Methodology for fiber-optic Raman mapping and FTIR imaging of metastases in mouse brains

The objectives of this study were to optimize the preparation of pristine brain tissue to obtain reference information, to optimize the conditions for introducing a fiber-optic probe to acquire Raman maps, and to transfer previous results obtained from human brain tumors to an animal model. Brain metastases of malignant melanomas were induced by injecting tumor cells into the carotid artery of mice. The procedure mimicked hematogenous tumor spread in one brain hemisphere while the other hemisphere remained tumor free. Three series of sections were prepared consecutively from whole mouse brains: dried, thin sections for FTIR imaging, hematoxylin and eosin-stained thin sections for histopathological assessment, and pristine, 2-mm thick sections for Raman mapping. FTIR images were recorded using a spectrometer with a multi-channel detector. Raman maps were collected serially using a spectrometer coupled to a fiber-optic probe. The FTIR images and the Raman maps were segmented by cluster analysis. The color-coded cluster memberships coincided well with the morphology of mouse brains in stained tissue sections. More details in less time were resolved in FTIR images with a nominal resolution of 25 microm than in Raman maps collected with a laser focus 60 microm in diameter. The spectral contributions of melanin in tumor cells were resonance enhanced in Raman spectra on excitation at 785 nm which enabled their sensitive detection in Raman maps. Possible reasons why metastatic cells of malignant melanomas were not identified in FTIR images are discussed.     

Chemistry at level of individual aerosol particle using multivariate curve resolution of confocal Raman image

Airborne particles with aerodynamic diameter in the 10-1 microm range have been collected in an industrial/urban zone by impaction and have been investigated by automated confocal Raman microspectrometry. The computer-microcontrolled XY scanning and Z focusing of Raman images provided many pixel Raman spectra which are characteristics of complex mixture at level of individual particle. The large heterogeneity was not resolved by the spatial resolution of the instrument which is limited by the optical diffraction. The severe spectral overlaps generated by heterogeneity were resolved by multivariate curve resolution (MCR) methods. The purity based method (SIMPLISMAX) was used to resolve both luminescence spectra and pure Raman spectra without prior information. The MCR-alternating least square (ALS) was used as a refined method of both spectra and spectral concentrations. The reconstructing Raman images of the respective spectral contribution supply a versatile potential to characterize the chemistry of atmospheric aerosols at the level of the individual particles.     

Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy

The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular emphasis on the advantageous interaction between multivariate data analysis and MIR spectroscopy.     

Mathematical topographical correction of XPS images using multivariate statistical methods

For rough heterogeneous samples, the contrast observed in XPS images may result from both changes in elemental or chemical composition and sample topography. Background image acquisition and subtraction are frequently utilized to minimize topographical effects so that images represent concentration variations in the sample. This procedure may significantly increase the data acquisition time. Multivariate statistical methods can assist in resolving topographical and chemical information from multispectral XPS images. Principal component analysis (PCA) is one method for identification of the highest correlation/variation between the images. Topography, which is common to all of the images, will be resolved in the first most significant component. The score of this component contains spatial information about the topography of the surface, whereas the loading is a quantitative representation of the topography contribution to each elemental/chemical image. The simple‐to‐use self‐modelling mixture analysis (Simplisma) method is a pure variable method that searches for the source of most differences in the data and therefore has the potential to distinguish between chemical and topographical phases in images. The mathematical background correction scheme is developed and validated by comparing results to the experimental background correction for samples with differing degrees of topography. Copyright © 2004 John Wiley & Sons, Ltd.     

Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images

Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time‐of‐flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd.     

Evaluating the Conservation State of Naturally Aged Paper with Raman and Luminescence Spectral Mapping: Toward a Non-Destructive Diagnostic Protocol

Micro-Raman and luminescence spectroscopy were combined with morphological analysis to study the conservation state of differently degraded paper samples, dated from 1873 to 2021. The aim of the work reported in this paper was to obtain ageing markers based on variations of Raman and fluorescence spectral features. Raman and luminescence spectra were acquired by scanning non-printed areas of books, and Raman and fluorescence maps were built by contrasting spectral parameters point by point, obtaining a micron-scale space resolved imaging of the degradation pattern. Complementary information on paper morphology and surface compactness were obtained by high-resolution scanning electron and atomic force microscopy. The proposed non-destructive procedure is particularly interesting for precious and ancient samples to analyze their degradation processes and to evaluate the performance and effectiveness of restoration treatments.     

