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.     

荧光褪色效应与拉曼光谱基线干扰消除

荧光是直接测定的拉曼光谱中背景的最主要来源,需要采用真实、准确的方法消除,以得到纯净的拉曼响应。基线拟合消除和查找荧光贡献扣除是解决背景问题的两条思路,目前多采用基线拟合方法,其优点是满足用户“视觉”要求,无需额外硬件,但并非机理或实质上的解释,因而难以保证数据的真实性与合理性;查找荧光的方法,更为真实,但是目前提出的方法,需要增加光源等额外设计和成本。另外,在实验方法上,也有采用消荧光剂和长时间照射漂白的,存在操作繁琐、效率低等不足。利用稳定体系中拉曼和荧光的时间差异解决体系中荧光问题。在微小的时间段内,例如几个毫秒,激发光不会导致体系性质发生显著变化,荧光具有寿命周期,会随激发时间延长强度下降的“褪色”,“褪色”的强度差异可以被认为是整体荧光的一个微元;与此同时,由于体系组成未发生显著变化,拉曼光对于短时间照射可以保持稳定。利用此差异可以区分出混合信号中的荧光和拉曼光。根据该原理,提出了荧光褪色差分法(FBDA),实现拉曼光谱的背景校正。方法的主要步骤：测量微小时刻内的多张直接拉曼光谱,求取系列光谱的差分,对差分值作高频滤波降噪,可获得荧光强度微元;然后,多个荧光微元平均归一化后,得到荧光强度单元。以拉曼光谱2 000～2 500 cm-1的静默区,即通常不会出现拉曼信号的频段为基准,对荧光单元作逆差分,逆差分累计值与原始光谱在此频段一致时,得到整体荧光响应;最终,从原始光谱中扣除荧光成分,完成背景扣除和基线校正。以盐酸二甲双胍片的拉曼测量为例,说明和讲解了所提出的原理和方法,验证方法的有效性。与目前效果较好的基线校正方法(不对称最小二乘和自适应迭代再加权惩罚偏最小二乘)进行了对比,表明FBDA方法更为客观真实,FBDA的另一个优势是不需要额外的设计和成本,所有数据都是在现有设备直接采集和完成。需要说明的是,微小时刻光谱差异的要求,可以确保FBDA光谱实时性,长时间的光谱差异,将会影响结果的准确性;另外,对于光化学反应体系和其他非荧光引起的复杂背景,FBDA的适用性有待改善。     

Background removal from polarized Raman spectra of tooth enamel using the wavelet transform

A wavelet transformation method is introduced to remove the large fluorescence background from polarized Raman spectra of stained tooth enamel. This method exploits the wavelet multiresolution decomposition where the experimental Raman spectrum is decomposed into signals with different frequency components, and where the lowest frequency background and highest frequency noise are removed. This method is optimized using a simulated collection of parallel‐polarized and cross‐polarized Raman spectra of the enamel and then applied to a set of experimental data. The results show that the wavelet transform technique can extract the pure spectra from background and noise, with the depolarization ratio used to discriminate between early dental caries and sound enamel preserved. Copyright © 2010 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.     

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 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.     

对强荧光背景拉曼光谱定量分析的研究

针对拉曼光谱定量分析中的难点问题—对具有强荧光背景物质的定量分析,文章分别对具有较强荧光背景的不同浓度下的纯甲醇溶液及不同浓度比例的甲醇和乙醇混合溶液的拉曼光谱数据采用新的归一化法结合基线校准技术进行拉曼光谱定量分析;同时,对由数据采集时的时空差异所引起的样品数据组间的波动性进行了研究。模拟时空变化来采集定量分析中所需的谱数据,并使用统计学方法评价样品的组间差异,讨论了此方法对样品组间差异的消除效果。研究证明,此方法具有较好的分析精度和消除不同样品组数据的组间差异的能力,对甲醇的定量分析,其相对误差为4.7%,组间数据的相对标准偏差为4.2%。从而使对具有强荧光背景干扰的溶液进行简单、快速、精确的定量分析成为可能。     

Range‐independent background subtraction algorithm for recovery of Raman spectra of biological tissue

