Quantitative analysis of NMR spectra with chemometrics

The number of applications of chemometrics to series of NMR spectra is rapidly increasing due to an emerging interest for quantitative NMR spectroscopy e.g. in the pharmaceutical and food industries. This paper gives an analysis of advantages and limitations of applying the two most common chemometric procedures, Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), to a designed set of 231 simple alcohol mixture (propanol, butanol and pentanol) (1)H 400 MHz spectra. The study clearly demonstrates that the major advantage of chemometrics is the visualisation of larger data structures which adds a new exploratory dimension to NMR research. While robustness and powerful data visualisation and exploration are the main qualities of the PCA method, the study demonstrates that the bilinear MCR method is an even more powerful method for resolving pure component NMR spectra from mixtures when certain conditions are met.     

代谢组学数据分析方法及在糖尿病研究中的应用

对NMR波谱数据的统计分析是基于NMR代谢组学研究的关键问题之一. 鉴于NMR波谱信号可以近似为样品中各种成分谱信号的线性叠加,本文将非负矩阵分解(NMF)方法引入基于NMR代谢组学的数据处理中,并与代谢组学中常用的统计方法--主成分分析(PCA)进行比较. 通过NMF和PCA两种方法对健康志愿者与2型糖尿患者血液和尿液的NMR谱图的统计分析,对所获取的特征代谢物进行比较和验证,并探讨了PCA方法可能存在的不足之处及其原因;阐明了NMF方法是基于NMR的代谢组学研究中较理想的数据分析方法. 最后,讨论了基于NMR代谢组学在糖尿病研究中的前景.     

Wavelet domain de-noising of time-courses in MR image sequences

Magnetic resonance images acquired with high temporal resolution often exhibit large noise artifacts, which arise from physiological sources as well as from the acquisition hardware. These artifacts can be detrimental to the quality and interpretation of the time-course data in functional MRI studies. A class of wavelet-domain de-noising algorithms estimates the underlying, noise-free signal by thresholding (or 'shrinking') the wavelet coefficients, assuming the underlying temporal noise of each pixel is uncorrelated and Gaussian. A Wiener-type shrinkage algorithm is developed in this paper, for de-noising either complex- or magnitude-valued image data sequences. Using the de-correlation properties of the wavelet transform, as elucidated by Johnstone and Silverman, the assumption of i.i.d. Gaussian noise can be abandoned, opening up the possibility of removing colored noise. Both wavelet- and wavelet-packet based algorithms are developed, and the Wiener method is compared to the traditional Hard and Soft wavelet thresholding methods of Donoho and Johnstone. The methods are applied to two types of data sets. In the first, an artificial set of complex-valued images was constructed, in which each pixel has a simulated bimodal time-course. Gaussian noise was added to each of the real and imaginary channels, and the noise removed from the complex image sequence as well as the magnitude image sequence (where the noise is Rician). The bias and variance between the original and restored paradigms was estimated for each method. It was found that the Wiener method gives better balance in bias and variance than either Hard or Soft methods. Furthermore, de-noising magnitude data provides comparable accuracy of the restored images to that obtained from de-noising complex data. In the second data set, an actual in vivo complex image sequence containing unknown physiological and instrumental noise was used. The same bimodal paradigm as in the first data set was added to pixels in a small localized region of interest. For the paradigm investigated here, the smooth Daubechies wavelets provide better de-noising characteristics than the discontinuous Haar wavelets. Also, it was found that wavelet packet de-noising offers no significant improvement over the computationally more efficient wavelet de-noising methods. For the in vivo data, it is desirable that the groups of "activated" time-courses are homogeneous. It was found that the internal homogeneity of the group of time-courses increases when de-noising is applied. This suggests using de-noising as a pre-processing tool for both exploratory and inferential data analysis methods in fMRI.     

小波包自适应阈值语音降噪新算法

为了克服低信噪比输入下,语音增强造成语音清音中的弱分量损失,造成重构信号包络失真的问题。论文提出了一种新的语音增强方法。该方法根据语音感知模型,采用不完全小波包分解拟合语音临界频带,并对语音按子带能量进行清浊音区分处理,在阈值计算上,提出了一种清浊音分离,基于子带信号能量的小波包自适应阈值算法。通过仿真实验,客观评测和听音测试表明,该算法在低信噪比输入时较传统算法,能够更加有效地减少重构信号包络失真,在不损伤语音清晰度和自然度的前提下,使输出信噪比明显提高。将该算法与能量谱减法结合,进行二次增强能进一步提高降噪输出的语音质量。     

