A time-domain algorithm for NMR spectral normalization

Recently, a new method for quantitatively comparing NMR spectra of control and treated samples, in order to examine the possible occurring variations in cell metabolism and/or structure in response to numerous physical, chemical, and biological agents, was proposed. This method is based upon the utilization of the maximum superposition normalization algorithm (MaSNAl) operative in the frequency domain and based upon maximizing, by an opportune sign variable measure, the spectral region in which control and treated spectra are superimposed. Although the frequency-domain MaSNAl algorithm was very precise in normalizing spectra, it showed some limitations in relation to the signal-to-noise ratio and to the degree of diversity of the two spectra being analyzed. In particular, it can rarely be applied to spectra with a small number of visible signals not buried in the noise such as generally in vivo spectra. In this paper, a time-domain normalization algorithm is presented. Specifically, it consists in minimizing the rank of a Hankel matrix constructed with the difference of the two free induction decay signals. The algorithm, denoted MiRaNAl (minimum rank normalization algorithm), was tested by Monte Carlo simulations as well as experimentally by comparing two samples of known contents both with the new algorithms and with an older method using a standard. Finally, the algorithm was applied to real spectra of cell samples showing how it can be used to obtain qualitative and quantitative biological information.     

A subspace time-domain algorithm for automated NMR spectral normalization

Recently, two methods have been proposed for quantitatively comparing NMR spectra of control and treated samples, in order to examine the possible occurring variations in cell metabolism and/or structure in response to numerous physical, chemical, and biological agents. These methods are the maximum superposition normalization algorithm (MaSNAl) and the minimum rank normalization algorithm (MiRaNAl). In this paper a new subspace-based time-domain normalization algorithm, denoted by SuTdNAl (subspace time-domain normalization algorithm), is presented. By the determination of the intersection of the column spaces of two Hankel matrices, the common signal poles and further on the components having proportionally varying amplitudes are detected. The method has the advantage that it is computationally less intensive than the MaSNAl and the MiRaNAl. Furthermore, no approximate estimate of the normalization factor is required. The algorithm was tested by Monte Carlo simulations on a set of simulation signals. It was shown that the SuTdNAl has a statistical performance similar to that of the MiRaNAl, which itself is an improvement over the MaSNAl. Furthermore, two samples of known contents are compared with the MiRaNAl, the SuTdNAl, and an older method using a standard. Finally, the SuTdNAl is tested on a realistic simulation example derived from an in vitro measurement on cells.     

Elemental quantitation of natural organic matter by CPMAS 13C NMR spectroscopy

Cross-polarized magic-angle-spinning NMR (CPMAS-NMR) techniques are assumed to be only semi-quantitative in the assessment of carbon distribution in humic substances or natural organic matter, due to a number of interferences such as spinning side bands (SSB) in spectra, paramagnetic species in samples, and low or remote protonation of aromatic carbons. Fast rotor spin rates or direct polarization NMR techniques are normally applied to improve quantitative signal detectability. Variable contact time pulse sequences were used here to obtain CPMAS-NMR spectra of organic compounds of known structure and different humic substances. Integration of spectral areas, previously subtracted of SSB, and relative stoichiometric factors were used for mathematical elaboration to calculate the elemental content in samples. These values did not significantly differ from those obtained by direct determination of elemental content with quantitative elemental analysis. Our results showed that the carbon observed CPMAS-NMR provides a quantitative representation of the whole carbon content in humic substances.     

