基于稀疏模型的气相色谱-质谱数据解析及其在严重重叠峰解析中的应用

提出一种气相色谱-质谱（GC-MS）数据解析算法。以色谱峰顶点处的质谱作为待测谱,在谱库中检索一定量相关参考谱,求解关于各纯组分色谱响应值的方程。质谱检索采取分步策略,先利用质谱碎片规律建立高速索引进行粗选,然后使用强峰高概率出峰准则和耐挤压性准则排除无关质谱。为求解色谱响应值方程,提出基于稀疏模型的回归算法,相比传统算法,稀疏算法利于提取待测谱的主要结构,避免"过拟合"。实验结果表明,该质谱检索算法具有较高的精度和规模较小的剩余参考谱集,而所提稀疏模型算法可有效解析严重重叠峰。该算法可作为GC-MS重叠峰解析,特别是严重重叠峰解析的一种有效解决方案。     

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。     

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。     

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...     

谱峰漂移校正技术结合三维平行因子分析方法用于分辨中药重叠高效液相色谱信号

谱峰漂移校正技术结合平行因子分析方法被应用于中药色谱重叠峰信号分辨. 谱峰漂移校正技术的使用, 使得数据的三线性得以加强, 更适合于平行因子分析方法的分辨. 本文通过对中药延胡索色谱数据分辨, 并与经典多元曲线分辨方法对比来验证谱峰漂移校正技术结合平行因子分析方法的正确性.     

法拉第吸脱附偶联过程循环伏安行为的有限元分析

法拉第吸脱附偶联过程的电化学行为较为复杂,难以定量获得其表界面反应动力学信息. 本文通过COMSOL有限元软件对法拉第吸脱附偶联过程的循环伏安行为进行数值分析,研究了反应物或产物不同吸附条件下的循环伏安行为. 结果表明：当反应物或产物弱吸附时,可通过阴、阳极峰电流之差实现饱和吸附量的定量表征. 随着吸附平衡常数的增大,反应由弱吸附向强吸附过渡,峰电流由扩散峰与吸脱附峰相互重叠过渡到相互分离的吸脱附“前波”或“后波”特征. 该吸脱附特征峰的形状和位置与电势依赖的吸附平衡常数有关. 吸附平衡常数及其电势依赖程度越大,吸脱附峰偏离扩散峰越远,吸脱附峰越尖锐. 该模型为法拉第吸脱附偶联过程的循环伏安研究提供了一种定量研究方法,能够帮助研究者从复杂的吸脱附伏安行为中定量获得饱和吸附量和吸附平衡常数等信息,并对涉及吸脱附的电催化研究具有一定指导意义.     

热解吸色谱方法中采用空心圆管扩散器的研究

在热解吸色谱装置中,采用适当的空心圆管扩散器后不仅能得到对称的脱附峰,有助于提高测量峰面积的精确度,而且也能有效地减少热扩散峰,实现对小比表面积的精密测量。我们考察了脱附峰在空心圆管中扩展的动力学因素,提出了热扩散峰在空心圆管中扩展和叠加的数学模型,及在测定小比表面积试样时脱附峰流出曲线的数学表达式,并阐明了由此计算氮气吸附量的方法。本实验采用连续流动色谱法,使用经我们稍加改动后的国产ST-03型表面与孔径测定仪。     

便携式气相色谱仪中嵌入式谱峰识别算法设计

设计了嵌入式气相色谱谱峰识别算法,用于便携式气相色谱仪的数据分析处理,现场使用时能够不连接计算机就得出分析结果。该算法提出了用于谱峰识别的序列映射方法,减小了对斜率阈值的依赖,增强了谱峰分析的准确性和稳定性。通过实验对比可知,即使对于复杂组分的样本谱图,该嵌入式分析算法也能较好地分析提取出样品的谱峰数据,其分析结果与实验室色谱工作站软件分析结果非常接近,能够满足用户现场使用的分析需求,提高了便携仪器在现场使用的快捷性和便利性。     

