Abstract：Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with PC-OSC algorithm, EOSC algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity in 112 orange samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with EOSC algorithm were satisfactory and the EOSC algorithm was not as susceptible to overfitting the data as PC-OSC algorithm. The correlation between actual and predicted values of calibration samples (Rc) obtained by the EOSC acidity model was 0.888 0, and prediction samples (Rp) was 0.885 6. The RMSEP was 0.081 65. The results proved that the portable NIR analyzer combined with EOSC algorithm and GRNN can be a feasible tool for the determination of acidity in oranges.
杨 帆，邱晓臻，郝 睿，高 帆，杜 薇，张卓勇*. 基于主成分正交信号校正算法和加强正交信号校正算法对柑桔酸度的检测[J]. 光谱学与光谱分析, 2012, 32(07): 1931-1934.
YANG Fan, QIU Xiao-zhen, HAO Rui, GAO Fan, DU Wei, ZHANG Zhuo-yong*. Determination of Acidity in Oranges Based on Emphatic Orthogonal Signal Correction and Principal Component Orthogonal Signal Correction. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(07): 1931-1934.
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