摘要
运用db1-db10等10个小波基4尺度分解对120个赣州脐橙样品的近红外光谱进行消噪处理,分别建立了各种小波消噪光谱的脐橙维生素C（VC）含量的PLS模型。通过PLS模型预测精度比较表明：所有db小波基变换都能有效消除脐橙VC近红外光谱噪声,PLS模型预测精度均高于不处理光谱建立的模型预测精度。其中,消噪效果最好的小波基是db5,模型预测值与实测值的相关系数R达到0.942 7、内部交叉验证均方差RMSECV仅为2.02 mg/（100 g）。因此,对脐橙VC含量近红外光谱消噪预处理的最优小波基是db5小波。
By using db1-db10 （10 in total） wavelet transform with the decomposing level of 4, the nearinfrared spectra （NIRS） signals obtained from 120 umbilical orange samples were denoised, the PLS models of vitamin C （VC） content were established by each wavelet base. Through comparing the prediction accuracy of PLS models, it shows that all db wavelet transform can eliminate the noise in VC NIRS effectively, the prediction accuracy of PLS models are higher than models not being treated. When the wavelet was dbS, the best prediction effect was obtained, with the correlation coefficient R between the prediction and true values being 0. 942 7 and the expected variance R MSECV being as low as 2.02 mg/（100 g）. In conclusion, the db5 is the best wavelet for NIRS denoising of VC content in umbilical orange.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2009年第4期143-146,共4页
Transactions of the Chinese Society for Agricultural Machinery
关键词
脐橙
近红外光谱
小波基
消噪
Umbilical orange, Near-infrared spectra, Wavelet base, Denoising
