Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy

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.

Predicting wheat kernels’protein content by near infrared hyperspectral imaging

The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat kernels.Seventy-nine samples from 11 breeds of wheat kernels were collected.The protein percentage of each sample measured by semimicro-Kjeldahl method was taken as the reference value.After comparing the prediction models of principal components regression(PCR)and partial least squares regression(PLSR)with various pretreatment methods,PLSR preprocessed by zero mean normalization(z score)function of MATLAB was found to obtain better prediction results than other regression models.Based on 10 latent variables of PLSR,the radial basis function(RBF)neural network was applied to improve the prediction,in which the coefficients of determination(R2)were greater than 0.92 for both the calibration set and validation set,while the corresponding RMSE values were 0.3496 and 0.4005,respectively.Therefore,hyperspectral imaging can provide a fast and non-destructive method for predicting the wheat kernels’protein content.