The potential of visible-near infrared (vis/NIR) spectroscopy (400 nm to 1100 nm) for classification of grape berries on the basis of multi inner quality parameters was investigated. Stored Vitis vinifera L. cv. Manicure Finger and Vitis vinifera L. cv. Ugni Blanc grape berries were separated into three classes based on the distribution of total soluble solid content (SSC) and total phenolic compounds (TP). Partial least squares regression (PLS) was applied to predict the quality parameters, including color space CIELAB, SSC, and TP. The prediction results showed that the vis/NIR spectrum correlated with the SSC and TP present in the intact grape berries with determination coefficient of prediction (RP2) in the range of 0.735 to 0.823. Next, the vis/NIR spectrum was used to distinguish between berries with different SSC and TP concentrations using partial least squares discrimination analysis (PLS-DA) with >77% accuracy. This study provides a method to identify stored grape quality classes based on the spectroscopy and distributions of multiple inner quality parameters.

Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%-100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves.

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We extended the current density convolution finite-difference time-domain (JEC-FDTD) method to plasma photonic crystals using the Crank–Nicolson difference scheme and derived the one-dimensional JEC-Crank–Nicolson (CN)-FDTD iterative equation of plasma photonic crystals. The method eliminated the Courant–Friedrich–Levy (CFL) stability constraint and became completely unconditional stable form. The incomplete Cholesky conjugate gradient (ICCG) algorithm is proposed to solve the equation with a large sparse matrix in the CN-FDTD method as the ICCG method improves the speed of convergence, enhances stability, and reduces memory consumption. The JEC-CN-FDTD method is applied to study the characteristics of time domain and frequency domain in the plasma photonic crystal objects. The high accuracy and efficiency of the JEC-CN-FDTD method are confirmed by computing the characteristic parameters of plasma photonic crystals under different conditions such as the electric field distribution of electromagnetic wave, reflection coefficients, and transmission coefficients. Simulation study showed that the algorithm performed stably and could reduce memory consumption and facilitate computer programming.