In-situ monitoring of the structure of a goethite-based catalyst during methane oxidation by X-ray absorption near-edge structure (XANES) spectroscopy assisted by chemometric methods

A goethite-based catalyst was evaluated using in-situ X-ray absorption near-edge structure (XANES) spectroscopy during methane oxidation under increasing reaction temperature. Determination of rank by median absolute deviation (DRMAD), evolving factor analysis (EFA), and multivariate curve resolution (MCR) were used to detect the species present in the catalyst during the reaction and determine their concentration profiles and their pure spectra. The reactants and reaction products were monitored on-line by mass spectrometer. The goethite-based catalyst was active for methane oxidation, with CO(2) and H(2)O as the main products. DRMAD and EFA were useful to determine the number of chemical species present in the catalyst structure during reactions. The catalyst presented phase transition during the reaction from goethite to maghemite according to XANES spectra determined by MCR. On the other hand, it was verified that the catalyst presented phase transition from goethite to wüstite in the process in the absence of the oxidant (O(2)).     

Multivariate hyperspectral Raman imaging using compressive detection

A multivariate hyperspectral imaging (MHI) instrument has been designed and constructed to achieve greatly increased Raman imaging speeds by utilizing a compressive spectral detection strategy. The instrument may be viewed as a generalized spectrometer, which can function either as a conventional monochromator or in a wide variety of other hyperspectral modalities. The MHI utilizes a spatial light modulator (SLM) to produce programmable optical filters to rapidly detect and map particular sample components. A sequence of Hadamard-transform or random filter functions may be used to regenerate full Raman spectra. Compressive detection is achieved either using multivariate signal processing filter functions or the actual component spectra. Compressive detection is shown to be capable of achieving sampling speeds exceeding 1 ms per image pixel and the collection of chemical images in less than a minute.     

ToF-SIMS analysis of chemical heterogenities in inkjet micro-array printed drug/polymer formulations

Three different formulations comprising two drugs, felodipine and hydrochlorothiazide (HCT) and two polymers, poly(vinyl pyrolidone) (PVP) and poly(lactic-co-glycolic acid) (PLGA) were inkjet printed as micro-dot arrays and analysed on an individual micro-spot basis by time-of-flight secondary ion mass spectrometry (ToF-SIMS). For the HCT/PLGA formulation, the spots showed heterogeneity of the drug and other chemical constituents. To further investigate these heterogeneities, multivariate curve resolution was applied to the ToF-SIMS hyperspectral image datasets. This approach successfully identified distinct chemical components elucidating the HCT, PLGA, substrate material, and contaminants based on sulphur, phosphorous and sodium chloride. Spots printed using either of the drugs with PVP exhibited full substrate coverage and a uniform distribution of the active ingredient along with all other constituents within the printed spot area. This represents the preferred situation in terms of stability and controlling the release of a drug from a polymer matrix.     

Exploring time-dependent structural changes during the cold crystallization process of isotactic polystyrene by infrared spectroscopy and multivariate curve resolution

The present study attempts an application of Fourier transform infrared (FT-IR) spectroscopy in conjunction with multivariate curve resolution (MCR) techniques to explore the structural evolution of isotactic polystyrene (iPS) during the cold crystallization process. The focus of the present study is placed on the performance of MCR techniques, e.g., orthogonal projection (OP), alternating least squares (ALS), and fixed-size moving window evolving factor analysis (FSMWEFA), and the interpretability of spectral changes in the investigated chemical process. As a result, valuable information and conclusions about the structural evolution of iPS during the crystallization process can be extracted: when the amorphous phase of iPS changes, the ordering of the phenyl rings takes place first, and then the polymer chains adjust their local conformations to form short 3(1) helix structures. Furthermore, according to intensity profiles of the spectral variations, the ordering of the phenyl rings proceeds more intensely than the formation of ordered local chains, and the structural evolution of iPS occurs even during the induction period. The spectral variations resulting from the conformational changes in the 3(1) helical structures depend on the sequence length of the helical chains: the longer the polymer chain is, the smaller the corresponding band variations are. It has been demonstrated that the combination of FTIR spectroscopy and chemometric MCR techniques is very promising for the analysis of the crystallization process of polymers. MCR is a powerful tool for analyzing and visualizing spectral data and integrating them with other information, making spectral intensity variations more amenable to interpretation in order to explore the molecular dynamics of polymers.     

