Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging

Chemical imaging method of vibrational spectroscopy, which provides both spectral and spatial information, creates a three‐dimensional (3D) dataset with a huge amount of data. When the components of the sample are unknown or their reference spectra are not available, the classical least squares (CLS) method cannot be applied to create visualized distribution maps. Raman image datasets can be evaluated even in such cases using multivariate (chemometric) methods for extracting the needed hidden information. The capability of chemometrics‐assisted Raman mapping is evaluated through the analysis of pharmaceutical tablets (considered as unknown) with the aim of estimating the pure component spectra based on the collected Raman image. Six chemometric methods, namely, principal component analysis (PCA), maximum autocorrelation factors (MAF), sample–sample 2D correlation spectroscopy (SS2D), self‐modeling mixture analysis (SMMA), multivariate curve resolution–alternating least squares (MCR‐ALS), and positive matrix factorization (PMF), were compared. SMMA was found to be the best choice to determine the number of components. MCR‐ALS and PMF provided the pure component spectra with the highest quality. MCR‐ALS was found to be superior to PMF in the estimation of Raman scores (which correspond to the concentrations) and yielded almost the same results as CLS (using the real reference spectra). Thus, the combination of Raman mapping and chemometrics could be successfully used to characterize unknown pharmaceuticals, identify their ingredients, and obtain information about their structures. This may be useful in the struggles against illegal and counterfeit products and also in the field of pharmaceutical industry when contaminants are to be identified. Copyright © 2011 John Wiley & Sons, Ltd.     

Raman spectroscopic analysis differentiates between breast cancer cell lines

Breast cancer incident rates are increasing in women worldwide with the highest incidence rates reported in developing countries. Major breast cancer screening approaches like mammography, ultrasound, clinical breast examination (CBE) and magnetic resonance imaging (MRI) are currently used but have their own limitations. Optical spectroscopy has attained great attention from biomedical researchers in recent years due to its non‐invasive and non‐destructive detection approach. Chemometrics is one of the powerful tools used in spectroscopic research to enhance its sensitivity. Raman spectroscopy, a vibrational spectroscopic approach, has been used to explore the chemical fingerprints of different biological tissues including normal and malignant types. This approach was used to characterize and differentiate two breast cancer and one normal breast cell lines (MDA‐MB‐436, MCF‐7 and MCF‐10A) using dispersive Raman spectroscopy. Raman spectra of the cell lines have revealed that basic differences in the concentration of biochemical compounds such as lipids, nucleic acids and protein Raman peaks were found to differ in intensity, and principal component analysis (PCA) was able to identify variations that lead to accurate and reliable separation of the three cell lines. Linear discriminant analysis (LDA) model of three cell lines was predicted with 100% sensitivity and 91% specificity. We have shown that a combination of Raman spectroscopy and chemometrics are capable of differentiation between breast cancer cell lines. These variations may be useful in identifying new spectral markers to differentiate different subtypes of breast cancer although this needs confirmation in a larger panel of cell lines as well as clinical material. Copyright © 2015 John Wiley & Sons, Ltd.     

Detailed spectroscopic analysis of complex multi‐component materials using a combination of Raman mapping with BTEM

The combined application of Raman microscopy and self‐modeling curve resolution techniques can address a wide range of material characterization problems. In particular, the combination of Raman microscopy and the Band‐Target Entropy Minimization (BTEM) algorithm has been applied to various organic, inorganic, pharmaceutical and bio‐material related problems. In the present contribution, the principles behind this type of analysis are reviewed, followed by a number of case‐by‐case studies. For each of these examples, a Raman microscopic mapping measurement (consisting of 100 s up to 1000 s of spectra) is performed, followed by BTEM analysis which provides the underlying pure component spectra of the constituents present in the system without the use of any a priori information. In most cases, outstanding signal‐to‐noise ratios for components at the 0.1‐1.0 % level can be obtained, and sometimes trace constituents can also be detected. Subsequently, the identity of the components can be determined by comparison to spectral libraries. Finally, the reconstructed pure component spectra can be further used to obtain the spatial distribution of the constituents present in the sample. Copyright © 2012 John Wiley & Sons, Ltd.     

