An intelligent background‐correction algorithm for highly fluorescent samples in Raman spectroscopy

Fluorescent background is a major problem in recoding the Raman spectra of many samples, which swamps or obscures the Raman signals. The background should be suppressed in order to perform further qualitative or quantitative analysis of the spectra. For this purpose, an intelligent background‐correction algorithm is developed, which simulates manual background‐correction procedure intelligently. It basically consists of three aspects: (1) accurate peak position detection in the Raman spectrum by continuous wavelet transform (CWT) with the Mexican Hat wavelet as the mother wavelet; (2) peak‐width estimation by signal‐to‐noise ratio (SNR) enhancing derivative calculation based on CWT but with the Haar wavelet as the mother wavelet; and (3) background fitting using penalized least squares with binary masks. This algorithm does not require any preprocessing step for transforming the spectrum into the wavelet space and can suppress the fluorescent background of Raman spectra intelligently and validly. The algorithm is implemented in R language and available as open source software ( http://code.google.com/p/baselinewavelet ). Copyright © 2009 John Wiley & Sons, Ltd.     

A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method

Noise removal is considered a primary and inevitable step for background correction in experimentally obtained Raman spectra. Employing an appropriate algorithm for a smoothing‐free background correction technique not only increases the speed but also eliminates unwanted errors from the smoothing algorithms. Herein, we show a new smoothing‐free method for background correction, which we developed by merging continuous wavelet transform and signal removal method, which in combination, could be applied to noisy signals without smoothing. We used wavelet transformation for suppressing the side effects of noise and eliminating peaks from the spectrum, thereby providing spectral sections purely related to the background to be used in the background correction process. We applied a range of statistical analyses to test the performance of this algorithm, wherein a low deviation in background correction procedure was observed. Additionally, when we tested this algorithm for experimentally obtained real Raman spectra, it showed good capability to correct background of noisy signals without the requirement of a smoothing process. Copyright © 2013 John Wiley & Sons, Ltd.     

A background subtraction approach based on complex wavelet transforms in EDXRF

Subtracting the background accurately is one of the most important issues in energy‐dispersive X‐ray fluorescence (EDXRF) spectra processing. This paper presents a novel approach to perform background subtraction based on dual‐tree complex wavelet transform. Compared with real wavelet transform, the proposed method has some attractive properties, including a smooth, nonoscillating, and nearly shift‐invariant magnitude with a simple near‐linear phase encoding of signal shifts. Therefore, it outperforms real wavelet transform to decompose background into low‐frequency components. The effectiveness of the proposed approach is demonstrated via two simulated spectra with different kinds of backgrounds and one measured spectrum from an energy‐dispersive X‐ray spectrometer. Both simulated and experimental results prove that the proposed approach can subtract background in EDXRF spectra effectively. Copyright © 2014 John Wiley & Sons, Ltd.     

A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy

Raman spectroscopy exploits the Raman scattering effect to analyze chemical compounds with the use of laser light. Raman spectra are most commonly analyzed using the ordinary least squares (LS) method. However, LS is known to be sensitive to variability in the spectra of the analyte and background materials. In a previous paper, we addressed this problem by proposing a novel algorithm that models expected variations in the analyte as well as background signals. The method was called the hybrid LS and principal component analysis (HLP) algorithm and used an unweighted Gaussian distribution to model the noise in the measured spectra. In this paper, we show that the noise in fact follows a Poisson distribution and improve the noise model of our hybrid algorithm accordingly. We also approximate the Poisson noise model by a weighted Gaussian noise model, which enables the use of a more efficient solver algorithm. To reflect the generalization of the noise model, we from hereon call the method the hybrid reference spectrum and principal components analysis (HRP) algorithm. We compare the performance of LS and HRP with the unweighted Gaussian (HRP‐G), Poisson (HRP‐P), and weighted Gaussian (HRP‐WG) noise models. Our experiments use both simulated data and experimental data acquired from a serial dilution of Raman‐enhanced gold‐silica nanoparticles placed on an excised pig colon. When the only signal variability was zero‐mean random noise (as examined using simulated data), HRP‐P consistently outperformed HRP‐G and HRP‐WG, with the latter coming in as a close second. Note that in this scenario, LS and HRP‐G were equivalent. In the presence of random noise as well as variations in the mean component spectra, the three HRP algorithms significantly outperformed LS, but performed similarly among themselves. This indicates that, in the presence of significant variations in the mean component spectra, modeling such variations is more important than optimizing the noise model. It also suggests that for real data, HRP‐WG provides a desirable trade‐off between noise model accuracy and computational speed. Copyright © 2013 John Wiley & Sons, Ltd.     

