Global dairy industry leader applies machine learning
- 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.
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.
Matthias Eisner, Innovation Manager at Yili explained in an Intellegens webinar: “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.”
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