基于小波和轮廓提取的色谱基线算法研究

把基于小波和轮廓提取的基线算法引入了色谱基线提取领域;基于轮廓提取的算法为:通过构造一个滑动窗,沿着色谱数据滑动求窗内的最小值。把这些最小值插值平滑就获得了色谱基线。基于小波和轮廓提取的算法为:先用小波初步提取基线,然后把色谱数据减去小波提取的基线后再用轮廓提取算法获得基线,把小波提取的基线和轮廓提取的基线相加即为原数据的基线。对这两个算法进行了比较实验研究,结果表明:基于小波和轮廓提取的算法比轮廓提取的算法效果好,能更准确地提取色谱基线。     

一种变压器油色谱峰识别算法的设计

在变压器油色谱峰识别领域,传统的一阶导数法需要斜率阈值来实现色谱峰识别,因此自动化程度低及容易失真。针对这些缺点,本文在一阶导数法的基础上进行了改进,将迭代移动平均及归一化分析技术应用到色谱峰识别中,通过对信号曲线及方波曲线多次迭代移动平均确定最优的归一化识峰参数,结合色谱峰的绝对保留时间及识峰窗口实现对色谱峰的准确识别。实验结果表明：该算法可以准确识别色谱峰,对噪声、色谱峰的峰宽及峰形变化不敏感,具有很强的自适应性,满足变压器油中气体在线监测装置的现场使用要求。     

一阶导数法在裂解色谱法中的应用

运用一阶导数数学方法对色谱馏出曲线的基本速率方程求导,应用导数光谱装置实现模拟微分,得到一阶导数色谱图。不但为裂解色谱特征的定性鉴别提供了又一个有力的手段,而且给色谱图数学方法的处理理论及信息转换以新的启迪。     

—阶导数法在裂解色谱法中的应用

运用一阶导数数学方法对色谱馏出曲线的基本速率方程求导，应用导数光谱装置实现模拟微分，得到一阶导数色谱图。不但为裂解色谱特征的定性鉴别提供了又一个有力的手段，而且给色谱图数学方法的处理理论及信息转换以新的启迪。     

基于近红外光谱的塑料分选技术预处理方法的优化

为进行不同塑料种类的识别,采集了尼龙（PA）、聚丙烯（PP）、聚苯乙烯（PS）、聚氯乙烯（PVC）4类塑料的近红外光谱数据,并针对光谱数据采集时存在的噪声、基线和光程问题,基于3点Savitzky-Golay卷积平滑（S-G）、一阶导数（FD）、二阶导数（SD）、标准正态变量变换（SNV）、多元散射校正（MSC）进行了预处理组合优化研究,以竞争性自适应重加权算法（CARS）进行特征波长提取,并运用支持向量机算法（SVM）建立模型。结果显示：所有预处理方法中,预处理组合S-G+FD+SNV获得的结果最优,S-G+FD+SNV+SVM模型的平均准确率高达96.67%,其训练集和验证集的平均准确率均为100%。上述预处理组合优化方法可为4类常见塑料的鉴别研究提供参考。     

气相色谱/离子阱质谱中组分图谱提取的主成分分析方法

由于离子阱质谱中容易发生分子-离子反应, 致使质谱图发生改变, 影响与主要由四极质谱建立的参照图谱的匹配效果. 本文采用主成分分析方法对气相色谱/离子阱质谱中组成色谱峰的所有全扫描质谱进行提取, 提取后的质谱图能够有效克服分子-离子反应和基线干扰的影响, 应用不同的匹配算法均能很好地和参照图谱匹配, 和传统方法相比匹配度显著提高, 对目标化合物定性准确.     

一阶和二阶导数相综合的色谱峰检测法

本文提出了一种介于一阶和二阶导数之间的色谱峰检测新方法，它能排除基线漂移的影响，具有抗噪声干扰能力，能判别重叠峰，还能够得色谱峰上各拐点，的位置并发现肩峰型重叠峰。该方法只需要设置一个起动参数即可自动进行峰检测，具有很强的自适应性和健壮性，极大地方便了用户。     

阶跃伏安法中离散数据处理方法Ⅲ.样条最小二乘法

本文对采用样条最小二乘法处理低信噪比的阶法离散数据的噪声滤除,导数卷积的应用原理作了详细讨论,从理论模拟数据和实验的测试结果表明,样条最小二乘法在处理波形复杂.含强白噪声的信号时,呈现了目前文献报道的分析信号处理方法所没有的优点,具有广阔的应用前景.     

小波变换在重叠化学信号解析中的应用

将小波变换应用于重叠化学信号的分辨与处理,由于在噪声较大的情况下二阶导数法很难从噪声中区分出微弱信号峰,故通过小波卷积法确定各个组成峰的位置.在信噪比相对较低的情况下明显优于二阶导数法,然后利用小波滤波器处理数据,处理后的峰可达到基线分离,峰位置和面积基本不变.将此方法应用于单一成分的正丁酮、正丁醛以及乙醇、正丙醇、异丙醇、正丁醇、正戊醇和仲辛醇6种醇类物质的混合物处理,得到了满意结果.     

