基于不同波段的枸杞多糖及总糖高光谱成像检测

摘要

采用高光谱图像技术对枸杞多糖和总糖含量进行检测,并探寻其最适宜的光谱波段.首先采用多元散射校正、Savitzky-Golay平滑(S-G平滑)和标准正态变量变换3种常用光谱预处理方法对原始光谱进行预处理,并对结果进行对比,选择多元散射校正预处理方法,以消除散射的影响;然后分别基于相关系数的数值及不同范围波长的特性,选择有效波段、可见光波段、近红外波段及全波段图像的平均光谱反射值作为特征参量;最后建立基于不同特征参量的枸杞多糖和总糖含量的BP神经网络预测模型.结果表明:基于全波段条件下光谱信息所建立的预测模型最佳,枸杞多糖含量预测正确率为97.59％,相关系数为0.997 4,均方根误差为0.077 7,枸杞总糖含量预测正确率为100％,相关系数为0.996 8,均方根误差为0.250 6.因此高光谱无损检测枸杞多糖和总糖含量具有可行性.%Hyperspectral imaging technology was used to detect the contents of polysaccharides and total sugar as two important quality indicators for rapid evaluation of Chinese wolfberry.For this purpose,the optimal spectral waveband was explored.Firstly,the original spectra were preprocessed using three commonly used methods,namely multiplicative scatter correction,Savitzky-Golay smoothing and standard normal variate,and comparison of the results obtained showed that multiplicative scatter correction was selected to eliminate the scattering effect.Then the average spectral reflectance value was extracted for use as characteristic parameters from hyperspectral images in the effective wavebands,the visible wavebands,the near-infrared wavebands and the full wavebands based on the correlation coefficients and the spectral characteristics in different waveband ranges.Finally,BP neural network models were established based on different characteristic parameters to predict the contents of polysaccharides and total sugar in Chinese wolfberry.The results showed that the prediction model based on the full bands was the best one.The correct prediction rate of the model for polysaccharide content was 97.59％ with a correlation coefficient of 0.997 4 and a root mean square error of 0.077 7.The correct prediction rate of the model for total sugar content was 100％ with a correlation coefficient of 0.996 8 and a root mean square error of 0.250 6.Therefore,it is feasible to detect the contents of polysaccharide and total sugar in Chinese wolfberry by hyperspectral imaging technology.