Two-dimensional correlation analysis of nuclear magnetic resonance metabonomics data

Two-dimensional (2D) correlation analysis has been used in this study to identify changes in complex nuclear magnetic resonance (NMR) metabonomics spectra of rat urine samples obtained during a study in which vasculitis (vascular injury), an important safety element in preclinical trials, was induced. Two types of correlation analysis were performed, along the variables and along the samples, and both 2D covariance and correlation coefficient maps were calculated. The binned and 'raw' NMR spectra were analyzed (0.04 and 0.001 ppm resolution, respectively). Good correlation was found among the major peaks of the binned spectra, and two groups of samples were identified using sample-sample 2D correlation maps. Much more complex correlation features were obtained from the 'raw' spectra, in which the specific, butterfly-like patterns were obtained in the covariance map but with only a few significant correlation coefficients in the corresponding 2D correlation maps. In terms of classification, the same group of the last nine spectra that indicated the end of the process and clustered in the 2D sample-sample covariance map of the binned data was also found in the 2D sample-sample covariance map of the raw NMR spectra but, again, not in the 2D correlation coefficient map. A discussion is given on the details of the application of the correlation analysis with regard to spectral data resolution, alignment, the effect of actual intensities of the NMR signal, and reference to various results from 2D correlation analysis of vibrational spectra.     

Sample-sample and wavenumber-wavenumber two-dimensional correlation analyses of attenuated total reflection infrared spectra of polycondensation reaction of bis(hydroxyethyl terephthalate).

Polycondensation reaction of bis(hydroxyethylterephthalate) was monitored in situ by attenuated total reflection (ATR)/infrared (IR) spectroscopy. The obtained spectra are analyzed by means of generalized two-dimensional (2D) sample-sample and wavenumber-wavenumber correlation spectroscopies. The sample-sample correlation analysis reveals the correlations among the concentration features of the components, and the wavenumber-wavenumber correlation analysis elucidates the relations among the spectral features. Before the experimental data are analyzed by the 2D correlation spectroscopies, a synthetic two-component spectral model composed of the first and the last experimental spectra, is examined. The results of an analysis of the real data are related to those obtained from the synthetic data. It is found that the sample-sample correlation analysis of the IR data of polycondensation explains the concentration variance in the system and classifies two groups of the samples. The wavenumber-wavenumber correlations are derived upon the results of the sample-sample correlations and explained in terms of the spectral variations of three components. The convolute patterns in both types of correlations are attributed to the weak presence of ethylene glycol.     

Probability-based differential normalized fluorescence bivariate analysis for the classification of tissue autofluorescence spectra

Differential normalized fluorescence (DNF) is an efficient and effective method for the differentiation of normal and cancerous tissue fluorescence spectra. The diagnostic features are extracted from the difference between the averaged cancerous and averaged normal tissue spectra and used as indices in tissue classification. In this paper, a new method, probability-based DNF bivariate analysis, is introduced based on the univariate DNF method. Two differentiation features are used concurrently in the new method to achieve better classification accuracy. The probability of each sample belonging to a disease state is determined with Bayes decision theory. This probability approach classifies the tissue spectra according to disease states and provides uncertainty information on classification. With a data set of 57 colonic tissue sites, probability-based DNF bivariate analysis is demonstrated to improve the accuracy of cancer diagnosis. The bivariate DNF analysis only requires the collection of a few data points across the entire emission spectrum and has the potential of improving data acquisition speed in tissue imaging.     

Application of sample-sample two-dimensional correlation spectroscopy to determine the glass transition temperature of poly(ethylene terephthalate) thin films

The glass transition temperatures (Tg) of poly(ethylene terephthalate) (PET) thin films with different thicknesses are determined by analyzing their in situ reflection-absorption infrared (RAIR) spectra measured over a temperature range of 28 to 84 degrees C. The criterion of standard deviation of the covariance matrices is used as a graphical indicator for the determination of the Tg present in the sample-sample two-dimensional (2D) correlation spectra calculated from the temperature-dependent RAIR spectra. After two data pretreatments of the first derivative of the spectral absorbance versus temperature and the mean normalization over the wavenumbers are sequentially carried out on the RAIR spectra, an abrupt change of the first-derivative correlation spectra with respect to temperature is quickly obtained. It reflects the temperature at which the apparent intensity changes in pertinent absorption bands of PET thin films take place due to the dramatic segmental motion of PET chain conformation. The Tg of the thin PET films is accordingly determined. The results reveal that it decreases with a great dependence on the film thickness and that sample-sample 2D correlation spectroscopy enables one to determine the transition temperature of polymer thin films in an easy and valid way.     

