In this study the feasibility of using VIS/NIR spectroscopy along with chemometrics was investigated to predict quality parameters (pH, TSS and firmness) of pomegranate fruit in a nondestructive manner. The effects of different pre-processing methods and spectra treatments, such a column pretreatment (mean centering) and rows pretreatments (including normalization (multiplicative scatter correction (MSC), standard normal variate transformation (SNV)), smoothing (median filter, Savitzky-Golay and wavelet) and transformation (first derivative and second derivative) were analyzed. The results showed that in each studied smoothing techniques SNV gave slightly better results than MSC method. Withal between three studied smoothing techniques namely median filter, Savitzky-Golay and wavelet, the median filter introduced better models. The prediction models were developed by principal component analysis (PCA) and partial least square regression (PLS). The obtained result using first derivative was better for TSS, firmness but second derivative was better for pH. The correlation coefficients (r), RMSEC and RPD for the calibration models were calculated: r=0.95, RMSEC=0.22 Brix and RPD=6.7 Brix for TSS; r=0.85, RMSEC=0.068 and RPD=4.58 for pH; r=0.94, RMSEC=0.65 N and RPD=5.65 N for firmness. Also these parameters for the validation models was found to be: r=0.94, RMSEP =0.21 Brix and RPD=6.72 Brix for TSS; r=0.86, RMSEP =0.069 and RPD =4.43 for pH; r=0.94, RMSEP =0.68 N and RPD =5.33 N for firmness. It was concluded that VIS/NIR spectroscopy and chemometrics combined with different preprocessing techniques could be an accurate and fast method for nondestructive prediction of key pomegranate quality attributes.

Keywords