振动胁迫下双孢蘑菇高光谱成像品质检测

Hyperspectral Imaging for Quality Detection of Agaricus bisporus Under Vibration Stress

为快速有效识别双孢蘑菇轻微损伤,以不同振动时间后不同损伤程度的双孢蘑菇为研究对象,采集400~1000 nm的完好无损、振动60 s和振动120 s双孢蘑菇的近红外高光谱图像,发现3种类型的双孢蘑菇在450~750 nm的光谱曲线有明显差异。比较标准正态变量变换、SG(Savitzky-Golay)平滑和多元散射校正等预处理方法,确定SG平滑为最优预处理方法。并将处理后的数据采用连续投影算法和竞争性自适应重加权算法提取不同损伤程度的特征波段;基于灰度共生矩阵提取500 nm波长特征图像感兴趣区域的纹理特征,分别将光谱信息和纹理特征信息作为输入,建立偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)、BP(back propagation)神经网络和极限学习机损伤程度识别模型。结果表明,两种特征集建模,PLS-DA模型均表现出最好的识别效果,PLS-DA模型训练集和测试集平均识别准确率为93.33%、91.11%和88.89%、86.67%。最后基于光谱-纹理融合信息建立PLS-DA模型,训练集和测试集总体识别正确率分别为97.78%、95.56%。结果表明,光谱-纹理融合信息建模预测效果优于单一特征信息建立的判别模型。因此,采用高光谱融合信息建模可以提高不同损伤程度的双孢蘑菇检测精度,为双孢蘑菇贮藏、分类提供理论支撑。

In order to quickly and effectively identify the slight damage of white button mushroom(Agaricus bisporus),the near-infrared hyperspectral images(400‒1000 nm)of the intact mushroom and the mushroom with different degrees of damage caused by vibration for 60 and 120 s were recorded.It was found that the spectra of the three types of samples in the wavelength range 450‒750 nm were obviously different.Compared to standard normal variable transformation and multivariate scattering correction,Savitzky Golay(SG)smoothing was determined as a better pretreatment method.The successive projection algorithm(SPA)and competitive adaptive reweighted sampling(CARS)were applied to extract the vibration-specific wavelengths.Based on the gray-level co-occurrence matrix,the texture features of the region of interest of the feature image at 500 nm were extracted.A model to discriminate the degree of damage of mushrooms was established using partial least square discriminant analysis(PLS-DA),BP neural network and extreme learning machine(ELM)based on the spectral information and the texture feature information,separately.The results showed that the PLS-DA model with each of the two feature sets as input had better performance than the other two models.With the spectral information as input,the average recognition accuracies for the training set and the test set were 93.33%and 91.11%,while those with the texture feature information as input were 88.89%and 86.67%,respectively.Finally,a PLS-DA classification model was established based on the spectrum-texture fusion information,whose overall classification accuracies for the training set and the test set were 97.78%and 95.56%,respectively.The predictive performance of the model based on the spectrum-texture fusion information was better than the model based on the single information.Therefore,the application of spectrum-texture fusion information-based modeling can improve the detection accuracy of white button mushrooms with different degrees of damage,which provides theoretical support for the storage and classification of white button mushrooms.