文献类型： 外文期刊

作者： Badia-Melis, R. ¹ ; Qian, J. P. ² ; Fan, B. L. ² ; Hoyos-Echevarria, P. ³ ; Ruiz-Garcia, L. ¹ ; Yang, X. T. ² ;

作者机构： 1.Univ Politecn Madrid, ETSI Agron, Dept Ingn Agroforestal, Edificio Motores,Avda Complutense 3, E-28040 Madrid, Spain

2.Beijing Acad Agr & Forestry Sci, NERCITA, Beijing 100097, Peoples R China

3.Univ Politecn Madrid, EUIT Agr, Dept Prod Agr, Avda Complutense S-N, E-28040 Madrid, Spain

关键词： Cold chain;Temperature estimation;Artificial neural networks;Thermal image;Food monitoring

期刊名称：FOOD AND BIOPROCESS TECHNOLOGY （ 影响因子：4.465； 五年影响因子：4.793 ）

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收录情况： SCI

摘要： The inability to correctly implement and safeguard a product cold chain leads to premature product spoilage and increased product waste. Special care is required to both implement and monitor the cold chain for perishable goods in order to preserve them. Many technologies are available on the market today with varying levels of success. This article presents a new technique, namely thermal imaging predicts surface temperature over a pallet of apples whilst comparing packaging (plastic boxes and cardboard boxes). This temperature data was then introduced as an input in artificial neural network (ANN) software to estimate the temperature across the entire pallet. Results obtained (root mean squared error [RMSE]) indicate that the estimation with plastic boxes has an error of 0.41 A degrees C whilst the error, taking as a reference the surface temperature, would be RMSE 2.14 A degrees C. In the case of cardboard boxes, the estimation error is 0.086 A degrees C whilst only taking into account the thermal image, data would be RMSE 3.56 A degrees C. This article proves the concept of the possibility of temperature monitoring by ANN through thermal imaging technology.