Development of a dynamic predictive model for quality changes in strawberries under fluctuating temperatures

Area of Science:

• Food Science
• Agricultural Engineering
• Supply Chain Management

Background:

• Maintaining strawberry quality and safety during the supply chain is crucial.
• Predictive modeling aids in understanding and managing post-harvest quality changes.

Purpose of the Study:

• To develop a dynamic predictive model for strawberry quality changes.
• To ensure the safety and quality of strawberries throughout the supply chain.
• To provide a reference for determining strawberry shelf life.

Main Methods:

• Strawberries were stored at various temperatures (4, 10, 20, 30°C).
• Quality parameters including weight loss, firmness, total soluble solids (TSS), titratable acidity (TA), and vitamin C (Vc) were analyzed.
• Zero-order reaction kinetics and the Arrhenius equation were used to model quality changes.

Main Results:

• Weight loss increased, while firmness, TSS, and Vc content decreased over storage.
• Firmness and Vc changes were accurately described by the zero-order reaction kinetics model.
• The Arrhenius equation effectively predicted the reaction rate (k) with R² > 0.900.

Conclusions:

• Dynamic predictive models integrating reaction kinetics and the Arrhenius equation were successfully established.
• These models accurately predict strawberry quality changes within the 4-30°C logistics range.
• The study provides a valuable tool for optimizing strawberry shelf life determination.