An important requirement for the use of Raman spectroscopy for tissue diagnostic applications is an appropriate algorithm that can faithfully retrieve weak tissue Raman signals from the measured raw Raman spectra. Although iterative modified polynomial‐fitting‐based automated algorithms are widely used, these are sensitive to the choice of the fitting range, thereby leading to significantly different Raman spectra for different start and stop wavenumber selection. We report here an algorithm for automated recovery of the weak Raman signal, which is range independent. Given a raw Raman spectrum and the choice of the start and the stop wavenumbers, the algorithm first truncates the spectrum to include the raw data within this wavenumber range, linearly extrapolates the truncated raw spectrum beyond the points of truncation on the two sides by using coefficients of linear least‐square fit, adds two Gaussian peaks of appropriate height and width on the extrapolated linear wings on either side and then iteratively smoothens the data with all these add‐ons such that the smaller of the ordinate values of the smoothed and the starting raw data serve as the input to each successive round of iterative smoothing until the added Gaussian peaks are fully recovered. The algorithm was compared with the modified polynomial‐based algorithms using mathematically simulated Raman spectrum as well as experimentally measured Raman spectra from various biological samples and was found to yield consistently range‐independent and artifact‐free Raman signal with zero baseline. Copyright © 2012 John Wiley & Sons, Ltd.     

基于自适应免疫算法重叠拉曼谱峰的解析

拉曼光谱测量速度快,可以实现原位实时测量,现已成为过程控制中物料检测的一种重要手段。但由于环境的复杂性以及拉曼光谱信号特点,目前在线检测时难免会出现一些重叠谱峰。基于免疫算法特点,将该方法用于芳烃重叠拉曼谱峰信号的解析中,提取混合物质中单个组分拉曼谱峰信息进行分析,结果表明该方法解析快速、定量准确,相对误差低于1%,是解析重叠拉曼光谱信号的有效方法。针对现场样品检测中出现的重叠谱峰伴随荧光背景信号,提出了结合独立成分分析的自适应免疫算法,有效地解析出荧光背景信号,为复杂样品的拉曼光谱检测分析提供了新的手段。     

拉曼光谱定量分析乙醇含量的非线性回归方法研究

传统拉曼特征峰峰比法一般采用线性回归法建立乙醇浓度与峰峰比的线性关系从而反演乙醇浓度实现乙醇定量分析,但仅在较低浓度范围适用。针对这一问题,采用自主研制的激光拉曼乙醇含量检测系统实验研究了不同浓度乙醇溶液拉曼光谱特征峰(非对称CH₂伸缩振动2 924.0 cm-1)与本底水峰(3 350 cm-1)相对强度关系,提出适用于大范围乙醇浓度测量的非线性回归分析方法。利用邻域平均算法去除拉曼光谱突变噪声,结合多点插值处理实现光谱基线校准。基线校准及归一化处理后,可有效消除突变噪声及强荧光背景的影响。分别采用二次多项式和e指数数学模型对拉曼峰值强度比随乙醇浓度变化关系进行非线性回归并与线性回归分析进行对比。结果表明,线性拟合相关系数约为0.991,线性回归模型乙醇浓度准确测量的适用范围为15%～60%;非线性拟合相关系数高于0.997,非线性回归模型乙醇浓度精确测量的适用范围为3%～97%。非线性数学模型可为乙醇溶液浓度定量分析提供理论基础,将该数学模型应用于乙醇含量检测系统,可实时反演较为精确的乙醇浓度,从而实现大浓度范围内具有荧光背景干扰的乙醇溶液快速、实时、准确的定量分析。     

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.     

SVD‐based method for intensity normalization,background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations

A semiautomated method combining intensity normalization with effective elimination of the solvent signal and non‐Raman background is presented for Raman spectra of biochemical and biological analytes in aqueous solutions. The method is particularly suitable for rapid and effortless preprocessing of extensive datasets taken as a function of gradually varied physicochemical parameters, e.g. analyte and/or ligand concentration, temperature, pH, pressure, ionic strength, time, etc. For intensity normalization, the strong Raman OH stretching band of water in the range of 2700–3900 cm⁻¹ recorded together with the analyte spectrum in the fingerprint region below 1800 cm⁻¹ is employed as internal intensity standard. Concomitant dependences of the solvent Raman spectra are taken into account and, in some cases, turned into advantage. Once the Raman spectra of the solvent are acquired for a particular range of the parameter varied, solvent contribution can be subtracted correctly from any analyte spectrum taken within this range. The procedure presented can be efficiently applied only for the analytes having their own Raman signal in the range of OH stretching vibrations much weaker than that of the solvent. However, this is the case for a great number of biochemical and biological samples. Accuracy, reliability and robustness of the method were tested under the conditions of spontaneous Raman, resonance Raman and surface‐enhanced Raman scattering. Serviceability of the method is demonstrated by several real‐world examples. Copyright © 2011 John Wiley & Sons, Ltd.     