太赫兹时域光谱在邮件隐匿危险品检测中的应用

随着电子商务的快速发展,邮件数量剧增,在邮件中隐匿危险品已经成为犯罪分子重要的犯罪手段,威胁公共安全和社会稳定。邮件的安全检查变得尤为重要,而常规的检测技术不能准确识别危险品。太赫兹波是介于红外和微波之间的电磁波,邮件中隐匿的爆炸物、毒品和有害生物因子等在太赫兹波段存在特征吸收光谱,而邮件常用的非极性包装材料可以被太赫兹波穿透。太赫兹波还具有低能性、相干性等特性,这些特性使得太赫兹技术可以实现邮件隐匿危险品高灵敏度的无损检测。文章介绍了太赫兹技术的特性,太赫兹时域光谱系统的组成和获取光学常数的菲涅尔公式解析法。该方法通过样品透射或反射信号和参考信号来获取包括吸收光谱在内的材料参数。将样品的太赫兹特征吸收光谱和已建立的各种危险品的光谱特征数据库进行比对,可以判断是否为危险品以及危险品种类。对爆炸物、毒品在太赫兹波段的特征吸收光谱的研究成果,及在各种非极性材料遮挡下吸收光谱的特异性的研究进展进行了总结。获取吸收光谱的解析法适用于较厚样品,针对薄样品,还介绍了一种P-谱法。该方法不需要参考信号就能准确获取覆盖物下样品的吸收光谱。除直接利用吸收光谱做检测外,近些年还提出了很多其他识别危险品的方法,如光谱动力学分析法,化学计量学方法和基于太赫兹时域光谱的成像分析法等。其中,光谱动力学分析法可以很好的区分吸收频率有重叠的物质;化学计量学方法可以对混合物进行成分的定性和定量分析;光谱成像法可以完成较大面积的隐匿危险品识别。分析了太赫兹时域光谱技术识别有害生物因子的可行性,以及针对有害生物因子携带量小的特点,总结了太赫兹时域光谱技术在提高生物因子检测灵敏度方面的研究进展。探讨了太赫兹技术在邮件安检应用中一些有待解决的问题,如太赫兹功率有限、受环境因素影响较大、缺乏统一的标准等,展望了未来的发展趋势。     

Gas Chromatography-Fourier Transform Infrared Spectrometry

Abstract

Difficult mixture analysis problems can usually be simplified when chromatographic separating techniques are employed to isolate individual mixture components. Many different discrimination methods based on observation of physical or chemical properties have been developed to detect substances Separated by gas chromatography. These detectors facilitate quantitative analysis when mixture component identities are known but provide little structural information for identification of unknown mixture components. On the other hand, mass spectrometry and infrared spectroscopy can provide a wealth of structural information. Mass spectra and infrared spectra are representative of eluent structure in a complementary manner. Mass spectrometry is often used to obtain molecular weight information and can be used to distinguish homologues. Infrared spectra reveal molecular functionalities and are particularly useful for isomer discrimination.     

Optimized fuzzy C-means clustering algorithm for the interpretation of the near-infrared spectra of rocks

The rock near-infrared spectrum contains information of its composition and structure. The interpretation of rock near-infrared spectrum is one of the important approaches in the qualitative and quantitative analyses of the alteration minerals in rock. The rock near-infrared spectra are classified using optimized fuzzy C-means clustering algorithm, and the main mineral composition is obtained for different rock samples through the analysis of cluster centers. The minimum Spectral Correlation Coefficient is used as the objective function to classify the simulation data. In this study, the classification method was first tested for parameter setting using simulation data, which was the mixture of several standard mineral spectra quantified in terms of reflectivity in the near-infrared band. Classification accuracies under different fuzzy index values are compared. When the fuzzy index value is 1.5, the classification accuracy of the simulation samples is 83%. The initial values of different cluster centers were shown to affect the classification result. In the practical application, the initial values of cluster centers need to be rationally chosen based on the knowledge of mineral spectroscopy. This method is applied in the clustering analysis of the rock near-infrared spectra, which were also quantified in terms of reflectivity in the near-infrared band. These actual rock near-infrared spectra were measured by a spectrometer, while the classification results were compared with X-ray diffraction analysis to show the effectiveness of our algorithm. Our study has shown that, with the optimized fuzzy C-means clustering algorithm, the interpretation of rock near-infrared spectra can help us obtain information of the mineral composition and structure more effectively in terms of accuracy and speed. This method is suitable for the rapid processing of massive rock near-infrared spectra and may become an important technology in geological survey and geological prospecting.     

Simulation study on the quantitative phase retrieval method under two-step phase-shifting technique

Based on two-step phase-shifting technique, we present a new derivative method for phase information extraction in quantitative phase imaging (QPI). By acquiring two phase-shifted interferograms and numerically calculating their 1st order derivatives, one can directly obtain the quantitative phase information. We demonstrate the proposed method by comparing our simulated results with the experimental results of the red blood cell and the HeLa cell, respectively. It shows that our method can be generally applied to any QPI, such as on-axis and off-axis interferometry, especially for slightly off-axis interferometry, and it has the feature of efficient utility of camera spatial bandwidth and the ability of fast data processing.     

