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Effect of signal intensity normalization on the multivariate analysis of spectral data in complex ‘real‐world’ datasets
Abstract
Spectral signal intensities, especially in ‘real-world’ applications with nonstandardized sample presentation due to uncontrolled variables/factors, commonly require additional spectral processing to normalize signal intensity in an effective way. In this study, we have demonstrated the complexity of choosing a normalization routine in the presence of multiple spectrally distinct constituents by probing a dataset of Raman spectra. Variation in absolute signal intensity (90.1% of total variance) of the Raman spectra of these complex biological samples swamps the variation in useful signals (9.4% of total variance), degrading its diagnostic and evaluative potential.
Using traditional spectral band choices, it is shown that normalization results are more complex than generally encountered in traditionally designed sample sets investigating limited chemical species. We demonstrate that no choice of a single band proves to be appropriate for predicting all the reference parameters, instead requiring a tailored normalization routine for each parameter.
Of the reference parameters studied in the chosen system, signals from pathogenic adducts in ocular tissues called advanced glycation endproducts were most prominent when normalizing about the 1550–1690 cm−1 region of the spectrum (17.5% of total variance, compared with 0.3% for unnormalized), while prediction of pentosidine and gender were optimized by normalization about the 1570 (R2 = 0.97 vs 0.57 for unnormalized) and 1003 cm−1 (p < 0.0000001 vs p < 0.01 for unnormalized) bands, respectively. The data obtained point to the extreme sensitivity of multivariate analysis to signal intensity normalization. Some general guidelines for making appropriate band choices are given, including the use of peak-finding routines. Copyright
... GC data were imported into Matlab and placed into a data matrix alongside the processed Raman data. Normalisation factors were calculated and separately applied using the mean intensity of the carbonyl stretching mode (approximately 1740 cm À1 ), the CH x scissoring mode (approximately 1440 cm À1 ) and linear combinations of the scores from the first three PCs (Beattie, Glenn, Boulton, Stitt, & McGarvey, 2009). The PC-based normalisation results are presented as they gave the most accurate baseline corrections on Raman spectra in this study. ...
... u. v.), multiplicative scatter correction (MSC), standard normal variate (SNV) transformation and Savitzky-Golay (1st derivative with nine points; 2nd derivative with 17 points). Raman data were assessed untreated and pre-treated using a combination of SVD-based baseline correction (Beattie, 2011), normalisation to a peak intensity suitable for use as an internal standard (mean intensity of CH 2 scissor band at 1440 cm À1 ; (Beattie, Bell, Borgaard, Fearon, & Moss, 2004a) and normalisation to the cumulative sums of scores over the first three PCs (Beattie et al., 2009). Techniques such as MSC and SNV are not relevant for Raman data and so were not employed (Afseth, Segtnan, & Wold, 2006;Beattie & McGarvey, 2013). ...
... In 'real-world' applications, it is typical to encounter complex mixtures of different signals that do not share a common feature suitable for standardising the intensity of the signal [61]. Beattie et al. [62] showed that selection of different signal features for intensity normalisation had a very profound effect on the variation within a 'real-world' dataset and the results obtained from the analyses of this data, as illustrated in Figure 10 where the 1 st PC loading after different normalisation routines are compared and exhibit wide variation in bandshape. Choosing one channel (or the average of a few adjacent channels) from the signal for calculating the intensity of the signal leads to the risk of very complex scaling unless the feature exists in a channel that exhibits zero contribution from any other constituent. ...
... The application of the PLS model to calculate the scaling factors resulted in a halving of the prediction variance compared with using traditional band area [44]. This PLS based approach does not solve the issues raised by Beattie et al. [62] regarding the unpredictability of normalisation by band area, but has the advantage of allowing established normalisation routines to benefit from the noise insensitivity, enhanced specificity etc that comes from using multivariate based processing. ...
Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength) that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since a signal contains multiple information points, they have rich information content but generally complex to apprehend. Multivariate analysis (MA) has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signal), consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals), accurate elimination of interfering signals (removal of reproducible but unwanted signals) and the standardisation of signal amplitude fluctuations. At present the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis), these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or where the variations in the signals can be complex. As science seeks to probe datasets in less and less tightly controlled situations the ability to provide high-fidelity corrections in a very flexible manner is becoming more critical and multivariate based signal processing has the potential to provide many solutions.
L'analyse multivariée, dont l'analyse en composantes principales (ACP), a transformé, dans des contextes concrets, l'étude de mesures complexes, formées de signaux chargés d'informations. Si la réduction de dimension permet de simplifier grossièrement un enchevêtrement de variations multidimensionnelles, elle est également plus robuste aux perturbations que les méthodes d'analyse univariées. Plus récemment, il est apparu que les propriétés des méthodes multivariées les rendaient propices à d'autres usages que statistiques, comme le traitement des signaux pour l'élimination des variations/fluctuations non pertinentes pour une analyse ultérieure. Il a été montré que l'exploitation spécifique de la réduction de dimension permet un débruitage précis (suppression de "non-signaux/perturbation" non reproductibles), la soustraction fiable et consistante de la ligne de base (suppression de "non-signaux/perturbation" reproductibles), l'éminination d'interférences (suppression de "signaux" reproductibles et inutiles), ainsi que la standardisation des fluctuations d'amplitude des signaux.
Si ce champ d'investigation est encore restreint, les possibilités de diffusion de ses applications sont considérables. En effet, ces améliorations, intrinsèquement liées aux signaux eux-mêmes, sont hautement reproductibles entre les répétitions, possèdent une grande capacité d'adaptation et d'application à des situations de bruit, ou de variations complexes dans les signaux. Alors que les disciplines scientifiques sondent des volumes de données toujours plus volumineux, dans des situations de moins en moins étroitement contrôlées, la capacité à apporter des corrections/améliorations précises/haute résolutions, de manière flexible, devient de plus en plus critique. Aussi les traitements de signaux multivariés offrent un éventail de solutions potentiellement très large.