Biomolecular component analysis of cultured cell nucleoli by Raman microspectrometry

Distinct cellular domains, such as structure–function compartments of the cell nucleus and cytoplasmic organelles, are responsible for numerous macromolecular processes essential for cell functions. Spectroscopic analysis of specific cellular domains opens a way for noninvasive characterization of their molecular content and monitoring of their function. Confocal Raman spectroscopy was employed here for characterization of the complex molecular organization of major structure–function compartment of the cell nucleus, the nucleolus. The Raman spectra obtained in the nucleoli were processed by biomolecular component analysis (BCA). BCA was used to determine the contribution of each major type of macromolecules (proteins, DNA, RNA and lipids) to the complex molecular composition of nucleoli. A notable cell‐to‐cell variability in the macromolecular composition of nucleolus was found. At the same time, we observed a correlation between the concentrations of major types of biomolecules in this nuclear compartment. In particular, the averaged concentration of RNA increases along with increase in protein concentration, while an inverse dependence between the concentrations of RNA and DNA was found. Variations in the nucleolar concentrations of lipids were also noticed. Manifestations in spectral variations of proteins for individual nucleoli, shown by BCA, are discussed and interpreted. We also compared utility of BCA and principal component analysis for biomolecular studies and conclude that BCA is a more powerful and informative technique for studies of macromolecular composition and its variations in specific subcellular domains. Copyright © 2012 John Wiley & Sons, Ltd.     

Morphology of InN nanorods using spectroscopic Raman imaging

We report the vibrational properties of vertical and oblique InN nanorods (NRs) grown by molecular beam epitaxy (MBE). Surface optical (SO) Raman mode at 561 cm⁻¹, belonging to E₁ symmetry [SO(E₁)], is identified along with symmetry allowed Raman modes of E₂(low), E₂(high), and E₁(LO) at 87, 489, and 589 cm⁻¹, respectively, corresponding to wurtzite InN phase. Usually, SO phonon modes arise due to breakdown of translational symmetry of surface potential at surface defects, which are attributed by the surface roughness. Intensity distribution of E₁(LO) and SO(E₁) phonon modes over a specified area have been analysed using Raman area mapping with an optical resolution of 400 nm. Imaging with E₁(LO) phonon mode, originating from the bulk of the sample, distinguishes the vertical NRs alone. We are able to resolve NR morphologies in both vertical and oblique cases with additional Raman mapping analysis of SO(E₁) phonon mode, emerging from the surface irregularities, which are confined to the tip of MBE grown NRs. Copyright © 2013 John Wiley & Sons, Ltd.     

Raman spectroscopic analysis and fast identification of several saturated monohydroxy alcohols

The Raman spectra of nine monohydroxy alcohols have been obtained by confocal Raman spectrometer at room temperature. Based on the Raman spectra, the density functional theory was used to analyze the characteristic Raman bands of monohydric alcohols. Through the discussion of the characteristic Raman bands and their corresponding assignment, four major Raman bands were selected to identify nine monohydric alcohols using principal component analysis and Euclidean distance. Finally, nine saturated monohydroxy alcohols can be distinguished exactly, and the recognition rate is 100%.     

基于拉曼光谱和多元统计学研究蓝色中性笔油墨

拉曼光谱作为一种无损检验的技术手段,近年来广泛用于文件检验领域中的书写材料种类检验。本实验采集了市面上收集到的28支蓝色中性笔油墨的拉曼光谱图,结合化学计量学中的相似度分析法和主成分分析法对谱图进行了分析,两种分析方法得到的分类结果基本一致,采用两种方法可以将28支蓝色中性笔油墨分为五类。实验结果表明,将拉曼光谱法得到的数据结合化学计量学分析方法进行数据分析,比直接比较谱图得到的分类结果更加客观、准确。     

New Approaches to Generalized Two‐Dimensional Correlation Spectroscopy and Its Applications

Abstract

This review is focused on the recent approaches to generalized 2D correlation spectroscopy, a technique widely used for the analysis of spectral data. A brief introduction of generalized 2D correlation spectroscopy is described first. Then the powerful combination of generalized 2D correlation spectroscopy and multivariate chemometircs techniques, such as the data reconstruction by principal component analysis (PCA), eigenvalue manipulation transformation (EMT), and self‐modeling curve resolution (SMCR) analysis are explored. Examples of successful applications of new approaches to generalized 2D correlation spectroscopy are highlighted.     