A spectral identity mapper for chemical image analysis

Generating chemically relevant image contrast from spectral image data requires multivariate processing algorithms that can categorize spectra according to shape. Conventional chemometric techniques like inverse least squares, classical least squares, multiple linear regression, principle component regression, and multivariate curve resolution are effective for predicting the chemical composition of samples having known constituents, but they are less effective when a priori information about the sample is unavailable. We have developed a multivariate technique called spectral identity mapping (SIM) that reduces the dependence of spectral image analysis on training datasets. The qualitative SIM method provides enhanced spectral shape specificity and improved chemical image contrast. We present SIM results of spectral image data acquired from polymer-coated paper substrates used in the manufacture of pressure sensitive adhesive tapes. In addition, we compare the SIM results to results from spectral angle mapping (SAM) and cosine correlation analysis (CCA), two closely related techniques.     

Multivariate curve resolution analysis on the multi-component water sorption process into a poly(2-methoxyethyl acrylate) film

In our previous study, sorption process of water into a biocompatible polymer film, poly(2-methoxyethyl acrylate) (PMEA) was monitored by time-resolved in situ attenuated total reflection infrared (ATR-IR) spectroscopy [S. Morita, et al., Langmuir 23, 3750 (2007)]. In the present study, noisy and heavily overlapped O-H stretching vibrational bands of diffusing water have been analyzed from the series spectra where the spectral shapes change irregularly with time. In spite of these complications, a powerful spectral analysis technique, multivariate curve resolution (MCR) by means of alternating least squares (ALS), yielded smooth and meaningful pure component spectra and detailed kinetic sorption profiles of each component, excluding noise. Ordinary smoothing techniques and Gaussian curve fitting would not achieve these significant results. The quantification of the kinetic parameters such as amplitudes (a) and relaxation time constants (tau) is significant for the systematic development of biocompatible materials and also for revealing the mechanisms of biocompatibility of a material. Moreover, the ratios of coefficients of each component at saturation corresponded well to the values obtained by Tanaka et al. measured by gravimetric analysis. This study is the first to report the detailed concentration profile of each water component whose sorption kinetics is discussed comprehensively.     

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.     

Chemometric strategy for modeling metabolic biological space along the gastrointestinal tract and assessing microbial influences

Over the past decade, the analysis of metabolic data with advanced chemometric techniques has offered the potential to explore functional relationships among biological compartments in relation to the structure and function of the intestine. However, the employed methodologies, generally based on regression modeling techniques, have given emphasis to region-specific metabolic patterns, while providing only limited insights into the spatiotemporal metabolic features of the complex gastrointestinal system. Hence, novel approaches are needed to analyze metabolic data to reconstruct the metabolic biological space associated with the evolving structures and functions of an organ such as the gastrointestinal tract. Here, we report the application of multivariate curve resolution (MCR) methodology to model metabolic relationships along the gastrointestinal compartments in relation to its structure and function using data from our previous metabonomic analysis. The method simultaneously summarizes metabolite occurrence and contribution to continuous metabolic signatures of the different biological compartments of the gut tract. This methodology sheds new light onto the complex web of metabolic interactions with gut symbionts that modulate host cell metabolism in surrounding gut tissues. In the future, such an approach will be key to provide new insights into the dynamic onset of metabolic deregulations involved in region-specific gastrointestinal disorders, such as Crohn's disease or ulcerative colitis.     

Visible reflectance hyperspectral imaging: characterization of a noninvasive,in vivo system for determining tissue perfusion

We characterize a visible reflectance hyperspectral imaging system for noninvasive, in vivo, quantitative analysis of human tissue in a clinical environment. The subject area is illuminated with a quartz-tungsten-halogen light source, and the reflected light is spectrally discriminated by a liquid crystal tunable filter (LCTF) and imaged onto a silicon charge-coupled device detector. The LCTF is continuously tunable within its useful visible spectral range (525-725 nm) with an average spectral full width at half-height bandwidth of 0.38 nm and an average transmittance of 10.0%. A standard resolution target placed 5.5 ft from the system results in a field of view with a 17-cm diameter and an optimal spatial resolution of 0.45 mm. The measured reflectance spectra are quantified in terms of apparent absorbance and formatted as a hyperspectral image cube. As a clinical example, we examine a model of vascular dysfunction involving both ischemia and reactive hyperemia during tissue reperfusion. In this model, spectral images, based upon oxyhemoglobin and deoxyhemoblobin signals in the 525-645-nm region, are deconvoluted using a multivariate least-squares regression analysis to visualize the spatial distribution of the percentages of oxyhemoglobin and deoxyhemoglobin in specific skin tissue areas.     