Applications of moving window two-dimensional correlation spectroscopy to analysis of phase transitions and spectra classification

Our recently proposed idea of moving window two-dimensional (2D) correlation spectroscopy, which partitions a data set into series of relatively small submatrices (windows) and calculates their covariance maps in succession, is tested for three convoluted data set. Phase-transition temperatures of oleic acid and poly-(N-isopropylacrylamide) in an aqueous solution are sought by analyzing covariances of their temperature-dependent near-infrared and infrared spectra, respectively, while Raman spectra of three kinds of polyethylene (PE) pellets are investigated to find the spectral differences among them and to classify randomly ordered spectra by a sample-sample (SS) covariance map. The criterion of mean of standard deviation of covariance matrices is used as an indicator of the crucial information present in these matrices so that only a few of them are discussed in details. The results are obtained quickly after very simple calculations and are studied at length. The baseline variation is not removed prior to the calculations but is found to be of use for the determination of the phase-transition temperatures. Randomly ordered Raman spectra of the PE pellets are classified by innovatively used and interpreted SS slice spectra, with the relation to principal component analysis discussed.     

A probability-based spectroscopic diagnostic algorithm for simultaneous discrimination of brain tumor and tumor margins from normal brain tissue

This paper reports the development of a probability-based spectroscopic diagnostic algorithm capable of simultaneously discriminating tumor core and tumor margins from normal human brain tissues. The algorithm uses a nonlinear method for feature extraction based on maximum representation and discrimination feature (MRDF) and a Bayesian method for classification based on sparse multinomial logistic regression (SMLR). Both the autofluorescence and the diffuse-reflectance spectra acquired in vivo from patients undergoing craniotomy or temporal lobectomy at the Vanderbilt University Medical Center were used to train and validate the algorithm. The classification accuracy was observed to be approximately 96%, 80%, and 97% for the tumor, tumor margin, and normal brain tissues, respectively, for the training data set and approximately 96%, 94%, and 100%, respectively, for the corresponding tissue types in an independent validation data set. The inherently multi-class nature of the algorithm facilitates a rapid and simultaneous classification of tissue spectra into various tissue categories without the need for a hierarchical multi-step binary classification scheme. Further, the probabilistic nature of the algorithm makes it possible to quantitatively assess the certainty of the classification and recheck the samples that are classified with higher relative uncertainty.     

荧光谱及平行因子分析在芝麻油与芝麻香精鉴别中的应用

利用FS920荧光光谱仪测量21种芝麻油和同品牌不同批次2种芝麻香精共23个样品的荧光光谱,并对激发-发射荧光谱数据矩阵(EEMs)进行平行因子分析,确定了平行因子分析模型的因子数及各因子的物质基础。综合分析同步三维荧光谱、激发-发射三维荧光谱及其等高线光谱图,给出了芝麻油及芝麻香精的峰位、峰数和峰强等特征信息;应用平行因子模型建立了芝麻油及芝麻香精的3因子激发、发射光谱轮廓图和样本因子相对含量图。证实荧光光谱技术和平行因子分析法对芝麻油和芝麻香精进行分析和鉴别的有效性。     

差分拉曼光谱结合XRF对塑料药瓶的多元分类研究

目的 构建一种药瓶的分类及预测模型。方法 利用差分拉曼光谱和X射线荧光光谱对54个不同品牌和产地的塑料药瓶进行分析检验。结果 得到了54个样品的差分拉曼谱图及Cl、Ca、Ti、Fe、Zn等元素的含量。利用主成分分析对差分拉曼光谱数据进行降维,再利用系统聚类将降维后的数据分为8类,并以此为依据建立判别分析模型,最终判别模型经交叉验证可知准确率达到90.7%,多层感知器的分类准确率为100%,分类效果较好。结论 差分拉曼光谱可以根据谱图中的特征峰推断样品的分子结构,并且可以根据峰位对样品进行分类,并建立分析模型,X射线荧光光谱可以通过各元素的种类和含量的不同对样品进行区分,实现组内的细化。差分拉曼光谱和X射线荧光光谱可以分别从有机和无机的角度对药瓶进行分类,在分析上可以优势互补,可为公安机关实际办案探索出一种新的光谱联用角度和方法。     

