光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2014年
1期
103-107
,共5页
姜承志%孙强%刘英%梁静秋%安岩%刘兵
薑承誌%孫彊%劉英%樑靜鞦%安巖%劉兵
강승지%손강%류영%량정추%안암%류병
拉曼光谱%谱峰识别%双尺度相关%局部信噪比%连续小波变换
拉曼光譜%譜峰識彆%雙呎度相關%跼部信譟比%連續小波變換
랍만광보%보봉식별%쌍척도상관%국부신조비%련속소파변환
Raman spectra%Peak recognition%Bi-scale correlation%Local signal to noise ratio%Continuous wavelet transform
拉曼谱峰识别是拉曼定性分析中的关键技术之一,针对现有拉曼谱峰识别方法中存在的缺陷和不足提出了一种双尺度相关拉曼光谱谱峰识别方法,即采用两个尺度下的相关系数与局部信噪比相结合来实现拉曼谱峰识别。利用MATLAB对所提算法与传统的连续小波变换法进行了对比分析,并通过实测拉曼光谱进行验证。分析结果:双尺度相关法识别一幅拉曼谱的平均时间为0.51 s ,连续小波变换法为0.71 s;当谱峰信噪比≥6时(现代拉曼光谱仪器均可达到较高的信噪比),双尺度相关法的谱峰识别准确率高于99%,连续小波变换法的谱峰识别准确率小于84%,且双尺度相关法谱峰位置识别误差的均值与标准差均要小于连续小波变换法。通过仿真对比分析和实验验证表明:双尺度相关法具有无需人工干预,无需做去噪及去背景等预处理操作,识别速度快,识别准确率高等特点,是一种切实可行的拉曼谱峰识别方法。
拉曼譜峰識彆是拉曼定性分析中的關鍵技術之一,針對現有拉曼譜峰識彆方法中存在的缺陷和不足提齣瞭一種雙呎度相關拉曼光譜譜峰識彆方法,即採用兩箇呎度下的相關繫數與跼部信譟比相結閤來實現拉曼譜峰識彆。利用MATLAB對所提算法與傳統的連續小波變換法進行瞭對比分析,併通過實測拉曼光譜進行驗證。分析結果:雙呎度相關法識彆一幅拉曼譜的平均時間為0.51 s ,連續小波變換法為0.71 s;噹譜峰信譟比≥6時(現代拉曼光譜儀器均可達到較高的信譟比),雙呎度相關法的譜峰識彆準確率高于99%,連續小波變換法的譜峰識彆準確率小于84%,且雙呎度相關法譜峰位置識彆誤差的均值與標準差均要小于連續小波變換法。通過倣真對比分析和實驗驗證錶明:雙呎度相關法具有無需人工榦預,無需做去譟及去揹景等預處理操作,識彆速度快,識彆準確率高等特點,是一種切實可行的拉曼譜峰識彆方法。
랍만보봉식별시랍만정성분석중적관건기술지일,침대현유랍만보봉식별방법중존재적결함화불족제출료일충쌍척도상관랍만광보보봉식별방법,즉채용량개척도하적상관계수여국부신조비상결합래실현랍만보봉식별。이용MATLAB대소제산법여전통적련속소파변환법진행료대비분석,병통과실측랍만광보진행험증。분석결과:쌍척도상관법식별일폭랍만보적평균시간위0.51 s ,련속소파변환법위0.71 s;당보봉신조비≥6시(현대랍만광보의기균가체도교고적신조비),쌍척도상관법적보봉식별준학솔고우99%,련속소파변환법적보봉식별준학솔소우84%,차쌍척도상관법보봉위치식별오차적균치여표준차균요소우련속소파변환법。통과방진대비분석화실험험증표명:쌍척도상관법구유무수인공간예,무수주거조급거배경등예처리조작,식별속도쾌,식별준학솔고등특점,시일충절실가행적랍만보봉식별방법。
The authors proposed a new Raman peak recognition method named bi-scale correlation algorithm .The algorithm uses the combination of the correlation coefficient and the local signal-to-noise ratio under two scales to achieve Raman peak identifica-tion .We compared the performance of the proposed algorithm with that of the traditional continuous wavelet transform method through MATLAB ,and then tested the algorithm with real Raman spectra .The results show that the average time for identif-ying a Raman spectrum is 0.51 s with the algorithm ,while it is 0.71 s with the continuous wavelet transform .When the signal-to-noise ratio of Raman peak is greater than or equal to 6(modern Raman spectrometers feature an excellent signal-to-noise rati-o) ,the recognition accuracy with the algorithm is higher than 99% ,while it is less than 84% with the continuous wavelet trans-form method .The mean and the standard deviations of the peak position identification error of the algorithm are both less than that of the continuous wavelet transform method .Simulation analysis and experimental verification prove that the new algorithm possesses the following advantages :no needs of human intervention ,no needs of de-noising and background removal operation , higher recognition speed and higher recognition accuracy .The proposed algorithm is operable in Raman peak identification .