鲜食葡萄物流质量安全评估方法研究

发布时间：2018-01-21 21:24

  本文关键词： 鲜食葡萄 物流质量安全评估 微生物 定量二氧化硫 无损检测　出处：《中国农业大学》2016年博士论文　论文类型：学位论文

【摘要】：鲜食葡萄在我国居民水果消费中占有重要地位,也是世界主要消费水果之一,但鲜食葡萄属于浆果类水果,不耐贮藏,其采收到最终消费的过程中,通常要经过多阶段的物流过程,因此,研究鲜食葡萄物流质量安全具有重要的现实意义。本文基于实地调研和相关文献总结,分析了鲜食葡萄物流过程特点和物流质量安全形成机理,并针对其中关键要素展开了研究。基于品质衰变量化分析,研究了鲜食葡萄物流质量安全评估方法；研究了SO2浓度对鲜食葡萄品质变化和物流质量安全评估的影响；基于近红外检测和气味识别,研究了基于无损检测的鲜食葡萄物流质量安全评估方法；在系统分析鲜食葡萄物流质量安全信息化需求的基础上设计了鲜食葡萄物流质量安全管理平台。主要研究结论如下：(1)本文研究中,表面霉菌生长是鲜食葡萄物流质量安全关键限制性因素,构建的霉菌生长动力学模型决定系数达到0.9以上,模型可靠性评价中,准确因子偏离值小于0.2,表明基于霉菌生长动力学的方法对鲜食葡萄物流品质变化预测与物流质量安全评估具有适用性。研究还表明,鲜食葡萄物流品质变化过程中的特定腐败菌除灰霉菌外还可能为青霉菌,且其生长过程存在不稳定性。(2)使用SO2控制表面霉菌生长条件下,质构变化和褐变成为鲜食葡萄物流质量安全关键限制性因素,可分别基于一级反应模型和零级反应模型建模,模型决定系数均达到0.8以上,基于化学动力学的方法适用于相应条件下的物流质量安全评估。研究还表明,冷链物流条件下,10ppm的稳定SO2浓度可以有效控制鲜食葡萄表面微生物生长,而常温条件下,需要维持20ppm的SO2浓度。(3)构建的基于近红外光谱的鲜食葡萄质构变化无损检测模型决定系数达到0.93,交叉验证中,模型预测准确度为0.81,表明近红外检测结合质构变化动力学模型的方法可用于鲜食葡萄物流质量安全评估。经传感器阵列优化,基于4气体传感器阵列构建的0℃条件下的鲜食葡萄品质新鲜度货架期当量无损检测模型,决定系数与验证准确度均达到0.8以上,相应方法可用于基于气味识别的鲜食葡萄物流质量安全评估,研究还表明,常温条件下的建模需要考虑SO2浓度的影响。(4)基于鲜食葡萄物流安全信息化需求分析和相关技术应用测试,互联网技术、GPRS技术和SMS预警技术的集成可用于提高鲜食葡萄物流安全信息管理效率,保障鲜食葡萄物流质量安全评估有效性。
[Abstract]:Fresh grape plays an important role in the consumption of fruit in our country and is also one of the main consumption fruits in the world. Usually, there are many stages of logistics process. Therefore, it is of great practical significance to study the quality and safety of fresh grape logistics. This paper is based on field research and related literature summary. The characteristics of fresh grape logistics process and the forming mechanism of logistics quality and safety were analyzed, and the key elements were studied. Based on the quantitative analysis of quality decay, the evaluation method of fresh grape logistics quality and safety was studied. The effect of SO2 concentration on quality change and logistics quality safety evaluation of fresh grape was studied. Based on near-infrared detection and odor recognition, the quality and safety evaluation method of fresh grape logistics based on non-destructive testing was studied. On the basis of systematic analysis of fresh grape logistics quality and safety information requirements, the quality and safety management platform of fresh grape logistics is designed. The main research conclusions are as follows: 1) in this paper. Surface mold growth is a key limiting factor for the quality safety of fresh grape logistics. The determinant coefficient of the established mold growth kinetic model is more than 0.9, and the reliability of the model is evaluated. The deviation value of the accurate factor is less than 0.2, which indicates that the method based on mold growth kinetics is applicable to the prediction of fresh grape logistics quality change and the evaluation of logistics quality safety. The specific spoilage bacteria in the process of changing the quality of fresh grape logistics may also be Penicillium, and its growth process is unstable. 2) SO2 is used to control the growth of surface fungi. The changes of texture and browning are the key limiting factors for the quality safety of fresh grape logistics, which can be modeled based on first-order reaction model and zero-order reaction model, respectively, and the determination coefficient of the model is more than 0.8. The method based on chemical kinetics is suitable for the evaluation of logistics quality and safety under the corresponding conditions. The study also shows that under the condition of cold chain logistics. Stable SO2 concentration of 10ppm can effectively control the growth of fresh grape surface microorganisms, but under normal temperature conditions. The determination coefficient of the nondestructive detection model for texture change of fresh grapevine based on near infrared spectroscopy was 0.93, which needed to be maintained at 20ppm SO2 concentration. The prediction accuracy of the model is 0.81. it shows that the method of near-infrared detection combined with the kinetic model of texture change can be used to evaluate the quality and safety of fresh grape logistics, which is optimized by sensor array. The non-destructive testing model of fresh grape quality shelf life equivalent at 0 鈩，

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