在传统的喇曼光谱检测过程中,喇曼光谱的有效信号有时会被荧光背景淹没,难以识别。为了准确、有效地分离差分信号和基线偏差,将差分喇曼技术和误差反向传播神经网络算法相结合,提出了差分喇曼解调和去噪算法,并进行了理论和实验验证。对市售的百草枯、补肾丸、机油以及海洛因等荧光较强的物质进行了检测分析。结果表明,可以得到有效的物质喇曼特征谱图。很好地解决了目前行业应用中检测的难题。

In the traditional Raman spectrum detection process, the effective signal of Raman spectrum is sometimes submerged by the fluorescent background and is difficult to be recognized. In order to separate differential signals and baseline deviations accurately and effectively, combining differential Raman technique with error back propagation algorithm neural network, a differential Raman demodulation and denoising algorithm was proposed. The theoretical and experimental validation was carried out. The fluorescent substances such as paraquat, kidney-tonifying pills, engine oil and heroin were detected and analyzed. The results show that Raman characteristic spectra can be obtained effectively. The study solves the difficult problem of detection in industry application.