拉曼光谱基线校正

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物理实验技术中的拉曼光谱测量技巧

物理实验技术中的拉曼光谱测量技巧

物理实验技术中的拉曼光谱测量技巧拉曼光谱是一种非常重要的光谱分析技术,广泛应用于物理、化学、材料科学等领域。

它能够提供样品的分子结构、化学键性质以及晶格振动等信息,对于研究物质的结构和性质具有重要意义。

而在拉曼光谱测量中,合理使用测量技巧能够提高实验的准确性和可靠性。

本文将重点介绍几种常用的拉曼光谱测量技巧。

首先,选择合适的激光光源是拉曼光谱测量中的关键之一。

在选择激光光源时,要考虑样品的特性以及所需的测量精度。

常用的激光光源有氩离子激光器、固体激光器和半导体激光器等。

氩离子激光器具有较高的功率和较窄的谱线宽度,适合于对强拉曼光谱的测量,但其成本较高。

固体激光器和半导体激光器则适用于对弱拉曼光谱的测量。

其次,调节激光光束的聚焦度是拉曼光谱测量中的另一个关键步骤。

激光光束的聚焦度直接影响到信号的强度和分辨率。

通常,聚焦度过大会导致信号强度分散,而聚焦度过小则会使信号集中在一个小区域内。

因此,我们需要通过适当调整进出激光光束的光学设备,如透镜、准直器等,来实现合适的聚焦度。

在实验过程中,还要注意样品与光束的相对位置,以获得最佳的信号强度。

此外,有效地抑制背景光对拉曼光谱的干扰也非常重要。

背景光包括散射光和荧光光,它们会掩盖样品的拉曼信号,降低测量的精确性。

为了有效抑制背景光,可以使用准直光栅或截断滤光片来选择特定波长范围的光信号。

此外,将样品放置在低荧光背景材料上,或使用液氮冷却系统降低样品的温度,都可以有效地减小荧光光的干扰。

此外,合理设计实验系统的光学路径也是拉曼光谱测量中需要注意的问题。

光学路径的设计应尽量减小信号丢失,并使信号成分尽可能均匀地投射到光谱仪探测器上。

为此,可以根据实验需要选取合适的光学元件和减小光学元件的反射和散射等损失。

此外,在样品固定位置的调整和光谱仪的参数设置方面也要进行细致的调试。

最后,数据处理是拉曼光谱测量中的最后一环节。

数据处理的目标是提取出样品中的拉曼信号,并去除背景干扰、噪音等因素。

拉曼光谱的数据初步处理

拉曼光谱的数据初步处理

摘要本文主要目的是熟悉拉曼光谱仪原理,并掌握拉曼光谱仪的实验测量技术以及拉曼光谱的数据初步处理。

文章首先论述了拉曼光谱仪开发设计、安装调试中所应用的基本理论、设计原理与关键技术,介绍了激光拉曼光谱仪的发展动态、研究方向和国内外总体概况。

其次阐述了拉曼散射的经典理论及其量子解释。

并说明了分析拉曼光谱数据的各种可行的方法,包括平滑,滤波等。

再次根据光谱仪器设计原理详细论述了分光光学系统的结构设计和激光拉曼光谱仪的总体设计,并且对各个部件的选择作用及原理做了详细的描述。

最后,测量了几种样品的拉曼光谱,并利用文中阐述的光谱处理方法进行初步处理,并且进行了合理的分析对比。

总之,本文主要从两个方面来分析拉曼光谱仪的实验测量和光谱数据处理研究:一、拉曼光谱仪的结构,详细了解拉曼光谱仪的工作原理。

二、拉曼光谱数据处理分析,用合理的方法处理拉曼光谱可以有效便捷的得到较为理想的实验结果.通过对四氯化碳、乙醇、正丁醇的光谱测量以及光谱数据分析,得到了较为理想实验效果,证明本文所论述方法的可行性和正确性。

