Yin Limei, Jayan Heera, Cai Jianrong, El-Seedi Hesham R, Guo Zhiming, Zou Xiaobo
Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Foods. 2023 Aug 6;12(15):2968. doi: 10.3390/foods12152968.
In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO, O and CH, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (R) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.
在储存和冷链物流过程中，苹果容易受到物理碰撞或微生物感染，这很容易导致在微环境中变质，从而引发广泛感染并造成严重的采后经济损失。因此，开发监测苹果变质并提供变质预警的方法已成为减少采后损失的重点。因此，在本研究中，通过测量苹果变质过程中挥发性成分的产生并结合化学计量分析，开发了一种变质监测与预警系统。设计了一个苹果变质监测原型，包括一个能够测量挥发性有机化合物（如一氧化碳、氧气和甲烷）的气体监测阵列，并与温度和湿度传感器集成在一起。该原型获取了模拟苹果仓库的传感器信息，并基于各种建模方法建立了苹果变质的多因素融合预警模型。模拟退火-偏最小二乘法（SA-PLS）是最优模型，预测集相关系数（R）和预测均方根误差（RMSEP）分别为0.936和0.828。通过将最优监测与预警模型加载到微控制器中，成功实现了对变质情况的实时评估。构建了一个苹果远程监测与预警平台，以可视化苹果仓库的传感器数据和变质水平。结果表明，基于特征气体传感器阵列的原型能够有效地监测和预警苹果变质。
