Computational optimization of food formulation and extrusion conditions for nutritional quality and…

AI / informaticsCurrent research in food science· 2026

Computational optimization of food formulation and extrusion conditions for nutritional quality and antioxidant capacity.

Xu Zhou, Keer Ni, Pranav Gupta, Ilias Tagkopoulos

Large Language Models in CareAI / informaticsQ1IF 8.6

Optimizing extrusion temperature and time raised combined nutrition-antioxidant scores by 10% on average.

Computational optimization of food formulation and extrusion conditions for nutritional quality and antioxidant capacity.

Xu Zhou … Ilias Tagkopoulos

Current research in food science · 2026

Background

Processed foods are important to modern dietary patterns, yet current classification systems are qualitative and provide limited guidance for designing healthier products.

10%

Results

10%

Combined nutrition-antioxidant score improved 10% on average, up to 44%, versus reference extrusion conditions.

n = 503 ingredient records

More results

Bioactive and nutrient concentrations varied by several orders of magnitude among ingredients, with a median within-ingredient coefficient of variation of 36% across supplier-level entries.

Ingredient selection alone accounted for a mean Nutrient Rich Foods index (NRF 9 .

More results

Under a simulated reference extrusion condition, the model predicted that NRF 9 .

3 than extrusion, although extrusion had a greater effect in formulations rich in heat-labile vitamins.

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Conclusion

As a whole, this work provides a generalizable framework that integrates formulations and process parameters for systematic optimization of nutritional and bioactivity targets in food processing.

Computational optimization of food formulation and extrusion conditions for nutritional quality and antioxidant capacity.

Computational optimization of food formulation and extrusion conditions for nutritional quality and antioxidant capacity.

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