Application of data mining and adaptive neuro-fuzzy structure to predict color parameters of walnuts (Juglans regia L.)

Abstract

Quality is the primary factor designating consumer satisfaction and the market price of agricultural commodities. Colorand general appearance are the basic quality indicators for agricultural products. Surface colors are assessed through colorimetricmeasurements including L*, a*, and b* color parameters. In the present study, L*, a*, and b* color parameters of Bilecik, Fernette, Fernor,Kaman-1, Maraş-12, Maraş-18, Sunland, Şen-2, Yalova-1, and Yalova-3 walnut cultivars (color parameters of 100 randomly selectedwalnuts from each cultivar) were measured with a chroma meter (CR-5 Konica Minolta). Based on L*, a*, and b* measurements,equations from which color index (CI), chroma (C*), and hue (h*) angle parameters could be calculated were developed with the FindLaws algorithm of PolyAnalyst. The color parameters obtained from these newly developed equations were used in training of adaptiveneuro-fuzzy structure. Then color index (CI), chroma (C*), and hue (h*) angle parameters were predicted by adaptive neuro-fuzzyapproach. Root mean square error values of the adaptive neuro-fuzzy-based approach were respectively identified as 0.02 for Bilecik,0.01 for Fernette, 0.02 for Fernor, 0.01 for Kaman-1, 0.01 for Maraş-12, 0.01 for Maraş-18, 0.01 for Sunland, 0.01 for Şen-2, 0.01 forYalova-1, and 0.01 for Yalova-3 walnuts. The obtained equations can be used as a viable alternative instead of equations that varydepending on whether a* and b* are negative or positive.