Au@4-硝基苯硫酚@Ag@牛血清白蛋白内标化表面增强拉曼散射探针的制备及其在细胞拉曼成像中的应用

制备了Au@4-硝基苯硫酚@Ag内标化表面增强拉曼散射（SERS）探针,进一步以牛血清白蛋白（BSA）置换探针表面的稳定剂十六烷基三甲基溴化铵（CTAB）,发展了Au@NT@Ag@BSA内标化SERS探针。Au@NT@Ag@BSA探针保留了原探针的单分散性和高灵敏度,同时显著提高了信号稳定性和生物相容性。进一步将Au@NT@Ag@BSA探针和SMMC7721肺癌细胞共孵育,实现了细胞的探针标记和拉曼光谱成像。Au@NT@Ag@BSA内标化SERS探针在活体生物成像等方面展示了良好的应用潜力。     

Imaging the cell wall of living single yeast cells using surface-enhanced Raman spectroscopy

The surface of a living yeast cell (Saccharomyces cerevisiae strain W303-1A) has been labeled with silver (Ag) nanoparticles that can form nanoaggregates which have been shown to have surface-enhanced Raman scattering (SERS) activity. The cell wall of a single living yeast cell has been imaged by use of a Raman microspectroscope. The SERS spectra measured from different Ag nanoaggregates were found to be different. This can be explained on the basis of detailed spectral interpretation. The SERS spectral response originates from mannoproteins which cover the outermost regions of the yeast cell wall. Analysis of SERS spectra from the cell wall and the extracted mannoproteins from the yeast has been performed for the clarification of variation in SERS spectra.     

FTIR and Raman microspectroscopy of normal,benign, and malignant formalin-fixed ovarian tissues

Ovarian cancer is the sixth most common cancer among women worldwide, and mortality rates from this cancer are higher than for other gynecological cancers. This is attributed to a lack of reliable screening methods and the inadequacy of treatment modalities for the advanced stages of the disease. FTIR and Raman spectroscopic studies of formalin-fixed normal, benign, and malignant ovarian tissues have been undertaken in order to investigate and attempt to understand the underlying biochemical changes associated with the disease, and to explore the feasibility of discriminating between these different tissue types. Raman spectra of normal tissues indicate the dominance of proteins and lower contents of DNA and lipids compared to malignant tissues. Among the pathological tissues studied, spectra from benign tissues seem to contain more proteins and less DNA and lipids compared to malignant tissue spectra. FTIR studies corroborate these findings. FTIR and Raman spectra of both normal and benign tissues showed more similarities than those of malignant tissues. Cluster analysis of first-derivative Raman spectra in the 700–1700 cm⁻¹ range gave two clear groups, one corresponding to malignant and the other to normal+benign tissues. At a lower heterogeneity level, the normal+benign cluster gave three nonoverlapping subclusters, one corresponding to normal and two for benign tissues. Cluster analysis of second-derivative FTIR spectra in the combined spectral regions of 1540–1680 and 1720–1780 cm⁻¹ resulted into two clear clusters corresponding to malignant and normal+benign tissues. The cluster corresponding to normal+benign tissues produced nonoverlapping subclusters for normal and benign tissues at a lower heterogeneity level. The findings of this study demonstrate the feasibility of Raman and FTIR microspectroscopic discrimination of formalin-fixed normal, benign, and malignant ovarian tissues.     

Quantification of rubber in high impact polystyrene by Raman spectroscopy. Comparison of a band fitting method and chemometrics

An analytical method for the detection and the quantification of polybutadiene (rubber) in high impact polystyrene is described. The percentage of this product in high impact polystyrene pellets influences many of its properties, more the concentration is great, and more the mechanical resistance is high. The analytical method currently used to estimate the proportion of polybutadiene in high impact polystyrene pellets is nuclear magnetic resonance. Nevertheless, the application of Raman spectroscopy to polymers becomes more popular because of its sensitivity to the CC vinyl bond. This technique offers various advantages such as the absence of sample preparation. Raman spectroscopy is furthermore non destructive, and is a reproducible method. Information about conformations of polybutadiene can be easily accessible. Two different quantitative procedures were compared to evaluate which one is the best to estimate the percentage of polybutadiene in high impact polystyrene pellets. The first conventional method was based on the bands fitting of Raman spectra, and yielded a good prediction with a R² regression coefficient equal to 0.96. The second was based on chemometric techniques with a 0.98 − R². Consequently, others evaluation factors of the procedures such as the root-mean-square error, the bias, or the number of principal components must have some influence to select the most appropriate quantitative model.     

表面增强拉曼光谱在毒品检测中的应用进展

表面增强拉曼光谱(surfaced-enhanced Raman spectroscopy, SERS)作为一种借助贵金属纳米材料可以增强目标分子信号的拉曼光谱技术,由于其具有指纹识别、高灵敏、高准确度、快速无损、不受水分子干扰等特点,在法庭科学领域中的痕量毒品检测方面逐渐受到人们的关注.SERS不仅用于毒品纯品的检测...     

