摘要
应用便携式近红外光谱分析仪对112个柑桔进行无损检测,运用主成分正交信号校正、加强正交信号校正结合广义回归神经网络的方法分别建立柑桔酸度定量分析模型。结果表明:采用EOSC方法能够使模型具有良好的预测能力并能够防止对数据造成过度校正。EOSC柑桔酸度模型校正集相关系数Rc=0.888 0,预测集相关系数Rp=0.885 6,RMSEP=0.081 65。研究结果表明EOSC预处理方法结合广义回归神经网络可以用于柑桔样本的酸度测定。
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 （Re） obtained by the EOSC acidity model was 0. 888 0, and prediction samples （R,） 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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2012年第7期1931-1934,共4页
Spectroscopy and Spectral Analysis
基金
北京市属高等学校人才强教计划项目(PHR20100718)资助