多波长消荧光拉曼光谱仪的研制及应用研究

拉曼光谱技术作为探究分子、晶体及其结构特征的有力手段,具有快速、无损、样品用量小、无需前处理且适应性强等优点,已被广泛应用于食品安全、石油化工等领域。但在拉曼光谱应用中,常常受到荧光背景干扰,导致拉曼信号降低,严重的情况下拉曼信号甚至会淹没在荧光背景中。为解决拉曼技术在实际应用中荧光背景干扰的问题,从仪器角度出发,采用二色镜对多波长拉曼光谱进行光路耦合设计,研制了近红外拉曼光谱与移频差分拉曼复合一体的多波长消荧光拉曼光谱检测系统,其中近红外拉曼光谱采用1 064 nm激光光源设计,移频差分拉曼光谱选取784.5和785.5 nm两组激光光源进行时分复用,在移频差分拉曼光谱检测的同时,亦可获得两组单波长拉曼光谱数据。通过对比同步测试和分时逐次测试的强度及峰位稳定性,验证了多波长消荧光拉曼光谱仪的同步测试性能;选取了多种荧光背景强弱不同的样品,进行了单波长拉曼、近红外拉曼及移频差分拉曼光谱的对比分析。针对丙酮、乙腈等荧光背景较弱的样品,可采用单波长拉曼光谱对样品进行定量及定性分析;针对食用油、红色塑胶微粒等荧光背景与拉曼信号强度相当的样品,可采用近红外拉曼光谱对样品进行定量及定性分析;针对红酒、棕色塑胶微粒等荧光背景较强的样品,需结合近红外拉曼光谱和差分拉曼光谱对样品进行定性分析。研究表明：通过多波长消荧光拉曼光谱检测系统的研制,在常规单波长拉曼光谱技术的基础上,将两种抑制荧光干扰技术有机结合,有效扩充了应用领域及样品检测范围。     

Automatic standardization method for Raman spectrometers with applications to pharmaceuticals

An effective and practical method, based on automatic peak detection, Lorentzian fitting, and polynomial fitting, is developed for aligning spectra, which can significantly reduce or eliminate systematic differences between Raman spectrometers. In this work, Lorentzian fitting of the experimental spectra is performed to precisely locate the peak positions. Then the standardization procedure is illustrated on the spectra of a chemical standard measured on primary and secondary instruments, and the spectra can be successfully aligned to each other after shift correction. It is shown that the similarity of pharmaceutical spectra acquired on three Raman spectrometers is then considerably improved after removal of the Raman shift difference. The result shows that the similarity of the spectra between primary and secondary spectrometers increases dramatically from about 0.8 to 0.95 after spectra standardization. This means that the proposed standardization method can effectively reduce or eliminate systematical errors and enhance spectral compatibility across different instruments. Copyright © 2014 John Wiley & Sons, Ltd.     

A shifted‐excitation Raman difference spectroscopy (SERDS) evaluation strategy for the efficient isolation of Raman spectra from extreme fluorescence interference

A biochemical characterization of pathologies in biological tissue can be provided by Raman spectroscopy. Often, the raw spectrum is severely affected by fluorescence interference. We report and compare various spectra‐processing approaches required for the purification of Raman spectra from heavily fluorescence‐interfered raw spectra according to the shifted‐excitation Raman difference spectroscopy method. These approaches cover the entire spectra‐processing chain from the raw spectra to the purified Raman spectra. In detail, we compared (1) area normalization versus z‐score normalization, (2) direct reconstruction of the difference spectra versus reconstruction of zero‐centered difference spectra and (3) collective baseline correction of the reconstructed spectra versus piecewise baseline correction of the reconstructed spectra and, finally, (4) analyzed the influence of the shift of the excitation wavelength on the quality of the reconstructed spectra. Statistical analysis of the spectra showed that – in our experiments – the best results were obtained for the z‐score normalization before subtraction of the normalized spectra, followed by zero‐centering of the difference spectra before reconstruction and a piecewise baseline correction of the pure Raman spectra. With our equipment, a wavelength shift from 784 to 785 nm provided reconstructed spectra of best quality. The analyzed specimens were different tissue types of pigs, tissue from the oral cavity of humans and a model solution of dye dissolved in ethanol. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd.     