... Tissue autofluorescence was subtracted via an automated 5th order modified polynomial fitting method using the full wavenumber range [24]. The processed spectra were binned to 5 cm −1 and normalized to four different normalizations schemes: the mean normalized intensity of the entire spectrum (600-1700 cm −1 ), the 1003 cm −1 peak (phenylalanine ring vibration) intensity, the 1443 cm −1 peak (CH 2 bend of proteins/lipids) intensity, and the 1658 cm −1 peak (Amide I stretch of proteins/lipid) intensity, all of which have been recommended based on previous comparative studies [25,26]. All normalization methods were carried through subsequent analysis. ...
... The choice of normalization scheme proved to be an important consideration, as demonstrated by previous comparative studies [25,26]. Following each scheme through discriminant analysis, normalization to the 1443 cm −1 peak provided maximum sensitivity and specificity (93% and 91%, respectively), followed closely by normalization to the 1658 cm −1 peak (92% and 90%) and mean (87% and 92%), while 1003 cm −1 peak normalization yielded a much poorer performance (84% and 75%). ...
Current diagnostics for lysosomal storage disorders such as mucopolysaccharidosis (MPS) rely on evaluation of ex vivo bodily fluids, which has several shortcomings. In this study, we evaluated whether Raman spectroscopy could noninvasively diagnose MPS in a murine model. Via confocal sampling of the murine outer ear, Raman spectra were obtained at multiple depths. Partial least-squares discriminant analysis of the processed Raman spectra showed a 93% sensitivity and 91% specificity for disease. The discriminant algorithm relied on several Raman bands related to glycosaminoglycans (GAGs) that typically accumulate in MPS. These findings indicate the possibility for a new, noninvasive diagnostic tool for MPS.
... 28 A principal component analysis (PCA)-based background removal 31-34 model derived from an archive of 1500 nails was applied to the data which was then normalised to the first PC score. 35,36 Spectra were acquired from 400 to 1800 cm À1 . ...
Bisphosphonate-based pharmacological therapy of osteoporosis reduces risk of fracture, but modulation of bone mineral density does not solely explain this. Additional bone quality aspects affecting fragility need to be better understood, alongside methods to monitor them. Systemic factors that influence bone colla-gen remodelling also remodel keratin in parallel processes. In this study, human fingernail clippings from subjects with and without bone active pharmaceutical intervention are compared. A discriminant model was able to distinguish between the nails from patients that were treatment-naive and treated with an area under the curve of 71% in the test set. A time series of changes relative to baseline revealed that after 1 year, the scores of the treated group (95 confidence interval 37% to 377% change) differed from both the 12 week measurements (À198% to 34%) and scores from untreated subjects (À92% to À674%). Analysis of the spectral differences and model coefficients revealed features that were inverse to those observed in three previous osteoporosis models, indicating that treatment was reverting damaged protein structure. This study provides preliminary evidence that bone active medication systemi-cally influences keratin structure in humans and provides some discussion on the underlying mechanism. The study demonstrates that bisphosphonates have a direct influence on protein structure, warranting further investigation of these effects. K E Y W O R D S bisphosphonate, bone, fracture risk, osteoporosis, Raman spectroscopy
... All regression models in the previous sections were obtained on baseline corrected Raman spectra as displayed in Fig. 2. Several studies show the importance of a normalization step in the pre-processing of Raman spectra, to account for, e.g., differences in sample presentation, focusing or sampling volumes, instrument drift (i.e., laser intensity fluctuations), and to aid for calibration transfer between instruments when conducting analysis outside of a laboratory setting over time. 42,43 In many cases, normalization will result in a simplification of the regression models (i.e., fewer PLS components used). 44 Table III compares regression results for baseline corrected spectra with baseline corrected and normalized (SNV) spectra. ...
Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by e.g. high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters fat and protein content were estimated using partial least squares regression (PLSR). Performance of PLSR models from RS was compared with near infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross validation (R2CV) from 0.73 to 0.96 and root mean square error of cross validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R2CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). Performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability for RS to analyse bulk composition in heterogeneous food matrices, and paves way for future applications of RS in routine food analyses.
... Once this region of the spectrum is selected, the first step in the preprocessing is to correct the baseline, which can be degraded due to instrument fluctuations or background signal influence (19,21). Then, the spectra are normalized to avoid the absolute intensity from masking the variation of signals of interest (22,23). It is also possible to align and/or smooth the Raman signal, but these steps can introduce noise to the measurements or remove relevant information and thus should be carefully considered. ...
Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction.
... The final pre-processing step, normalization, attempts to minimize the impact of variables in the data collection process that are independent and extraneous to the hypothesis driving the LRS investigation [32]. Common confounding variables include changes in sample temperature, ambient lighting, laser power drift, and CCD/CMOS chip temperature. ...
Laser Raman spectroscopy (LRS) is a highly specific biomolecular technique which has been shown to have the ability to distinguish malignant and normal breast tissue. This paper discusses significant advancements in the use of LRS in surgical breast cancer diagnosis, with an emphasis on statistical and machine learning strategies employed for precise, transparent and real-time analysis of Raman spectra. When combined with a variety of “machine learning” techniques LRS has been increasingly employed in oncogenic diagnostics. This paper proposes that the majority of these algorithms fail to provide the two most critical pieces of information required by the practicing surgeon: a probability that the classification of a tissue is correct, and, more importantly, the expected error in that probability. Stochastic backpropagation artificial neural networks inherently provide both pieces of information for each and every tissue site examined by LRS. If the networks are trained using both human experts and an unsupervised classification algorithm as gold standards, rapid progress can be made understanding what additional contextual data is needed to improve network classification performance. Our patients expect us to not simply have an opinion about their tumor, but to know how certain we are that we are correct. Stochastic networks can provide that information.
... (Liu et 252 al., 2015;Wahl et al., 2020). Then, the spectra are normalized to avoid that the absolute intensity 253 masks the variation of signals of interest(Beattie et al., 2009;Gautam et al., 2015). It is also possible 254 ...
