Ingredient Informatics: Previously disconnected data sets expedite food and beverage product (re)formulation

May 20, 2022 | Case Study

Product development in consumer package goods (CPG) is traditionally done manually and benefits from years of experience as there are often intangible and poorly resolved testing methods available that lead to frequent failures when the product comes to market. Luckily, the advent of ingredient informatics and machine learning has allowed for new insights to be drawn from a variety of sources: broad market surveys, specific social media interactions, consumer preference testing, or those derived from analytical testing, among countless other public or proprietary ideas. Companies in this space claim to be able to correlate one or more of these data sets to be able to expedite product formulation and better understand what worked, what didn't, what other products have done, and how CPG companies can improve their product faster and with fewer iterations. Recent partnerships from companies like CP Kelco with Shiru, Sumitomo with Nuritas, and Danone with Brightseed have already made major steps in connecting the dots between functionality and product development.

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