A robust method for automated background subtraction of tissue fluorescence

This paper introduces a new robust method for the removal of background tissue fluorescence from Raman spectra. Raman spectra consist of noise, fluorescence and Raman scattering. In order to extract the Raman scattering, both noise and background fluorescence must be removed, ideally without human intervention and preserving the original data. We describe the rationale behind our robust background subtraction method, determine the parameters of the method and validate it using a Raman phantom against other methods currently used. We also statistically compare the methods using the residual mean square (RMS) with a fluorescence‐to‐signal (F/S) ratio ranging from 0.1 to 1000. The method, ‘adaptive minmax’, chooses the subtraction method based on the F/S ratio. It uses multiple fits of different orders to maximize each polynomial fit. The results show that the adaptive minmax method was significantly better than any single polynomial fit across all F/S ratios. This method can be implemented as part of a modular automated real‐time diagnostic in vivo Raman system. Copyright © 2007 John Wiley & Sons, Ltd.     

乙醇含量拉曼光谱检测中噪声与背景同时消除方法研究

乙醇含量拉曼光谱检测中,拉曼光谱信号中的各种噪声及光谱荧光造成的基线漂移和样品池背景等,影响了校正模型的预测精度。利用总体平均经验模态分解,将光谱信号分解成若干无模态混叠的内在模式分量,根据排列熵的信号随机性检测判据判断出代表背景信息和噪声信息的内在模式分量,将其置零即可同时消除拉曼光谱中的噪声与背景。将总体平均经验模态分解与排列熵相结合的预处理方法应用于乙醇含量的拉曼光谱检测中,并与小波变换和平均平滑滤波做了对比。实验结果表明：应用总体平均经验模态分解与排列熵相结合的方法能够有效的同时消除乙醇含量拉曼光谱检测中的噪声和背景信息,提高校正模型的预测精度,且使用简便,无需参数设置,对乙醇含量拉曼光谱检测具有实用价值。     

Measurement and calculation of the Q‐branch spectrum of nitrogen using inverse Raman spectroscopy and cw Raman‐induced polarization spectroscopy

The techniques of inverse Raman spectroscopy, Raman‐induced polarization spectroscopy (RIPS), and optical heterodyne RIPS (OHD‐RIPS) are compared by probing the Q‐branch of the nitrogen molecule. The signal is measured employing either a photomultiplier tube (low background level–RIPS) or a photodetector (high background level–IRS and OHD‐RIPS). The measurements are performed using atmospheric mixtures of N₂ Ar with concentrations varying from 0 to 79% N₂. This strategy permits estimation of detection limits using the different techniques. Pump and probe energy levels are varied independently to study signal dependence on laser irradiance. A theoretical treatment is presented on the basis of the Raman susceptibility equations, which permits the calculation of spectra for all three techniques. Calculated Q‐branch spectra are compared with the measured spectra for the interactions of a linearly polarized probe beam with a linearly or circularly polarized pump beam. The polarizer angle in the detection path for OHD‐RIPS has a dramatic effect on the shape of the spectrum. The calculated and experimental OHD‐RIPS spectra are in good agreement over the entire range of investigated polarizer angles. Detection limits using these techniques are analyzed to suggest their applicability for measuring other species of importance in combustion and plasma systems. Copyright © 2007 John Wiley & Sons, Ltd.     

Deconvolution of pure component FT‐Raman spectra from thermal emission of barium sulfate and graphite samples using the BTEM algorithm

Band‐target entropy minimization (BTEM) was applied for the extraction of pure component Raman spectra from samples exhibiting a significant thermal background due to sample emission. In this method, singular value decomposition was first used to calculate the right singular vectors of the spectroscopic data matrix. Then the non‐noise right singular vectors were examined for localized spectral features, which were subsequently used for spectral recovery. These local features were targeted with the BTEM algorithm to recover the pure component Raman spectra. Accordingly, the interfering thermal background was removed. This method of analysis is applied to graphite and barium sulfate Raman spectroscopic data. Copyright © 2006 John Wiley & Sons, Ltd.     