基于判别分析与ANN的药品铝塑包装泡罩XRF分析

目的针对案件现场常见的药品铝塑包装泡罩,为达到对其分类识别的目的,提出系列检验分析、数据处理方法。方法采用X射线荧光光谱法对45个药品铝塑包装泡罩样本所含元素进行检验并讨论分析。对检验结果进行无监督的系统聚类,利用离差平方和法计算欧氏距离进而将未知样本分为5类。结果将分类结果作为变量进行判别分析,选取累积方差百分比为97.8%的2个判别函数,其类内平方和与总平方和之比为0.015和0.394,具有较强的解释能力。绘制的样本判别分类图将5类样本类之间相互区分开来,样本总体判别正确率为95.6%。提取样本在判别函数上的判别得分构建了人工神经网络,最终分类正确率为97.8%。结论利用X射线荧光光谱法对药品铝塑包装泡罩进行检验,将元素种类及含量作为变量进行了分类,并构建了45个药品铝塑包装泡罩样本的人工神经网络分类模型,可借助该模型进一步实现对于案件现场未知类别的药品铝塑包装泡罩样本的分类识别。     

2D correlation spectroscopy and multivariate curve resolution in analyzing pH-dependent evolving systems monitored by FT-IR spectroscopy,a comparative study

Multivariate curve resolution (MCR) and 2D correlation spectroscopy (2D-CoS), including sample-sample correlation, have been applied to the analysis of evolving midinfrared spectroscopic data sets obtained from titrations of organic acids in aqueous solution. In these data sets, well-defined species with significant differences in their spectra are responsible for the spectral variation observed. The two fundamentally different chemometric techniques have been evaluated and discussed on the basis of experimental and supportive simulated data sets. MCR gives information that can be directly related to the chemical species that is of importance from a practical point of view, whereas 2D-CoS results normally require more interpretation. The obtained conclusions are regarded valid for similar evolving data, which are increasingly being encountered in analytical chemistry when multivariate detectors are used to follow dynamic processes, including separations as well as chemical reactions, among others.     

An inverse problem approach to stiffness mapping for early detection of breast cancer: tissue phantom experiments

Early detection of breast cancer will continue to be crucial in improving patient survival rates. Our ultimate goal is to develop an electro-mechanical device to automate and refine the manual breast exam process, and use inverse techniques to generate a tissue stiffness map of the breast tissue. We have previously presented computational simulations of the stiffness mapping approach, which employs static indentations of the tissue and measurements of surface displacements. In this paper, we report on experimental validation of the technique with tissue phantom experiments. We tested 12 tissue phantom samples without simulated tumours and 14 tissue phantom samples with simulated tumours. Our stiffness mapping approach correctly identified all 26 samples.     

Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis

The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.     

Moving window two-dimensional correlation spectroscopy and determination of signal-to-noise threshold in correlation spectra

In this paper we report two new developments in two-dimensional (2D) correlation spectroscopy; one is the combination of the moving window concept with 2D spectroscopy to facilitate the analysis of complex data sets, and the other is the definition of the noise level in synchronous/asynchronous maps. A graphical criterion for the latter is also proposed. The combination of the moving window concept with correlation spectra allows one to split a large data matrix into smaller and simpler subsets and to analyze them instead of computing overall correlation. A three-component system that mimics a consecutive chemical reaction is used as a model for the illustration of the two ideas. Both types of correlation matrices, variable-variable and sample-sample, are analyzed, and a very good agreement between the two is met. The proposed innovations enable one to comprehend the complexity of the data to be analyzed by 2D spectroscopy and thus to avoid the risks of over-interpretation, liable to occur whenever improper caution about the number of co-existing species in the system is taken.     

