为探索不同光谱预处理方法对太赫兹(THz)光谱的影响,采用平滑、多元散射校正、基线校正和归一化相以及多元散射校正和归一化结合等预处理方法。为优化模型、减少运算量,采用主成分分析(PCA)对太赫兹光谱进行波段压缩,以降低数据维数,基于压缩后的数据分别建立反向传播神经网络 (BPNN)和广义回归神经网络(GRNN)检测模型。实验结果表明:经多元散射校正结合归一化校正处理后的GRNN模型效果最佳,得到的预测相关系数为0.9967,预测均方根误差为0.0050。本实验验证了THz光谱检测技术对奶粉中违禁添加剂三聚氰胺检测的可行性,并建立了较优的掺杂三聚氰胺奶粉样品的GRNN检测模型。该研究对促进奶粉行业的健康发展具有较为重要的意义。

To explore the influences of different spectral preprocessing methods on the terahertz (THz) spectrum, preprocessing methods such as smoothing, multivariate scattering correction, baseline correction and normalization, and combination of multiple scattering correction and normalization are used. In order to optimize the model and reduce the computation amount, principle component analysis is used to compress the THz spectrum to reduce data dimension. The backpropagation neural network (BPNN) and generalized regression neural network (GRNN) detection models are established based on the compressed data. Experimental results show that the effect of the GRNN model with multiple scattering correction and normalization correction is the best. The predicted correlation coefficient is 0.9967 and the predicted root mean square error is 0.0050. This experiment verifies the feasibility of the THz spectrum detection technology for the detection of the melamine in milk powder, and establishes a better GRNN detection model for melamine adulterated milk powder samples. This study is of great significance to promote the healthy development of milk powder industry.