关键词: 拉曼光谱仪光栅光谱分析AbstractPurpose of this paperisfamiliar withRamanSpectrometer,and mastery of experimental measurements ofRaman spectroscopyandRaman spectroscopytechniquespreliminarydataprocessing。

The article firstdiscusses theRaman spectrometerdevelopment, design,installation and commissioningin theapplication of the basictheory,designprinciples andkeytechnologies,laserRaman spectrometerdevelopments,research direction andoverall profileat home and abroad. The second section describesthe classical theoryof Ramanscatteringandquantumexplanation。

基于多项式拟合的拉曼光谱基线漂移校正方法

基于多项式拟合的拉曼光谱基线漂移校正方法

基于多项式拟合的拉曼光谱基线漂移校正方法在拉曼光谱分析中,光谱基线漂移是一个普遍存在的问题。

拉曼光谱中的基线漂移会干扰峰信号的判断和峰位的计算,因此对光谱进行准确的基线漂移校正非常重要。

而基于多项式拟合的方法是一种常用的拉曼光谱基线漂移校正方法。

1. 多项式拟合原理在拉曼光谱分析中,基线漂移是由一些非拉曼散射效应引起的,例如荧光、连续光等。

这些非拉曼散射效应对光谱的基线产生干扰,导致基线的漂移。

我们可以利用多项式拟合来对这些非拉曼散射效应进行建模,从而实现基线漂移的校正。

2. 多项式拟合方法多项式拟合是一种通过拟合多项式函数来逼近一组数据的方法。

在拉曼光谱基线漂移校正中,我们可以选择合适的多项式阶数,利用最小二乘法来拟合基线漂移的干扰部分,然后将拟合结果与原始光谱进行相减,从而实现基线漂移的校正。

3. 多项式拟合的优势基于多项式拟合的方法具有一定的优势。

多项式拟合是一种简单而有效的方法,不需要对光谱数据做过多的假设,适用于各种类型的拉曼光谱。

多项式拟合可以通过选择不同阶数的多项式来适应不同的基线漂移情况,具有一定的灵活性和通用性。

多项式拟合方法的计算速度较快,可以在较短的时间内完成基线漂移校正。

4. 个人观点与理解在我的个人观点和理解中,基于多项式拟合的方法是一种简单而有效的拉曼光谱基线漂移校正方法。

通过选择合适的多项式阶数和利用最小二乘法来拟合基线漂移的干扰部分,可以较好地实现基线漂移的校正。

然而,需要注意的是在选择多项式阶数时,要避免过拟合或欠拟合的情况,以免影响基线漂移校正的准确性和可靠性。

总结回顾基于多项式拟合的方法是一种常用的拉曼光谱基线漂移校正方法。

通过对非拉曼散射效应进行多项式拟合来建模,然后将拟合结果与原始光谱进行相减,可以较好地实现基线漂移的校正。

多项式拟合方法具有简单、灵活、通用和高效的特点,但需要注意选择合适的多项式阶数,避免过拟合或欠拟合的情况。

在实际应用中需要综合考虑光谱特征和数据情况,选择合适的方法来进行基线漂移校正。

拉曼光谱基线校正

拉曼光谱基线校正

拉曼光谱基线校正
拉曼光谱的基线校正可以通过多种方法实现,以下是一些常用的方法:
1.峰值消除:通过寻找并消除光谱中的峰值,可以消除基线校正中的噪声和干扰。

2.多项式拟合逼近基线:利用多项式拟合的方法逼近基线,能够得到较为平滑的基线。

这种方法需要选择合适的多项式阶数,以避免过拟合或欠拟合。

