for our clients, because they are interested in how much trust they can place in a flavor profile (\({\it F}\)). For example if they were to ask us for a report on a product \({\it X}\) then we would provide them with the Objective Flavor Profile and the perceived quality, both of which are computed from among the trusted reviews (I will explain how I determine which reviews are trustworthy). So in short our clients can get a concrete flavor analysis on \({\it X}\) computed with trust based weights derived from the reviews.

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Every consumer has his or her own preferences. What is in our favorite drinks that sparks our euphoria? This momentous task is made greatly simpler by our unique sensory system, the Gastrograph. In detail, trained testers and ordinary consumers (with a bit of guidance) can easily map out what they love and hate in a particular product. Thanks to the Objective Flavor Profile (OFP), one can compare products and assess their similarities and differences on a scale of zero to five of increasing intensity for each of the twenty-four possible flavors.

What happens when two products have similar tastes but perform completely different in the market? The OFPs for such products can be compared to reveal differences. What about when a new product is released and is a similar style to a successful product? Is it possible to determine how similar it is to the successful product and where the differences are? The machinery to answer both questions is nearly the same. A quick, dirty model for this problem would be simple principal component analysis (PCA) with a simple application of dot products in \(\mathbb{R}^{24}\).

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At Analytical Flavor Systems, our job is to monitor our clients’ products for flaws, contaminations, and batch variations in real time. Clients review their products regularly, assigning each a score from 0 to 5 along 24 universal flavor dimensions. Additionally, every reviewer assigns the product an overall Perceived Quality (PQ) score between one and seven.

Our algorithms run 24 hours a day, 365 days a year, protecting our clients from shipping a bad batch and hurting their brand. Our clients are alerted to any significant drop in quality through email and phone alerts. This blog post will explain how we updated our models from the Western Electric rules to a more precise and accurate Adaptive Bayesian model.

The image above is a representation of the Western Electric rules we used to use for data analysis. Each data point represents the weighted average (or \(\bar x\)) of a week’s worth of Perceived Quality values. The grey line represents the mean of all Perceived Quality values we have for this product. The other horizontal lines represent distance from the mean (in standard deviations).

The original model we used was the Western Electric rules, which operates on the assumption that the \(\bar x\) values (or sequences of \(\bar x\) values) outside a certain number of standard deviations from the mean have a very low probability of being generated by random processes, and could thus be a symptom of meaningful variation (such as a beer flaw like dimethyl sulfide).

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