Skip to content
intellegens logo
  • Home
  • Products
    • Alchemite™ Analytics
    • Alchemite™ Engine
    • Alchemite™ Success
    • Ichnite™
    • Integration partnerships
    • How to Buy
  • Applications
    • Materials & Chemicals
    • Life Sciences
    • Manufacturing Industries
    • Data Science
    • Academic Research
    • All Featured Applications
  • Events
    • Webinars
    • All upcoming Events
  • Resources
    • White Papers
    • Case Studies
    • Publications
    • Intellegens Blog
    • Latest News
    • Newsletter
    • FAQs
  • About
    • About Intellegens
    • Our Technology
    • Careers
    • Our Team
  • Contact
Menu
  • Home
  • Products
    • Alchemite™ Analytics
    • Alchemite™ Engine
    • Alchemite™ Success
    • Ichnite™
    • Integration partnerships
    • How to Buy
  • Applications
    • Materials & Chemicals
    • Life Sciences
    • Manufacturing Industries
    • Data Science
    • Academic Research
    • All Featured Applications
  • Events
    • Webinars
    • All upcoming Events
  • Resources
    • White Papers
    • Case Studies
    • Publications
    • Intellegens Blog
    • Latest News
    • Newsletter
    • FAQs
  • About
    • About Intellegens
    • Our Technology
    • Careers
    • Our Team
  • Contact

Developing food formulations at Yili

  • Intellegens Case Studies, Intellegens news
  • in: Food and beverage, Formulations design

UHT whipped cream case study

“We relatively quickly could drop out a number of the ingredients we had been testing… This wasn’t obvious if you just looked at them one-by-one, because you always have some cross-interactions. This was a big learning and helped speed up the development.”

Matthias Eisner, Innovation Manager, Yili

  • Machine learning was able to provide valuable insights into the behaviour of cream formulations over time.
  • Yili was able to remove some ingredients from consideration, speeding up development.
  • The project identified useful cross-correlations and was able to predict likely behaviour in later months from earlier test results.
Yili food formulation case study

Summary

Global dairy products leader, Yili, applied Alchemite™ machine learning to study UHT whipping cream formulations – a commercially-significant product-line for food services such as large-scale bakery operations. The product must show reliable properties over a nine month shelf life, achieved by using ingredients including stabilisers and emulsifiers, and by controlling processing. The project team built a reliable machine learning model based on 2-3 years of time series formulations data, and showed how this model could be evolved and applied as new data was continuously added. Missing data was imputed and a hierarchical modelling approach was employed to allow earlier months’ tests to be used as inputs to improve predictions of later shelf life. A key learning was understanding which ingredients impacted target properties, enabling some additives to be dropped, speeding up the development process.

DOWNLOAD FULL CASE STUDY (PDF)
VIEW RECORDED WEBINAR

Related information

  • Alchemite™ for chemicals
  • Alchemite™ product information
  • Request a demo

Company number : 10591395 | Vat number: 267525774
Address: The Studio, Chesterton Mill, Cambridge, United Kingdom, CB4 3NP
Privacy policy

Copyright 2023 Intellegens Limited – All rights reserved
Linkedin Twitter

Search...