Convolutional decoding of thermographic images to locate and quantify honey adulterations.

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Abstract

In this research, 56 samples of pure honey have been mixed with different concentrations of rice syrup simulating a set of adulterated samples. A thermographic camera was used to extract data regarding the thermal development of the honey. The resulting infrared images were processed via convolutional neural networks (CNNs), a subset of algorithms within deep learning. The CNNs have been trained and optimized using these images to detect the commonly elusive rice syrup in honey in concentrations as low as 1% in weight, as well as quantify it. Finally, the model was successfully validated using images which were initially isolated from the training database. The result was an algorithm capable of identifying adulterated honey from different floral origins and quantifying rice syrup with accuracies of 95% and 93%, respectively. Therefore, CNNs have complemented the thermographic analysis and have shown to be a compelling tool for the control of food quality, thanks to traits such as high sensitivity, speed, and being independent of highly specialized personnel.
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