拖尾峰的类型

在通常的情况下,色谱峰大多是不对称的拖尾峰。对于拖尾峰的分类,我们认为应根据谱峰的描述函数来进行分类,这有助于了解物质在色谱过程中运动规律,有利于解释色谱曲线和进行谱峰拟合,有利于探求影响谱峰扩张的因素和了解扩张机理,因而能在本质上反映各类型拖尾峰的特征。 近来,越来越多的作者采用高斯函数的指数衰减(EMG)模型来描述拖尾峰,能用EMG模型描述的色谱峰称之为EMG峰,其描述函数为,     

一种基于光谱分析的二元不完全重叠色谱峰的分离解析方法

基于二极管阵列检测器获得的色谱-光谱数据,建立了一种二元不完全重叠液相色谱峰的解析方法: 色谱数据经过去噪、归一化处理后,计算各时间点的光谱差异并进行系统聚类分析,提取特征光谱后,利用非负最小二乘法对色谱-光谱矩阵进行解析,得到基于特征光谱的流出曲线,进而得到分离后的色谱峰。将解析结果和纯标样的色谱峰进行比较,解析后的光谱图和纯标样的光谱图无显著差异,保留时间相差小于0.01 min。实验结果表明,该方法在二元不完全重叠液相色谱峰的解析方面能取得良好的效果。     

A continuous wavelet transform algorithm for peak detection

Contactless conductivity detector technology has unique advantages for microfluidic applications. However, the low S/N and varying baseline makes the signal analysis difficult. In this paper, a continuous wavelet transform-based peak detection algorithm was developed for CE signals from microfluidic chips. The Ridger peak detection algorithm is based on the MassSpecWavelet algorithm by Du et al. [Bioinformatics 2006, 22, 2059-2065], and performs a continuous wavelet transform on data, using a wavelet proportional to the first derivative of a Gaussian function. It forms sequences of local maxima and minima in the continuous wavelet transform, before pairing sequences of maxima to minima to define peaks. The peak detection algorithm was tested against the Cromwell, MassSpecWavelet, and Linear Matrix-assisted laser desorption/ionization-time-of-flight-mass spectrometer Peak Indication and Classification algorithms using experimental data. Its sensitivity to false discovery rate curve is superior to other techniques tested.     

Preserving the chromatographic integrity of high-speed supercritical fluid chromatography separations using time-of-flight mass spectrometry

The advantage of high-speed time-of-flight-mass spectrometry (TOF-MS) detection for ultrafast qualitative supercritical fluid chromatography/mass spectrometry (SFC/MS) applications allows the superior resolving power of SFC to be exploited in high-throughput analysis. A chromatographic comparison of quadrupole MS and TOF-MS shows high-speed TOF total ion current data point sampling to be more indicative of fast SFC separations and corresponding short (1-2 s) baseline peak widths. Results shown for analysis of a six-compound mixture with two peaks eluting at 0.86 and 0.89 min exhibit >50% resolution by high-speed TOF data sampling, whereas the same peaks appear to coelute using quadrupole MS data sampling. Additionally, a marked improvement in the peak baseline widths is afforded by fast TOF data acquisition of 0.1 s/spectrum, resulting in a reduction in the baseline width, 1.6 s, of sulfanilamide in a four-compound mixture that is more than 2-fold greater than that achieved at the slower data acquisition of 0.5 s/spectrum. The resulting increase in resolution and improved peak shapes allow automatic integration routines to perform more effectively. For most classes of compounds amenable to high performance liquid chromatography, including druglike species, steroids, and polymers, the union of SFC with TOF-MS provides the maximum density of chemical information per unit time available with any high-speed chromatographic/mass spectrometric method.     

An automatic peak finding method for LC‐MS data using Gaussian second derivative filtering

A highly automated procedure for localising and characterising peaks in the chromatographic time domain of LC‐MS data has been developed. The work was initiated by an identified need to facilitate the detection and tracking of chromatographic peaks during method development for the analysis of impurities in pharmaceutical products. The algorithm is mainly based on a digital filter for which the settings are automatically adapted to the data set under study. The procedure was evaluated for synthetic data sets with various S/N levels, peak widths and baseline properties. It was found that even for the worst case tested with S/N=10 and a high variability in the baseline, 94% of the simulated analytical peaks could be detected without producing any false‐positive identifications. Furthermore, the number of correctly estimated peak heights and peak widths falling within a 10% error of the true values were 94 and 91%, respectively. For experimental data sets, peak height, and width estimations were more difficult, but the processed reconstructions showed an excellent agreement with the analytical signals of the raw data, and also a clearly improved visualisation in total ion‐ and base‐peak chromatograms.     