A Raman spectroscopic study of teeth affected with molar–incisor hypomineralisation

Raman spectroscopy was used to chemically map lesions associated with molar–incisor hypomineralisation in human teeth. Three teeth with hypomineralised lesions of differing severity, described as white, yellow or brown, were mapped using integral ratios of major component bands (hydroxyapatite, amide I and b‐type carbonate) and principal component analysis scores values. These lesions were found to contain depleted levels of mineral (hydroxyapatite) compared with those of healthy enamel. Principal component analysis also highlighted changes in the phosphate structure and variations in various organic constituents. These variations were consistent with increased disorder in the mineral component of the hypomineralised tooth lesions. Scanning electron microscopy–energy dispersive X‐ray spectroscopy supported the findings based on Raman spectroscopy. Copyright © 2015 John Wiley & Sons, Ltd.     

Nano‐imaging through tip‐enhanced Raman spectroscopy: Stepping beyond the classical limits

The spatial resolution in optical imaging is restricted by so‐called diffraction limit, which prevents it to be better than about half of the wavelength of the probing light. Tip‐enhanced Raman spectroscopy (TERS), which is based on the SPP‐induced plasmonic enhancement and confinement of light near a metallic nanostructure, can however, overcome this barrier and produce optical images far beyond the diffraction limit. Here in this article, the basic phenomenon involved in TERS is reviewed, and the high spatial resolution achieved in optical imaging through this technique is discussed. Further, it is shown that when TERS is combined with some other physical phenomena, the spatial resolution can be dramatically improved. Particularly, by including tip‐applied extremely localized pressure in TERS process, it has been demonstrated that a spatial resolution as high as 4 nm could be achieved.     

Near infrared Raman spectroscopic characterization of blood plasma of normal,oral premalignant and malignant conditions—a pilot study

The metabolic end products from cells/tissues that are released into the circulating blood stream and any changes in their level because of pathological conditions may be used as markers in disease diagnosis. Raman spectroscopy has been exploited to characterize the biomolecules present in the blood plasma of clinically confirmed normal group, premalignant (Oral Sub Mucous Fibrosis) and malignant (Oral Squamous Cell Carcinoma) at 784.15 nm. Raman spectral signatures show relatively less intense Raman bands of phenylalanine, lipid and antioxidant beta carotene but higher intense bands for proteins, DNA base components and amino acids (tyrosine and tryptophan) for malignant group than that of normal group. However premalignant group possess high intense Raman bands for amino acids (tyrosine and tryptophan) at 830, 1020 and 1620 cm⁻¹ and protein peaks at 913, 978 and 1646 cm⁻¹ when compared to that of malignant and normal group. Principal component analysis coupled with linear discriminant analysis (PCA‐LDA) yielded a diagnostic sensitivity of 96.3% and 91.2%, and a specificity of 80.0% and 96.7% in the classification of normal from premalignant and normal from malignant, respectively. This indicates that Raman spectroscopy of blood plasma has the potential in classifying normal and oral malignancy conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Bacterial mixture identification using Raman and surface‐enhanced Raman chemical imaging

The ability of normal Raman and surface‐enhanced Raman scattering (SERS) to identify and detect bacteria has shown great success in recent studies. The addition of silver nanoparticles to bacterial samples not only results in an enhanced Raman signal, but it also suppresses the native fluorescence associated with biological material. In this report, Raman chemical imaging (RCI) was used to analyze individual bacteria and complex mixtures of spores and vegetative cells. RCI uses every pixel or a binned pixel group (BPG) of the Raman camera as an independent Raman spectrograph, allowing collection of spatially resolved Raman spectra. The advantage of this technique resides primarily in the analysis of samples in complex backgrounds without the need for physically isolating or purifying the sample. Using a chemical imaging Raman microscope, we compare normal RCI to SERS‐assisted chemical imaging of mixtures of bacteria. In both cases, we are able to differentiate single bacterium in the Raman microscope's field of view, with a 60‐fold reduction in image acquisition time and a factor of 10 increase in the signal‐to‐noise ratio for SERS chemical imaging over normal RCI. Copyright © 2010 John Wiley & Sons, Ltd.     

