Citrus greening or Huanglongbing (HLB) is one of most serious citrus diseases in the world. Once a tree is infected, there is no cure. The feasibility was investigated for discriminating citrus greening by use of near infrared (NIR) spectroscopy and least square support vector machine (LS-SVM). The spectra of sound and citrus greening samples were recorded in the wavenumber range of 4000-9000 cm<sup>-1</sup>. The preprocessing method of second derivative with a gap of seven was adapted to eliminate spectral baseline. The spectral variables were optimized by principal component analysis (PCA) and (UVE) algorithms. The unknown samples were used to access the performance of the models. Compared to the PLS-DA model, the LS-SVM was better with the input vector of the first 15 principal components and linear kernel function. The regularization factor (γ) of linear kernel function was 1.8756, and the operation time of LS-SVM model was 0.86s. The recognition error of the LS-SVM model was zero. The results showed that the combination of LS-SVM and NIR spectroscopy could detect citrus greening nondestructively and rapidly.
Two different near infrared spectrometric systems were used to determine soluble solids content (SSC) of intact apple,
including a portable near infrared (NIR) spectrometer and an online NIR system. The pretreatment methods were applied to
improve the predictive results. The moving average smoothing was significant. The effective wavelength regions were chosen
by interval partial least squares (iPLS) and backward iPLS (Bipls). Then the models were developed by partial least square
regression (PLSR) and least square support machine (LS-SVM). Performance comparisons were made in the context of 30
unknown samples prediction. The LS-SVM models were better than others with correlation coefficient (R) and root mean
square error of prediction (RMSEP) of (0.88, 0.80ºBrix) and (0.82, 1.01ºBrix) for portable and online measurement mode,
respectively. The results demonstrated that the online measurement mode was not as well as the portable.
The photodiode array (PDA) spectrometer combined with partial least square (PLS) was developed to rapid measure the
internal quality indices of Gannan navel orange nondestructively in the wavelength range of 550-950nm. The original spectra
were processed by standard normal variate (SNV) and Savitzky-Golay (SG) smooth method. The optimal models of
internal quality indices were determined after different spectral windows chosen. The optimal model of soluble solids
content (SSC), total acidity (TA) and ratio of them were developed with RMSECV = 0.5118Brix%, 0.0856% and 2.0617
by PLS method, respectively. The optimal spectral windows were 700-950nm, 600-750nm and 600-950nm for measuring
internal indices nondestructively by PDA. The results illustrated that PDA with PLS method were a rapid tool to measure the
internal quality indices of Gannan navel orange nondestructively.