基于小波变换动态时间规整的近红外光谱模型传递方法

为解决因测量环境及仪器差异而导致的近红外光谱模型通用性较差的不足,提出一种基于小波变换动态时间规整算法的模型传递方法(Wavelet transform combined with dynamic time warping,WDTW),从而实现不同仪器之间模型的共享。首先,该方法将光谱进行小波变换预处理,然后利用动态时间规整算法(Dynamic time warping,DTW)找到近红外光谱波长点之间最优的对应关系并建立回归方程。使用近红外药品光谱数据集和汽油数据集建立传递模型,验证了基于小波变换动态时间规整模型传递方法的有效性。汽油光谱数据集C7、C8、C9和C10成分的预测标准偏差(SEP)分别为0.414 4、0.801 1、1.090 4和1.290 8;药品光谱数据集活性、硬度和重量的SEP分别为2.585 6、0.434 5和2.270 3,均小于传统方法。上述实验结果表明,所建立的模型传递方法能有效消除源机光谱和目标机光谱之间的差异,提高模型的稳定性和准确性,实现模型传递的效果。

A Near Infrared Spectroscopy Model Transfer Method Based on Wavelet Transform Combined with Dynamic Time Warping

In order to solve the problem of poor generality of near infrared spectroscopy(NIR) model caused by the difference of measuring environment and spectroscopy instruments,a model transfer method based on wavelet transform combined with dynamic time warping(WDTW) algorithm was proposed to realize the sharing of models among different instruments.First,the infrared spectra were preprocessed with wavelet transform.Then,the best match wavelength point relationship between two spectroscopy was acquired by using WDTW algorithm to build the regression model,and the performance of the developed model transfer method was verified with the experiment built transfer model using the near infrared spectroscopy dataset from drug and gasoline samples.The spectral standard deviation of prediction(SEP) for the gasolin datasets C7,C8,C9 and C10 were 0.414 4,0.801 1,1.090 4 and 1.290 8,respectively.The spectral SEP in activity,hardness and weight components of the drug datasets were 2.585 6,0.434 5 and 2.270 3,respectively,which were lower than those of the traditional methods.The experimental results showed that the WDTW model transfer algorithm has low standard error of prediction.It could not only eliminate the difference between the master and slave machine spectroscopy,but also improve the stability and accuracy of the model.