The performance of nu-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near-infrared spectroscopy.
The nu-support vector regression (nu-SVR) was used to construct the calibration model between soluble solids content (SSC) of apples and acousto-optic tunable filter near-infrared (AOTF-NIR) spectra. The performance of nu-SVR was compared with the partial least square regression (PLSR) and the back-propagation artificial neural networks (BP-ANN). The influence of SVR parameters on the predictive ability of model was investigated. The results indicated that the parameter nu had a rather wide optimal area (between 0.35 and 1 for the apple data). Therefore, we could determine the value of nu beforehand and focus on the selection of other SVR parameters. For analyzing SSC of apple, nu-SVR was superior to PLSR and BP-ANN, especially in the case of fewer samples and treating the noise polluted spectra. Proper spectra pretreatment methods, such as scaling, mean center, standard normal variate (SNV) and the wavelength selection methods (stepwise multiple linear regression and genetic algorithm with PLS as its objective function), could improve the quality of nu-SVR model greatly.
Zhu
Dazhou
D
College of Food Science and Nutritional Engineering, China Agricultural University, Tsinghua East Road 17, Haidian District, Beijing 100083, People's Republic of China.
Ji
Baoping
B
Meng
Chaoying
C
Shi
Bolin
B
Tu
Zhenhua
Z
Qing
Zhaoshen
Z