3.减去基线:在拉曼光谱中减去基线,可以突出光谱中的特征峰。

常用的基线校正算法有分段线性拟合法、局部极值中值法、多项式拟合法等。

4.小波变换法:小波变换法是一种有效的信号处理方法,可以对拉曼光谱进行多尺度分析和处理,从而提取出光谱中的特征信息并消除噪声。

5.插值法:插值法是一种数学方法,通过插值可以获得更加平滑的基线。

常用的插值方法包括线性插值、多项式插值、样条插值等。

6.最小二乘法:最小二乘法是一种数学优化技术,通过最小化误差的平方和来寻找最佳函数匹配。

在拉曼光谱基线校正中,可以利用最小二乘法对光谱数据进行拟合,从而得到较为准确的基线。

以上是一些常用的拉曼光谱基线校正方法,根据具体情况选择合适的方法可以提高校正的准确性和稳定性。

1。

拉曼仪使用方法

拉曼仪使用方法

拉曼光谱仪使用方法
一、开机测样
1、打开计算机和拉曼仪器,预热大约1小时;
2、打开计算机桌面上的程序操作界面,口令是“OPUS”;
3、点击测样图标,在基本设置中调入所测试样的种类并调
节激光强度;在高级设置中更改名称和更改保存路径并
设置扫描次数和时间,根据样品类型自行选择分辨率;
4、装样;(注意不可以污染镜片)
5、样品检测:选中拉曼光谱图,待出现预览谱图后,前后
调节样品座位置(点击Forward 和backward)到峰值显
示最大幅度;
6、回到基本设置点击样品测试开始测试样品;
二,谱图处理
1、谱图处理:点击放大图标将谱图放大,基线校正,标峰
位,点击打印-新建打印模板,将左侧显示栏中的第一
个谱图图标拖入模板,再将第二个峰值图标拖入模板,
在模板空白处新建一个表格,将峰值图标拖入表格中,
剪切掉模板中多余部分包括Bruker标志(在空白处右击
显示属性-范围-显示Bruker标志);
2、保存谱图:右击-复制,选中最后一项;打开写字板,
黏贴,保存写字板;
3、保存数据:选中左侧显示栏图标,右击-显示参数-Raman,
复制数据,黏贴到写字板。

三、关机
1、先关闭计算机,再关闭插座(即关闭激光);
2、关闭左侧仪器背面开关按钮,拔掉插头。

拉曼光谱的局域动态移动平均全自动基线校准算法

拉曼光谱的局域动态移动平均全自动基线校准算法

MMA 窗 口半宽度和控制平滑迭代 次数 , 最大程度地避免 了基线校准过度和基线欠校准现象 。 无论对 于凸形 基线 、 指数形基线 、 反 曲线形基线模拟拉曼光谱 , 还是真实物质 的拉曼光谱 ,L D MA全 自动基线校准算法都
取得 了很好 的基线校准效果 。 关键 词 拉曼光谱 ; 基线校准 ; 光谱平滑 ;窗 口平均
拉 曼 光谱 的局 域 动 态移 动 平均 全 自动 基 线校 准算 法
高鹏 飞 , 杨 蕊 , 季 江 Hale Waihona Puke ,郭汉明 , 瑚 琦 , 庄松林
1 .上海市 现代光学系统重点实验室 , 光学仪器与系统教育部工程研究 中心 , 上海 理工大学光 电信息与计算机工程学院,上海 2 .上海 医疗器械高等专科学校医学影像工程 系,上海 2 0 0 0 9 3 2 0 0 0 9 3
局域动态移动平均 ( L D MA) 全 自动基线校 准算法 ,并且详 细 阐明了该算法 的基本 思想和具 体算法步骤 。该 算法采用 了改进移动平 均算 法( MMA) 实现拉曼光谱峰 的逐渐剥离 , 通过 自动识别原始拉曼光谱 的基线子 区
间来将整个拉 曼光 谱 区 间 自动分 割 为多 个 拉曼 峰 子 区间 ,从 而 实现 了在每 个 拉曼 峰 子 区间 中动 态改 变
文献标识码 : A D O I : 1 0 . 3 9 6 4 / j . i s s n . 1 0 0 0 — 0 5 9 3 ( 2 0 1 5 ) 0 5 — 1 2 8 1 — 0 5
中图分类号 : 06 5 7 . 3

