基于导数直方图和神经网络的色谱基线提取算法

根据基线信号的变化特征,提出了一种新的色谱基线提取算法－基于一阶导数直方图和神经网络的色谱基线提取算法。鉴于信号总是存在着噪声,含噪声的基线信号一阶导数值也较大,并且分布无规律,而采用了多分辨率小波变换滤波技术。通过选择阈值门限,本算法可精确提取出信号的基线,尤其适用于非基线对称类的色谱信号的基线（趋势函数）提取。     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优     

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.     

高效液相色谱全轮廓指纹图谱结合化学计量学区分不同地区的紫苏叶

利用高效液相色谱全轮廓指纹图谱结合化学计量学方法对不同栽培地区的紫苏叶样品(共84个)进行区分。全轮廓色谱数据经自适应迭代加权最小二乘法(airPLS)和相关优化翘曲法(COW)校正后,基线和保留时间漂移现象均得到明显改善。经预处理后的色谱数据采用主成分分析(PCA)进行解析,结果表明不同来源的样品能按其特性各自聚为一类;而分段间隔压缩变量后的色谱数据经主成分分析处理可得到与全轮廓色谱数据为输入变量时相一致的结果。此外,偏最小二乘判别分析(PLS-DA)对于紫苏叶样品分类的识别能力和预报能力分别为92.8%和89.6%。     

小波包分析用于重叠分析化学信号的处理

对小波包分析的算法进行了改进,并将此算法成功地应用于多组分重叠色谱信号的解析.结果表明,本文提出的算法解决了MRSD算法的不足,更适合处理分析化学信号,用于重叠信号的解析时不需重构(逆变换),简化了数据处理步骤,加快了数据解析速度,具有较强的解析能力.对于重叠色谱信号的解析,小波包分析比小波分析具有更强的解析能力.     

小波神经网络用于色谱数据的滤噪与压缩

介绍了小波神经网络（Ｗａｖｅｌｅｔ Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋ）的结构和算法，并将其应用于色谱数据的滤噪与压缩。在小波神经网络中，采用了Ｂｕｂｂｌｅ母小波和一维搜索变步长共轭梯度优化方法。结果表明，小波神经网络将原始数据压缩至１：２０时，仍能得到很好的重建谱图。重建的色谱图不仅能保留原始的谱图特征而且有光滑滤噪的效果。对于色谱数据的存储与平衡滤噪具有重要意义。     

小波变换用于重叠色谱峰组分信息的提取

提出了一个小波变换用于提取重叠色谱中组分信息的理论公式，并将它应用于三组分和五组分重叠色谱实验数据的解析。结果表明：在定定分离度范围内，当相邻色谱峰峰宽相近时，根据此公式的计算结果，可以将各组分的信息从重叠色谱峰信号中提取出来。洒工作对小波变换用于重叠峰的解析具有重要意义。     

基于气相色谱-质谱的拟靶向代谢组学方法分析大米中脂肪酸

建立了一种基于气相色谱-质谱的拟靶向代谢组学分析方法对大米中脂肪酸进行分析,共检测到16种脂肪酸,并研究了不同大米中脂肪酸的轮廓差异。以提取到饱和脂肪酸和不饱和脂肪酸的总量为评价指标,比较了6种提取方法及4种提取溶剂对脂肪酸提取效率的影响。将该方法用于5种不同大米（稻花香、吉星、金浪子、农大、状元）中脂肪酸的分析,发现稻花香大米中脂肪酸轮廓与其他4种均有较大差异;而金浪子与农大、状元间脂肪酸差异也较大,与吉星脂肪酸轮廓较为相似。该方法简单,有较好的稳定性和准确性,可为大米品质和营养价值改善研究提供基础数据。     

小波包分析-减法聚类-RBF网络同时测定痕量铁、锰

提出一种解析分光光度吲时测定数据的小波色分析-减法聚类-RBF刚络新方法。该方法用小波包分析处理吸收光潜数据,滤除信号中的噪声;采用基于山峰函数的减法聚类算法,按照自适应聚类的结果构成预测各未知样的校正集,实现校准模型的优化;由此,使RBFM络在对未知样预测时能更有效地提取光潜数据中的特征信息,提高预测结果的准确性把该算法用于模拟汽油中铁、锰合成样预测,计算表明,该方法可以显著降低测定结果的相对误差,预测结果令人满意。     

基于有限脉冲响应滤波器的实时小波算法及其在色谱信号解析中的应用

对色谱信号进行正确、实时地解析是利用色谱仪器系统进行过程检测的关键技术。小波分析方法能对色谱信号进行有效解析,但实时性一直是制约该方法在色谱信号处理中广泛应用的一个瓶颈。多分辨率分析与重构算法(MALLAT)是小波分析的一种快速算法,但是该算法的实时性仍然需要进一步提高。针对这一问题,以MALLAT算法原理为基础,利用在分解重组过程中两组滤波器系数之间的关系提出了一种基于有限脉冲响应滤波器的实时小波分析算法。仿真结果表明,与经典的MALLAT小波快速算法相比,该算法在保持信号有效分解与重构的基础上,运行耗时明显缩短,实时性得到较大提高。     

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.     

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.     

Total ion chromatographic fingerprints combined with chemometrics and mass defect filter to predict antitumor components of Picrasma quassioids

A method of total ion chromatogram combined with chemometrics and mass defect filter was established for the prediction of active ingredients in Picrasma quassioides samples. The total ion chromatogram data of 28 batches were pretreated with wavelet transformation and correlation optimized warping to correct baseline drifts and retention time shifts. Then partial least squares regression was applied to construct a regression model to bridge the total ion chromatogram fingerprints and the antitumor activity of P. quassioides. Finally, the regression coefficients were used to predict the active peaks in total ion chromatogram fingerprints. In this strategy, mass defect filter was employed to classify and characterize the active peaks from a chemical point of view. A total of 17 constituents were predicted as the potential active compounds, 16 of which were identified as alkaloids by this developed approach. The results showed that the established method was not only simple and easy to operate, but also suitable to predict ultraviolet undetectable compounds and provide chemical information for the prediction of active compounds in herbs.     