基于CEM的高光谱图像小目标检测算法

针对高光谱图像中小目标检测问题,提出了一种基于约束能量最小化(Constrained Energy Minimization,CEM)的目标检测算法.该算法首先对原始图像进行背景信息抑制从而抑制背景地物、突出低概率的小目标,用迭代误差分析的自动端元提取算法找出目标的端元光谱,然后把目标端元光谱代入CEM滤波器得到该目标的检测结果图.用高光谱数据进行了实验研究,并与CEM滤波器进行了比较.结果表明,其检测性能与直接采用CEM方法的检测性能相当,但是相对于CEM方法,该算法不需要目标的先验光谱信息,更具有实用性.     

Quantitative X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry characterization of the components in DNA

The great diversity of techniques to synthesize and use DNA microarrays has made them extremely flexible for a variety of applications. This flexibility also has made standardization difficult, leading to problems comparing data from these different systems. In this work, we use the surface science techniques of X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS) to analyze the components of DNA. The atomic ratios of the components of nucleobases, nucleosides, and nucleotides were characterized by XPS. The chemical shifts in the high-resolution XPS spectra allow for their relatively easy resolution. The unique positive and negative ions from the nucleobases, nucleosides, and nucleotides in their TOF-SIMS spectra were identified. This information was used to build a comprehensive table of all of the molecular ions. These standard spectra of DNA components can be used to predict the relative amounts of the bases within more complex molecules either by univariate analysis (i.e., by relating the base molecular ions to the sugar fragment ions within the nucleotides) or by multivariate analysis (e.g., principal component analysis). Our preliminary examination of four oligonucleotides shows promising results in that we can distinguish between two oligomers of similar composition using univariate and multivariate analysis, although additional studies are needed to expand this method to more complex oligomers.     

Interpretation of static time-of-flight secondary ion mass spectra of adsorbed protein films by multivariate pattern recognition

Multivariate analysis has become increasingly common in the analysis of multidimensional spectral data. We previously showed that the multivariate analysis technique principal component analysis (PCA) is an excellent method for interpreting the static time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of adsorbed protein films. PCA is an unsupervised pattern recognition technique that loses resolution between spectra of different proteins as more proteins are added to the data set due to large within-group variation. The supervised pattern recognition techniques discriminant principal component analysis (DPCA) and linear discriminant analysis (LDA), which aim to control within-group variation while maximizing between-group separation to enhance discrimination between groups, were compared with PCA using data sets of TOF-SIMS spectra of proteins adsorbed onto mica and PTFE substrates. DPCA and LDA quantitatively improved discrimination between groups and provided different information about the data than PCA. LDA was able to classify unknown samples with a misclassification rate lower than PCA or DPCA. Both unsupervised and supervised pattern recognition techniques are useful for the interpretation and classification of static TOF-SIMS spectra of adsorbed protein films.     

Characterization and mapping of carotenoids in the algae Dunaliella and Phaeodactylum using Raman and target orthogonal partial least squares

A method was developed for the characterisation of carotenoid pigments in algal species using Raman spectroscopy in combination with multivariate hyperspectral analysis. Target orthogonal partial least squares (T-OPLS) operates by designating one known reference spectrum as the target. The target spectrum is put as the single y column in an OPLS regression model where the X matrix consists of the unfolded image spectra as variables in its columns. The spectral shape of the OPLS first orthogonal target score enabled us to verify the peak positions of the standard, and detect new peaks, not present in the reference standard. It was shown that the mixture of carotenoids present in the algae did not fully match the reference spectrum, however, the method provided enough information to make an analysis possible also in this case. The image results were constructed from the OPLS loading vectors that were showing a correlation map for the reference spectrum from the predictive loadings and maps of the occurrence of deviations from the orthogonal loadings.     

Multivariate image analysis: A review with applications

Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction.     

Evolving factor analysis,a new multivariate technique in chromatography

Overlapping peaks are a general problem in chromatography. Modern multichannel detectors such as the diode-array detector allow multivariate techniques for a computational resolution. Evolving factor analysis (EFA) is a recently developed method for a completely model-free resolution of overlapping peaks into concentration profiles and absorption spectra. EFA is successfully tested with real chromatograms. The requirements concerning the quality of the measured data are discussed and related to the scope and fields of application of EFA.     