Enhancement of chromatographic detail using infrared spectra from chromatography/infrared spectrometer systems

Working with chromatography/infrared spectroscopy data, the technique of local baseline correction in the calculation of the chromatographic intensities from each spectrum is used to provide improved chromatographic detection. The technique reduces chromatographic baseline drift, and produces broad-band (universal) chromatograms with noise levels approaching those obtained from narrow-band specific chromatograms. Chromatograms produced from the ratio of two selected band chromatograms can quickly profile a trend in a homologous series of compounds, test a chromatographic peak for compositional uniformity, or show shifts in composition across a polymer chromatographic series.     

Semi-Quantitative MALDI Measurements of Blood-Based Samples for Molecular Diagnostics

Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.     

Analysis of peak asymmetry in chromatography

The knowledge of the symmetry of chromatographic peaks is extremely important regarding the digital signal processing. The significant deviation of the peak shape from the symmetrical peak makes hardly possible the acquisition of chromatographic signal information, such as the retention time, the peak area, the peak width at half peak height, the peak overlapping, etc. In the literature one can find many methods for the determination of the asymmetry factor. For example it is suitable to calculate the skewness from the third central moment. However in case of noisy baseline the value of the skewness oscillates highly depending on the number of points used for the mathematical calculation. In this work a new method is presented for the determination peak shape asymmetry. We order mathematical function to the chromatographic peaks by fitting, and then symmetrical curve is generated with the same peak maximum position and height, the peak width is fitted. The difference of the two functions is constituted and areas of the data differences are calculated, which are really characteristics of the peak asymmetry. Correlation between the area of the difference signal and the asymmetry factor is established. The method was applied for different types of chromatographic peak shapes and the results were interpreted.     

Multi‐scale Gaussian/Haar wavelet strategies coupled with sub‐window factor analysis for an accurate alignment in nontargeted metabolic profiling to enhance herbal origin discrimination capability

Metabolic dataset can provide an overview of different herbal origin, which is conducted by some statistical procedures. Such results often deviate to a certain degree, due to peaks shifts in chromatographic signals. In order to solve this problem, an improved algorithm of combining sub‐window factor analysis with the mass spectrum information is proposed. The algorithm uses a peak detection approach derived either from multi‐scale Gaussian function or Haar wavelet to locate the peaks with different application scope; the candidate drift points at each peak are estimated by Fast Fourier transform cross correlation; Specifically, the best drift points at each candidate peaks are confirmed by sub‐window factor analysis and mass spectrum information in nontargeted metabolic profiling. Finally, the peak regions were aligned against a reference chromatogram, and the non‐peak regions were used linear interpolation. The chromatographic signals of 30 Bupleurum samples were aligned as an illustration of this algorithm, and they could be well distinguished using some statistical procedures. The result demonstrates that the presented method is stronger than other mass‐spectra based algorithms, when facing the alignment of some co‐eluted peaks.     

Probabilistic peak detection for first-order chromatographic data

We present a novel algorithm for probabilistic peak detection in first-order chromatographic data. Unlike conventional methods that deliver a binary answer pertaining to the expected presence or absence of a chromatographic peak, our method calculates the probability of a point being affected by such a peak. The algorithm makes use of chromatographic information (i.e. the expected width of a single peak and the standard deviation of baseline noise). As prior information of the existence of a peak in a chromatographic run, we make use of the statistical overlap theory. We formulate an exhaustive set of mutually exclusive hypotheses concerning presence or absence of different peak configurations. These models are evaluated by fitting a segment of chromatographic data by least-squares. The evaluation of these competing hypotheses can be performed as a Bayesian inferential task. We outline the potential advantages of adopting this approach for peak detection and provide several examples of both improved performance and increased flexibility afforded by our approach.