In this study, Visible/near-infrared (Vis/NIR) diffuse reflectance spectroscopy at 530-1560 nm region was investigated
for the analysis of the soluble solids content (SSC) and color of pear. Least squares support vector regression (LSSVR)
has been proven to be a powerful tool for modeling complex samples through the use of adapted kernel functions.
However, one of the major drawbacks of LSSVR is that the optimization of the regularization and kernel
meta-parameters is time-consuming during training the model, and the modeling results are sensitive to spectral noise.
Wavelet compression pretreatment is an effective method for spectral information extraction and noise elimination. The
calibration set was composed of 75 pear samples and 32 pear samples were used as the validation set. The raw and
pretreated spectra by wavelet compression were modeled using LSSVR, It was shown that wavelet compression
procedure not only shortened the modeling time, but also improved the predictive precision. The correlation coefficient
(r) was improved from 0.78 to 0.93 for SSC, and from 0.95 to 0.96 for color, respectively. The root mean square error of
prediction (RMSEP), optimization time and calibration variables were reduced from 0.68, 0.33s and 1031 to 0.41, 0.03s
and 24 for SSC, while from 1.10, 0.33s and 1031 to 1.07, 0.03s and 40 for color. The results indicated that Vis/NIR
spectroscopy combined with wavelet compression procedure and LSSVR is a reliable approach for predicting the SSC
and color of pear.