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.
To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibra- tion techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.