Application of data mining and adaptive neuro-fuzzy structure to predict color parameters of walnuts (Juglans regia L.)
Quality is the primary factor designating consumer satisfaction and the
market price of agricultural commodities. Color
and general appearance are the basic quality indicators for agricultural
products. Surface colors are assessed through colorimetric
measurements 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 selected
walnuts 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 Find
Laws algorithm of PolyAnalyst. The color parameters obtained from these
newly developed equations were used in training of adaptive
neuro-fuzzy structure. Then color index (CI), chroma (C*), and hue (h*)
angle parameters were predicted by adaptive neuro-fuzzy
approach. 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 for
Yalova-1, and 0.01 for Yalova-3 walnuts. The obtained equations can be
used as a viable alternative instead of equations that vary
depending on whether a* and b* are negative or positive.
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