种不需要预先设置优化参数 、不需要人 工干预 、对不 同基
MMA窗 口半宽度和控制平滑迭代 次数 ,实现 了有 效的全 自

拉曼光谱基线处理

拉曼光谱基线处理

拉曼光谱基线处理
拉曼光谱基线处理是对拉曼光谱中的基线进行去除或调整,以提高光谱数据的准确性和可视化效果。

基线指的是光谱中的背景信号,可能来自仪器噪声、样品自发辐射或散射等因素,而且可能会掩盖或干扰目标信号的分析。

下面是一些常见的拉曼光谱基线处理方法:
1. 多项式拟合法:利用多项式函数对整个光谱曲线进行拟合,然后将拟合的曲线视为基线,并将其从原始光谱中减去。

2. 线性插值法:选择光谱曲线中的几个谷底或谷峰点,通过这些点之间的线性插值来拟合基线,然后将其从原始光谱中减去。

3. Small Region Iterative Standard Deviation(依标准偏差进行
小区域迭代)法:这是一种自动的基线处理方法,通过在光谱中选择一段小区域,在该区域内计算标准偏差,并将位于该区域之外的数据点视为异常点,然后将这些点剔除,重新计算标准偏差,循环迭代直到标准偏差达到稳定。

4. Whittaker Smoothing方法:这是一种光滑峰值式的方法,通
过对光谱数据进行平滑操作,去除光谱中的噪声和振荡,从而得到基线。

以上只是一些简单的基线处理方法,根据实际情况和需求,还可以结合其他方法进行基线处理,如小波变换、曲线拟合、光
谱差异谱等方法。

最终目的是去除基线的干扰,凸显光谱中的目标信号,以便进行更准确的光谱分析和解释。

基于临近比较的快速拉曼光谱基线校正方法

基于临近比较的快速拉曼光谱基线校正方法

基于临近比较的快速拉曼光谱基线校正方法许英杰;范贤光;林智乐;王昕;左勇【摘要】Raman imaging is a kind of modern testing technology based on Raman scattering,which has been widely applied in manufacturing and scientific research.However,due to the fluorescent effect and instruments drift,the baseline shift could easily occur,which has a strong impact on the feature extraction of the Raman signals.Therefore,the baseline correction is necessary and inevitable in the signal processing of Raman spectra.The traditional baseline correction methods can only correct the Raman spectrum one by one,so once a large amount of Raman imaging signals have to be processed,the processing time is too long to accept.In this paper,the baseline correction base on comparison which considers the correlation between the Raman spectra and the same background is proposed to realize the fast baseline correction and improve the processing speed of Raman imaging data.%拉曼光谱成像技术是基于拉曼散射效应所开发的一项现代检测技术,在现代生产、科学研究过程中使用非常广泛.拉曼光谱信号受荧光效应和仪器等方面的影响,往往会产生基线漂移,严重影响对信号特征的进一步提取.因此,必须对拉曼光谱信号进行基线校正.传统的基线校正方法,只针对单一光谱信号,计算量较大,在处理由大量拉曼信号组成的成像数据时,耗时较长且效果不佳.该文提出一种基于临近比较的快速基线校正方法,根据在相同背景下采集的光谱之间的相关性,实现快速基线校正,提高了拉曼成像数据的处理速度.【期刊名称】《分析测试学报》【年(卷),期】2017(036)008【总页数】4页(P1047-1050)【关键词】拉曼光谱;基线校正;临近比较;快速成像【作者】许英杰;范贤光;林智乐;王昕;左勇【作者单位】厦门大学航空航天学院,福建厦门361005;厦门大学航空航天学院,福建厦门361005;厦门大学航空航天学院,福建厦门361005;厦门大学航空航天学院,福建厦门361005;北京长城计量测试技术研究所国防科技工业第一计量测试研究中心,北京100095【正文语种】中文【中图分类】O657.37拉曼光谱(Raman spectroscopy)又称拉曼效应,由印度科学家C.V.Raman发现并命名[1-2]。

