Development of shelf-life prediction models and programs for 'Xuxiang' kiwifruit stored at different temperatures

Abstract

The perishability and microbial contamination of fresh kiwifruit reduce its shelf-life, and the thick skin and deep color make it challenging for consumers and merchants to assess its freshness visually, resulting in losses. Relevant predictive models are crucial in minimizing losses by quickly determining the shelf-life. The appearance, physiological and biochemical properties, and microbial populations of kiwifruit stored under different temperatures were assessed through simulated storage. Shelf-life prediction models were developed based on deterioration quality using a combination of dynamic and Arrhenius equations, as well as total mold and yeast changes using a combination of the Gompertz model and Belehradek equation. Furthermore, a shelf-life prediction program was established for both types of models using Python, and the accuracy of the models was validated and compared. The results indicated that the quality index based on the zero-order kinetic model fits better. The average relative errors of the shelf-life prediction model were all below 10 % except for the weight loss, and the prediction accuracy of the model based on L* was the highest. The average relative error in the shelf-life prediction model based on total mold and yeast count was within 25 %. This study can provide theoretical support for developing shelf-life prediction models and reducing postharvest losses of kiwifruit.