基于高光谱成像的大米中蛋白质含量的可视化研究

为了研究大米蛋白质含量在品种和产地之间的差异,将采集的大米按照品种、产地进行分类。将提取的感兴趣区域光谱信息与化学方法测定的蛋白质含量相结合,建立全波长预测模型,通过对比确定最优的模型为偏最小二乘回归(partial least-squares regression,PLSR)。连续投影算法(successive projection algorithm,SPA)筛选特征波段,建立PLSR的特征波长模型,其性能与全波长模型相当。提取特征波长下的高光谱图像,将提取的特征图像上的所有像素点的光谱数据导入已建好的SPA-PLSR模型,预测各像素点的蛋白质含量,并把高光谱灰度图像进行伪彩色处理,得到不同品种产地大米的蛋白质含量分布图。结果表明,利用高光谱成像技术对大米中蛋白质含量分布进行可视化研究具有可行性,为后期筛分大米的产地和品种提供依据。

Visualization of Protein Content in Rice Based on Hyper-spectral Imaging

In order to study the difference of protein content in rice from different varieties and places of origin,the collected rice was classified according to varieties and places of origin.Combining the extracted spectral information of the region of interest with the protein content determined by the chemical method,a full-wavelength prediction model was established,and the optimal model was determined as partial least-squares regression(PLSR)by comparison.Successive projection algorithm(SPA)was used to select characteristic bands and build the characteristic wavelength model of PLSR,which has similar performance to the full wavelength model.The hyper-spectral image at characteristic wavelength was extracted,and the spectral data of all the pixels on the extracted feature image was imported into the established SPA-PLSR model.Then the protein content of each pixel was predicted,and the hyper-spectral gray-scale image was pseudo-color processed to obtain the protein content distribution maps of rice from different varieties.The results showed that it was feasible to visualize the protein content distribution in rice by hyper-spectral imaging,which provides a basis for screening varieties and origins of rice in the later stage.