李汴生, 彭红梅, 张德润, 阮征. 基于感官品质的油梨常温后熟进程预测模型[J]. 农业工程学报, 2019, 35(13): 285-290. DOI: 10.11975/j.issn.1002-6819.2019.13.034
    引用本文: 李汴生, 彭红梅, 张德润, 阮征. 基于感官品质的油梨常温后熟进程预测模型[J]. 农业工程学报, 2019, 35(13): 285-290. DOI: 10.11975/j.issn.1002-6819.2019.13.034
    Li Biansheng, Peng Hongmei, Zhang Derun, Ruan Zheng. Prediction model of avocado ripening process based on sensory quality at room temperature[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(13): 285-290. DOI: 10.11975/j.issn.1002-6819.2019.13.034
    Citation: Li Biansheng, Peng Hongmei, Zhang Derun, Ruan Zheng. Prediction model of avocado ripening process based on sensory quality at room temperature[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(13): 285-290. DOI: 10.11975/j.issn.1002-6819.2019.13.034

    基于感官品质的油梨常温后熟进程预测模型

    Prediction model of avocado ripening process based on sensory quality at room temperature

    • 摘要: 针对中国油梨市场油梨进口储运过程中储藏时间和品质变化难以把控及油梨损坏严重等问题,对常温常湿 ((25±1 )℃、75%相对湿度) 条件下果皮色泽、果肉硬度及感官品质指标进行测定,监测其在常温后熟过程中的内在变化规律,对色差、硬度、呼吸速率、质量损失率与果肉受喜爱程度进行皮尔逊相关性分析,利用无损检测指标色差a*值与储藏时间之间logistic方程,建立油梨后熟时间预测模型。结果表明:油梨平均单果质量随储藏时间的延长而减少,每日质量损失率和呼吸速率均呈现先增加后减少的趋势,果皮色泽在储藏时间小于3 d时显著增加,其后趋于平衡,而果肉硬度表现相反的趋势,干基质量损失率、色泽(L*,a*和b*值)和硬度均与感官喜爱度呈显著相关性(P<0.01)。基于色差测定的无损、快速、便捷,选取色差a*值进行预测模型的建立,色差a*值的logistic方程拟合效果较好,R2为0.993。模型的检验发现,在色差a*值<4时,模型预测值与实测值有很好的线性相关性,决定系数为0.996,说明该模型在果皮色差a*值<4时可以用于油梨常温后熟期的预测,研究结果可为油梨品质控制提供指导和依据。

       

      Abstract: Abstract: 'Hass' avocado is enjoyed by consumers worldwide due to its rich flavor, high overall quality and health related attributes. The nutritional and dense phytochemical composition of avocado is attracting more consumers. Avocados do not ripen on the tree and must be ripened after harvest, which means that most or all of the ripening process needs to be carefully controlled in the commercial postharvest environment. In view of the problems that the storage time and quality changes of avocado during the storage and transportation in the Chinese avocado market are difficult to be controlled, as well as that avocado is easy to be damaged, the color of the avocado peel, the hardness of the avocado pulp, and the sensory quality indicators at room temperature (25±1 ℃, 75% RH) were measured to monitor their intrinsic variation during ripening in this study. The Pearson correlation analysis between the affection degree of avocado flesh and the color difference, hardness, respiration rate, weight loss rate, respectively, was also performed. At last, the prediction model for the ripening of avocado was developed based on first-order functional equation, logistic equation of dry weight loss rate-and the color difference a* value-storage time. The results showed that the weight of avocado fruit decreased from 195.2(0.9 to 181.2(0.8 with the prolongation of storage time (0-9 d), and the daily weight loss rate and respiration rate increased first at the storage time of 0~3 d and then decreased sharply. Conversely, the peel color, characterized by L* (34.66(1.27), a* ((10.69(1.29) and b* values (18.31±1.56), changed significantly when the storage time was less than 3 d, and then tended to constant (~25.63, ~4.19, and~3.45, respectively). However, the avocado flesh hardness decreased from 144.2N to 8.04N during storing for 3 d and reached equilibrium when the storage time exceeded 3 h. Additionally, apart from the respiratory rate (r=(0.221), the dry weight loss rate, peel color (L*, a* and b* values), and avocado flesh hardness during storing at room temperature were significantly correlated with sensory preference scores (P<0.01), in which the dry weight loss rate (r=0.840)and a* value (r=0.915) were positively related to sensory preference scores, and flesh hardness (r=(0.954), L* (r=(0.947), and b* values (r=(0.952) were negatively related to sensory preference scores. Based on determination of the color of avocado peel were nondestructive, fast, convenient, herein a* value was selected as key indicators to establish the prediction model of avocado quality change during storing. At the storage time of 0-9 d, the a* value was fitted well based on logistic equation. R2 values was 0.993. In the validation experiments, when a* value less than 4, the predicted and the measured values have a good linear correlation, the decision coefficient is 0.996, indicating that the developed model can be used to predict the ripening period of avocado at room temperature when a* value less than 4. The research results in this work can provide favorable guidance and basis for quality control of avocado.

       

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