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Rcharacterization for its ability to obtain information on vibrations from samples. It can also be used for on-line monitoring using a fiber-optic Raman probe (1,2). The Raman spectra show the characteristics for species in sharp and dense peaks. However, during the application of Raman spectroscopy, fluorescence of organic compounds in the samples, which are sometimes several orders of magnitude more intense than the weak Raman scatter, can interfere with the Raman signals (3). A phenomenon of baseline drift shows up, making the resolution and analysis of Raman spectra impractical.Both instrumental (4) and mathematical methods have been developed to reduce the drifted baseline caused by fluorescence. The use of an excitation wavelength such as 785–1064 nm lasers, which does not eliminate fluorescence (5), is the most traditional instrumental method. Raman scattering is directly proportional to the fourth power of frequency; as the excitation wavelength increases, the sen-sitivity of the Raman becomes severely reduced. The use of anti-Stokes Raman spectroscopy is another method, based on theory (6). Mathematical methods (7–10) include the first and second order derivatives, wavelet transform, me-dian filter, and manual polynomial fitting. These methods are useful in certain situations, but still have some limita-tions. For example, derivatives are effective, but as a result the shape of the Raman spectrum is changed; wavelet trans-form can be differentiable in the high- and low-frequency components of the signals; however, it is difficult to choose a decomposition method. Manual polynomial fittings re-quire the user to identify the “non-Raman” locations manu-ally (11), and afterwards the baseline curve is formed by fitting these locations. Consequently, the result involves the inevitable subjective factors and, in addition, the workload is always heavy. Therefore, it is important to choose an op-timal decomposition method.Piecewise linear fitting based on critical-point-seeking was proposed in this study. The method determines an op-timum corrected spectrum by correlation analysis, which can conquer these limitations. A Raman spectrum from the sulfamic acid catalytic reaction of an aspirin system was used as a study subject. By using this method, the Raman spectrum drifted baseline was automatically eliminated, leaving only the corrected spectrum.Theory and MethodBasis of Qualitative and Quantitative Raman Analysis A Raman spectrum is a plot of the intensity of Raman scattered radiation as a function of its frequency differ-ence from the incident radiation (usually in units of wave-numbers, cm -1). This difference is called the Raman shift, which is the basis of qualitative analysis (12). The intensity or power of a normal Raman peak depends in a complex way upon the polarizability of the molecule, the intensityKuo Sun, Hui Su, Zhixiang Yao, and Peixian HuangThe correction of baseline drift is an import part for data preprocessing. An interval linear fitting method based on automatic critical-point-seeking was improved, which made it possible for the baseline to drift automatically. Experimental data were acquired from the sulfamic acid catalytic reaction of the aspirin system, which consisted of different proportions of aspirin. A simulated base-line with different interval values of moving average smoothing determined setting parameters in this method. After baseline drifts caused by fluorescence are removed, the differences of character-istic aspirin peaks proved the efficiency of this method.Baseline Correction for Raman Spectra Based on Piecewise Linear Fittingtively lower the frequency and sensi-tivity as shown in equation 2: ()smooth 21k,x k ix ++=[2]where ω is the interval number of themoving average smoothing window, which must be an odd number.Step 2: Locating Local ExtremaSuppose that the function f (x ) has an extreme value at point x = x 0 in a certain neighborhood (x 0 – δ, x 0 + δ)where the derivative of the function is defined and is not 0. If x ϵ (x 0 – δ, x 0) the derivative is positive, whereas if x ϵ (x 0 + δ, x 0) the derivative is nega-tive, f (x 0) is maximum, otherwise f (x 0) is minimum. After finding the local minimum, we can get a set of minimal critical points λi (i = 1, 2, . . ., n ). The Raman spectrum was divided into n - 1 intervals by λi .Step 3: Fitting BaselineEvery interval (λ1,λ2), (λ2,λ3), . . .,(λi–1,λi ) can be fitted in a linear equa-tion as shown in equation 3:φ(x )=(f (λi )–f (λi -1))((x –)+(λi +λi -1)(λi –λi -1)2 (f (λi )+f (λi -1))x ∈)+)(λi -1,λi )i =(2,3,...,n )2[3]where φ(x ) is the fitting baseline.Step 4: Removing BaselineA corrected spectrum F (x ) was acquired after the fitting baseline was removed from the original spectrum.F (x )=f (x )–φ(x )[4]Step 5: Performing Correlation Analysis A correlation analysis method was conducted between the original spec-trum X and the corrected spectrum Y as shown in equation 5:r coe =(X –X )(Y –Y )(X –X )2 (Y –Y )2[5]The flowchart is shown in Figure 1.Laser aperture Raman spectrometerFiber CableWater bathFiber-optic probeSpectroscopy softwareFigure 2: The sampling Raman spectrum device.Input original spectrumSmoothLocate intervalFit baselineRemove baselineCorrelation analysesDetermine corrected spectrum (εmin = (1– r i ))Hit list (r 1, r 2 ...r n )Set range of span number (ω1, ω2 .... ωn )Figure 1: The process of interval linear fitting baseline correction.Experimental SectionRaman Platform SettingA laser with the wavelength of 785 nm was used as the excitation light source (Laser-785, Ocean Optics), and a Raman spectrometer (Scientific-grade QE65000, Ocean Optics) was used for the detector. The Raman information was obtained using a fiber-optic probe (BAC100-785-OEM, Ocean Optics), with Spectrasuite spectroscopy soft-ware (Ocean Optics); the x axis on the workstation menu was selected to be Raman shift, and the selected integral time was 1/s to obtain the Raman spec-trum for the 0–2000 cm -1 spectral range (1044 data points).Experimental DataWe saved the Raman spectrum of ace-tylsalicylic acid (AR, Tianjin Guangfu Fine Chemical Research Institute) first. According to the literature (15), we pre-cisely weighed acetic anhydride to 41.0 g (AR, ChengDu KeLong Chemical Co.,Ltd.), salicylic acid to 27.7 g (AR, Tianjin Guangfu Fine Chemical Re-search Institute), and sulfamic acid to 0.5 g (AR, ShanTou XiLong chemical factory). The acetic anhydride, salicylic acid, and sulfamic acid were transferred sequentially to a 100-mL, three-necked, round-bottomed flask that was main-tained at a temperature of 81 °C in a-1). With fitting, and output were integrated into a function based on Scilab 5.4.0 (/).Parameter SettingsIn this approach, the interval number of moving average smoothing is the primary parameter. A suitable inter-val for a simulated baseline is chosen. If a strong baseline slope exists in the Raman spectrum, another regulating parameter must be set, the derivative start point. The Raman spectrum ob-tained at the 18-min mark, with the maximum baseline drift, was selected for setting parameters.The baseline (red) was eliminated without both smoothing and setting of the derivative start point. The cor-rected spectrum is shown in Figure 4. Many Raman peaks (from 300 cm -1 to 1100 cm -1) of components in the reac-tion system were removed as the base-line because of the frequency shift and high sensitivity. An inverse peak (0–100 cm -1) exists because of the high slope in the estimated baseline (0–240 cm -1). With the setting of the derivative start point at 60, the original spectrum of object was handled with the selected interval values of 5, 15, 25, and 35, re-spectively. They are shown in Figures 5a–d.The inverse peak disappears (0–100 cm -1) because of the derivative start point setting, which increased match-ing with the original spectrum. When the interval value was 5 (Figure 5a), useful information (250–1000 cm -1)16000140001200010000800060004000200000500100015002000Raman shift (cm -1)Figure 3: Original Raman spectra of samples.SampleI n t e n s i t yBaselineCorrectionRaman shift (cm -1)200400600800100012001400160018002000Figure 4: Correction without setting parameters.inal spectrum, a group of correlation coefficients was used consisting of a hit list from the corrected spectrum in the range