净信号的局部建模算法及其在近红外光谱分析中的应用

提出了一种基于净信号分析的局部建模算法,以克服光谱定量分析中样本间差异性过大和样本待测性质与光谱之间存在非线性等问题。首先利用净信号分析方法得到校正样本和待测样本的净信号,然后用待测样本净信号和校正样本净信号之间的欧式距离作为样本相似性判据,选取一定数量的与待测样本最相似的校正样本组成局部校正子集,建立局部PLS回归模型。针对一组猪肉近红外光谱数据集的实验结果表明,该方法的预测精度显著优于全局建模方法和基于光谱欧式距离的局部建模方法。     

特征根回归法近红外光谱定量分析研究

本文以大豆样品为实验材料，研究了特征根回归法近红外光谱定量分析。用40个大豆样品的近红外光谱数据建立了测定大豆蛋白质含量的特征根回归模型，预测另外32个大豆样品的蛋白质含量，结果同PLS回归方法进行了比较，表明特征根回归模型可用于生物样品的近红外光谱定量分析。特征根回归法是对PCR建模方法改进的又一种化学计量学定量分析校正方法，该方法在对样品光谱提取主成份时考虑了待分析组分的作用，因此所建立的定量分析模型有好的分析效果。研究结果进一步表明，以样品近红外光谱建立定量分析模型，提取主成份时充分考虑被定量分析成份的作用是完全必要的。     

Quantitative phase detection with expanded principal component analysis method on interferometric microscopic cytometer

Based on interferometric microscopy, we develop a quantitative interferometric microscopic cytometer with expanded principal component analysis (PCA) phase retrieval method to obtain phase distributions of numerous biological samples with spatial resolution ~1.5 μm. The expanded PCA method realizes high-speed phase imaging from multiple microscopic interferograms captured by CCD camera when the biological samples are scanned in the field of view. This method is a time-domain algorithm which calculates faster than traditional frequency-domain algorithms and overcomes drawbacks induced by fast Fourier transform. The potential of this phase detecting system for studying biological systems is demonstrated with simulations and phase measurement of red blood cells in experiments.     

Fast and accurate characterization of biological membranes by EPR spectral simulations of nitroxides

A method by which it is possible to characterize the membranes of biological samples on the basis of the EPR spectral lineshape simulation of membrane-dissolved nitroxide spin probes is described. The presented simulation procedure allows the determination of the heterogeneous structure of biological membranes and fluidity characteristics of individual membrane domains. The method can deal with isotropic and anisotropic orientations of nitroxides introduced into the biological samples described by restricted fast motion with a correlation time between 0.01 and 10 ns. The linewidths of the Lorentzian lineshapes are calculated in a restricted fast-motion approximation. In the special case of samples with high concentrations of nitroxides or in the presence of paramagnetic ions, the lineshapes are calculated directly from the exchange-coupled Bloch equations. The parameters describing ordering, relaxation, polarity, and the portions of the individual spectral components are extracted by optimizing the simulated spectra to the experimental spectrum with either a Simplex or a Monte Carlo algorithm. To improve the algorithm's efficiency, a new way of characterizing the goodness of fits is introduced. The new criterion is based on the standard least-squares function, but with special weighting of the partial sums. Its benefits are confirmed with membrane spectral simulation. Two classes of examples-simulation and optimizations of synthetic spectra to evaluate the accuracy of the optimization algorithms and simulation and optimization of EPR spectra of nitroxides in liposome suspensions in the presence of a broadening agent and in human leukocytes are shown.     

Fractal Approach for <Superscript>1</Superscript>H-NMR Spectra Simplification and Data Processing

Nuclear magnetic resonance (NMR) is a powerful instrumental technique suited to characterize and identify organic substances, and has been successfully applied in the analysis of complex matrices such as biological and environmental samples. In a previous work, we demonstrated the ability of unsupervised contribution analysis (UCA) to process complex mixtures to identify the number of independent constituents and deconvolute mixed signals into specific signal sources. In this work, we evaluated the deconvolving ability of this algorithm to access underlying spectral information—we used UCA to estimate the number of contributing species and respective contributing sources and scores and with that information performed selective ¹H-NMR signal suppression. We found that, in optimal NMR conditions, independently of signal source type, UCA allows us to correctly (a) estimate the number of independent contributions, (b) retrieve specific signal sources and (c) respective mixing information, allowing us to (d) characterize each contribution using signal sources and (e) quantify each specific contribution by means of its mixing information. This unsupervised soft-modeling method allows (f) individual contribution estimation and (g) respective removal from collected spectra, thus (h) enhancing spectra information for minor contributing species.     

