小波变换在脐橙维生素C含量近红外光谱预测中的应用

 【目的】探索快速检测柑橘Vc含量的方法。【方法】利用不同分解水平的Daubechies3小波变换，对100个脐橙整果样品的近红外光谱信号进行了消噪处理，并利用消噪后的重构光谱对脐橙Vc含量进行了偏最小二乘法交叉验证。【结果】小波分解尺度水平不同，偏最小二乘法交叉验证效果各不相同，在分解水平为4时效果最好，其预测值与标准值的相关系数R达到0.9574，交叉验证预测均方差RMSECV仅为3.9 mg/100g。比较了11种光谱预处理方法，其中，小波消噪后的偏最小二乘法交叉验证模型预测效果最好，预测值与真值的相关系性最好。【结论】小波消噪后建立的近红外光谱模型能准确地对脐橙Vc含量进行无损快速的定量分析。

Application of Wavelet Transformation in Umbilical Orange Vitamin C Content Prediction with Near-Infrared Spectroscopy

In order to quest a approach for measure vitamin C content of orange, based on wavelet transformation by different decomposing level, the near-infrared spectroscopy signals of 100 intact orange samples were do-noised and some PLS-CV (partial least squared-cross validation ) operations were proposed for the prediction of orange Vc ( Vitamin C ) content with the reconstructed spectra after do-noised. The results show that the PLS-CV results were not the same when the wavelet decomposing level was different. PLS-CV result was best at a wavelet decomposing level of 4. Its R was 0.9574, and its RMSECV was 3.9mg/100g . Therefore, it is concluded that the FT-NIR model treated by wavelet do-noised can be feasible to detect Vc content of orange rapidly and nondestructively.