自适应的EEMD残余相关基线校正算法

基线校正是光谱分析的重要环节,现有算法通常需要设定关键参数,不具备自适应性。根据总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)残余量特点,提出用残余量拟合光谱基线。通过残余量与信号相关性、残余量自相关和互相关性(称为残余相关准则)判断残余量是否是基线组成部分,以此为基础提出一种自适应的EEMD残余相关基线校正算法。对叠加曲线背景和线性背景的模拟光谱数据进行实验,结果显示在已知基线数学假设情况下,EEMD残余相关法逊于多项式拟合,同非线性拟合相差不多,优于小波分解。在没有光谱背景知识情况下,对真实拉曼光谱数据进行试验。经过上述方法预处理过的玉米叶片光谱采用3层BP神经网络建立与叶绿素之间预测模型,经过残余相关基线校正的模型具有最大校正相关系数和预测相关系数,最小交叉验证标准差和相对分析误差。各种基线校正方法中,残余相关基线校正对特征峰峰位、峰强和峰宽影响最小。实验表明,该算法可用于拉曼谱图基线校正,无需分析样品成分的先验知识,无需选择合适的拟合函数、拟合数据点、拟合阶次以及基函数和分解层数,也无需基线信号分布的数学假设,自适应性很强。     

基于表面增强拉曼的饮料中山梨酸钾快速定量检测方法

基于实验室自行搭建的拉曼光谱点扫描系统,利用表面增强拉曼技术对橙味饮料中山梨酸钾的含量进行了定量快速检测研究。通过与山梨酸钾标准品拉曼光谱及其水溶液表面增强拉曼光谱等比较分析,确定了山梨酸钾1 648.4,1 389.3和1 161.8 cm-1处的表面增强特征拉曼位移。通过山梨酸钾橙味饮料平行样品的拉曼位移峰强重现性实验并计算其峰强的相对标准偏差证实了该表面增强拉曼方法具有较好的重复性。采集了山梨酸钾浓度范围为1.706～0.180 7 g·kg-1的33个橙味饮料样品的表面增强拉曼光谱,所有原始光谱经S-G 5点平滑及Baseline基线去除荧光背景预处理后分别用一元线性回归分析、多元线性回归分析和偏最小二乘回归分析方法,建立了山梨酸钾的定量预测模型。经比较,选取三个山梨酸钾拉曼特征位移1 161.8,1 389.3和1 648.4 cm-1所建立的多元线性回归模型校正集的相关系数(R2_C)和均方根误差(RMSEC)分别为0.983 7和0.051 7 g·kg-1,验证集的相关系数(R2_P)和均方根误差(RMSEP)分别为和0.969 9和0.052 8 g·kg-1,比一元线性回归模型和偏最小二乘回归模型误差小、精度高。基于表面增强拉曼完全可以实现橙味饮料中山梨酸钾的定量快速预测,为各类食品中山梨酸钾含量的快速监测奠定了技术基础。     

Laser wavelength dependence of background fluorescence in Raman spectroscopic analysis of synovial fluid from symptomatic joints

Gout is a disease process where the nucleation and growth of crystals in the synovial fluid of joints elicit painful arthritis‐like symptoms. Raman spectroscopy is evolving as a potential diagnostic tool in identifying such crystals; however, attainment of sufficient Raman signal while overcoming the background fluorescence remains as a major challenge. The current study focused on assessing whether excitation in 532–700 nm range will provide greater signal intensity than the standard 785 nm while not being impeded by background fluorescence. We characterized the fluorescence spectra, absorption spectra and Raman spectra of synovial fluid from patients who presented ‘gout‐like symptoms’ (symptomatic) and controls (asymptomatic). A digestion and filtration method was developed to isolate crystals from synovial fluid while reducing the organic burden. Spectral profile and photobleaching dynamics during Raman spectroscopy were observed under an excitation wavelength range spanning 532 to 785 nm. Absorbance and fluorescence profiles indicated the digestion and filtration worked effectively to extract crystals from symptomatic synovial fluid without introducing additional fluorescence. Raman spectral analyses at 532 nm, 660 nm, 690 nm and 785 nm indicated that both asymptomatic and symptomatic samples had significant levels of fluorescence at excitation wavelengths below 700 nm, which either hindered the collection of Raman signal or necessitated prolonged durations of photobleaching. Raman‐based diagnostics were more feasible at the longest excitation wavelength of 785 nm without employing photobleaching. This study further demonstrated that a near‐infrared (NIR) OEM‐based lower‐cost Raman system at 785 nm excitation has sufficient sensitivity to identify crystals isolated from the synovial fluid. In conclusion, while lower excitation wavelengths provide greater signal, the fluorescence necessitates NIR wavelengths for Raman analysis of crystal species observed in synovial aspirates. Copyright © 2013 John Wiley & Sons, Ltd.     

