Honey characterization using computer vision system and artificial neural networks

• Published in: Food chemistry 2014-09, Vol.159, p.143-150
• Main Authors: Shafiee, Sahameh, Minaei, Saeid, Moghaddam-Charkari, Nasrollah, Barzegar, Mohsen
• Format: Article
• Language: English
• Subjects: Antioxidant; Antioxidants - analysis; Ash content; Biological and medical sciences; Color; Colorimetry; Colour; Computer vision; Computer vision system; Eye and associated structures. Visual pathways and centers. Vision; Food toxicology; Fundamental and applied biological sciences. Psychology; Honey; Honey - analysis; Image Processing, Computer-Assisted - instrumentation; Learning theory; Mathematical models; Medical sciences; Neural networks; Neural Networks (Computer); Phenols - analysis; Toxicology; Vertebrates: nervous system and sense organs
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2014.02.136

•Computer vision system combination with ANN is a good tool to honey characterization.•Computer vision system can measure honey colour accurately.•Antioxidant activity of honey can be predicted by computer vision system.•Total phenolic content of honey can be measured by CVS and ANN combination.•Honey ash content can be predicted precisely by CVS. 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 R2 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.