Food

Is the Future of Food Quality in the Hands of Machine Learning?

In a Q&A with Food Safety Tech, Fero founder Berk Birand briefly discusses how machine learning can benefit food companies from the standpoint of streamlining manufacturing processes and improve product quality:

Food Safety Tech: How does machine learning help food manufacturers maximize production without sacrificing quality?

Berk Birand: Machine learning can help food manufacturers boost volume and yield while also reducing quality issues waste, and cycle time. With a more efficient process powered by machine learning, they can churn out products faster without affecting quality.

Additionally, machine learning helps food producers manage raw material variation, which can cause low production volume. In the chemicals sector, a faulty batch of raw ingredients can be returned to the supplier for a refund; in food, however, the perishable nature of many food ingredients means that they must be used, regardless of any flaws. This makes it imperative to get the most out of each ingredient. A good machine learning solution will note those quality differences and recommend new parameters to deal with them.

FST: How does integrating machine learning into software predict quality violations in real-time, and thus help prevent them?

Birand: The power of machine learning can predict quality issues hours ahead of time and recommend the optimal settings to prevent future quality issues. The machine learning software analyzes all the data produced on the factory floor and “learns” how each factor, such as temperature or length of a certain process, affects the final quality.

By leveraging these learnings, the software can then help predict quality violations in real-time and tell engineers and operators how to prevent them, whether the solution is increasing the temperature or adding more of a specific ingredient.

FST: How does machine learning technology reveal & uphold sustainability improvements?

Birand: Due to the increase in climate change, sustainability continues to become a priority for many manufacturers. Explainable machine learning software can reveal where sustainability improvements, such as reducing heat or minimizing water consumption, can be made without any effect on quality or throughput. By tapping into these recommendations, factories can produce more food with the same amount of energy.

Originally published in Food Safety Tech.

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