EEMD在土壤剖面反射光谱消噪中的应用

实测光谱常含有大量干扰信息,消噪在光谱数据处理和分析中极为重要,它直接影响后续的定量分析和信息挖掘。因此,选择适当的消噪方法是改善光谱分析精度,提升光谱分析能力的一个关键性突破。集合经验模态分解(EEMD)方法是一个以信号固有特征尺度为度量的时空滤波过程,能充分保留信号本身的非线性和非平稳特征,在信号的滤波和消噪中具有较大的优势。结合EEMD的多尺度滤波特性,提出了一种新的EEMD阈值光谱消噪方法,并应用于新疆塔里木河中游典型绿洲33个土壤剖面反射光谱数据的预处理。为探讨EEMD阈值法在土壤剖面反射光谱消噪中的效用,对EEMD阈值法和小波阈值法的消噪结果进行了对比分析。结果表明：与传统的小波阈值法相比,EEMD阈值法消噪结果的信噪比从14.836 6 dB提高到34.275 7 dB,均方根误差由6.786 1×10-5降到7.240 6×10-6,相关系数从0.982 5提高到0.999 8,EEMD阈值法的三个消噪效果衡量指标均优于小波阈值法。证明了EEMD阈值法可有效地去除土壤剖面光谱噪声,较好地保留了光谱的细节信息,提高了光谱的定量分析精度,且与小波阈值消噪方法相比具有较强的可靠性和自适应优势,作为光谱数据预处理的一种新方法,其应用前景良好。     

Feature separation using ICA for a one-dimensional time series and its application in fault detection

Principal component analysis (PCA) is a method that transforms multiple data series into uncorrelated data series. Independent component analysis (ICA) is a method that separates multiple data series into independent data series. Both methods have been used in fault detection. However, both require signals from at least two separate sensors. To overcome this requirement and utilize the fault detection capability of ICA and PCA, we propose to use wavelet transform to pre-process the data collected from a single sensor and then use the coefficients of the wavelet transforms at different scales as input to ICA and PCA. The effectiveness of this method is demonstrated by applying it to both a simulated signal series and a vibration signal series collected from a gearbox. The results show that the method of combining wavelet transform and ICA works better than the method of combining wavelet transform and PCA for impulse detection based on a one-dimensional vibration data series.     

光谱信号的小波去噪新技术

光谱分析中,噪声的存在常影响分析的准确度和检测限。现有滤波方法在光谱信号除噪方面有种种缺陷。文章充分利用小波在信号处理方面的优良特性,提出了Mexican Hat小波滤波算法,它选用Mexican Hat小波函数构造滤波项,利用滤波项与原始信号作用,从而实现信号与噪音的分离。该方法无论对低频或高频信号均适用,除噪完全,即使对信噪比为1的高噪声信号也能获取满意的处理结果。运用这一技术处理光谱信号,简单快速、结果可靠,处理后峰位置、峰高、峰面积误差分别小于0.2%,3.2%,1.1%。大量实验表明,本方法能有效提高光谱分析的准确度。     

基于小波变换的分水岭图像分割方法

图像分割技术在数字图像处理中占有重要地位.提出了一种基于小波变换的图像分割方法,有效地将小波分析、小波包分解与数学形态学中的分水岭方法相结合.首先,通过小波包对图像有效降噪,在一定程度上减少了分水岭方法的过分割现象.然后利用小波变换得到的梯度向量进行分水岭变换,有效保持边缘信息.实验结果证明该算法是可行的,与基于形态梯度的分割结果相比,得到了较好的分割效果.     

基于小波降噪与支持向量机的恒星光谱识别研究

提出了一种对恒星光谱识别的新方法。 根据恒星光谱数据的特性,我们以支持向量机为核心技术构建光谱识别器。 由于恒星光谱数据通常含有较高的噪声,如果直接进行分类,识别率往往较低。 因此作者首先采用小波分析的方法对原始光谱数据进行降噪预处理,提取光谱的特征,然后馈送到支持向量机完成对光谱数据的最终识别。 利用实际光谱数据(Jacoby, 1984)对所提出的技术进行检测,实验结果表明使用这种小波分析结合支持向量机的技术的识别效果要优于使用支持向量机结合主分量分析降维技术的识别方法。 另外,作者还比较了支持向量机与传统甄别分析的分类性能,对实际及合成光谱进行实验的结果显示了支持向量机的识别正确率不但优于常见的5种甄别分析方法的识别率,而且有较强的泛化能力。     

一维核磁共振谱数据的小波变换压缩

本成功地将小波变换的多尺寸信号分解（ＭＲＳＤ）方法用于一维核磁共振（ＮＭＲ）信号的数据压缩。将一幅由３２７６８个数据点构成的ＮＭＲ谱图压缩至５１２个数据点,仍能在均方差很小（２．７７６９×１０＾－４）的情况下很好地重建原始谱图。     

Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.     