Imaging of freeze-fractured cells with in situ fluorescence and time-of-flight secondary ion mass spectrometry

Bioanalytical imaging techniques have been employed to investigate cellular composition at the single-cell and subcellular regimes. Four imaging modes have been performed sequentially in situ to demonstrate the utility of a more integrated approach to imaging cells. The combination of bright-field, scanning ion, and fluorescence microscopy complements TOF-SIMS imaging of native biomolecules. Bright-field microscopy provides a blurred visualization of cells in frozen-hydrated samples, while scanning ion imaging provides a morphological view of freeze-fractured cells after TOF-SIMS analysis is completed. With the use of selective fluorescent labels, fluorescence microscopy allows single mammalian cells to be located in the complex ice matrix of freeze-fractured samples, a task that has not been routine with either bright-field or TOF-SIMS. A fluorescent label, DiI (m/z 834), that does not interfere with the mass spectra of membrane phosphatidylcholine, has been chosen for fluorescence and TOF-SIMS imaging of membrane phospholipids. In this paper, in situ fluorescence microscopy allows the distinction of single cells from ice and other sample debris, previously not possible with bright-field or scanning ion imaging. Once cells are located, TOF-SIMS imaging reveals the localization of membrane lipids, even in the membrane of a single 15-microm rat pheochromocytoma cell. The utility of mapping lipids in the membranes of single cells using this integrated approach will provide more understanding of the functional role of specific lipids in functions of cellular membranes.     

Near-infrared luminescence of cadmium pigments: in situ identification and mapping in paintings

A comprehensive study of the luminescence properties of cadmium pigments was undertaken to determine whether these properties could be used for in situ identification and mapping of the pigments in paintings. Cadmium pigments are semiconductors that show band edge luminescence in the visible range and deep trap luminescence in the red/infrared range. Emission maxima, quantum yields, and excitation spectra from the band edge and deep trap emissions were studied for sixty commercial cadmium pigments that span the color range from yellow to red (reflectance transition 470 to 660 nm). For paints containing cadmium pigments, luminescence from deep traps was more readily observable than that from the band edge, although the yield varied widely from zero to around 4.5%. Optimal excitation for emission is found to be in the visible for both pigments in powder form and mixed with a medium. The maxima of the deep trap emission shift with the band gap energy, providing a potentially useful way to assign pigment type even when used in pigment mixtures. The usefulness of the results of the study on mockups was demonstrated by the mapping of cadmium pigments of different hues with the aid of calibrated luminescence imaging spectroscopy in a painting by Edward Steichen, entitled Study for 'Le Tournesol' (1920). Analysis of the luminescence image cube reveals at least six unique spectral components, associated with emission from white pigments, paint binder, and cadmium red and yellow pigments. The results were compared with those from X-ray fluorescence spectrometry (XRF) and fiber-optic reflection spectroscopy (FORS) and the results obtained on paint samples containing cadmium pigments. These results show that, when present, the emission from traps can be used as an analytical tool to identify cadmium pigments, to distinguish among cadmium sulfide, cadmium zinc sulfide, and cadmium sulfoselenide, and to map cadmium pigments, even in mixtures.     

Tea classification and quality assessment using laser-induced fluorescence and chemometric evaluation

Laser-induced fluorescence was used to evaluate the classification and quality of Chinese oolong teas and jasmine teas. The fluorescence of four different types of Chinese oolong teas-Guangdong oolong, North Fujian oolong, South Fujian oolong, and Taiwan oolong was recorded and singular value decomposition was used to describe the autofluoresence of the tea samples. Linear discriminant analysis was used to train a predictive chemometric model and a leave-one-out methodology was used to classify the types and evaluate the quality of the tea samples. The predicted classification of the oolong teas and the grade of the jasmine teas were estimated using this method. The agreement between the grades evaluated by the tea experts and by the chemometric model shows the potential of this technique to be used for practical assessment of tea grades.     

