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根据卡莎规则[12],分子所发射的光(荧光或磷光)只能从某一多重态中的最低态激发,因此对于激发光的微小偏移并不影响背景(样品受激发射光谱)。获得的喇曼光谱的荧光轮廓几乎不发生变化。而喇曼光谱作为散射光谱特征峰出现的位置与激发光源频谱位置有固定关系,当激发光频率移动时喇曼特征峰会跟着移动。JOHANNES用宽谱光源进行光谱成像,很形象地说明了这一现象[13]。图 1a中x轴代表的是波长,颜色谱代表了接收到的光谱强度。可以看出, 中间有很强的一片区域是中心对称的,代表了不随激发光频率变化的受激发射光谱,而窄窄的倾斜光谱则是喇曼光谱。图 1b表示的两个邻近的激发波长λ1,λ2采集的喇曼光谱,可以从放大部分看出喇曼特征峰有微小偏移[14]。
图 2表示差分喇曼技术采用物理和数学相结合的荧光处理方法,是常规的流程[15]。
首先,需要获得采用具有已知微小波长错位的光源激发的喇曼光谱,再通过归一化、重建以及基线矫正等,将光谱的基线对齐。理论上对齐后光谱相减生成的曲线中仅包含喇曼光谱的差分信息。根据下式[16]:
$\begin{array}{l} \Delta S(\nu )f(\nu ) = R\left( {\nu , {\nu _1}} \right) - R\left( {\nu , {\nu _2}} \right) = \\ R( - \nu ) \otimes \left[ {\delta \left( {\nu - {\nu _1}} \right) - \delta \left( {\nu - {\nu _2}} \right)} \right] \end{array} $
(1) 式中, ν1和ν2分别表示两种激发光源的频率,ν代表绝对频率,S是绝对频率下的总的信号,f是通过用已知的真实光源的光谱除以测量光源的光谱计算出的形状因子,R代表的是喇曼光谱的强度,R(-ν)表示的是真实的喇曼位移谱的镜像。对于单色性比较好的激光束,激发光谱可以表示为狄拉克函数δ(ν-νi)。
可以将差分曲线看作喇曼光谱横轴颠倒后与两个δ函数差的卷积:
$\begin{array}{l} \quad\quad{R_{\Delta \nu }}( - \nu ) \equiv R( - \nu ) \otimes \\ \int {\left[ {\delta (\nu + \Delta \nu /2) - \delta (\nu - \Delta \nu /2)} \right]} {\rm{d}}\nu \end{array} $
(2) $\int \Delta S(\nu )f(\nu ){\rm{d}}\nu = {R_{\Delta \nu }}( - \nu ) \otimes \delta (\nu - \bar \nu ) $
(3) 对差分曲线积分后,可以看作喇曼光谱与一个方波函数的卷积,通过解卷积的方式获得真实喇曼光谱, (2)式中Δν表示的是两种激发光源频率的差值的绝对值,(3)式中ν表示种激发光源频率的平均值。
但是,实际操作中虽然两次差分光谱虽然相差不大,但是由于激光的漂白作用或激光的波动,焦点位置的微小变化等各种因素都可能导致光谱曲线的变化[17]。错误的对齐将在差分喇曼光谱重建过程中被放大,降低喇曼特征提取效果。虽然希望通过光谱对齐, 通过差分扣除光谱中的荧光部分,分离荧光和喇曼信号;但是只有知道准确的荧光才可能让光谱荧光完美地对齐[18-20]。而如果已经可以清除地区分荧光和喇曼信号,则没有必要对光谱信号进行差分。为了解决这一问题,常规做法是近似,根据整个光谱面积或关键的局部面积作为基准对谱图进行缩放和平移,使谱图的匹配基本匹配。然后在对差分后产生的曲线进行矫正,如交替最小二乘法(alternating least squares, ALS)拟合[21]。但是由于拟合目标是未知的,所以该方法还存在很多不确定性。
基于差分喇曼技术在抑制荧光中的应用研究
Application research of fluorescence suppression based on differential Raman technique
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摘要: 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。Abstract: 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.
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Key words:
- spectroscopy /
- differential Raman /
- fluorescence suppression /
- characteristic spectrum
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