光谱滤波法提高激光诱导击穿光谱对蔬菜中元素Pb的检测精度

Spectral Filtering Method for Improvement of Detection Accuracy of Lead in Vegetables by Laser Induced Breakdown Spectroscopy

激光诱导击穿光谱(Laser induced breakdown spectroscopy,LIBS)原始光谱中包含较多噪声信号,为探究不同滤波方法对LIBS光谱预处理的影响,本研究以实验室Pb污染处理的蔬菜为研究对象,采集波长范围在400.45~410.98 nm的LIBS谱线信息,分别利用相邻平均(Adjacent averaging)、Savitzky-Golay(S-G)滤波器、快速傅里叶变换(Fast Fourier transformation,FFT)对采集的LIBS光谱进行平滑、去噪,并结合偏最小二乘法(PLS)定量分析模型对光谱处理效果进行评价。结果表明,S-G平滑效果最优,当S-G滤波器窗口宽度为15,拟合阶次为3时,PLS定量模型效果最佳,其验证集均方根误差(RMSEP)为0.26、平均相对误差(ARE)为3.7%。结果表明,选择适合的滤波方法有助于提高LIBS光谱质量以及检测模型的精度。

There are many noise signals in original laser induced breakdown spectroscopy （ LIBS） spectra. To explore the effect of spectral pretreatment on LIBS information by different filter methods, the LIBS spectra of Pb-polluted cabbage in wavelength range of 400. 45-410. 98 nm was investigated and preprocessed by adjacent averaging, Savitzky-Golay （ S-G） and fast Fourier transformation （ FFT）. Then partial least square （ PLS） model was established for evaluating the spectral treatment effect. The result showed that the root mean square error of prediction （RMSEP） and average relative error of S-G method were 0. 26 and 3. 7 % , suggesting a superior smoothing effect than other methods. Experimental results indicated that an appropriate filtering method could help to improve the spectral quality and raise the precision of model checkout.