Microbial cells experience physiological changes due to environmental change, such as pH and temperature, the release of bactericidal agents, or nutrient limitation. This, has been shown to affect community assembly and other processes such as stress tolerance, virulence or cell physiology. Metabolic stress is one such physiological changes and is typically quantified by measuring community phenotypic properties such as biomass growth, reactive oxygen species or cell permeability. However, community measurements do not take into account single-cell phenotypic diversity, important for a better understanding and management of microbial populations. Raman spectroscopy is a non-destructive alternative that provides detailed information on the biochemical make-up of each individual cell.
Here, we introduce a method for describing single-cell phenotypic diversity using the Hill diversity framework of Raman spectra. Using the biomolecular profile of individual cells, we obtained a metric to compare cellular states and used it to study stress-induced changes. First, in two Escherichia coli populations either treated with ethanol or non-treated. Then, in two Saccharomyces cerevisiae subpopulations with either high or low expression of a stress reporter. In both cases, we were able to quantify single-cell phenotypic diversity and to discriminate metabolically stressed cells using a clustering algorithm. We also described how the lipid, protein and nucleic acid composition changed after the exposure to the stressor using information from the Raman spectra. Our results show that Raman spectroscopy delivers the necessary resolution to quantify phenotypic diversity within individual cells and that this information can be used to study stress-driven metabolic diversity in microbial communities.
Importance
Microbes that live in the same community respond differently to stress. This phenomemon is known as phenotypic diversity. Describing this plethora of expressions can help to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behaviour of the community. In this work, we present a way to quantify the phenotypic diversity of single cells using Raman spectroscopy - a tool that can describe the molecular profile of microbes. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. We also show its potential as an ‘alarm’ system to detect when communities are changing into a ‘stressed’ type.
... This might depend on the objectives of the specific research project. 257 One option to affect normalization on a per cell basis is to normalize to DNA by using the 783 cm −1 composite nucleic acids peak, but this is complicated by changes in nucleic acid scattering due to the cell cycle, culture expansion phase, cell health and a required correction for RNA scattering contributions. 93,118,258 Effective normalization on a per cell basis would permit analysis and quantification of biomolecular changes due to normal or induced metabolic processes. ...
Therapies based on injecting living cells into patients offer a huge potential to cure many degenerative and deadly diseases, with hundreds of clinical trials ongoing. Due to their complex nature, a basic understanding of their biochemical and functional characteristics, how to manufacture them for safe and efficacious therapy, and how to effectively implement them in clinical settings are very challenging. Raman spectroscopy could provide an information-rich, non-invasive, non-destructive analytical method to complement the use of conventional sample-based, infrequent and destructive biochemical assays typically employed to analyze and validate the quality of therapeutic cells. This article provides an overview of the current state of emerging cell therapies, and then reviews the related Raman spectroscopic state of the art analysis of human cells. This includes spectroscopic data processing considerations, the scope offered by technical variants of Raman spectroscopy, and analytical difficulties encountered by spectroscopists working with therapeutic cells. Finally, we outline a number of salient challenges as cell therapy products are translated from the laboratory to the clinic, and propose how Raman spectroscopy-based solutions could address these challenges.
... [1][2][3]5,6 In the case of † data acquired from complex biological molecules for which no internal standard is used, the choice of normalization method remains an open question. 7 It usually relies on the operator's experience and habits. It can also be chosen according to the performances of classification models making pre-processing dependent from the final model and increasing the risk of false positive through increasing the hypotheses tested. ...
Raman spectroscopy is a candidate technique for diagnosis applications in medicine due to its high molecular specificity. Optimizing the pre-treatment applied for Raman data is important for exploiting Raman signal and ensuring its relevance in medical diagnosis. One of the crucial steps in data pre-processing, normalization, can affect significantly the results interpretation. To select the appropriate normalization method, a strategy based on validity indices (VI) is proposed in this study. VI are based on measuring the quality of data partitioning without involving a full sequence of supervised classification. The approach was tested on Raman data acquired from control and in vitro glycated proteins (albumin and collagen). Protein glycation is a process involved in the molecular ageing of tissues that leads to the formation of products altering the functional and structural properties of proteins. Different methods of normalization were applied on the data sets: integrated intensity of phenylalanine band, integrated intensity of amide I band, standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) that performs simultaneously baseline correction and normalization. Following normalization, principal component analysis (PCA) was applied and VI were calculated from PCA scores resulting from each of the normalization methods mentioned. Based on VI quantitative values, our experiments permit to illustrate the effect of normalization on the data separability of control and glycated samples, and to determine the most appropriate normalization and simultaneously the most discriminant principal components to exploit vibrational information associated with glycation-induced modifications. In parallel, principal component analysis – linear discriminant analysis (PCA-LDA) was carried out for positioning the interest of VI in regards to a common chain of data processing.
... Spectral processing was as previously described [10] with cosmic rays manually removed, baseline correction [24,25] and principle component analysis (PCA) based normalisation [26,27]. Data reduction of the normalised data was performed using PCA followed by a t-test to check of significant differences between groups. ...
Pharmacological therapy of osteoporosis reduces bone loss and risk of fracture in patients. Modulation of bone mineral density cannot explain all effects. Other aspects of bone quality affecting fragility and ways to monitor them need to be better understood. Keratinous tissue acts as surrogate marker for bone protein deterioration caused by oestrogen deficiency in rats. Ovariectomised rats were treated with alendronate (ALN), parathyroid hormone (PTH) or estrogen (E2). MicroCT assessed macro structural changes. Raman spectroscopy assessed biochemical changes. Micro CT confirmed that all treatments prevented ovariectomy-induced macro structural bone loss in rats. PTH induced macro structural changes unrelated to ovariectomy. Raman analysis revealed ALN and PTH partially protect against molecular level changes to bone collagen (80% protection) and mineral (50% protection) phases. E2 failed to prevent biochemical change. The treatments induced alterations unassociated with the ovariectomy; increased beta sheet with E2, globular alpha helices with PTH and fibrous alpha helices with both ALN and PTH. ALN is closest to maintaining physiological status of the animals, while PTH (comparable protective effect) induces side effects. E2 is unable to prevent molecular level changes associated with ovariectomy. Raman spectroscopy can act as predictive tool for monitoring pharmacological therapy of osteoporosis in rodents. Keratinous tissue is a useful surrogate marker for the protein related impact of these therapies.The results demonstrate utility of surrogates where a clear systemic causation connects the surrogate to the target tissue. It demonstrates the need to assess broader biomolecular impact of interventions to examine side effects.