Optical sectioning using single‐plane‐illumination Raman imaging

Confocal Raman imaging is widely used for optical sectioning of materials. However, for biological applications it often suffers from poor axial resolution, photodamage to the sample of interest, substrate interference, and long acquisition times. We have applied the principles of light sheet microscopy to Raman imaging and show for the first time that optically sectioned Raman images can be obtained in significantly lower acquisition times. Copyright © 2010 John Wiley & Sons, Ltd.     

Fourier‐transform Raman analysis of milk powder: a potential method for rapid quality screening

In this work, FT‐Raman spectroscopy was explored as a fast and reliable screening method for the assessment of milk powder quality and the identification of samples adulterated with whey (1–40% w/w). Raman measurements can easily differentiate milk powders without the need of sample preparation, whereas the traditional methods of quality control, including high‐performance liquid chromatography, are laborious and slow. The FT‐Raman spectra of whole, low‐fat, and skimmed milk powder samples were obtained and distinguished from commercial milk powder samples. In addition, the exploratory analysis employing data from Raman spectroscopy and principal component analysis (PCA)allowed the separation of milk powder samples according to type,identifying differences between samples in the same group. Multivariate analysis was also developed to classify the adulterated milk powder samples using PCA and partial least squares discriminate analysis (PLS‐DA). The resulting PLS‐DA model correctly classified 100% of the adulterated samples. These results clearly demonstrate the utility of FT‐Raman spectroscopy combined with chemometrics as a rapid method for screening milk powder. Copyright © 2011 John Wiley & Sons, Ltd.     

A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy

Raman spectroscopy exploits the Raman scattering effect to analyze chemical compounds with the use of laser light. Raman spectra are most commonly analyzed using the ordinary least squares (LS) method. However, LS is known to be sensitive to variability in the spectra of the analyte and background materials. In a previous paper, we addressed this problem by proposing a novel algorithm that models expected variations in the analyte as well as background signals. The method was called the hybrid LS and principal component analysis (HLP) algorithm and used an unweighted Gaussian distribution to model the noise in the measured spectra. In this paper, we show that the noise in fact follows a Poisson distribution and improve the noise model of our hybrid algorithm accordingly. We also approximate the Poisson noise model by a weighted Gaussian noise model, which enables the use of a more efficient solver algorithm. To reflect the generalization of the noise model, we from hereon call the method the hybrid reference spectrum and principal components analysis (HRP) algorithm. We compare the performance of LS and HRP with the unweighted Gaussian (HRP‐G), Poisson (HRP‐P), and weighted Gaussian (HRP‐WG) noise models. Our experiments use both simulated data and experimental data acquired from a serial dilution of Raman‐enhanced gold‐silica nanoparticles placed on an excised pig colon. When the only signal variability was zero‐mean random noise (as examined using simulated data), HRP‐P consistently outperformed HRP‐G and HRP‐WG, with the latter coming in as a close second. Note that in this scenario, LS and HRP‐G were equivalent. In the presence of random noise as well as variations in the mean component spectra, the three HRP algorithms significantly outperformed LS, but performed similarly among themselves. This indicates that, in the presence of significant variations in the mean component spectra, modeling such variations is more important than optimizing the noise model. It also suggests that for real data, HRP‐WG provides a desirable trade‐off between noise model accuracy and computational speed. Copyright © 2013 John Wiley & Sons, Ltd.     

