Honey characterization using computer vision system and artificial neural networks.
This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry.
Department of Agricultural Machinery Engineering, Tarbiat Modares University, Tehran, Iran.
Sahameh
S
Shafiee
Department of Agricultural Machinery Engineering, Tarbiat Modares University, Tehran, Iran. Electronic address: minaee@modares.ac.ir.
Saeid
S
Minaei
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Nasrollah
N
Moghaddam-Charkari
Department of Food Science and Technology , Tarbiat Modares University, Tehran, Iran.
Mohsen
M
Barzegar
D000975:Antioxidants / Q000032:analysis; D003116:Color; D003124:Colorimetry; D006722:Honey / Q000032:analysis*; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation; D016571:Neural Networks, Computer; D010636:Phenols / Q000032:analysis
D000975:Antioxidants; D010636:Phenols
Antioxidant; Ash content; Colour; Computer vision system; Honey