In this study, the potential of visible and near infrared spectroscopy was investigated to classify the maturity stage and to predict the quality attributes of pomegranate variety “Ashraf” such as total soluble solids content, pH, and titratable acidity during four distinct maturity stages between 88 and 143 days after full bloom. Principal component analysis was used to distinguish among different maturities. The prediction models of internal quality attributes of the pomegranate were developed by partial least squares regression. The transmission spectra of pomegranate were obtained in the wavelength range from 400 to 1100 nm. In this research several preprocessing methods were utilized including centering, smoothing (Savitzky–Golay algorithm, median filter), normalization (multiplicative scatter correction and standard normal variate) and differentiation (first derivative and second derivative). It concluded that different preprocessing techniques had effects on the classification performance of the model using the principal component analysis method. In general, standard normal variate and multiplicative scatter correction gave better results than the other pretreatments. The correlation coefficients (r), root mean square error of calibration and ratio performance deviation for the calibration models were calculated: r = 0.93, root mean square error of calibration = 0.22 °Brix and ratio performance deviation = 6.4 °Brix for total soluble solids; r = 0.84, root mean square error of calibration = 0.064 and ratio performance deviation = 4.95 for pH; r = 0.94, root mean square error of calibration = 0.25 and ratio performance deviation = 5.35 for titratable acidity.

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