... The data were then normalised to the first PC score, using MATLAB 2013a (Natick, MA, USA). 35,36 Spectra were acquired from 400 to 1800 cm −1 , and this full spectral range was used for the data processing and analysis (see below). ...
Studies have shown that Raman spectroscopic analysis of fingernail clippings can help differentiate between post-menopausal women who have and who have not suffered a fracture. However, all studies to date have been retrospective in nature, comparing the proteins in nails sourced from women, post-fracture. The objective of this study was to investigate the potential of a prospective test for hip fracture based on spectroscopic analysis of nail tissue. Archived toenail samples from post-menopausal women aged 50 to 63 years in the Nurses’ Health Study were obtained and analysed by Raman spectroscopy. Nails were matched case-controls sourced from 161 women; 82 who underwent a hip fracture up to 20 years after nail collection and 81 age-matched controls. A number of clinical risk factors (CRFs) from the Fracture Risk Assessment (FRAX) tool had been assessed at toenail collection. Using 80% of the spectra, models were developed for increasing time periods between nail collection and fracture. Scores were calculated from these models for the other 20% of the sample and the ability of the score to predict hip fracture was tested in model with and without the CRFs by comparing the odds ratios (ORs) per 1 SD increase in standardised predictive values. The Raman score successfully distinguished between hip fracture cases and controls. With only the score as a predictor, a statistically significant OR of 2.2 (95% confidence interval [CI]: 1.5-3.1) was found for hip fracture for up to 20 years after collection. The OR increased to 3.8 (2.6-5.4) when the CRFs were added to the model. For fractures limited to 13 years after collection, the OR was 6.3 (3.0-13.1) for the score alone. The test based on Raman spectroscopy has potential for identifying individuals who may suffer hip fractures several years in advance. Higher powered studies are required to evaluate the predictive capability of this test.
... Detrending (DT) of second-order and standard normal variate (SNV) preprocessing were also evaluated to correct collinearity and multiplicative interferences of scatter such as baseline shift from data set. [20][21][22][23] Spectral data were first visualized by PCA where scores and loading were plotted in a representation of lower dimension with the goal of identifying potential outlier samples and the best spectral region that would be suitable for building the calibration models. Calibration models for mechanical properties were then developed using reference from ASTM methods including Young's traction modulus (ASTM D-638), elongation at yield on traction (ASTM D-638), tensile strength at yield (ASTM D-638), 24 and flexural modulus (1% secant) (D790). ...
A nondestructive and faster methodology to quantify mechanical properties of polypropylene (PP) pellets, obtained from an industrial plant, was developed with Raman spectroscopy. Raman spectra data were obtained from several types of samples such as homopolymer PP, random ethylene–propylene copolymer, and impact ethylene–propylene copolymer. Multivariate calibration models were developed by relating the changes in the Raman spectra to mechanical properties determined by ASTM tests (Young’s traction modulus, tensile strength at yield, elongation at yield on traction, and flexural modulus at 1% secant). Several strategies were evaluated to build robust models including the use of preprocessing methods (baseline correction, vector normalization, de-trending, and standard normal variate), selecting the best subset of wavelengths to model property response and discarding irrelevant variables by applying genetic algorithm (GA). Linear multivariable models were investigated such as partial least square regression (PLS) and PLS with genetic algorithm (GA-PLS) while nonlinear models were implemented with artificial neural network (ANN) preceded by GA (GA-ANN). The best multivariate calibration models were obtained when a combination of genetic algorithms and artificial neural network were used on Raman spectral data with relative standard errors (%RSE) from 0.17 to 0.41 for training and 0.42 to 0.88% validation data sets.
... The Raman data collected from the nails were processed using singular value decomposition-based background removal, 25,26 normalized to the first principal component (PC) score, using Matlab 2013a. 27,28 Spectra were acquired from 400 to 1800 cm −1 , and this full spectral range was used for the data processing and analysis (see below). calibration and validation. ...
Raman spectroscopy was applied to nail clippings from 633 postmenopausal British and Irish women, from six clinical sites, of whom 42% had experienced a fragility fracture. The objective was to build a prediction algorithm for fracture using data from four sites (known as the calibration set) and test its performance using data from the other two sites (known as the validation set). Results from the validation set showed that a novel algorithm, combining spectroscopy data with clinical data, provided area under the curve (AUC) of 74% compared to an AUC of 60% from a reduced QFracture score (a clinically accepted risk calculator) and 61% from the dual-energy X-ray absorptiometry T-score, which is in current use for the diagnosis of osteoporosis.
Raman spectroscopy should be investigated further as a noninvasive tool for the early detection of enhanced risk of fragility fracture.
... - treated using a combination of SVD - based baseline correction ( Beattie , 2011 ) , normalisation to a peak intensity suitable for use as an internal standard ( mean intensity of CH 2 scissor band at 1440 cm À1 ; ( Beattie , Bell , Borgaard , Fearon , & Moss , 2004a ) and normalisation to the cumulative sums of scores over the first three PCs ( Beattie et al . , 2009 ) . Techniques such as MSC and SNV are not relevant for Raman data and so were not employed ( Afseth , Segtnan , & Wold , 2006 ; Beattie & McGarvey , 2013 ) . PLSR quantitative models for NT , IT and TT were developed separately for each sample type and for all samples using a number of spectral ranges decided by intensities of regressi ...
... Spectra obtained from Raman microscopy were normalized using Peakfit v4.12 (USA) software. The Gaussian amplitude in the second derivative method was chosen for fitting the peaks [11]. ...
