林炎娟,周丹蓉,方智振,陈文光,叶新福.温度对李果实采后品质的影响及货架期预测模型建立[J].食品安全质量检测学报,2022,13(20):6748-6754 |
温度对李果实采后品质的影响及货架期预测模型建立 |
Effects of storage temperature on the postharvest quality of Prunus salicina Lindl. and establishment of shelf life prediction model |
投稿时间:2022-07-19 修订日期:2022-10-13 |
DOI: |
中文关键词: 李 温度 货架期预测 Arrhenius方程 |
英文关键词:Prunus salicina Lindl. temperature shelf life prediction Arrhenius equation |
基金项目:福建省对外合作项目(2022I0032)、福建省属公益类科研院所基本科研专项(2020R10280016) |
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中文摘要: |
目的 研究不同温度下李果实采后品质变化及建立李果实采后货架期预测模型。方法 本研究设置了3种温度(4、15、25℃), 并定期测定生理生化指标。采用零级动力学模型和一级动力学模型结合Arrhenius方程建立基于品质变化的李果实采后货架期预测模型, 并验证模型的预测精确度。结果 低温明显延缓了果皮强度和果肉硬度的下降和果实失重率的上升, 同时也延缓了花色苷含量的积累, 对总酸含量影响不明显(除贮藏20 d)。根据果皮强度、果肉硬度和失重率变化建立货架期预测模型, 通过验证发现基于失重率变化的货架期预测模型具有最好的精确度, 相对误差值低于10%。结论 货架期预测模型的建立, 可快速预测果实剩余货架期, 为贮藏和货架提供技术指导。 |
英文摘要: |
Objective To study the changes of the postharvest quality of Prunus salicina Lindl. at different temperatures and establish a prediction model for the postharvest shelf life of Prunus salicina Lindl.. Methods This study kept Prunus salicina Lindl. under 3 kinds of shelf temperatures (4, 15, 25℃), the physiological and biochemical indicators were regularly measured. The zero-order kinetic model and the first-order kinetic model combined with the Arrhenius equation to establish postharvest shelf life prediction model based on quality changes of Prunus salicina Lindl., and verify the prediction accuracy of the model. Results The low temperature significantly delayed the decrease of peel strength and pulp firmness, delayed the increase of weight loss rate, delayed the accumulation of anthocyanin content at the same time, and had no obvious effect on total acid content (except storage for 20 d). The shelf life prediction model established according to the changes in peel strength, pulp hardness and weight loss rate. Through verification, it found that the shelf life prediction model based on weight loss rate changes had the best accuracy, with a relative error value of less than 10%. Conclusion The establishment of shelf life prediction model can quickly predict the remaining shelf life of fruits and provide technical guidance for storage and shelf life. |
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