阿司匹林合成过程的在线拉曼光谱研究

利用在线拉曼光谱的方法,跟踪阿司匹林合成反应过程的实验.直接观测到反应过程中体系的拉曼光谱随时间的变化,用小波变换的方法去除光谱的本底之后, 又利用多波长线性回归的方法对实验数据进行实时的处理,得到了实验中各组分的相对浓度随时间的变化.     

能量色散X射线荧光光谱背景扣除方法的探讨

能量色散X射线荧光分析使得特征X射线的全能峰叠加在背景之上,针对能量色散X射线荧光谱线背景扣除算法的研究,简述了剥峰法和小波变换法扣除背景的原理,并将这两种方法用于对实际谱线的处理,取得了良好的效果.同时对两种方法的算法和处理效果进行了对比研究,表明剥峰法相对简单,而小波变换法需要考虑小波基和分解层次等的影响,但是对整个谱线的综合处理效果相对要好一些.     

小波变换用于近红外光谱性质分析

以汽油研究法辛烷值分析为例,研究了小波变换在近红外光谱分析中的应用。对近红外光谱的小波特性、小波变换参数以及变量提取方法进行了详细研究。研究结果表明：光谱噪音、有用信息和背景分别分布在小波高、中和低频区域;母小波函数对性质分析结果影响很大;小波变换可以同时扣除光谱背景、去除噪音和压缩变量,具有运算速度快、分析精度高以及无需去噪后处理等优点,在近红外光谱分析中具有很好的应用前景。     

基于小波-反向搜索及表面增强拉曼的食品中色素的光谱定性分析

使用金纳米粒子为增强因子的表面增强拉曼光谱技术,通过连续小波变换将拉曼光谱信号转化到小波空间(墨西哥帽小波作为小波基)。该步骤能够减轻信号中基线变化及随机噪音的影响并找到峰位置和最佳小波尺度系数。依据小波空间中的信息,对混合物光谱及标准谱光谱进行反向搜索得到反向搜索匹配系数(Reverse match quality,RMQ),作为判断混合物中目标成分是否存在的依据。该算法可对混合物中的目标物质进行准确定性,并已成功应用于多种食品中色素鉴定。食品中色素的检出率达到99%,且结果稳健,其效果明显优于传统的命中质量系数法(Hit quality index,HQI)。这证实了小波空间反向搜索方法是一种快速而准确的拉曼光谱定性算法。     

基于料酒的三维同步荧光光谱模式识别研究

提出了一种应用同步荧光光谱技术无损快速鉴别料酒品牌的新方法.利用主成分分解法和小波变换法对料酒样品的同步荧光光谱信号进行了压缩处理,分别采用同步荧光光谱数据的第一主成分和小波细节系数为特征变量进行主成分分析和聚类分析,分类结果表明小波系数作为料酒的特征变量对料酒品牌分类正确率更高.利用偏最小二乘和径向基人工神经网络方法...     

Sr元素X荧光快速分析方法

为了实现Sr元素的快速、准确测量,讨论了现场X荧光分析方法(XRF),测量得到Sr元素特征X射线全能峰的峰面积,采用基于傅里叶变换的本底估计方法扣除散射本底,计算得到Sr元素的含量。结果表明,XRF法测得Sr的含量与实验室分析结果的平均相对误差为-9.3%,XRF法对现场快速分析Sr含量有指导意义。     

近红外分析中光谱预处理及波长选择方法进展与应用

光谱预处理和波长选取方法在近红外光谱分析技术中相当重要。本文综述了常用的NIR预处理和波长选取方法及这一领域的最新进展,详细介绍正交信号校正(OSC)、净分析信号(NAS)和小波变换(WT)等新光谱预处理方法以及无信息变量消除(UVE)和遗传算法(GA)等波长选取方法,并给出了这些方法的具体算法和一些应用实例。     

小波变换-分段直接校正法用于近红外光谱模型传递研究

提出了一种新的传递算法(WT-PDS)———小波变换-分段直接校正法,并详细讨论了模型传递参数和传递结果。首先利用小波变换对光谱进行压缩处理,采用PDS算法消除不同仪器之间压缩数据的差异,最后利用经校正的压缩数据进行分析,实现模型传递。本方法能够扣除不同仪器之间的大部分差异,大幅度改善分析精度。传递后模型分析精度与源机模型稳健性紧密相关。如果源机模型稳健性强,则能够实现不同仪器之间的共享。本方法能够实现源机的0#轻柴十六烷值、凝点、馏出温度;-10#轻柴十六烷值、凝点以及-10#军柴凝点和馏出温度共10个模型在5台仪器之间共享,简化了建模的成本。与传统的PDS相比,WT-PDS方法具有传递和建模变量少、速度快、光谱校正性能高等优点,而其模型分析精度与传统PDS基本一致。     

