A portable near infrared spectroscopy system was developed for assessing the quality of Nanfeng mandarin fruit.One hundred and fifty-three Nanfeng mandarin samples were used to measure the performance of the system.Several pretreatment methods were adopted to process the spectra.Then Support Vector Machine(SVM),Back Propagation Neural Network(BPNN)and Partial Least Square(PLS)were used to build models for soluble solids content(SSC),titratable acidity(TA),vitamin C and surface color.The best results were obtained by SVM.The correlation coefficient(R)and root mean square error of prediction(RMSEP)were(0.93,0.65°Brix),(0.66,0.09%),(0.81,2.7mg/100g)and(0.57,0.81)for SSC,TA,vitamin C and color,respectively.The results demonstrated that the portable near infrared spectroscopy was feasible for determining the Nanfeng mandarin quality nondestructively.
Variable selection is applied widely for visible-near infrared(Vis-NIR)spectroscopy analysis of internal quality in fruits.Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content(SSC)in navel oranges.Moving window partial least squares(MW-PLS),Monte Carlo uninformative variables elimination(MC-UVE)and wavelet transform(WT)combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges.The performances of these methods were compared for modeling the Vis NIR data sets of navel orange samples.Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation cofficient(r)of 0.89 and lower root mean square error of prediction(RMSEP)of 0.54 at 5 fruits per second.It concluded that Vis NIR spectroscopy coupled with WT-MC-UVE may be a fast and efective tool for online quantitative analysis of SSC in navel oranges.
