基于NLMS自适应滤波的近红外光谱去噪处理方法研究

为了去除直接采集的近红外(NIR)光谱中含有的噪声,将归一化最小均方(NLMS)自适应滤波方法引入到NIR光谱去噪领域中。以51份土壤样品的NIR光谱为研究对象,探讨NLMS自适应滤波方法在NIR光谱预处理中的应用,并将处理后的结果与土壤中有机质的含量相关联,建立模型。结果表明,通过NLMS自适应滤波去噪后的光谱,预测集的相关系数r由处理前的0.8284提高至0.9654,预测均方根误差(RMSEP)由处理前的0.3385降至0.1606。由此可见,NLMS自适应滤波对NIR光谱的去噪有显著效果,可以有效地提高光谱的分析精度和模型的稳健性,为NIR光谱的预处理提供了一种新方法。

Preprocessing Methods of Near-Infrared Spectrum Based on NLMS Adaptive Filtering

The normalized least mean square(NLMS) adaptive filtering method is introduced to get the preprocessing of near-infrared (NIR) spectrum in order to deduct the noise from Near-infrared spectrum. Fifty-one soil samples are served as the target and the application of NLMS adaptive filtering method in NIR spectrum preprocessing is discussed. The result after denoising is related to the real organic content of soil samples, then constructing a model according to this. Experimental results show that the correlation coefficient of the prediction set is improved from 0.8284 to 0.9654, and the root mean square error of prediction (RMSEP) is reduced from 0.3385 to 0.1606 after denoising with NLMS adaptive filter. So NLMS adaptive filter has a good effect in denoising the NIR spectrum. And it is also very useful to make the final model more representative, stable and robust. NLMS adaptive filter provides a new method for near-infrared sprctrum preprocessment.