Improved methods for fluorescence background subtraction from Raman spectra

Raman spectroscopy has attracted interest as a non‐invasive optical technique to study the composition and structure of a wide range of materials at the microscopic level. The intrinsic fluorescence background can be orders of magnitude stronger than the Raman scattering, and so, background removal is one of the foremost challenges for quantitative analysis of Raman spectra in many samples. A range of methods anchored in instrumental and computational programming approaches have been proposed for removing fluorescence background signals. An enhanced adaptive weighting scheme for automated fluorescence removal is reported, applicable to both polynomial fitting and penalized least squares approaches. Analysis of the background fitting results for ensembles of simulated spectra suggests that the method is robust and reliable and can significantly improve the background fit over the range of signal, shot noise and background parameters tested, while reducing the subjective nature of the process. The method was also illustrated by application to experimental data generated from aqueous solutions of bulk protein fibrinogen mixed with dextran. Copyright © 2013 John Wiley & Sons, Ltd.     

一种改进小波算法及其在拉曼光谱预处理的应用

针对现有用于光谱预处理的小波变换算法对光谱噪声和背景荧光等处理效果不佳的局限性,本文提出了一种改进的小波变换算法——小波变换频率分量相关选择法,首先对拉曼光谱进行小波棱镜分解,然后计算各个频率分量与待测质量指标的相关系数,设定相关系数的相对阈值,提取高于阈值的小波频率分量波长点光谱数据作为校正模型的有效输入数据。将其应用于汽油低分辨率拉曼光谱的预处理,并采用预处理后光谱建立的偏最小二乘模型预测值的最大正负误差和交叉检验的均方误差作为指标。实验结果证明,与其他常见预处理方法比较,该方法并能够很好地减弱荧光背景干扰和高频噪声,显著提高了基于偏最小二乘方法建立的汽油辛烷值的模型预测精度,其均方误差减少为0.23;此外,采用该预处理方法的偏最小二乘模型的均方误差随主元数变化不大,稳健性也比采用其他预处理方法的效果好。     

基于特征提取的低信噪比拉曼光谱去噪方法研究

为了提高拉曼光谱检测系统的时间分辨率,常常需要采用较短的采样积分时间,此时带有分子结构振动谱的有用拉曼信号可能完全淹没在噪声中,严重影响信号的进一步分析,因此有必要对测量所得的光谱信号进行噪声消除处理。传统的消噪方法是基于信号与噪声在频域或统计特性之间的差异,通过平滑滤波或取平均值的方法来消除噪声,一般适用于噪声强度不高的情况,对于信噪比较低的情况处理效果并不理想。针对传统去噪方法的不足,从信号重构的角度,利用基于小波变换的谱峰识别、半峰宽检测提取光谱特征参数,再利用最小二乘拟合的方法,能够有效地提取淹没于强噪声背景下的有用拉曼信号。在仿真中,运用该算法得到的光谱曲线光滑,峰位置准确,信噪比改善明显。在实验中,分别利用该方法处理头孢呋辛酯片和罗红霉素拉曼光谱数据,得到了清晰的谱峰位置、幅值及半峰宽信息,实现了对短积分时间、强噪声背景的拉曼信号的有效还原,提高了检测系统的时间分辨率。仿真和实验结果表明,该方法需要调整参数少,易于实现,在信噪比比较低的情况下依然能够得到良好的去噪效果,为进一步分析光谱数据提供准确可靠的信息。     

Experimental research into time–frequency characteristics of cavitation noise using wavelet scalogram

The cavitation has become the main cause of the damage to the hydraulic machine. Cavitation detection is very important to guarantee the safe running of the hydraulic machine. The sound, especially the audible sound, based methods are becoming attractive due to their simplicity and logicality in the application. However, the cavitation noise is easy to be contaminated by the background noise. In order to understand the characteristics of the cavitation noise deeply, using the wavelet scalogram analysis, this paper presents an experimental study to investigate the time–frequency characteristics of the cavitation noise of various cavitation states and the relation between the cavitation noise and the cavitation process. In addition, the method of parameters optimization for the wavelet is used to improve the transform performance of the wavelet scalogram. The results show that: the cavitation noise is composed of the components with wide band frequency and obvious impulse feature; but the cavitation noise of different cavitation stages has different time–frequency characteristics and compositions; in addition, the cavitation noise can be distinguished from the background noise because they have totally different frequency characteristic. This study validates that the cavitation noise can be used to detect the cavitation state and monitor the cavitation process.     

