Artificial Neural Networks and Thermal Image for Temperature Prediction in Apples

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...

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Bibliographic Details
Published in:Food and bioprocess technology 2016-07, Vol.9 (7), p.1089-1099
Main Authors: Badia-Melis, R., Qian, J. P., Fan, B. L., Hoyos-Echevarria, P., Ruiz-García, L., Yang, X. T.
Format: Article
Language:English
Subjects:
Agriculture
Apples
Artificial neural networks
Biotechnology
Boxes
Cardboard
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Cold storage
Consumer goods
Food Science
Heat detection
Neural networks
Original Paper
Packaging
Root-mean-square errors
Spoilage
Supply chains
Surface temperature
Thermal imaging
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Summary: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 °C whilst the error, taking as a reference the surface temperature, would be RMSE 2.14 °C. In the case of cardboard boxes, the estimation error is 0.086 °C whilst only taking into account the thermal image, data would be RMSE 3.56 °C. This article proves the concept of the possibility of temperature monitoring by ANN through thermal imaging technology.
ISSN:1935-5130
1935-5149
DOI:10.1007/s11947-016-1700-7