Multivariate analysis of combined Raman and fibre‐optic reflectance spectra for the identification of binder materials in simulated medieval paints

The non‐invasive identification of paint materials used in works of art is essential, both for preserving and restoring them, and also for understanding and verifying the history surrounding their creation. As such, the development of suitable non‐invasive techniques has received much interest in recent years. We have investigated the use of Fourier transform (FT)‐Raman spectroscopy and fibre‐optic reflectance spectroscopy (FORS), together with multivariate principal‐component analysis (PCA) techniques, in order to identify the pigment and binding materials used in made‐up samples representative of real artwork. We demonstrate that both types of spectroscopy provide complementary information which can be used to identify the pigments and binders in paint samples. We show that PCA with FT‐Raman spectra can be used to assist in the identification of oil‐based binders, and that the additional data provided by FORS spectra enables PCA on combined spectra to identify more complex proteinaceious and polysaccharide‐based binding media. The results presented here demonstrate that multivariate analyses of lead‐based paints, using data measured by FT‐Raman and FORS in conjunction, have much potential for identifying individual pigments and binders in paint samples. This provides a path towards computer‐assisted characterisation of paint materials on artwork. Copyright © 2013 John Wiley & Sons, Ltd.     

Improved DOSY NMR data processing by data enhancement and combination of multivariate curve resolution with non-linear least square fitting

The quality of DOSY NMR data can be improved by careful pre-processing techniques. Baseline drift, peak shift, and phase shift commonly exist in real-world DOSY NMR data. These phenomena seriously hinder the data analysis and should be removed as much as possible. In this paper, a series of preprocessing operations are proposed so that the subsequent multivariate curve resolution can yield optimal results. First, the baseline is corrected according to a method by Golotvin and Williams. Next, frequency and phase shift are removed by a new combination of reference deconvolution (FIDDLE), and a method presented by Witjes et al. that can correct several spectra simultaneously. The corrected data are analysed by the combination of multivariate curve resolution with non-linear least square regression (MCR-NLR). The MCR-NLR method turns out to be more robust and leads to better resolution of the pure components than classic MCR.     

基于中值滤波和提升小波分析的图像去噪方法研究

针对现有算法大多对单一高斯噪声或脉冲噪声进行图像滤波的问题,在对二维图像平滑去噪的过程中,采用基于中值滤波和提升小波变换相结合的图像去噪方法。在中值滤波基础上,构造基于脉冲检测的中值滤波器,找出混合噪声中脉冲噪声并进行滤波;与此同时,对原始小波进行提升,构造提升小波,然后采用提升小波阈值去噪方法抑制高斯噪声。实验结果表明:采用本文方法,混合噪声得到有效抑制,去噪效果好。     

Wavelet-based ultra-high compression of multidimensional NMR data sets

The application of a lossy data compression algorithm based on wavelet transform to 2D NMR spectra is presented. We show that this algorithm affords rapid and extreme compression ratios (e.g., 800:1), providing high quality reconstructed 2D spectra. The algorithm was evaluated to ensure that qualitative and quantitative information are retained in the compressed NMR spectra. Whilst the maximum compression ratio that can be achieved depends on the number of signals and on the difference between the most and the least intense peaks (dynamic range), a compression ratio of 80:1 is affordable even for the challenging case of homonuclear 2D experiments of large biomolecules.     

红外光谱预处理中去噪的研究

红外(IR)光谱由于包含了噪声等各种外界干扰因素.应该先进行光谱预处理,以便降噪,提高分析准确度.本文采用了一种基于最优小波包基的信号去噪算法,该算法根据最小代价原理,采用不同的阈值算法对光谱的高频和低频信号进行量化处理,用量化后的系数重构得到去噪信号,从而达到较好的去噪效果.实验表明,本方法处理后光谱曲线非常光滑、噪声消除效果明显.     

基于提升小波的红外图像去噪算法与实现

介绍了提升小波原理,以及把提升小波应用于红外图像去噪的算法.研究了提升小波的硬件实现方法,根据FPGA器件具有快速逻辑处理能力的特点,采用流水线的加法及桶状移位操作指令,设计了一种适合FPGA实现的提升小波图像去噪的硬件结构.实验结果表明,以FPGA来实现基于小波变换的图像去噪方法与传统方法相比,在速度方面得到了很大的提高.     

线性辨别分析与人工神经网络用于血清光谱对于肝癌、肝硬化的诊断

本文探测了人体血清的自体荧光-拉曼光谱,采用多元统计方法中的主元分析法(PCA)对光谱进行分析,并利用线性辨别分析(LDA)作为诊断算法,与此同时,用人工神经网络进行交叉认证。PCA-LDA的灵敏度和特异性分别为88.00%和79.14%,PCA-ANN的为89.29%和94.74%。