Implementation of a novel low‐noise InGaAs detector enabling rapid near‐infrared multichannel Raman spectroscopy of pigmented biological samples

Pigmented tissues are inaccessible to Raman spectroscopy using visible laser light because of the high level of laser‐induced tissue fluorescence. The fluorescence contribution to the acquired Raman signal can be reduced by using an excitation wavelength in the near infrared range around 1000 nm. This will shift the Raman spectrum above 1100 nm, which is the principal upper detection limit for silicon‐based CCD detectors. For wavelengths above 1100 nm indium gallium arsenide detectors can be used. However, InGaAs detectors have not yet demonstrated satisfactory noise level characteristics for demanding Raman applications. We have tested and implemented for the first time a novel sensitive InGaAs imaging camera with extremely low readout noise for multichannel Raman spectroscopy in the short‐wave infrared (SWIR) region. The effective readout noise of two electrons is comparable to that of high quality CCDs and two orders of magnitude lower than that of other commercially available InGaAs detector arrays. With an in‐house built Raman system we demonstrate detection of shot‐noise limited high quality Raman spectra of pigmented samples in the high wavenumber region, whereas a more traditional excitation laser wavelength (671 nm) could not generate a useful Raman signal because of high fluorescence. Our Raman instrument makes it possible to substantially decrease fluorescence background and to obtain high quality Raman spectra from pigmented biological samples in integration times well below 20 s. Copyright © 2015 John Wiley & Sons, Ltd.     

胃粘膜组织共焦显微拉曼光谱分类研究

采用基于小波变换的光谱去噪和背景扣除预处理技术,对32例(其中:13例正常,19例癌变)胃粘膜组织拉曼光谱进行分析,克服了手动背景扣除的一些缺点,并观察到一些新的光谱特征。基于此,给出能对所有正常组织和癌变组织进行有效分类的特征量和判据。通过研究这些特征量与胃粘膜组织癌变发展阶段之间的定量关系,可望实现胃癌的早期诊断。     

基于LCEEMD的低信噪比拉曼光谱自适应去噪方法研究

在生物体拉曼光谱快速采集或低功率采集过程中,往往会获得低信噪比拉曼光谱。针对低信噪比光谱数据,提出应用补充总体经验模态方法(CEEMD)分解拉曼光谱,并且依据特征模态分量的归一化排列熵值(NPE)按比例扣除噪声成分的方法,称为局部补充总体均值经验模分解方法(LCEEMD)。LCEEMD方法不仅解决了经验模态(EMD)分解中高频信号与噪声的模态混叠问题,还有效降低了总体经验模态分解法(EEMD)中的残留噪声。仿真数据实验显示,LCEEMD方法在处理10db信噪比模拟光谱时获得了39.615 0 db信噪比,0.001 17标准差和0.999 9相关系数。在人体皮肤拉曼光谱试验中,LCEEMD方法滤波后数据准确呈现出角质层脂质酰胺I带激发拉曼强谱峰以及甘油三酸酯中(CO)酯微弱谱峰。在水稻叶片可溶性糖定量预测模型中,LCEEMD方法取得了0.871 7预测相关系数和0.912 0预测标准误差,优于EMD和EEMD软阈值去噪(0.511 4,1.647 8和0.638 2,1.508 8)。LCEEMD方法实施过程中,根据去噪性能指标反馈调整归一化排列熵阈值,直至获得最佳去噪效果,滤波过程无需参数设置,可以自适应实现。     

Survival of molecular information under surfaced‐enhanced resonance Raman (SERRS) conditions