Objectives
Our aim was to determine the origin of the red fluorescence of carious dentine observed with the Soprolife® camera.
Methods
We conducted in vitro studies to evaluate the origin of the red fluorescence using acids and matrix metalloproteinase (MMP) to mimic caries and methylglycoxal (MGO) to evaluate the effect of glycation reactions on the red fluorescence. In every step of these models, we detected the changes of dentin photonic response with Soprolife® in daylight mode and in treatment mode. A Raman spectroscopy analysis was performed to determine the variations of the dentin organic during the in vitro caries processes. Raman microscopy was performed to identify change in the collagen matrix of dentine.
Results
The red fluorescence observed in carious dentine using a Soprolife® camera corresponds to the brownish color observed using daylight. Demineralization using nitric acid induces a loss of the green fluorescence of dentine. The red fluorescence of carious dentine is resistant to acid treatment. Immersion of demineralized dentine in MGO induces a change of color from white to orange-red. This indicates that the Maillard reaction contributes to lesion coloration. Immersion of demineralized dentine in an MMP-1 solution followed by MGO treatment results in a similar red fluorescence. Raman microspectroscopy analysis reveals accumulation of AGE's product in red-colored dentine.
Conclusions
Our results provide important information on the origin of the fluorescence variation of dentine observed with the Soprolife® camera. We demonstrate that the red fluorescence of carious dentine is linked to the accumulation of Advanced Glycation End products (AGE).
Clinical relevance
The study provides a new biological basis for the red fluorescence of carious dentine and reinforces the importance of the Soprolife® camera in caries diagnostics.
... 9. The absolute Raman intensity is very difficult to measure reproducibly, being dependant on a very large number of factors (58, 62 ) and for this reason it is necessary to standardise the intensity prior to quantitative analysis. 10. ...
Every organ compromises of several different cell types. When studying the effects of a chosen compound within this organ or tissue uptake, localisation, metabolism, and the effect itself can be expected to differ between cells. Using the example of Vitamin E in pulmonary tissue we introduce confocal Raman Microscopy as a superior method to localise lipid-soluble compounds within tissues and cells. We describe the analyses of vitamin E, its oxidation products, and metabolites as well as pulmonary surfactant phospholipids in fixed lung tissue sections. Examples of main structural membrane lipids (PC, cholesterol) and an example of a lipid-signalling molecule (ceramide) are also included. Confocal Raman microscopy is a non-destructive optical method of analysing chemical and physical composition of solids, liquids, gases, gels, and solutions. The method is rich in information allowing discrimination of chemically similar molecules (including geometric isomers) and sensitive monitoring of subtle physical interactions. Additionally, Raman spectroscopy is relatively insensitive to water allowing the analysis of aqueous solutions and suspensions typical in biochemistry. In contrast, Raman spectroscopy is sensitive to non-polar molecules making it ideal for lipidomics research.
In aluminium‐killed calcium‐treated steels, the characterisation of multiphase oxides found in the CaO–MgO–Al2O3 system is essential for the study of inclusion formation, modification, and the control process. In this study, Raman spectroscopy is used as a characterisation technique for the multiphase mixtures of MgO·Al2O3 spinel and calcium aluminate phases of C12A7, C3A and CA. Multiphase samples of C12A7–CA–MA and C12A7–C3A–MA were synthesised from MgO·Al2O3 spinel and the calcium aluminates with varying phase content. The relative intensity of the Raman peaks was qualitatively used for the most primary phases present in the sample. Regression models were also identified for predicting the phase composition. Prediction model identification was carried out by using the successive projection algorithm (SPA) for feature selection and the partial least squares regression model (PLS) as a regressor. The identified prediction models showed a reasonably good prediction performance. This study demonstrates using the time‐gated Raman spectroscopy technique to characterise the multiphase mixture of magnesium aluminate spinel and calcium aluminate phases of CA, C12A7, C3A and the potential application for inclusion characterisation.
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Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the “quality” of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.
Patients diagnosed with osteoporosis have reported loss of fingernail resilience as the disease progresses. Keratin is the predominant protein in human nail tissue, and its structure has been postulated to be different in fingernails clipped from subjects who have sustained fragility fractures and those who have not, which may offer a window into the donor's bone health. This study was designed to qualify these differences, which may lead to the development of a novel screening tool for fracture risk. Raman spectroscopy was used to measure the fingernails of 633 postmenopausal women who presented at six fracture clinics located across the UK and Ireland. The Raman signals from donor's fingernails were compared between (1) fracture and nonfracture and (2) osteoporotic versus non-osteoporotic donors The data presented show differences in the protein changes observed for pervasive osteoporosis compared to a general increased risk of fragility fracture. For fracture risk, compositional changes falling into broad classes of amino acid residue (aliphatic, aromatic, acidic, amide and sulphurous) were observed, while a difference in disulphide bonding levels was reaffirmed. For pervasive osteoporosis, the disulphide mode suggested increasing disorder in disulphide bonding orientation. Fractures were associated with a transition from alpha helical secondary structure to random, while the pervasive osteoporosis cases were associated with a transition to beta sheet structure. General fracture risk is associated with a change in the structure and composition of the keratin protein. Osteoporosis is associated with different protein structural changes and an increase in free acid groups. Copyright
Calibration transfer of Raman spectra was investigated on the basis of instrument response function, which is affected by the resolution of the spectrometer. A simplified procedure is presented allowing the transfer of Raman spectra from higher to lower resolution instruments (or conditions). Needn't to measure a set of well-prepared samples to construct a statistical transfer model, the method employed in preent study, a convolution step involving a Gaussian transfer function, which theoretically describes the instrumental difference from source to target. And with the help of established methods for wavenumber calibration and relative intensity correction, an integrated algorithm for Raman spectra transfer is provided. Two spectral experiments of three analytes were performed for certification: (1) Transfer on a Fourier transform (FT) Raman spectrometer under various resolution conditions; (2) Transfer between two dispersive Raman spectrometers equipped with charge-coupled device (CCD). In almost all cases, the correlation coefficient between the measured spectra and transferred spectra could reach to 0.99. It suggested that this procedure may be applicable for all kinds of Raman spectra transfer and even works universally in other spectroscopy.