在线小波变换用于伏安分析仪的研制

将在线小波变换应用于伏安信号的在线处理,研制了具有小波变换功能的伏安分析仪。该系统利用在线小波变换对采集到所数据进行实时处理,具有控制灵活,分辨率高,数据处理方便等特点。通过对低浓度Ｃｙ＾２＋和Ｆｅ＾３＋混合样品的阶梯斜坡扫描伏安珠滤噪及定性,定量分析,结果表明可获得满意的分析结果。     

Inter-domain linker prediction using amino acid compositional index

Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate and reliable prediction of protein domain linkers and boundaries is often considered to be the initial step of protein tertiary structure and function predictions. In this paper, we introduce CISA as a method for predicting inter-domain linker regions solely from the amino acid sequence information. The method first computes the amino acid compositional index from the protein sequence dataset of domain-linker segments and the amino acid composition. A preference profile is then generated by calculating the average compositional index values along the amino acid sequence using a sliding window. Finally, the protein sequence is segmented into intervals and a simulated annealing algorithm is employed to enhance the prediction by finding the optimal threshold value for each segment that separates domains from inter-domain linkers. The method was tested on two standard protein datasets and showed considerable improvement over the state-of-the-art domain linker prediction methods.     

Hotelling trace criterion as a figure of merit for the optimization of chromatogram alignment

We present a methodology for optimization of chromatogram alignment using a class separability measure called the Hotelling trace criterion (HTC). This metric is a multi‐class distance measure that accounts for within‐class and between‐class variation. We chose the correlation optimized warping algorithm as our alignment method and used the HTC to judge the effectiveness of the alignment based on algorithm parameters called segment length and max warp. Biodiesel feedstock samples representing classes of soy, canola, tallow, waste grease, and hybrid were used in our experiments. Fatty acid methyl esters in each biodiesel were separated using gas chromatography‐mass spectroscopy. The entire data set was baseline corrected, aligned, normalized, and mean‐centered prior to principal components (PCs) analysis. The aligned, baseline corrected data sets were used to compute a figure of merit called warping effect, while the PC‐transformed data sets were used to evaluate the HTC. The segment length and max warp parameters that maximized the warping effect and/or HTC were then determined. Scores plots of pairs of PCs, along with 95% confidence ellipses, were created and analyzed. The results demonstrated that the parameters derived from maximizing the HTC more effectively aligned the data, as evidenced by better clustering of the biodiesels in the scores plots. This behavior was robust to the number of PCs used in the computation of the HTC. We conclude that the HTC is an objective measure of alignment quality that allows for optimal class separability and can be applied to optimize other methods of chromatogram alignment. Copyright © 2015 John Wiley & Sons, Ltd.     

A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables

An algorithm is proposed for extracting relevant information from near-infrared (NIR) spectra for multivariate calibration of routine components in complex plant samples. The algorithm is a combination of wavelet transform (WT) data compression and a procedure for uninformative variable elimination (UVE). After compression of the NIR spectra by WT, the UVE approach is used to eliminate the irrelevant wavelet coefficients. Finally, a calibration model is built from the retained wavelet coefficients to enable prediction. Because irrelevant information can be removed from the spectra used for multivariate calibration, the model based on the extracted relevant features is better than those obtained with full-spectrum data. Both prediction precision and calculation speed are improved.     

Classification of Rat FTIR Colon Cancer Data Using Waveletsand BPNN

本文提出了一种新的基于水平衰减全反射-傅里叶变换红外光谱（HATR-FTIR）的小波特征提取与反向传播人工神经网络模式分类方法以提高FTIR对早期大鼠结肠癌的诊断准确率．对60只DMH诱导的SD大鼠,44只诱导鼠的第二代鼠,36只正常SD大鼠的结肠正常组织、异常增生、早癌及进展期癌组织所获得的的HATR-FTIR,利用连续小波多尺度分析法提取12个特征量,采用反向传播人工神经网络进行分类,识别准确率分别为100%、94%、97.5%及100%．实验结果表明此方法对早期结肠癌具有较高的诊断率．     

Wavelet transforms in separation science for denoising and peak overlap detection

Wavelet transform is a versatile time‐frequency analysis technique, which allows localization of useful signals in time or space and separates them from noise. The detector output from any analytical instrument is mathematically equivalent to a digital image. Signals obtained in chemical separations that vary in time (e.g., high‐performance liquid chromatography) or space (e.g., planar chromatography) are amenable to wavelet analysis. This article gives an overview of wavelet analysis, and graphically explains all the relevant concepts. Continuous wavelet transform and discrete wavelet transform concepts are pictorially explained along with their chromatographic applications. An example is shown for qualitative peak overlap detection in a noisy chromatogram using continuous wavelet transform. The concept of signal decomposition, denoising, and then signal reconstruction is graphically discussed for discrete wavelet transform. All the digital filters in chromatographic instruments used today potentially broaden and distort narrow peaks. Finally, a low signal‐to‐noise ratio chromatogram is denoised using the procedure. Significant gains (>tenfold) in signal‐to‐noise ratio are shown with wavelet analysis. Peaks that were not initially visible were recovered with good accuracy. Since discrete wavelet transform denoising analysis applies to any detector used in separation science, researchers should strongly consider using wavelets for their research.