In the present paper, we discuss the molecular information that can be derived from surface‐enhanced resonance Raman Scattering (SERRS) experiments performed with different excitation wavenumbers, which are close to resonance with an excited electronic state of the molecule [surface‐enhanced Raman dispersion spectroscopy (SERADIS)]. We specifically consider the situation, where a molecule is physisorbed to a site characterized by a local electric field with a direction independent of the direction of the external, exciting field. The molecular information available in this experimental situation is compared with the information available in a corresponding Raman dispersion spectroscopy (RADIS) experiment performed on a free molecule or a molecule physisorbed to a site, where the local field is isotropic. The consequences for resonance Raman scattering (RRS) and RADIS, when the molecule is adsorbed in the highly anisotropic hot spot (HS), are discussed; here it is shown that only the molecular information originating from the symmetric part of the scattering tensor can survive in SERRS and in SERADIS. Besides, it is shown that the depolarization ratio can no longer be used to discriminate between totally and non‐totally symmetric modes in the polarized surface‐enhanced Raman scattering (SERS) spectra. These results have implications for the resonance Raman spectra, but even more important for the application of the resonance Raman effect in the investigation of excited vibronic molecular states, in general, and in the investigation of electronic states in larger bio‐molecules, such as the various metallo‐porphyrins. Copyright © 2009 John Wiley & Sons, Ltd.     

A background elimination method based on linear programming for Raman spectra

In this paper, we consider a new background elimination method for Raman spectra. As a background is usually slowly varying with respect to wavelength, it could be approximated by a slowly varying curve. However, the usual curve‐fitting method cannot be applied because there is a constraint that the estimated background must be beneath a measured spectrum. To meet the requirement, we adopt a polynomial as an approximating function and show that background estimation could be converted to a linear programming problem which is a special case of constrained optimization. In addition, we present an order selection algorithm for automatic baseline elimination. According to the experimental results, it is shown that the proposed method could be successfully applied to experimental Raman spectra as well as synthetic spectra. Copyright © 2011 John Wiley & Sons, Ltd.     

Rapid screening of sildenafil and tadalafil adulterated in healthcare products by Micro‐Raman spectroscopy

Sildenafil and tadalafil are inhibitors of phosphodiesterase type 5, which are frequently added into healthcare products. The objective of this study was to evaluate the possibility of using micro‐Raman spectroscopy as a non‐destructive technique to screen for sildenafil and tadalafil in adulterated healthcare products. Using a viewing microscope, the suspect area of healthcare products was selected, which had a discernable crystal form or shape from the surrounding zone. Optimization of instrumental parameters of the Raman spectrometer was chosen to reduce the background fluorescence, and the Raman spectra were collected. The spectra collected were compared with the standard Raman spectra of pure sildenafil and tadalafil. Samples with an identifiable Raman signature to that of sildenafil or tadalafil could be confirmed using liquid chromatography–mass spectrometry (LC/MS). Additionally, wavelet denoising combined with similarity calculation was used to establish an automated approach for discrimination of adulterated healthcare products. Correlation coefficient was chosen for similarity calculation based on the spectra collected and the standard Raman spectra of pure sildenafil and tadalafil. We compared ten samples, secured by administrative authorities in Shanghai, to analyse and demonstrate the capabilities of our proposed method. We established six samples containing sildenafil or tadalafil warranting analysis using LC/MS. Thus, the use of micro Raman spectroscopy provides a quick, convenient and non‐destructive method for screening adulterated chemicals in healthcare products. Raman spectroscopy combined with similarity calculation requires little training after spectra library is developed, thus showing great promise to identify the adulterated healthcare products in the future. Copyright © 2012 John Wiley & Sons, Ltd.     

一种改进的小波除噪方法用于含噪ICP-AES光谱的处理

提出了一种改进的小波除噪方法。它基于噪声具有频率较高和幅度较小的特点 ,先排除信号中频率较高的成分 ,再丢弃余下的系数较小的成分。对模拟的含噪电感耦合等离子体原子发射光谱 (ICP AES)的处理结果表明 ,该法能克服小波平滑和小波去噪的一些缺陷 ,可去除更多噪声 ,而信号强度不受影响。同时 ,基线变得平坦 ,有利于峰高的定量计算。用该法处理实测ICP AES光谱 ,效果满意。     

基于反向传播神经网络的拉曼光谱去噪方法

拉曼光谱技术作为一种典型的光学检测方法,因其独特的非侵入性、快速、原位和极高的特异性,在生物分析、疾病诊断及分子识别等众多领域得到广泛应用.拉曼光谱的指纹特性使其成为生物医学分析领域的重要工具,但拉曼散射信号微弱,数据处理分析大量依赖分析人员、自动化处理能力低等因素都会极大影响该技术在实际中的应用.实验设备、环境产生的...