Raman spectroscopy of fibroblast cells from a Huntington's disease patient

ABSTRACT

We present a Raman spectroscopy case study of living fibroblast (skin) cells from a patient who developed Huntington’s disease, with fibroblasts from a healthy volunteer as a control. Spectra were processed to remove cosmic rays, had a spectrum of the quartz substrate subtracted, and were flattened to remove cellular autofluorescence. We achieved an accuracy of 95% in discriminating individual cells, and assign spectral differences to (i) the reduction of cholesterol, (ii) the reduction of lipids, and (iii) an increase in beta-sheet proteins for fibroblasts with Huntington’s disease. All these biochemical changes have been previously measured by other methods. Averages over all the cells in this study yield a difference which is extremely statistically significant [p?相似文献   

Raman imaging and stress quantification in self‐assembled graphene oxide fiber ‘Latin Letters’

To probe the intrinsic stress distribution in terms of spatial Raman shift (ω) and change in the phonon linewidth (Γ), here we analyze self‐assembled graphene oxide fibers (GOF) ‘Latin letters’ by confocal Raman spectroscopy. The self‐assembly of GOF ‘Latin letters’ has been explained through surface tension, π–π stacking, van der Waals interaction at the air–water interface and by systematic time‐dependent investigation using field emission scanning electron microscopy analysis. Intrinsic residual stress due to structural joints and bending is playing a distinct role affecting the E_2g mode (G band) at and away from the physical interface of GOF segments with broadening of phonon linewidth, indicating prominent phonon softening. Linescan across an interface of the GOF ‘letters’ reveals Raman shift to lower wavenumber in all cases but more so in ‘Z’ fiber exhibiting a broader region. Furthermore, intrinsic stress homogeneity is observed for ‘G’ fiber distributed throughout its curvature with negligible shift corresponding to E_2g mode vibration. This article demonstrates the significance of morphology in stress distribution across the self‐assembled and ‘smart‐integrable’ GOF ‘Latin letters’. Copyright © 2016 John Wiley & Sons, Ltd.     

Improving chemometric results by optimizing the dimension reduction for Raman spectral data sets

In this contribution a new method for improving the accuracy of classification and identification experiments is presented. For this purpose the four most applied dimension reduction methods (principal component analysis, independent component analysis, partial least square dimension reduction and the linear discriminant analysis) are used as starting point for the optimization method. The optimization is done by a specially designed genetic algorithm, which is best suited for this kind of experiments. The presented multi‐level chemometric approach has been tested for a Raman dataset containing over 2200 Raman spectra of eight classes of bacteria species (Bacillus anthracis, Bacillus cereus, Bacillus licheniformis, Bacillus mycoides, Bacillus subtilis, Bacillus thuringiensis, Bacillus weihenstephanensis and Paenibacillus polymyxa). The optimization of the dimension reduction improved the accuracy for classification by 6% compared with the accuracy, if the standard dimension reduction is applied. The identification rate is improved by 14% compared with the dimension reduction. The testing in a classification and identification experiment showed the robustness of the algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

基于主成分分析结合拉曼光谱快速识别血液归属的研究

以3种不同的动物(3×10)血样以及21个人血液作为分析对象,采用主成分分析(PCA)结合拉曼光谱进行血液定性识别检测,通过矢量归一化对拉曼信号进行预处理,以及杠杆值与残差值得分图剔除异样点,使得人与动物血样的识别率均高于95%,并在此基础上进一步采用PCA进行动物血样之间的识别,使得个体的识别率高于90%。实验结果表明PCA在血液识别检测中具有较好的应用前景和可行性,该方法也可以为刑侦、生命科学等应用领域提高借鉴。     

Comparison of connective tissue invaded by Lewis lung carcinoma to healthy connective tissue by means of micro‐Raman spectroscopy

We have measured the micro‐Raman spectra of mouse tissues invaded by Lewis lung carcinoma (LLC). We have also carried out categorical principal component analysis (CATPCA) on the acquired spectra. The results indicate that the tumor tissues can be well discriminated from normal tissues by the first two principal components extracted from the spectra. Furthermore, we have found that the concentrations of nucleic acids and lipids/fatty acids in the tumor are considerably higher than those in the normal tissue, whereas the collagen concentration is lower. These differences can be detected and characterized by Raman images using the 788 cm⁻¹ DNA/RNA band and the 1301 cm⁻¹ lipid/fatty acid band. Copyright © 2009 John Wiley & Sons, Ltd.