自动化色谱谱图解析——谱峰的自动识别与快速解析

结合基于高阶导数的谱峰识别方法和面积重现法,建立了一种完全自动化的对色谱曲线进行分割、识别与快速解析的方法。其中,DW(Durbin-Watson)测试的引入和区分信号与噪声判据的采用减少了在色谱解析过程中的人为干预,降低了对操作人员专业知识和经验的要求,为实现色谱解析的自动化奠定了基础。通过对模拟色谱和实验色谱的比较,验证了该方法是一个很有用的工具,可以为色谱分析工作提供有力的帮助。     

化学谱图重叠峰检测及基线校正

本文提出一种谱峰曲线与其一个阶差分曲线相结合的算法，经宽松变换处理后，用于区分化学谱图中谱峰的终点是否真正落回到基线上，并由此建立了一种具有健壮性的重叠峰检测方法和及时准确的谱图基线校正方法。由于此算法的阈值参数可以固定，从而保证了重叠峰检测较少地受到人为的影响，进一步提高了化学谱图数据处理的自动化水平。     

Recognizing chromatographic peaks with pattern recognition methods III. Application of the algorithm for peak recognition in trace analysis

Summary The application of a newly developed peak recognition algorithm is shown. This algorith is based on the KNN method, one of the pattern recognition methods. It is shown that peaks with a S/N-ratio down to one can be safely recognized. This is also possible if the baseline has not only detector noise, but has other disturbances, e.g., noise signals which are generated by a reaction detector. The recognition ability of the algorithm is demonstrated by a standard chromatogram with three different concentrations and with two different sampling rates. The improvement against the classical algorithm is demonstrated. Some properties of the algorithm are discussed.     

基于无基底扣除的数据趋势累积谱峰检测算法

谱峰的检测分析在色谱技术研究中具有十分重要的作用,但在色谱数据采集、传输的过程中,不同程度的噪声干扰给谱峰检测带来了极大的困难。目前传统的谱峰检测算法普遍通过基底扣除的方式对谱峰的形态进行预定义,将谱峰分为单峰、重叠峰等多个种类。针对不同种类的谱峰采用不同的检测方法,这就导致了传统的谱峰检测算法具有高复杂度、低自动化程度以及容易失真等缺点。因此,该文从另一个角度出发提出了一种新型的谱峰检测算法。该算法取消基底扣除以及谱峰分类这一步骤,直接在源数据曲线的基础上进行谱峰检测,主要分为离散差分、趋势累积以及遍历寻峰3个步骤。首先通过信号量表征数据升降趋势;然后进行数据趋势累积,根据累积总和定位谱峰,采用三点定位的方式,即峰起点、极值点和峰终点描述一个谱峰的位置;最后根据遍历排序的方式进行谱峰的筛选。此外,通过谱峰扣除的方式得到曲线基底部分。采用C语言设计编写了算法程序,并对多个动态比表面积分析仪测定的色谱图进行了检测分析,结果显示使用该算法可以精准区分谱峰部分与基底部分,受数据曲线毛刺、震荡等噪声干扰很小,谱峰的三点定位十分准确,且不受其复杂形态的影响,具有很强的普适性。与其他算法相比,该算法定位准确,结构清晰,具有较好的稳定性以及可靠性。该文报道了无基底扣除以及趋势累积等新型谱峰检测思想在吸脱附色谱曲线中的应用,证明了其在吸脱附色谱峰检测中的有效性和良好的应用前景。     

PeakSelect: preprocessing tandem mass spectra for better peptide identification

We present a new preprocessing method, PeakSelect, to improve the accuracy and efficiency of Tandem Mass-Spec peptide (protein) identification. The fundamental difference between noise and fragment ions in spectra is that ions have isotopes but noise does not. We propose a new and important concept of an Isotope Pattern Vector (IPV) which characterizes the isotope cluster of fragment ions. Then the noise and real peaks can be distinguished by the quantitative IPV values. PeakSelect first uses a new method of the Gaussian Mixture Model and Expectation-Maximization (EM) algorithm to find the base intensity level (baseline) in a spectrum. Then PeakSelect selects features based on the IPV and baseline, and constructs a decision tree to automatically classify the peaks into different categories such as noise, single ion peaks, and overlapping peaks. Experiments show that PeakSelect can help to reduce the Mascot searching time and increase the reliability of peptide identifications. In particular, PeakSelect performs well on complex spectra with a large number of peaks from large peptides, and supports more sequence identification than other well-known systems.     

A general-purpose baseline estimation algorithm for spectroscopic data

A common feature of many modern technologies used in proteomics - including nuclear magnetic resonance imaging and mass spectrometry - is the generation of large amounts of data for each subject in an experiment. Extracting the signal from the background noise, however, poses significant challenges. One important part of signal extraction is the correct identification of the baseline level of the data. In this article, we propose a new algorithm (the “BXR algorithm”) for baseline estimation that can be directly applied to different types of spectroscopic data, but also can be specifically tailored to different technologies. We then show how to adapt the algorithm to a particular technology - matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry - which is rapidly gaining popularity as an analytic tool in proteomics. Finally, we compare the performance of our algorithm to that of existing algorithms for baseline estimation.The BXR algorithm is computationally efficient, robust to the type of one-sided signal that occurs in many modern applications (including NMR and mass spectrometry), and improves on existing baseline estimation algorithms. It is implemented as the function baseline in the R package FTICRMS, available either from the Comprehensive R Archive Network (http://www.r-project.org/) or from the first author.     

