| Title: | Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics |
| Author(s): | Yin L; Jayan H; Cai J; El-Seedi HR; Guo Z; Zou X; |
| Address: | "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. Pharmacognosy Group, Department of Pharmaceutical Biosciences, Biology Medical Center, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden. International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China" |
| ISSN/ISBN: | 2304-8158 (Print) 2304-8158 (Electronic) 2304-8158 (Linking) |
| Abstract: | "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(2), O(2) and C(2)H(4), 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(p)) 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" |
| Keywords: | apple early warning gas sensor logistics control simulated annealing spoilage monitoring; |
| Notes: | "PublisherYin, Limei Jayan, Heera Cai, Jianrong El-Seedi, Hesham R Guo, Zhiming Zou, Xiaobo eng 2022YFD2100604/National Key R&D Program of China/ Switzerland 2023/08/12 Foods. 2023 Aug 6; 12(15):2968. doi: 10.3390/foods12152968" |
