Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp

  • Authors:

  • Mohebbat Mohebbi;Mohammad-R Akbarzadeh-T;Fakhri Shahidi;Mahmoud Moussavi;Hamid-B Ghoddusi

  • Affiliations:

  • Department of Food Science and Technology, Ferdowsi University of Mashhad, Iran;Department of Electrical Engineering, Ferdowsi University of Mashhad, Iran;Department of Food Science and Technology, Ferdowsi University of Mashhad, Iran;Department of Chemical Engineering, Ferdowsi University of Mashhad, Iran;Microbiology Research Unit, School of Human Sciences, London Metropolitan University, London, UK

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2009

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Abstract

This paper presents a method based on computer vision systems (CVS) to estimate shrimp dehydration level by analyzing color during drying process. Since the most commonly used color space in food industry is L*a*b, transformation of RGB digital images to L*a*b units was carried out using direct two steps model with @c factor. Experimental data obtained from images captured at different drying temperatures (100-130^oC) and several time intervals (15-180min) were analyzed with a complete randomized block design (CRBD), and the means were compared with Duncan's multi-range test. Multiple linear regression (MLR) and artificial neural networks (ANN) were applied for correlating the color features to moisture content of dried shrimp determined chemically. Results obtained with these two models lead to 0.80 and 0.86 correlation coefficients in MLR and ANN models, respectively. While there is no statistical difference at p