过氧草酰类化学发光反应及其在分析化学中的应用

在化学发光试剂中，过氧草酰类试剂是发光量子产率最高的一类试剂，本文对这类试剂的合成、性质、发光机理及其在液相色谱、薄层色谱及流动注射分析等方面的应用进行了评述。引用文献９３篇。     

A MAP-based algorithm for spectroscopic semi-blind deconvolution

Spectroscopic data often suffer from common problems of bands overlapping and random noise. In this paper, we show that the issue of overlapping peaks can be considered as a maximum a posterior (MAP) problem and be solved by minimizing an object functional that includes a likelihood term and two prior terms. In the MAP framework, the likelihood probability density function (PDF) is constructed based on a spectral observation model, a robust Huber-Markov model is used as spectra prior PDF, and the kernel prior is described based on a parametric Gaussian function. Moreover, we describe an efficient optimization scheme that alternates between latent spectrum recovery and blur kernel estimation until convergence. The major novelty of the proposed algorithm is that it can estimate the kernel slit width and latent spectrum simultaneously. Comparative results with other deconvolution methods suggest that the proposed method can recover spectral structural details as well as suppress noise effectively.     

Mixture models for two-dimensional baseline correction,applied to artifact elimination in time-resolved spectroscopy

Baseline correction and artifact removal are important pre-processing steps in analytical chemistry. We propose a correction algorithm using a mixture model in combination with penalized regression. The model is an extension of a method recently introduced for baseline estimation in the case of one-dimensional data. The data are modeled as a smooth surface using tensor product P-splines. The weights of the P-splines regression model are computed from a mixture model where a datapoint is either allocated to the noise around the baseline, or to the artifact component. The method is broadly applicable for anisotropic smoothing of two-way data such as two-dimensional gel electrophoresis and two-dimensional chromatography data. We focus here on the application of the approach in femtosecond time-resolved spectroscopy, to eliminate strong artifact signals from the solvent.     

基于马氏距离和稀疏矩阵技术的LIBS煤质灰分分析

为了提高激光诱导击穿光谱(LIBS)定量测量煤质的精度问题,先对原始数据进行预处理,包括异常值剔除、基线校正,谱线筛选,再将LIBS与偏最小二乘回归法(PLSR)结合建立定量模型以应用于煤质灰分的分析。结果表明,经过预处理后训练样品的拟合度(R²)从0.9740提高到0.9841,均方根误差(RMSE)从0.9613降低到了0.7527,预测均方根误差(RMSEP)从2.2731降到2.0017,同时平均绝对误差(MAE)和平均相对误差分别从1.9747、0.1094降低到1.5572、0.0757。研究表明,基于马氏距离(MD)的异常数据剔除算法结合基于稀疏矩阵技术的基线估计与降噪算法(BEADS),能够在一定程度上能够改善数据的稳定性和光谱信噪比,有利于提高数据建模的预测精度。     

Post-optimization of Py-GC/MS data: A case study using a new digital chemical noise reduction filter (NOISERA) to enhance the data quality utilizing OpenChrom mass spectrometric software

Noise is a known drawback in the mass spectrometric analysis of chromatographic data. Different techniques exist to avoid or to remove noise before and after data acquisition. Noise occurs in two different types, electronic and chemical. The former can be lowered by applying smoothing filters. The latter needs more sophisticated methods since the distinction between chemical noise and real chromatographic data is not straightforward. Several approaches have been published with different scopes and algorithms. This work describes a new algorithm (NOISERA - Noise Reduction Algorithm) for a dynamic reduction of chemical noise in mass spectrometric, chromatographic data of nominal mass resolution. It offers an approach to detect and to reduce chemical noise with a minimum of required user interaction and adjustments. Due to the assumption, that chemical noise varies in its intensity and is dependent of the time, the algorithm tries to calculate and reduce noise locally along the retention time axis. Electronic noise will not be covered by the presented algorithm. It can be reduced by applying further filters, since OpenChrom provides methods for smoothing.The presented algorithm NOISERA is capable of reducing noise with minimal user interaction. It combines existing ideas and extends them to achieve a parameter-free chemical noise detection and reduction. NOISERA improves the signal-to-noise ratio of peaks, but as a drawback of all chemical noise filters, it tends to remove either too much, respectively, not enough data from chromatograms than necessary. That is why the algorithm should not be used without supervision of an analyst. The noise reduction filter NOISERA is available as a plug-in for OpenChrom and is released under the Eclipse Public License 1.0 (EPL). OpenChrom is available free of charge at http://www.openchrom.net. The noise reduction algorithm NOISERA is included in the latest OpenChrom release.