基于小波变换的柑橘维生素C含量近红外光谱无损检测方法

Approach to nondestructive measurement of Vitamin C content of orange with near-infrared spectroscopy treated by wavelet transform

为了探索快速检测柑橘维生素C含量的方法,利用不同分解水平的Daubechies3小波变换,对100个柑橘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柑橘维生素C含量进行了偏最小二乘法交叉验证(PLC-CV)。结果表明,小波分解尺度水平不同,PLC-CV效果各不相同,在分解水平为4时,PLC-CV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/(100 g)。因此,小波消噪后建立的近红外光谱模型能准确地对柑橘维生素C含量进行无损快速的定量分析。

In order to explore a approach to measure vitamin C content of orange, based on wavelet transform by different decomposing levels, the near-infrared spectroscopy signals of 100 intact orange samples were de-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 de-noised. The results show that the PLS-CV results were not the same when the wavelet decomposing level was different. PLS-CV result was the best at a wavelet decomposing level of 4. Its R was 0. 9574, and its RMSECV was 3.9 mg/（100 g）. Therefore, it is concluded that the FT-NIR model treated by wavelet de-noised is feasible to detect VC content of orange rapidly and nondestructively.