The Raman effect was predicted by Schmekal1 in 1923 and independently discovered in 1928 by two Indian physicists, Raman and Krishna.2,3 In principle, monochromatic light is inelastically scattered at a quantiffed structure like the vibrational states of a molecule. The occurring energy shifts are an indirect representation of the vibrational states of the molecule and, thus, are molecule specific. If this principle is spectroscopically used, an ensemble of molecules is measured and the result is called a Stokes-Raman spectrum, or shorter a Raman spectrum. The Stokes-Raman spectrum is the part of inelastically scattered light, which is shifted to lower energies.4,5 This is the dominant effect at room temperatures, which is the reason for skipping the attribute. Due to the ensemble mixing the Raman spectrum is not representing the vibrational states of one molecule but of a mixture of molecules. Thus, the Raman spectrum is a superposition of Raman spectra of substances within the excitation focus. Because the unmixing of this superposition is only possible for limited cases, the Raman spectrum is used as a vibrational fingerprint. This fingerprint is either interpreted with a certain set of reference Raman spectra or evaluated by means of statistical methods. The latter procedure is often applied, if heterogonous mixtures like cells or tissues are investigated, while the former method is used, if pure substances or easy mixtures are studied. As investigations on biological samples, like cells or tissue are the topic of the review, we will focus on biological samples in the following. Therefore, a Raman spectrum is used as vibrational fingerprint.
Diffuse Reflectance Spectroscopy (DRS) is a leading technique for the detection of head and neck cancers. It can capture information regarding tissue absorption and scattering. In this research work, we propose a novel method for the identification of normal and Squa-mous Cell Carcinoma (SCC) mucosa tissues using the Bag Of Words (BOW) approach. The study included 70 spectra from normal mucosa tissue sites and 70 spectra from SCC mucosa tissue sites. First, the spectra are preprocessed by extracting the useful wavelength range, denoising and reducing the inter and intra patient variability. Subsequently, features are extracted from each spectrum by continuously sliding a window with a pre-defined length along each spectrum to extract a group of local segments. Discrete Wavelet transform (DWT) is then employed for each segment. Next, we construct the codebook to represent each spectrum by a histogram of codewords at which each bin in the histogram is a count of a codeword appeared in the spectrum. Finally, the histogram representation is used as input for classification. The maximum accuracy reported is 94.28% with sensitivity and specificity of 91.42% and 97.14% respectively.
Using home-built Raman optical activity (ROA) spectrometer and a relative intensity correction for different instrument responses, we report ROA spectra beyond the traditionally utilized spectral region of 200–2500 cm−1. With three different interchangeable gratings covering partially overlapping spectral regions, we can study ROA in the whole region of fundamental molecular vibrations (150–4000 cm−1). Complete panoramic spectra are assembled from subparts collected with different gratings after a relative intensity correction based on the National Institute of Science and Technology standards known from the analogous application in Raman and fluorescence spectroscopy. Using this setup, we report the still little known ROA from C–H and C–D stretching region of the testing substances α-pinene and a tricyclic spirodilactam. The intensity-corrected experimental data were compared with calculated ROA and Raman spectra of these substances both with and without anharmonic corrections. A comparison revealed that above 1200 cm−1, the anharmonic correction provides a clear improvement of the agreement. While the calculation of Raman spectra achieves already good accuracy, the analogous ROA calculations still need further development. Copyright © 2014 John Wiley & Sons, Ltd.
A semiautomated method combining intensity normalization with effective elimination of the solvent signal and non-Raman background is presented for Raman spectra of biochemical and biological analytes in aqueous solutions. The method is particularly suitable for rapid and effortless preprocessing of extensive datasets taken as a function of gradually varied physicochemical parameters, e.g. analyte and/or ligand concentration, temperature, pH, pressure, ionic strength, time, etc. For intensity normalization, the strong Raman OH stretching band of water in the range of 2700–3900 cm−1 recorded together with the analyte spectrum in the fingerprint region below 1800 cm−1 is employed as internal intensity standard. Concomitant dependences of the solvent Raman spectra are taken into account and, in some cases, turned into advantage. Once the Raman spectra of the solvent are acquired for a particular range of the parameter varied, solvent contribution can be subtracted correctly from any analyte spectrum taken within this range. The procedure presented can be efficiently applied only for the analytes having their own Raman signal in the range of OH stretching vibrations much weaker than that of the solvent. However, this is the case for a great number of biochemical and biological samples. Accuracy, reliability and robustness of the method were tested under the conditions of spontaneous Raman, resonance Raman and surface-enhanced Raman scattering. Serviceability of the method is demonstrated by several real-world examples. Copyright © 2011 John Wiley & Sons, Ltd.
Raman microscopy has become established as a key probe technique in
biology and biomedicine. In combination with imaging and mapping it has
been employed in the investigation of a diverse array of problems
ranging from ex vivo and in vivo single cell studies to elucidation of
the often complex, interacting structures which constitute human and
animal tissues. This chapter emphasises the unique attributes of Raman
microscopy as a bioimaging technique, including its non-invasive,
spectral multiplexing ability, allied with high spatial resolution and
underpinned by a range of multivariate data processing methods. A number
of illustrative examples have been selected for discussion from the
fields of molecular biology, ophthalmology, respiratory medicine as well
as some non-medical examples. Recent advances and pointers to future
activity in the uses of Raman microscopy as a structurally and
functionally informative bioimaging technique are briefly considered.
