Taste characterization of orange using image processing combined with ANFIS

Alternative methods for taste evaluation of fruits are of great interest to the food industry. This paper deals with the application of ANFIS-based image processing to characterize orange taste. For this purpose, images of 300 orange samples (Bam, Khooni and Thompson varieties) were acquired using a camera and the relevant features were extracted. The features were RGB component, HSV component, texture features, major and minor diameters, area, circumference, R/G and R/B color component ratio and diameter ratio. A sensory test performed by ten panelists was used to provide the reference data for image analysis. Then, the features were entered as input to ANFIS, and taste class of the fruit was the output of ANFIS. Based on the results, the success rate for the taste classification of Bam, Khooni and Thompson orange varieties were found to be 96.6%, 93.3% and 93.3%, respectively.

[1]  Nikos C. Tsourveloudis,et al.  Modeling and optimization of olive stone drying process , 2005 .

[2]  Mahdi Ghasemi-Varnamkhasti,et al.  Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools , 2012, Expert Syst. Appl..

[3]  Dmitry Kurtener,et al.  Evaluation of Ecological Conditions Using Bioindicators: Application of Fuzzy Modeling , 2008, ICCSA.

[4]  Mahmoud Omid,et al.  Application of ANFIS to predict crop yield based on different energy inputs , 2012 .

[5]  F. S. Davies,et al.  Soluble solids accumulation in 'Valencia' sweet orange as related to rootstock selection and fruit size. , 2004 .

[6]  Michel Tenenhaus,et al.  Measurement of the reliability of sensory panel performances , 2006 .

[7]  C. Mahanta,et al.  ANFIS Modeling Based on Full Factorial Design , 2006, 2006 IEEE International Conference on Industrial Technology.

[8]  H. Rogers,et al.  Characterizing root distribution with adaptive neuro-fuzzy analysis , 2011 .

[9]  H. Ertunç,et al.  Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system , 2008 .

[10]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[11]  L. Cronbach.  Coefficient alpha and the internal structure of tests , 1951 .

[12]  Jagdev Singh,et al.  Modelling for tensile strength of friction welded aluminium pipes by ANFIS , 2010, Int. J. Intell. Eng. Informatics.

[13]  Bruce E. Mackey,et al.  Determinants of flavor acceptability during the maturation of navel oranges , 2009 .

[14]  Da-Wen Sun,et al.  Inspection and grading of agricultural and food products by computer vision systems—a review , 2002 .

[15]  Haruhiko Murase,et al.  Machine vision based quality evaluation of Iyokan orange fruit using neural networks , 2000 .

[16]  Chi-Bin Cheng,et al.  Neuro-fuzzy and genetic algorithm in multiple response optimization , 2002 .

[17]  M. Omid,et al.  Estimating volume and mass of citrus fruits by image processing technique , 2010 .

[18]  Saeid Minaei,et al.  Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges , 2012 .