基于低分辨率色散型拉曼光谱仪的汽油苯含量快速分析

针对汽油苯含量的快速分析问题,提出了基于低分辨率色散型拉曼光谱仪的新的检测方法。由于色散型拉曼光谱中存在着严重的测量噪声和荧光背景干扰,应用多项式平滑滤波除噪声和迭代多项式拟合基线校正方法减少荧光背景的干扰。随后,分别采用岭回归、主成分回归、偏最小二乘回归方法,对炼油厂的汽油样本建立了三个苯含量的快速分析模型并对其进行验证。实验表明,基于低分辨率色散型拉曼光谱仪进行汽油苯含量分析,结合常规建模方法,重复性和再现性均满足SH/T0713—2002标准的要求,此外由于其具有低成本、小型易携带等优点,适用于日常快速分析。     

实用化拉曼光谱水下温度遥测系统研究

海水水下温度测量对研究海洋环境和气候监测及自然灾害的早期预报等十分重要。利用蓝绿激光在海水中良好的透射性,拉曼光谱技术可应用于大面积海水水下温度的快速遥感监测。但目前能够实现现场连续水温监测功能的实用化拉曼光谱水温遥测系统还尚未见报道。研制了实用化低成本的拉曼光谱水下温度遥测系统,开发了光谱实时采集和数据处理软件。数据处理中结合了面阵CCD的空间累加与时间积分及本底扣除算法,有效增强了拉曼光谱的信噪比和光谱系统的探测灵敏度。为了提高实际测温精度,以短波段拉曼谱的面积(S_HB)与长波段拉曼谱的面积(S_NHB)之比作为光谱信标与水温建立关系,研究了测温精度与光谱积分范围和拟合阶次的关系。实验测量了五百多组不同水温的拉曼光谱,分别选用比值S_HB/S_NHB和S_NHB/S_HB与水温进行线性拟合和二阶多项式拟合。研究结果显示,分界波长对面积比值变化范围影响很大,而拟合阶次对面积比与温度的拟合关系的准确度影响很大,两者最终都影响水温测量误差。为了更客观地反应不同面积比法、分界波长和拟合阶次对水温测量误差的影响,分析了温度测量误差与不同分界波长的关系。结果显示,温度测量误差受分界波长影响较小,受面积比法和拟合阶次影响较大;相同情况下2阶多项式拟合结果优于相应的线性拟合结果;而采用比值S_HB/S_NHB与水温进行线性拟合时测温精度较高,且拟合参数易于调整。进一步研究了不同面积比方法和分界波长对系统抗干扰性能的影响。研究结果显示,比值S_HB/S_NHB法抗干扰能力随分界波长减小而减小,而比值S_NHB/S_HB法抗干扰能力随分界波长减小而增大。上述研究结果提高了温度反演算法参数设置的合理性和拉曼散射系统测温精度及系统抗干扰能力。综合考虑上述研究结果,数据处理中设定649.3 nm作为分界波长计算拉曼光谱面积比S_HB/S_NHB与水温进行线性拟合。最后通过实验检验了拉曼光谱水下温度遥测系统的连续实时测温能力和测温精度。结果显示,拉曼光谱系统测温值与高精度同步温度传感器测量温度一致,最大测温误差为±0.5 ℃,测温误差的标准差约0.21 ℃。     

Classification of Raman spectra using the correlation kernel

The classification of Raman spectra can be very useful in a wide range of diagnostic applications including bacterial identification. Before any form of classification can be carried out on the Raman spectra, some form of pre‐processing is commonly applied. This pre‐processing greatly affects the accuracy of the results and introduces user bias and over‐fitting effects. In this paper, we propose using support vector machines with the correlation kernel. The use of the correlation kernel on Raman spectra has not been presented before in any published work. Our results illustrate that the correlation kernel is ‘self‐normalizing’ and produces superior classification performance with minimal pre‐processing, even on highly noisy data obtained using inexpensive equipment. Such effective classification approaches can lead to clinically valuable diagnostic applications of Raman Spectroscopy. Copyright © 2010 John Wiley & Sons, Ltd.