Effect on the partial least-squares prediction of yarn properties combining raman and infrared measurements and applying wavelength selection

The combination of Raman and infrared spectroscopy on the one hand and wavelength selection on the other hand is used to improve the partial least-squares (PLS) prediction of seven selected yarn properties. These properties are important for on-line quality control during production. From 71 yarn samples, the Raman and infrared spectra are measured and reference methods are used to determine the selected properties. Making separate PLS models for all yarn properties using the Raman and infrared spectra, prior to wavelength selection, reveals that Raman spectroscopy outperforms infrared spectroscopy. If wavelength selection is applied, the PLS prediction error decreases and the correlation coefficient increases for all properties. However, a substantial wavelength selection effect is present for the infrared spectra compared to the Raman spectra. For the infrared spectra, wavelength selection results in PLS prediction errors comparable with the prediction performance of the Raman spectra prior to wavelength selection. Concatenating the Raman and infrared spectra does not enhance the PLS prediction performance, not even after wavelength selection. It is concluded that an infrared spectrometer, combined with a wavelength selection procedure, can be used if no (suitable) Raman instrument is available.     

Analyzing Raman maps of pharmaceutical products by sample-sample two-dimensional correlation

Sample-sample (SS) two-dimensional (2D) correlation spectroscopy is applied in this study as a spectral selection tool to produce chemical images of real-world pharmaceutical samples consisting of two, three, and four components. The most unique spectra in a Raman mapping spectral matrix are found after analysis of the covariance matrix. (This is obtained by multiplying the original mapping data matrix by itself.) These spectra are identified by analyzing the slices of the covariance matrix at the positions where covariance values are at maxima. Chemical images are subsequently produced in a univariate fashion by visually selecting the wavenumbers in the extracted spectra that are least overlapped. The performance of SS 2D correlation is compared with principal component analysis in terms of highlighting the most prominent spectral differences across the whole data set (which typically comprises several thousand spectra) and determining the total number of species present. In addition, the selection of the unique spectra by SS 2D correlation is compared with the selection obtained by the orthogonal projection approach (OPA). Both comparisons are found to be satisfactory and demonstrate that a quite simple SS 2D correlation routine can be used for producing reliable images of unknown samples. The main benefit of using SS 2D correlation is that it is based on a few data processing commands that can be executed separately and produce results that are closely related to the chemical features of the system.     

Dual-enzyme fiber-optic biosensor for glutamate based on reduced nicotinamide adenine dinucleotide luminescence.

Response characteristics are presented for a dual-enzyme fiber-optic biosensor for glutamate. An enzyme layer composed of glutamate dehydrogenase (GDH) and glutamate-pyruvate transaminase (GPT) is used to produce reduced nicotinamide adenine dinucleotide (NADH) at the tip of a fiber-optic probe. NADH luminescence is monitored through this probe and the measured fluorescence intensity is related to the concentration of glutamate. GDH catalyzes the formation of NADH, and GPT drives the GDH reaction by removing a reaction product and regenerating glutamate. Optimal response is obtained in a pH 7.4 Tris-HCl buffer maintained at 25 degrees C in the presence of 4 mM NAD+ and 10 mM L-alanine. The temperature profile reveals a strong negative temperature effect which is attributed to the temperature dependency of NADH luminescence. Under optimal conditions, the sensor sensitivity is 0.127 nA/microM over the 1-10 microM concentration range, the detection limit is 0.13 microM, and response times range from 4 to 8 min. The sensor response is stable for 12 days when stored at 4 degrees C. Selectivity for glutamate is excellent over most of the common amino acids as well as ascorbic acid, uric acid, taurine, and GABA. Only slight responses were observed for glutamine and lysine. The effect of ammonia on the glutamate response was found to be minimal at total ammonia nitrogen concentrations as high as 200 microM.     

An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images

Brain tumor and brain stroke are two important causes of death in and around the world. The abnormalities in brain cell leads to brain stroke and obstruction in blood flow to brain cells leads to brain stroke. In this article, a computer aided automatic methodology is proposed to detect and segment ischemic stroke in brain MRI images using Adaptive Neuro Fuzzy Inference (ANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The brain image is enhanced using Heuristic histogram equalization technique. Then, texture and morphological features are extracted from the preprocessed image. These features are optimized using Genetic Algorithm and trained and classified using ANFIS classifier. The performance of the proposed ischemic stroke detection system is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and Mathew's correlation coefficient.