基于时间分辨荧光光谱法同时测定己酸乙酯与乙酸乙酯

提出了一种基于时间分辨荧光光谱分析法同时测定己酸乙酯和乙酸乙酯混合液中各组分浓度的方法。实验测得两种单体物质的荧光发射光谱重叠度达到78%。对于荧光发射光谱重叠度很高的物质使用普通荧光方法结合算法也很难进行定量分析。而通过量子化学计算结果,对两种单体的分子结构分析,得到己酸乙酯的荧光寿命长于乙酸乙酯。通过时间分辨荧光光谱测量得到己酸乙酯和乙酸乙酯的荧光寿命分别为1.0和5.3 ns。两种物质的荧光发射光谱重叠严重,但是其荧光衰减曲线有明显的差别,所以提出使用时间分辨荧光光谱法对两种物质进行定量分析。通过分析两种物质不同体积比下的荧光衰减曲线的变化趋势,建立两种物质体积比的预测模型,并对其进行检验。实验结果表明：该方法能够实现混合溶液中各组分体积比的测量,其测量的平均残差控制在1%以内,具有一定的实用价值。     

In vitro method for medical risk assessment of laser fumes

Laser processing of different materials may produce toxic fumes. In preventive occupational medicine it is necessary to evaluate valid hygienic standards for work places. The basis for such hygienic standards is the classification of laser fumes by their fibrogenic, emphysematous, immunological or other harmful potencies in biological assay systems. This paper is part of a European project on laser safety. Our part in this project is the development of a method for the investigation of lung responses using in vitro cell assays. The appropriate laser fume samples will be supplied by other groups in this European project. In contrast to the cell assays usually used in risk assessment, our method is based on isolated target cells in the lung, such as alveolar macrophages. The test criteria are mediator release, surfactant reactions, release of reactive oxygen species and cell proliferation. As demonstrated in the lung response to other dusts (minerals, fibres etc) these parameters are medically relevant factors in the pathogenic alveolar dust response. The paper gives basic information about the method using lung cell assays and the results of known substances, in comparison with a dust generated by laser processing.     

应用CARS和SPA算法对草莓SSC含量NIR光谱预测模型中变量及样本筛选

采用光谱技术对水果进行定量或定性分析,如何获得一个简单、有效的校正模型对后续模型的应用和维护都非常关键。以草莓内部品质近红外光谱预测为例,从关键变量和特征样本优选两方面进行研究。采用竞争性自适应重加权CARS算法对光谱变量进行初次选择,随后采用连续投影算法SPA对校正集样本进行优选,获得98个特征样本,针对优选后的变量/样本子集利用SPA算法作二次关键变量提取,获得25个关键变量。为了验证CARS算法的性能,蒙特卡罗无信息变量消除MC-UVE和连续投影算法SPA用于比较研究。CARS算法在消除无信息变量的同时可以对共线性信息进行去除。同样,为了评估SPA算法在特征样本选择中的性能,经典的Kennard-Stone算法也用于比较分析。SPA算法能够用于校正集特征样本的优选。针对最终优选后的变量/样本(25/98)子集建立PLS和MLR模型对草莓内部可溶性固形物含量SSC含量进行定量预测。结果表明,两个模型利用原始变量/样本的0.59%/65.33%的信息均能够获得比基于原始变量/样本所建模型更好的性能,且MLR模型比PLS模型性能略优,r2_pre,RMSEP和RPD分别为0.909 7,0.348 4和3.327 8。     

Simultaneous Identification and Quantitative Determination of Amino Acids in Mixture by NMR Spectroscopy Using Chemometric Resolution

Nuclear magnetic resonance (NMR) spectroscopy receives increasing interest in biological and metabolomic studies. It is still, however, a great challenge to analyze the NMR spectra of multicomponent samples, containing overlapping responses of the compositions. In this work, nonnegative independent component analysis (nonnegative ICA) and immune algorithm (IA) were employed for simultaneous identification and quantitative determination of three amino acids in mixture samples from measured multicomponent NMR signals. Results show that the correlation coefficients (R) between the extracted and measured spectra are higher than 0.97 and that those between the calculated relative contents and experimental ones are higher than 0.99. The method may provide an alternative technique for analyzing multicomponent samples like biofluids by using NMR spectroscopy.     

