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Quality Monitoring and Analysis of Xinjiang 'Korla' Fragrant Pear in Cold Chain Logistics and Home Storage with Multi-Sensor Technology (CROSBI ID 269080)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Liu, Jingjing ; Xu, Zhang ; Li, Zhigang ; Zhang, Xiaoshuan ; Jemrić, Tomislav ; Wang, Xiang Quality Monitoring and Analysis of Xinjiang 'Korla' Fragrant Pear in Cold Chain Logistics and Home Storage with Multi-Sensor Technology // Applied sciences (Basel), 9 (2019), 18; 3895, 14. doi: 10.3390/app9183895

Podaci o odgovornosti

Liu, Jingjing ; Xu, Zhang ; Li, Zhigang ; Zhang, Xiaoshuan ; Jemrić, Tomislav ; Wang, Xiang

engleski

Quality Monitoring and Analysis of Xinjiang 'Korla' Fragrant Pear in Cold Chain Logistics and Home Storage with Multi-Sensor Technology

Korla fragrant pear is prestigious for its special texture and unique flavor but suffers storage and supply chain diffculties for its deterioration-prone properties. In order to improve the storage quality of Korla fragrant pears during the whole cold chain from the orchard to the customers, the paper deeply researches multiple influencing factors of cold chain logistics and home storage of Korla fragrant pears with multi- sensor technology (MST), such as the temperature, relative humidity, concentrations of oxygen (O2), carbon dioxide (CO2) and ethylene (C2H4). Cold chain logistics are assessed by sensory evaluation and physiological index measurement, and home storage environments are classified by using back propagation neural network (BPNN) in both refrigerators and ordinary rooms. Experimental results show that the MST-based detectors can improve the accuracy of continuous sensor data acquisition, such that the preservation quality of Korla fragrant pears is effectively enhanced by data analysis on gas contents, firmness, pH, and total soluble solids. These results indicate that Korla fragrant pears stored in refrigerators have a higher acceptance for customers.

‘Korla’ fragrant pears ; cold chain logistic ; the multi-sensor technology ; back propagation neural network

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Podaci o izdanju

9 (18)

2019.

3895

14

objavljeno

2076-3417

10.3390/app9183895

Trošak objave rada u otvorenom pristupu

Povezanost rada

Informacijske i komunikacijske znanosti, Poljoprivreda (agronomija)

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