The aim of this paper is to provide an overview of advances in the field of Raman spectroscopy as reflected in articles published each year in the Journal of Raman Spectroscopy as well as in trends across related journals publishing in this research area. The context for this review is derived from statistical data on article counts obtained from Thomson Reuters ISI Web of Knowledge by year and by subfield of Raman spectroscopy. Additional information is gleaned from presentations featuring Raman spectroscopy presented at the International Conference on Advanced Vibrational Spectroscopy in Kobe Japan in August 2013 and at SCIX 2013 sponsored by the Federation of Analytical Chemistry and Spectroscopy Societies in Milwaukee, Wisconsin, USA, October 2013. Papers published in the Journal of Raman Spectroscopy in 2012 are highlighted in this review and reflect topics and advances at the frontier of Raman spectroscopy, a field that is expanding rapidly as a sensitive photonic probe of matter at the molecular level in an ever widening sphere of novel applications. Copyright © 2013 John Wiley & Sons, Ltd.
The present study is designed to understand further implications of using multivariate loadings for the correction of back-ground signal, which has previously been shown to be highly reproducible even for very low quality signals. Singular value decomposition (SVD)-based background correction was compared with the traditional per-signal paradigm for a biomedical dataset to generate qualitative and quantitative models. The qualitative effect on a principal component analysis model and the quantitative effect on a partial least square regression model were assessed for these background correction meth-ods. The chosen quantitative parameter was the concentration of a pathologically relevant protein modification, pentosidine. Of the approaches tested, the SVD-based paradigm provided the regression model with the highest correlations, highest accuracy (lowest standard error of prediction) and repeatability (lowest sampling error). Contrasted against the traditional approaches, it was determined that the improved accuracy and repeatability of the SVD-based approach arises from its ability to simultaneously handle very complex background shapes alongside the complex variation in biochemical species that resulted in Raman signals with incompatible baseline regions. A better understanding of the interaction of SVD-based base-line correction, and data will give the reader more insight into the potential applicability of the procedure for other datasets.
Raman spectroscopy is a noninvasive, nondestructive tool for capturing multiplexed biochemical information across diverse molecular species including proteins, lipids, DNA, and mineralizations. Based on light scattering from molecules, cells, and tissues, it is possible to detect molecular fingerprints and discriminate between subtly different members of each biochemical class. Raman spectroscopy is ideal for detecting perturbations from the expected molecular structure such as those occurring during senescence and the modification of long-lived proteins by metabolic intermediates as we age. Here, we describe the sample preparation, data acquisition, signal processing, data analysis and interpretation involved in using Raman spectroscopy for detecting age-related protein modifications in complex biological tissues.
The goals of this trial were, first, to produce a Raman mapping of decay and sound dentin samples, through accurate analysis of the Raman band spectra variations of mineral and organic components. The second goal was to confirm the correlation between the Raman signal and the signal of a fluorescent camera, by assaying the concentration of pentosidine and natural collagen fluorescent crosslink using reverse phase high-pressure liquid chromatography. The first correlation assumed a possible relationship between the signal observed with the camera and Raman spectroscopy. The second correlation assumed an association with the Maillard reaction. Absence of a correlation for this trial was that no association could be found between Raman spectra characteristics, fluorescence variation and the HPLC assay. Our results void this absence (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
In clinical situations carious dentine tissues can be discriminated by most caries fluorescence detection tools, including a new fluorescence intra-oral camera. The objectives of this study were: (i) to analyze the Raman spectra of sound, carious, and demineralized dentine, (ii) to compare this spectral analysis with the fluorescence variation observed when using a fluorescence camera, and (iii) to evaluate the involvement of the Maillard reaction in the fluorescence variations. The first positive hypothesis tested was that the fluorescence of carious dentine obtained using a fluorescence camera and the Raman spectra variation were closely related. The second was that the variation of fluorescence could be linked with the Maillard reaction. Sound dentine, sound dentine demineralized in aqueous nitric acid solution, carious soft dentine, sound dentine demineralized in lactic acid solution, sound dentine demineralized in aqueous nitric acid solution and immersed in methylglycoxal solution, and sound dentine demineralized in aqueous nitric acid solution and immersed in methylglycoxal and glucose solutions, were studied using micro-Raman spectroscopy. Modifications in the band ratio of amide, phosphate, and carbonate were observed in the decayed and demineralized groups compared with the sound dentine group. The results indicate that a close relationship exists between the Maillard reaction and fluorescence variation.
Raman microscopy is used to investigate the spectral features of selected compounds known to be involved in the development of the eye disease age-related macular degeneration (AMD). Diagnostic features were identified in synthetic samples of these compounds and in a biological matrix. The study demonstrates the potential of Raman microscopy for the development of diagnostic markers of the onset of AMD. Copyright © 2008 John Wiley & Sons, Ltd.
Raman spectroscopy has been revolutionised in recent decades by major technological advances such as lasers, charge-coupled detectors (CCD) and notch/edge filters. In contrast the development of signal processing algorithms has progressed at a slower pace. Spectroscopic applications increasingly focus on ‘real-world’ applications that are not under highly controlled conditions and with more stringent limitations placed on acquisition conditions (e.g. low power for in vivo and explosives analysis). Often it is necessary to work with signals of a quality traditionally considered poor. In this study an alternative paradigm for signal processing poor quality signals is presented and rigorously assessed. Instead of estimating the background on the individual signals it is estimated on the results of a multivariate analysis. Under this paradigm prediction reproducibility is unaffected by the signal processing, unlike the traditional paradigm of correcting individual signals which induces errors that propagate through to the prediction. The paradigms were tested on a ‘real-world’ dataset to predict the concentration of a pathologically relevant protein modification, carboxymethyl lysine (CML). Use of the new paradigm allowed signals with a signal to noise ratio (SNR) of 2.4 to give a prediction with variance just 8.7% of the mean, with the traditional paradigm giving a variance of over 140% of the mean. Significant improvement in reproducibility could even be observed with signals as good as SNR 85. The ability to obtain reproducible predictions from low quality signals allows shorter acquisition (e.g. mapping or on-line analysis), use of low powers (in vivo diagnostics, hazardous materials analysis (HAZMAT)) or use of cheaper equipment. Copyright © 2011 John Wiley & Sons, Ltd.