Multivariate image analysis of a set of FTIR microspectroscopy images of aged bovine muscle tissue combining image and design information

In this paper we present an algorithm for analysing sets of FTIR microscopic images of tissue sections. The proposed approach allows one to investigate sets of many FTIR tissue images both with respect to sample information (variation from image to image) and spatial variations of tissues (variation within the image). The algorithm is applied to FTIR microscopy images of beef loin muscles containing myofibre and connective tissue regions. The FTIR microscopy images are taken of sub-samples from five different beef loin muscles that were aged for four different lengths of time. The images were investigated regarding variation due to the ageing length and due to the homogeneity of the connective tissue regions. The presented algorithm consists of the following main elements: (1) pre-processing of the spectra to overcome large quality differences in FTIR spectra and differences due to scatter effects, (2) identification of connective tissue regions in every image, (3) labelling of every connective tissue spectrum with respect to its location in the connective tissue region, and (4) analysis of variations in the FTIR microscopic images in regard to ageing time and pixel position of the spectra in the connective tissue region. Important spectral parameters characterising collagen and proteoglycan structure were determined. Figure Effective optical path length estimated by EMSC     

Overview of Popular Techniques of Raman Spectroscopy and Their Potential in the Study of Plant Tissues

Raman spectroscopy is one of the main analytical techniques used in optical metrology. It is a vibration, marker-free technique that provides insight into the structure and composition of tissues and cells at the molecular level. Raman spectroscopy is an outstanding material identification technique. It provides spatial information of vibrations from complex biological samples which renders it a very accurate tool for the analysis of highly complex plant tissues. Raman spectra can be used as a fingerprint tool for a very wide range of compounds. Raman spectroscopy enables all the polymers that build the cell walls of plants to be tracked simultaneously; it facilitates the analysis of both the molecular composition and the molecular structure of cell walls. Due to its high sensitivity to even minute structural changes, this method is used for comparative tests. The introduction of new and improved Raman techniques by scientists as well as the constant technological development of the apparatus has resulted in an increased importance of Raman spectroscopy in the discovery and defining of tissues and the processes taking place in them.     

Analysis of erlotinib and its metabolites in rat tissue sections by MALDI quadrupole time-of-flight mass spectrometry

A qualitative and quantitative analysis of erlotinib (RO0508231) and its metabolites was carried out on rat tissue sections from liver, spleen and muscle. Following oral administration at a dose of 5 mg/kg, samples were analyzed by matrix-assisted laser desorption ionization (MALDI) with mass spectrometry (MS) using an orthogonal quadrupole time-of-flight instrument. The parent compound was detected in all tissues analyzed. The metabolites following drug O-dealkylation could also be detected in liver sections. Sinapinic acid (SA) matrix combined with the dried-droplet method resulted in better conditions for our analysis on tissues. Drug quantitation was investigated by the standard addition method and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis on the tissue extracts. The presence of the parent compound and of its O-demethylated metabolites was confirmed in all tissue types and their absolute amounts calculated. In liver the intact drug was found to be 3.76 ng/mg tissue, while in spleen and muscle 6- and 30-fold lower values, respectively, were estimated. These results were compared with drug quantitation obtained by whole-body autoradiography, which was found to be similar. The potential for direct quantitation on tissue sections in the presence of an internal standard was also investigated using MALDI-MS. The use of alpha-cyano-4-hydroxycinnamic acid (CHCA) as the matrix resulted in better linearity for the calibration curves obtained with reference solutions of the drug when compared to SA, but on tissue samples no reliable quantitative analysis was possible owing to the large variability in the signal response. MS imaging experiments using MALDI in MS/MS mode allowed visualizing the distribution of the parent compound in liver and spleen tissues. By calculating the ratio between the total ion intensities of MS images for liver and spleen sections, a value of 6 : 1 was found, which is in good agreement with the quantitative data obtained by LC-MS/MS analysis.     

自适应拉曼光谱成像数据去噪及其在植物细胞壁光谱分析中的应用

拉曼光谱成像数据存在基线漂移与宇宙射线干扰峰两类噪声信号,无法直接用于光谱分析研究,必须去除。现有单光谱去噪方法处理结果不稳定、可重复性差。针对这一问题,本研究提出了一种自适应拉曼光谱成像数据新型去噪法,采用优化的自适应迭代惩罚最小二乘法( Adaptive iteratively reweighted penalized least-squares,airPLS)和基于主成分分析( PCA)的干扰峰消除算法修正光谱基线漂移和宇宙射线干扰峰,具有输入参数少、光谱失真小、处理速度快、去噪结果稳定等优点。利用本方法去除了芒草( Miscanthus sinensis)细胞壁拉曼光谱成像数据(9010条光谱)中的噪声信号,并对去噪后数据进行PCA和聚类分析(CA),成功区分非植物光谱与植物光谱,分类结果优于未去噪数据。预期本方法可应用于其它光谱成像数据去噪,为光谱的解译和定量分析提供可靠的研究基础。     

