基于高光谱成像技术的生鲜猪肉含水率无损检测

Non-destructive Detection of Water Content in Fresh Pork Based on Hyperspectral Imaging Technology

为了建立稳健的生鲜猪肉含水量高光谱预测模型,研究了样本集划分、光谱预处理和波段选择对模型预测效果的影响。实验结果表明,采用浓度梯度法划分样本结合多元散射校正、一阶导和标准化组合的光谱预处理方法建立的PLSR预测模型最优,交叉验证和预测相关系数分别为0.814和0.804,均方根误差分别为0.726%和0.686%。采用竞争性自适应重加权算法优选特征波段建模,显著提高了模型的预测精度,交叉验证和预测相关系数分别提高到0.926和0.924,均方根误差分别减小到0.467%和0.438%。

In order to build a robust model for predicting water content in fresh pork based on hyperspectral imaging technology,the influence of sample set partition,spectral preprocessing and wavelengh selection on model prediction result was discussed.The pork sample set was parted by concentration gradient( CG) firstly,and then the reflectance spectra was pre-processed with multiple scattering correlation( MSC), first derivative and autoscale successively.The partial least square regression( PLSR) model was built,which had the best prediction abilities.The water content values were predicted with the cross-validation and prediction correlation coefficients( Rc and Rp) of 0.814 and0.804,with the root mean square error( RMSECV and RMSEP) values of 0.726% and 0.686%,respectively.The feature wavelengths were identified by using competitive adaptive reweighted algorithm.The proposed PLSR model was again built by using the feature wavelengths,which had remarkable prediction abilities with Rc and Rp of 0.926 and 0.924,with RMSECV and RMSEP of 0.467% and0.438%,respectively.