Understanding tip‐enhanced Raman spectra of biological molecules: a combined Raman,SERS and TERS study

Although conventional Raman, surface‐enhanced Raman (SERS) and tip‐enhanced Raman spectroscopy (TERS) have been known for a long time, a direct, thorough comparison of these three methods has never been carried out. In this paper, spectra that were obtained by conventional Raman, SERS (on gold and silver substrates) and TERS (in ‘gap mode’ with silver tips and gold substrates) are compared to learn from their differences and similarities. Because the investigation of biological samples by TERS has recently become a hot topic, this work focuses on biologically relevant substances. Starting from the TER spectra of bovine serum albumin as an example for a protein, the dipeptides Phe–Phe and Tyr–Tyr and the tripeptide Tyr–Tyr–Tyr were investigated. The major findings were as follows. (1) We show that the widely used assumption that spectral bands do not shift when comparing SER, TER and conventional Raman spectra (except due to binding to the metal surface in SERS or TERS) is valid. However, band intensity ratios can differ significantly between these three methods. (2) Marker bands can be assigned, which should allow one to identify and localize proteins in complex biological environments in future investigations. From our results, general guidelines for the interpretation of TER spectra are proposed. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantitative Analysis of Simulated Illicit Street-Drug Samples Using Raman Spectroscopy and Partial Least Squares Regression

Modern drug laws require that a seized sample be characterized for both the illegal substances present and the quantity of each of those substances. The goal of this work was to develop a common approach to model development based on Raman spectroscopic analysis followed by partial least squares (PLS) regression that would allow us to obtain quantitative information from simulated street-drug samples. Each drug sample contained one drug surrogate—either isoxsuprine, norephedrine, benzocaine, or lidocaine—and up to 3 different cutting agents. All spectra were acquired on a homebuilt Raman instrument equipped with a rotating sample holder. The same steps were employed for developing separate models for each drug surrogate, including spectral preprocessing by Savitsky-Golay smoothing, differentiation, mean-centering, and autoscaling. PLS models were developed using 2 latent variables that yielded root mean square errors of calibration (RMSEC) values in the 3% range and root mean square error of prediction (RMSEP) values in the 4% range.     

三维荧光光谱结合Zernike图像矩快速鉴别掺伪芝麻油

为了实现对掺伪芝麻油的快速鉴别,应用FS920荧光光谱仪测定样品的三维荧光光谱数据。将三维荧光光谱图视为灰度图,在没有任何预处理的前提下,直接应用Zernike图像矩提取三维光谱灰度图的特征信息,然后采用类平均法对特征信息进行聚类分析,从定性角度实现掺伪芝麻油的鉴别,并解析其组成成分。最后应用广义回归神经网络(GRNN)对掺伪样本的成分进行定量分析。聚类分析能够以很高的辨识率来识别掺伪芝麻油,并能够正确解析其组成成分。定量模型预测了2组掺伪样本中各成分的相对体积,其平均相对误差分别为2.23%,8.00%,9.70%和9.70%。分析结果表明,Zernike矩能够有效提取光谱的特征信息,光谱数据的Zernike矩特征结合聚类分析以及GRNN模型能够获得良好的定性和定量分析结果,为掺伪芝麻油的鉴别提供了一种新的方法。     

基于波长选择的纸页厚度太赫兹时域光谱检测新方法

光谱数据的波长选择是太赫兹光谱定量分析的关键。以纸页厚度检测为例,提出了一种基于波长选择的纸页厚度太赫兹相位谱检测新方法。首先采用离散离子群算法进行太赫兹相位信号优选,并应用偏最小二乘法建立纸页厚度定量模型,以模型的预测均方根误差作为太赫兹相位信号优选的评价标准,选择出预测效果最优的太赫兹相位信号,进行纸页厚度预测。最后将本文方法的预测结果与采用全谱和单频相位信号的预测结果进行比较,结果表明,提出的方法预测结果最好。