Enzyme kinetics assay in ionic liquid-based reaction media by means of Raman spectroscopy and multivariate curve resolution

Enzymatic hydrolysis of p-nitrophenylphosphate by alkaline phosphatase in binary mixtures of water and 1-ethyl-3-methylimidazolium tetrafluoroborate (emimBF₄) was monitored with Raman microspectroscopy. Concentrations of emimBF₄ in the studied ionic liquid/water solvent systems ranged from 0 to 75% v/v. Multivariate curve resolution-alternating least squares (MCR-ALS) was successfully applied to the recorded Raman spectra in order to retrieve the concentration profiles and pure Raman spectra of the different species involved in the reaction. Michaelis-Menten constant (K_M) and maximum rate (V_max) of the reaction were calculated from the initial reaction phase for the different solvent systems studied, to investigate the effect of increasing concentration of the ionic liquid on the kinetic behavior. From this study, it was found that the ionic liquid inhibits the reaction under study decreasing both V_max and K_M.     

Detection of malathion in food peels by surface-enhanced Raman imaging spectroscopy and multivariate curve resolution

An analytical methodology was developed for detection of malathion in the peels of tomatoes and Damson plums by surface-enhanced Raman imaging spectroscopy and multivariate curve resolution. To recover the pure spectra and the distribution mapping of the analyzed surfaces, non-negative matrix factorization (NMF), multivariate curve calibration methods with alternating least squares (MCR-ALS) and MCR with weighted alternating least square (MCR-WALS) were utilized. Error covariance matrices were estimated to evaluate the structure of the error over all the data. For the tomato data, NMF-ALS and MCR-ALS presented excellent spectral recovery even in the absence of initial knowledge of the pesticide spectrum. For the Damson plum data, owing to heteroscedastic noise, MCR-WALS produced better results. This methodology enabled detection below to the maximum residue limit permitted for this pesticide. This approach can be implemented for in situ monitoring because it is fast and does not require extensive manipulation of samples, making its use feasible for other fruits and pesticides as well.     

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm⁻².     

Confocal Raman Spectroscopic Imaging for Evaluation of Distribution of Nano-Formulated Hydrophobic Active Cosmetic Ingredients in Hydrophilic Films

Film-forming systems are highly relevant to the topical administration of active ingredients (AI) to the body. Enhanced contact with the skin can increase the efficacy of delivery and penetration during prolonged exposure. However, after the evaporation of volatile solvents to form a thin film, the distribution of the ingredient should remain homogenous in order to ensure the effectiveness of the formula. This is especially critical for the use of hydrophobic molecules that have poor solubility in hydrophilic films. In order to address this concern, hydroxyphenethyl esters (PHE) of Punica granatum seed oil were prepared as a nanosuspension stabilised by poloxamers (NanoPHE). NanoPHE was then added to a formulation containing polyvinyl alcohol (PVA) as a film forming agent, Glycerol as a plasticiser and an antimicrobial agent, Sepicide^TM HB. Despite their reliability, reference methods such as high-performance liquid chromatography are increasingly challenged due to the need for consumables and solvents, which is contrary to current concerns about green industry in the cosmetics field. Moreover, such methods fail to provide spatially resolved chemical information. In order to investigate the distribution of ingredients in the dried film, Confocal Raman imaging (CRI) coupled to Non-negatively Constrained Least Squares (NCLS) analysis was used. The reconstructed heat maps from a range of films containing systematically varying PHE concentrations highlighted the changes in spectral contribution from each of the ingredients. First, using NCLS scores it was demonstrated that the distributions of PVA, Glycerol, Sepicide^TM HB and PHE were homogenous, with respective relative standard deviations (RSD) of 3.33%, 2.48%, 2.72% and 6.27%. Second, the respective relationships between ingredient concentrations in the films and their Raman responses, and the spectral abundance were established. Finally, a model for absolute quantification for PHE was be constructed using the percentage of spectral abundance. The prepared %w/w concentrations regressed against predicted %w/w concentrations, displaying high correlation (R² = 0.995), while the Root Mean Squared Error (0.0869% w/w PHE) confirmed the precision of the analysis. The mean percent relative error of 3.75% indicates the accuracy to which the concentration in dried films could be determined, further supporting the suitability of CRI for analysis of composite solid film matrix. Ultimately, it was demonstrated that nanoformulation of hydrophobic PHE provides homogenous distribution in PVA based film-forming systems independent of the concentration of NanoPHE used in the formula.     

Raman Method in Identification of Species and Varieties,Assessment of Plant Maturity and Crop Quality—A Review

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.