of selected interval val-ues from 3 to 35 (with the derivative start point setting at 60). The hit list is shown in Figure 6.background, and the baseline slope was eliminated, all correlation coefficients between the original spectrum and corrected spectra were less than 1. The correlation coefficients were less than 0.7 in the range of interval value from 3 to 7 because the Raman scattering of the measured object was removed. When the interval number was in the range from 9 to 21, the corrected spec-tra matched favorably with the original spectra. Although the correlation coef-ficient increased in the range from 29 to 35, the corrected spectra were distorted as shown in Figure 5d. Determining the range of the inter-val values is the most important step. In the program, the range of interval values from 9 to 21 is favorable, as seen in Figure 6. The correction spectrum was obtained respectively for every in-terval value in the setting range after correlation analyses were conducted and between the original spectrum and every corrected spectrum we obtained a hit list of correlation coefficients. When correlation coefficients on the list wereclosest to 1, the corrected spectrum wasoptimum.ApplicationThe six Raman spectra obtained di-rectly in the reaction system were cor-rected with this program.Figure 7 shows that the position ofRaman peaks could be discerned inthe corrected spectra of samples, whichmeans the information about compo-nents in the reaction system is pre-served. The Raman spectrum of aspirinis shown below the corrected spectraof the samples. The Raman spectrumof aspirin has characteristic bands inthe region from 700 cm-1 to 800 cm-1,which can be assigned to the aromaticring CH-deformation vibrations. Thefeature at 1045 cm-1 is attributed to theOH-bending vibration. Raman bandsin the 1606– 1630 cm-1 spectral regionare caused by both the CC-stretchingvibrations and CO-stretching vibra-tions of the carboxyl group (16,17).Raman shift (cm-1)Raman shift (cm-1)Raman shift (cm-1)Raman shift (cm-1) 05001000150020000500100015002000 05001000150020000500100015002000IntensityIntensityIntensityIntensity(a)(b)(d)(c)Figure 5: Correction with the derivative start point setting at 60 and interval numbers at (a) 5, (b)15, (c) 25, and (d) 35.357911131517192123252729313335Span numberCorrelationcoefficient0.7050.70.6950.690.6850.680.6750.67Figure 6: Hit list in the range of interval numbers.Scatter intensity in Raman shift re-gions (700–800 cm -1, 1040–1050 cm -1, and 1600–1630 cm -1) increased with increasing reaction time.ConclusionsTo eliminate the influence of a drifted baseline, a piecewise linear fitting method was developed that was able to automatically correct the base-line from acquired data, particularly for the fluorescence background in Raman spectra.The interval value of a moving av-erage smoothing is the primary pa-rameter in the programming. Proper parameters were selected according to correlation coefficient to the highest value (closest to 1) in the hit list. This method makes characteristic peaks identifiable for further analysis, which could improve the quality of Raman spectra in other fields.AcknowledgmentsThe authors would like to acknowl-edge foundation support and researchfellowships from the Biochemical Pro-cess Detection and Control Laboratory of the Department of Biology and Chemical Engineering, at Guangxi University of Science and Technology.References(1) H. Torii, A. Ishikawa, and M. Tasumi, J. Mol.Struct. 413, 73–79 (1997).(2) S.K. Khijwania, V.S. Tiwari, F.-Y. Yueh, andJ.P. Singh, Sens. Actuators, B 125, 563–568 (2007).(3) R.L McCreery, Raman Spectroscopy forChemical Analysis (Wiley-Interscience, New York, New York, 2000), pp. 25–26.(4) E.A.J. Burke, Lithos. 55, 139–158 (2001). (5) J. Funfschilling and D.F. Williams, Appl. Spec-trosc. 30, 446 (1976).(6) P.A. Mosier-Boss, S.H. 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Acta, are withFor more information on this topic,please visit our homepage at: Raman shift (cm -1)I n t e n s i t ySampleAspirin200400600800100012001400160018002000Figure 7: Hit list in the range of interval numbers.。

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