根据苯并(a)芘在特定光的照射下能够发出荧光的特性，采用紫外荧光法检测苯并(a)芘的浓度，构建了检测苯并(a)芘浓度的实验系统。为了精确地检测出苯并(a)芘的浓度，需要将实验中所得数据中所包含的噪声滤除掉，得到有效的荧光信号。将归一化最小均方自适应滤波(NLMS)的原理应用到光谱数据的噪声处理中，并将其与小波去噪的效果相比较，用Matlab 仿真出去噪效果图，从不同的角度分析去噪效果。经过对比得出，用归一化最小自适应滤波能够继续保持信号的非平稳特性，达到更加理想的去噪效果，去噪性能更优，此种方法适用于荧光法检测物质浓度的信号处理中。

According to the characteristic that benzo (a) pyrene (bap) will fluoresce under the irradiation of light, the ultraviolet fluorescence is used to detect the bap concentration, builds the dectetion of bap concentration experiment system. In order to accurately detect the bap concentration, the data from the experiment need remove noise with the filter contained in the experiment, to get the fluorescent signal effectively. The normalized least mean squared adaptive filter principle is applied to the noise spectral data processing, compared with the wavelet denoising effect, with Matlab to simulate out denoising effect, and analysis of the different denoising effect from different perspectives. After the contrast, using the minimum normalized adaptive filter can keep the non- stationary signal feature, and achieve more ideal denoising effect and better denoising performance. Hence, this method is suitable for the signal processing of fluorescence detection matter concentration.