粉体的密度作为一项很重要的物性参数,对粉体的流动性能有决定性作用,研究粉体的密度,对粉体加工、包装、输送、存储等具有重要意义。以小麦面粉作为实验对象,采用高光谱散射技术,结合高光谱散射图像特征提取方法中的矩方法,探索了面粉堆密度快速无损的检测方案。实验共采集了474个面粉样本在500~1000 nm范围内的高光谱散射图像,先对图像进行前期预处理,以消除图像噪声,并提取了散射图像的零阶矩特征和一阶矩特征;然后分别利用零阶矩特征、一阶矩特征,及零阶一阶联合特征建立了面粉堆密度的偏最小二乘预测模型。结果表明:相对于单一的零阶矩特征或一阶矩特征,联合特征获得了最好的建模结果;联合特征的预测相关系数RP为0.968,剩余预测偏差为3.95;矩方法可以有效提取高光谱散射图像特征,并可应用于粉体堆密度的高精度无损检测。

Powder density, as a very important physical parameter, has a huge influence on the fluidity of powder, therefore, studying the characteristic of powder density is of great significance to powder processing, packaging, transportation, storage and so on. The hyperspectral scattering technique combined with moment method in hyperspectral scattering image feature extraction is used for non-destructive detection of bulk density of wheat flour. Hyperspectral scattering images of 474 wheat flour samples are acquired at the wavelength of 500-1000 nm. Images are pretreated to eliminate the noises, and the zeroth-order moment (ZOM) and the first-order moment (FOM) of scatter images are extracted. Finally, the ZOM, FOM, and their combination (Z-FOM) are used for developing bulk density prediction models using partial least squares (PLS) algorithm. The results demonstrate that the prediction model developed by Z-FOM achieved the optimal performance compared with ZOM or FOM. The PLS model using Z-FOM obtained 0.968 of the predicted correlation coefficient RP, and 3.95 of the residual prediction deviation. Experiments show that the moment method is an effective method for extracting features of the hyperspectral scattering images and can be used for high-accuracy detection of bulk density of wheat flour.