基于Voigt峰的未知成分光谱拟合算法及其在甲醇汽油定量分析中的应用

提出了一种基于Voigt函数的未知成分拉曼光谱拟合算法,利用Voigt峰函数的叠加形式对样本中未知成分所产生的背景光谱进行拟合。在扣除背景光谱影响后,利用被测成分的光谱贡献权值与成分浓度之间建立线性关系模型。实验在3种成分不同的基础汽油中加入不同体积比例(2.5%~80.0%)的甲醇溶液,利用本方法对成分未知的基础汽油所产生的背景光谱进行拟合,扣除拟合光谱后,剩余光谱即可视为甲醇的光谱贡献。基于4个训练样本建立了甲醇光谱贡献权值与浓度之间的线性定量分析模型,模型的预测均方误差(RMSEP)为1.86%,复相关系数(R2)达到0.987。结果表明,此方法可有效解决混合物中光谱重叠问题,具有训练样本少、外推性强的优点。     

基于小波变换动态时间规整的近红外光谱模型传递方法

为解决因测量环境及仪器差异而导致的近红外光谱模型通用性较差的不足,提出一种基于小波变换动态时间规整算法的模型传递方法(Wavelet transform combined with dynamic time warping,WDTW),从而实现不同仪器之间模型的共享。首先,该方法将光谱进行小波变换预处理,然后利用动态时间规整算法(Dynamic time warping,DTW)找到近红外光谱波长点之间最优的对应关系并建立回归方程。使用近红外药品光谱数据集和汽油数据集建立传递模型,验证了基于小波变换动态时间规整模型传递方法的有效性。汽油光谱数据集C7、C8、C9和C10成分的预测标准偏差(SEP)分别为0.414 4、0.801 1、1.090 4和1.290 8;药品光谱数据集活性、硬度和重量的SEP分别为2.585 6、0.434 5和2.270 3,均小于传统方法。上述实验结果表明,所建立的模型传递方法能有效消除源机光谱和目标机光谱之间的差异,提高模型的稳定性和准确性,实现模型传递的效果。     

Application of wavelet transform to background correction in inductively coupled plasma atomic emission spectrometry

A background correction method based on wavelet transform was devised and applied to inductively coupled plasma atomic emission spectrometry (ICP-AES). The proposed approach separated background from analyte signal according to their different frequencies. Compared with the analyte signal, the background has a low frequency. By removal of the components attributed to the signal, the background over the spectral window of the analyte line can be fitted through wavelet reconstruction. The results showed that the wavelet transform technique could handle all kinds of background and low signal-to-background ratio spectra, and required no prior knowledge about the sample composition, no selection of suitable background correction points, and no mathematical assumption of the background distribution. This technique performed as well as the conventional three-point background correction method for linear backgrounds, and provided better results than the latter for curved backgrounds. The proposed procedure was illustrated, by processing real spectra, to be an effective and practical tool for background correction in ICP-AES.     

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing.

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.     

The Application of Complex Wavelet Transform to Spectral Signals Background Deduction

The accuracy of spectrograms may be affected by baseline excursion or drift when infrared spectrometers are used in the analyses of gases. Background deduction or baseline correction is one of the effective pretreatment methods that can improve measurement accuracy. This paper presents a novel methodology based on complex wavelet transform algorithm to perform background deduction. The complex wavelet transform methodology establishes a complex wavelet filter to decompose the spectral signals first, and set the decomposition coefficients in the high-frequency section to zero, and then reconstruct the background signals; finally, the background deduction can be realized by deducting the background signals. In this study, the complex wavelet established by Daubechies was selected to demonstrate background deduction aiming at simulative spectral signals with different backgrounds and the real spectral signal of SF6 decomposition gases. Compared with the results done by the real wavelet transform in the same conditions, the results indicate that complex wavelet transform methodology can perform background deduction more efficiently than real wavelet transform methodology, thus improving the effectiveness and precision of spectrogram measurements greatly, which is useful for SF6 gas decomposition compositions analysis

     

The Application of Complex Wavelet Transform to Spectral Signals Background Deduction

The accuracy of spectrograms may be affected by baseline excursion or drift when infrared spectrometers are used in the analyses of gases. Background deduction or baseline correction is one of the effective pretreatment methods that can improve measurement accuracy. This paper presents a novel methodology based on complex wavelet transform algorithm to perform background deduction. The complex wavelet transform methodology establishes a complex wavelet filter to decompose the spectral signals first, and set the decomposition coefficients in the high-frequency section to zero, and then reconstruct the background signals; finally, the background deduction can be realized by deducting the background signals. In this study, the complex wavelet established by Daubechies was selected to demonstrate background deduction aiming at simulative spectral signals with different backgrounds and the real spectral signal of SF6 decomposition gases. Compared with the results done by the real wavelet transform in the same conditions, the results indicate that complex wavelet transform methodology can perform background deduction more efficiently than real wavelet transform methodology, thus improving the effectiveness and precision of spectrogram measurements greatly, which is useful for SF6 gas decomposition compositions analysis     

