Development of Predictive Models for Shelf Life of Two Leafy Vegetables Under Different Storage Temperatures
Year: 2026
Type: article
Abstract: Postharvest vegetables are still undergoing metabolic processes primarily through respiration. Although modified atmosphere packaging (MAP) can extend the shelf life of vegetables, it is of great significance to accurately predict the shelf life of vegetables for the quality assurance. In this study, the modified atmosphere packaged spinach and Chinese chives were stored at 0, 5, 10, 15, and 20°C, and Pseudomonas spp. count was determined regularly. The modified Gompertz model was used as the primary model to construct the model of Pseudomonas spp. growth kinetics in spinach and Chinese chives at different temperatures. On the basis of the primary model, the Belehradek (square root) model was selected as the secondary model to explore the influence of temperature on maximum specific growth rate ( μ max ) and lag period ( λ ). Finally, the shelf life prediction model of spinach and Chinese chives was established and verified. From the results, the growth of Pseudomonas spp. showed an S‐shaped curve at different storage temperatures. With the increase of storage temperature, μ max of microorganisms increased and λ decreased. The modified Gompertz model can describe the growth dynamics of Pseudomonas spp. well, and μ max 0.5 and (λ ‐1 ) 0.5 have a good linear relationship with storage temperature. The accuracy of the shelf life prediction model was verified by measuring the growth state of Pseudomonas spp. in modified atmosphere packaged spinach and Chinese chives at the storage temperature of 8°C. The results showed that the relative error between the predicted value and the measured value was 2.39% for spinach and 0.43% for Chinese chives, respectively, indicating that the shelf life prediction model could effectively predict the shelf life of spinach and Chinese chives at the storage temperature range of 0°C–20°C.
Source: Journal of Food Quality
DOI: 10.1155/jfq/2939320
Language: English
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,570
Awards: G4291197954, G6476960094, G6981337409
OpenAlex ID: W7119480870