Design, construction, characterization, and application of a hyperspectral microarray scanner

We describe the design, construction, and operation of a hyperspectral microarray scanner for functional genomic research. The hyperspectral instrument operates with spatial resolutions ranging from 3 to 30 microm and records the emission spectrum between 490 and 900 nm with a spectral resolution of 3 nm for each pixel of the microarray. This spectral information, when coupled with multivariate data analysis techniques, allows for identification and elimination of unwanted artifacts and greatly improves the accuracy of microarray experiments. Microarray results presented in this study clearly demonstrate the separation of fluorescent label emission from the spectrally overlapping emission due to the underlying glass substrate. We also demonstrate separation of the emission due to green fluorescent protein expressed by yeast cells from the spectrally overlapping autofluorescence of the yeast cells and the growth media.     

A Bayesian Nonparametric Model for Temperature-Emissivity Separation of Long-Wave Hyperspectral Images

Long-wave infrared (LWIR) hyperspectral imagers (HSI) image a scene by collecting high-resolution spectra at each pixel. The data are similar to a camera image but have a large number of narrow spectral bands rather than the familiar broad three bands of red-green-blue in a traditional digital camera. Materials in LWIR are emissive (rather than reflective) and have unique spectra that can be used for material detection and identification. The measured spectra are a convolution of the material spectra (emissivity), the black body temperature (Planck curve), other interacting environmental spectral sources, and measurement error. One approach to material identification is temperature-emissivity separation (TES), which separates or deconvolves the material spectra from the temperature curve. To accomplish this task, we develop a unique flexible model which combines the mathematical model of the physical processes within a Bayesian nonparametric framework. In addition to offering interpretable estimates of model parameters, this model is able to identify material emissivity spectra and cluster pixels into appropriate material groups. We demonstrate our method using both a synthetic and measured dataset. The online supplementary material contains an appendix of the details of the sampling algorithm.     

Noninvasive method for the assessment of dermal uptake of pesticides using attenuated total reflectance infrared spectroscopy

Dermal absorption of pesticides is a primary exposure route for agricultural workers, but is not well characterized. Current measurement techniques are either invasive, such as tape-stripping, or require extensive sample preparation or analysis time, such as urinary metabolite monitoring or wipe sampling followed by gas chromatography analysis. We present the application of a noninvasive, spectroscopic approach for the measurement of pesticide absorption into skin. Attenuated total reflectance infrared spectroscopy (ATR-IR) was used to monitor directly the absorption of two pesticides--captan and azinphos-methyl--in ten volunteers over 20 min under occlusive conditions. We found substantial variability in absorption across subjects. Our results were comparable to those measured by the more traditional method of wipe-sampling followed by extraction and gas chromatography analysis. Multivariate data analysis, in the form of multivariate curve resolution (MCR), is a novel addition to this type of experiment, yielding time-resolved information unachievable by standard methods. These data are potentially more informative than the monitoring of blood or urinary metabolites because they can be acquired in essentially real-time, allowing observations of pesticide absorption on a rapid timescale rather than over hours or days.     

Characterization of a near-infrared laparoscopic hyperspectral imaging system for minimally invasive surgery

We developed and characterized a new imaging platform for minimally invasive surgical venues, specifically a system to help guide laparoscopic surgeons to visualize biliary anatomy. This platform is a novel combination of a near-infrared hyperspectral imaging system coupled with a conventional surgical laparoscope. Intraoperative tissues are illuminated by optical fibers arranged in a ring around a center-mounted relay lens collecting back-reflected light from tissues to the hyperspectral imaging system. The system consists of a focal plane array (FPA) and a liquid crystal tunable filter, which is continuously tunable in the near-infrared spectral range of 650-1100 nm with the capability of passing light with a mean bandwidth of 6.95 nm, and the FPA is a high-sensitivity back-illuminated, deep depleted charge-coupled device. Placing a standard resolution target 5.1 cm from the distal end of the laparoscope, a typical intraoperative working distance, produced a 7.6-cm-diameter field of view with an optimal spatial resolution of 0.24 mm. In addition, the system's spatial and spectral resolution and its wavelength tuning accuracy are characterized. The spectroscopic images are formatted into a three-dimensional hyperspectral image cube and processed using principle component analysis. The processed images provide contrast based on measured spectra associated with chemically different anatomical structures helping identify the main molecular chromophores inherent to each tissue. The principal component images were found to image swine gallbladder and biliary structures from surrounding tissues, in real time, during cholecystectomy surgery. Furthermore, it is shown that surgeons can interrogate selected image subregions for their molecular composition identifying biliary anatomy during surgery and before any invasive action is undertaken.