谢 晶, 张利平, 苏 辉, 黎 柳, 吴圣彬. 上海青蔬菜的品质变化动力学模型及货架期预测[J]. 农业工程学报, 2013, 29(15): 271-278. DOI: 10.3969/j.issn.1002-6819.2013.15.033
    引用本文: 谢 晶, 张利平, 苏 辉, 黎 柳, 吴圣彬. 上海青蔬菜的品质变化动力学模型及货架期预测[J]. 农业工程学报, 2013, 29(15): 271-278. DOI: 10.3969/j.issn.1002-6819.2013.15.033
    Xie Jing, Zhang Liping, Su Hui, Li Liu, Wu Shengbin. Quality kinetic model and shelf life prediction of green vegetable (Brassica rapa var. chinensis)[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2013, 29(15): 271-278. DOI: 10.3969/j.issn.1002-6819.2013.15.033
    Citation: Xie Jing, Zhang Liping, Su Hui, Li Liu, Wu Shengbin. Quality kinetic model and shelf life prediction of green vegetable (Brassica rapa var. chinensis)[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2013, 29(15): 271-278. DOI: 10.3969/j.issn.1002-6819.2013.15.033

    上海青蔬菜的品质变化动力学模型及货架期预测

    Quality kinetic model and shelf life prediction of green vegetable (Brassica rapa var. chinensis)

    • 摘要: 为探讨上海青蔬菜不同储藏温度下的货架期预测方法,试验测定了在5、10、15和20℃ 4个温度下贮藏的上海青(Brassica rapa var. chinensis)的还原型抗坏血酸、叶绿素、颜色参数L*(亮度)、b*(黄度)、△E(色差)和感官评价,并对这些指标进行了动力学分析。研究表明,在测定温度范围内,上海青储藏的温度越低,其品质指标变化越慢。动力学分析显示,零级动力学比一级动力学更适合表现所测品质的变化规律,并且除b*外的指标拟合较好。研究还采用Arrhenius 方程对品质变化速率常数k和温度T进行非线性拟合,得到还原型抗坏血酸、叶绿素、L*和ΔE活化能Ea分别为68.130、57.024、46.685和42.581 kJ/mol。最终得到以时间、温度和品质指标值为变量的上海青货架期预测方程,拟定不同的品质终点值能得到对应的货架期。同时,Arrhenius方程与依赖于感官终点的动态品质终点拟合方程结合预测的货架期曲线与感官寿命曲线能得到更好的契合。验证试验结果证明前者能较好地预测上海青不同剩余品质指标对应的货架期,另一方面,测量温度范围内,后者能较好地通过品质指标预测感官寿命(两者绝对差值小于0.3 d)。两者结合能得到较为全面的货架期预测参数,为上海青流通过程中的货架期预测提供理论基础。

       

      Abstract: Abstract: With widely utilization of kinetic theory in the field of shelf life prediction, and the necessity of shelf life prediction of perishable leafy vegetables, two methods of shelf life prediction of green vegetable (Brassica campestris L. ssp.chinensis) were conducted through a series of experiments and validation. Reduced ascorbic acid content, chlorophyll, color parameters such as L*(lightness), b*(yellowness), ΔE (color difference), and sensory evaluation of green vegetable stored at 5℃, 10℃, 15℃ and 20℃ were determined in this experiment, then a kinetic analysis of those quality indexes and sensory score were studied. The results showed that the lower temperature at which a green vegetable was stored, the slower quality parameters change in the temperature range was concerned, in other words, the kinetics rate constant of quality change was smaller in lower temperature. Kinetic analyses were proposed that zero-order law was more appropriate than first-order reaction kinetics to describe quality determined change, and zero-order law could describe quality degradation very well except b*(yellowness) (R2>0.84). Nonlinear fitting of reaction rate k and temperature T based on Arrhenius law was also studied, from which active energy Ea of Reduced ascorbic acid content, chlorophyll, L* and ΔE were 68.130 kJ/mol, 57.024 kJ/mol, 46.685 kJ/mol, and 42.581 kJ/mol, respectively. As a consequence, the prediction function of green vegetable shelf life based on time, temperature, and quality parameters was obtained (R2>0.87), from which shelf life could be calculated with different thresholds of quality indexes. A sensory score was also determined by zero-order kinetics and Arrhenius law in this paper, which was used for prediction of sensory shelf life. Sensory shelf life is 11.03, 7.11, 4.65, and 3.09 d with a score of 3.5 as the cut point for a green vegetable stored at 5, 10, 15, and 20℃, respectively, while predicted shelf life is 11.06, 7.15, 4.69, and 3.13d, by assuming final chlorophyll decreased by 36%, which was close to sensory shelf life by a small relative prediction error of 1.5%. However, because of lacking unitive standards for final shelf life in the fruit and vegetable industry, there were almost no references defining any accuracy thresholds of qualities to predict certain shelf life as close as possible with sensory shelf life. Based on above situation, shelf life resulted from Arrhenius law and linear-fitting function of dynamic final quality relied on sensory cutting point was tried in this research, the function curve of which corresponded better to the predicted curve of sensory life. The validated experiment proved that former models were a good approach to evaluate the shelf life of a green vegetable corresponding to relatively remained quality value; on the other hand, latter models could predict sensory shelf life via a quality index, of which the relative prediction error was less than 0.3 day. The overall shelf life prediction parameters can be obtained based on the combination of two models. This conclusion is expected to offer a theoretical basis for shelf life prediction of a green vegetable during transportation in circulation.

       

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