Abstract Rapid senescence and microbial infection lead to a short shelf life for postharvest Flammulina velutipes. Therefore, a model for predicting the shelf life for this product would be meaningful and enable necessary steps to be taken to reduce losses. We simulated shelf storage of F. velutipes at three temperatures (4, 15 and 25 °C). Sensory, biochemical and microbial evaluations of the samples were carried out at frequent intervals. The kinetic models combined with the Arrhenius equation were used to establish shelf life prediction models based on quality indexes; whereas, the Gompertz model combined with the Belehradek equation were used to establish a growth trend and shelf life prediction model for F. velutipes based on the presence of Pseudomonas spp. Moreover, the microbial growth model was verified by several indices including the correlation coefficient R2, accuracy factor Af and bias factor Bf. The results showed that the shelf-life kinetic models established according to four quality indicators were highly accurate, the R2 was >0.90 and the relative error between the measured and the predicted values were less than ±10%. In particular, the shelf-life prediction model established using the whiteness value was best. The mathematical model at different temperatures fitted the modified Gompertz model with a high correlation coefficient (R2 > 0.95). The Af and Bf of the Belehradek model were all between 0.9 and 1.05. The residual value between the predicted value and measured values was less than ±0.1. Using these validated models, the shelf life of F. velutipes can be estimated at any point in the cold chain if the temperature history is known. These models can serve as effective tools for predicting shelf life and developing new products for the fresh produce food sector.

中文翻译：

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根据蘑菇质量和特定腐败生物预测不同温度下采后金针菇的保质期

摘要 快速衰老和微生物感染导致金针菇采后的保质期较短。因此，用于预测该产品保质期的模型将是有意义的，并且能够采取必要的步骤来减少损失。我们模拟了 F. velutipes 在三种温度（4、15 和 25 °C）下的货架储存。样品的感官、生化和微生物评估频繁进行。利用动力学模型结合Arrhenius方程建立基于质量指标的货架期预测模型；而 Gompertz 模型结合 Belehradek 方程用于建立基于假单胞菌属存在的 F. velutipes 生长趋势和保质期预测模型。而且，微生物生长模型通过相关系数R2、精度因子Af和偏差因子Bf等多项指标进行验证。结果表明，根据四项质量指标建立的货架期动力学模型准确度高，R2>0.90，实测值与预测值的相对误差小于±10%。尤其是使用白度值建立的保质期预测模型效果最好。不同温度下的数学模型拟合修正Gompertz模型，相关系数高（R2>0.95）。Belehradek 模型的 Af 和 Bf 都在 0.9 和 1.05 之间。预测值与实测值的残差小于±0.1。使用这些经过验证的模型，F 的保质期。如果温度历史已知，则可以在冷链中的任何一点估计 velutipes。这些模型可以作为预测保质期和为新鲜农产品食品行业开发新产品的有效工具。