Continuous wavelet transform applied to removing the fluctuating background in near-infrared spectra

A novel method based on continuous wavelet transform (CWT) was proposed as a preprocessing tool for the near-infrared (NIR) spectra. Due to the property of the vanishing moments of the wavelet, the fluctuating background of the NIR spectra can be successfully removed through convolution of the spectra with an appropriate wavelet function. The vanishing moments of a wavelet and the scale parameter are two key factors that govern the result of the background elimination. The result of its application to both the simulated spectra and the NIR spectra of tobacco samples demonstrates that CWT is a competitive tool for removing fluctuating background in spectra.     

A Background and noise elimination method for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of near infrared (NIR) spectral signals. The method is based on the use of multi-resolution, which is one of the main advantages provided by wavelet transform. The signals are firstly split into different frequency components, which keep the same data points of the original signals. In conjunction with a modified uninformative variable elimination (mUVE) criterion, the new method can be used to remove the low-frequency varying background and the high-frequency noise simultaneously. The method is successfully applied to simulated spectral data set and experimental NIR spectral data, resulting in more parsimonious multivariate models with higher precision. In addition, the proposed strategy can be applied to other spectral signals as well.     

A novel method to calculate the approximate derivative photoacoustic spectrum using continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) using Haar wavelet function for approximate derivative calculation of analytical signals is proposed and successfully used in processing the photoacoustic signal. An approximate nth derivative of an analytical signal can be obtained by applying n times of the wavelet transform to the signal. The results obtained from four other different methods--the conventional numerical differentiation, the Fourier transform method, the Savitzky-Golay method, and the discrete wavelet transform (DWT) method--were compared with the proposed CWT method; it was demonstrated that all the results are almost the same for signals without noise, but the proposed CWT method is superior to the former four methods for noisy signals. The approximate first and second derivative of the photoacoustic spectrum of Pr(Gly)3Cl3.3H2O and PrCl3.6H2O were obtained using the proposed CWT method; the results are satisfactory.     

An On-Line Wavelet Transform for De-Noising of High Performance Liquid Chromatograms

ABSTRACT

A novel algorithm of wavelet transform, called the on-line wavelet transform, which can be used in on-line processing of signals of an instrumental analysis, is proposed. And application of the on-line wavelet transform in denoising of high performance liquid chromatograms was investigated. Results showed that the noise in chromatograms was cleanly removed by the method, peak position after the on-line de-noising dose not change, and the linearity of calibration curves of concentration versus peak area remained and even was improved. Quantitative determination of three mixed samples were investigated with five standard samples; the recoveries were between 94.0-105.0%.     

Passive Airborne Fourier Transform Infrared Remote Detection of Methanol by Use of Wavelet Analysis as A Feature Extraction Method

Wavelet analysis was evaluated as a data preprocessing tool in the construction of automated classifiers for the detection of volatile organic compounds from passive Fourier transform infrared remote sensing data collected in a downward-looking mode from an aircraft platform. The discrete wavelet transform was applied to single-beam spectra and patterns were formed with either the wavelet coefficients directly or with spectra reconstructed with selected resolution levels of the wavelet decomposition. Automated classifiers were constructed with support vector machines (SVM) and used to detect releases of methanol from an industrial site. A key issue in this work was the desire to use data collected during controlled experiments on the ground to train the SVM classifiers. Spectral backgrounds in these ground-collected data are different than those encountered as the aircraft flies, however, and the development of successful classification models requires spectral preprocessing to suppress background signatures. Biorthogonal wavelets were used to generate patterns and resulted in SVM models that produced no missed methanol detections and false detection rates of less than 0.1% when applied to prediction data not used in the development of the model. The SVM classifiers constructed with wavelet processing were compared to one based on unprocessed spectra and also to one computed with spectra preprocessed with Butterworth high-pass digital filters.     

基于多尺度小波变换的红外光谱谱峰识别算法

传统的谱峰检测方法一般分为3个步骤:谱线平滑、基线校正和谱峰识别.现有的基于小波变换的峰值检测方法能较好地将基线校正和谱峰识别两个步骤融为一步.在此基础之上,本研究将谱线平滑也很好地融入到小波变换的峰值检测算法中,使整个峰值检测算法成为一个整体.在峰值提取时,原始谱图直接处理,不再是处理加工过的谱图,减小了谱峰检测结果...