Fig 1.
Nine peach cultivars, Prunus persica L.
Fig 2.
Three major dimensions of peach.
Fig 3.
Structure of ANN model to predict total soluble solids, titratable acidity, ratio of total soluble solids to titratable acidity, vitamin C, anthocyanin, and total carotenoids contents of fresh peach fruit.
Table 1.
Mean fruit weight, juice volume, sphericity percent, TSS/titratable acidity, and Vitamin C for nine commercial peach cultivars.
Fig 4.
Mean TSS and titratable acidity evaluated for nine commercial fresh peach cultivars.
Fig 5.
Mean anthocyanin and total carotenoids evaluated for nine commercial fresh peach cultivars.
Fig 6.
Mean L*, Hue, and chroma for surface skin evaluated for nine commercial fresh peach cultivars.
Table 2.
Regression coefficients of different chemical compositions based on Eq (5) with coefficients of determination (R2) using training data set.
Table 3.
Error criteria for the prediction of chemical composition parameters in peach fruit using multiple linear regression and artificial neural network models for training data sets.
Fig 7.
Regression analysis between the desired output and the obtained output variables.
Table 4.
Error criteria for the prediction of chemical composition parameters in peach fruit using multiple linear regression and artificial neural network models for testing data sets.
Table 5.
Contribution percentage of six independent variables used in the artificial neural network model for predicting TSS, titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids in peach cultivars.