Raman spectroscopy is an effective probe of advanced glycation end products (AGEs) in Bruch's membrane. However, because it is the outermost layer of the retina, this extracellular matrix is difficult to analyze in vivo with current technology. The sclera shares many compositional characteristics with Bruch's membrane, but it is much easier to access for in vivo Raman analysis. This study investigated whether sclera could act as a surrogate tissue for Raman-based investigation of pathogenic AGEs in Bruch's membrane.
Human sclera and Bruch's membrane were dissected from postmortem eyes (n = 67) across a wide age range (33-92 years) and were probed by Raman spectroscopy. The biochemical composition, AGEs, and their age-related trends were determined from data reduction of the Raman spectra and compared for the two tissues.
Raman microscopy demonstrated that Bruch's membrane and sclera are composed of a similar range of biomolecules but with distinct relative quantities, such as in the heme/collagen and the elastin/collagen ratios. Both tissues accumulated AGEs, and these correlated with chronological age (R(2) = 0.824 and R(2) = 0.717 for sclera and Bruch's membrane, respectively). The sclera accumulated AGE adducts at a lower rate than Bruch's membrane, and the models of overall age-related changes exhibited a lower rate (one-fourth that of Bruch's membrane) but a significant increase with age (P < 0.05).
The results suggest that the sclera is a viable surrogate marker for estimating AGE accumulation in Bruch's membrane and for reliably predicting chronological age. These findings also suggest that sclera could be a useful target tissue for future patient-based, Raman spectroscopy studies.
Aging of the human retina is characterized by progressive pathology, which can lead to vision loss. This progression is believed to involve reactive metabolic intermediates reacting with constituents of Bruch's membrane, significantly altering its physiochemical nature and function. We aimed to replace a myriad of techniques following these changes with one, Raman spectroscopy. We used multiplexed Raman spectroscopy to analyze the age-related changes in 7 proteins, 3 lipids, and 8 advanced glycation/lipoxidation endproducts (AGEs/ALEs) in 63 postmortem human donors. We provided an important database for Raman spectra from a broad range of AGEs and ALEs, each with a characteristic fingerprint. Many of these adducts were shown for the first time in human Bruch's membrane and are significantly associated with aging. The study also introduced the previously unreported up-regulation of heme during aging of Bruch's membrane, which is associated with AGE/ALE formation. Selection of donors ranged from ages 32 to 92 yr. We demonstrated that Raman spectroscopy can identify and quantify age-related changes in a single nondestructive measurement, with potential to measure age-related changes in vivo. We present the first directly recorded evidence of the key role of heme in AGE/ALE formation.
The work presented here is aimed at determining the potential and limitations of Raman spectroscopy for fat analysis by carrying out a systematic investigation of C4−C24 FAME. These provide a simple, well-characterized set of compounds in which the effect of making incremental changes can be studied over a wide range of chain lengths and degrees of unsaturation. The effect of temperature on the spectra was investigated over much larger ranges than would normally be encountered in real analytical measurements. It was found that for liquid FAME the best internal standard band was the carbonyl stretching vibration ρ(C=O), whose position is affected by changes in sample chain length and physical state; in the samples studied here, it was found to lie between 1729 and 1748 cm−1. Further, molar unsaturation could be correlated with the ratio of the ρ(C=O) to either ρ(C=C) or δ(H−C=) with R
2>0.995. Chain length was correlated with the δ(CH2)tw/ρ(C=O) ratio, (where “tw” indicates twisting) but separate plots for odd- and even-numbered carbon chains were necessary to obtain R
2>0.99 for liquid samples. Combining the odd- and even-numbered carbon chain data in a single plot reduced the correlation to R
2=0.94–0.96, depending on the band ratios used. For molal unsaturation the band ratio that correlated linearly with unsaturation (R
2>0.99) was ρ(C=C)/δ(CH2)sc (where “sc” indicates scissoring). Other band ratios show much more complex behavior with changes in chemical and physical structure. This complex behavior results from the fact that the bands do not arise from simple vibrations of small, discrete regions of the molecules but are due to complex motions of large sections of the FAME so that making incremental changes in structure does not necessarily lead to simple incremental changes in spectra.
Raman spectroscopy has been used to predict the abundance of the FA in clarified butterfat that was obtained from dairy cows fed a range of levels of rapeseed oil in their diet. Partial least squares regression of the Raman spectra against FA compositions obtained by GC showed that good prediction of the five major (abundance >5%) FA gave R
2=0.74–0.92 with a SE of prediction (RMSEP) that was 5–7% of the mean. In general, the prediction accuracy fell with decreasing abundance in the sample, but the RMSEP was <10% for all but one of the 10 FA present at levels >1.25%. The Raman method has the best prediction ability for unsaturated FA (R
2=0.85–0.92), and in particular trans unsaturated FA (best-predicted FA was 18∶1tΔ9). This enhancement was attributed to the isolation of the unsaturated modes from the saturated modes and the significantly higher spectral response of unsaturated bonds compared with saturated bonds. Raman spectra of the melted butter samples could also be used to predict bulk parameters calculated from standard analyzes, such as iodine value (R
2=0.80) and solid fat content at low temperature (R
2=0.87). For solid fat contents determined at higher temperatures, the prediction ability was significantly reduced (R
2=0.42), and this decrease in performance was attributed to the smaller range of values in solid fat content at the higher temperatures. Finally, although the prediction errors for the abundances of each of the FA in a given sample are much larger with Raman than with full GC analysis, the accuracy is acceptably high for quality control applications. This, combined with the fact that Raman spectra can be obtained with no sample preparation and with 60-s data collection times, means that high-throughput, on-line Raman analysis of butter samples should be possible.
Raman microscopy, based upon the inelastic scattering (Raman) of light by molecular species, has been applied as a specific structural probe in a wide range of biomedical samples. The purpose of the present investigation was to assess the potential of the technique for spectral characterization of the porcine outer retina derived from the area centralis, which contains the highest proportion of cone:rod cell ratio in the pig retina.
Retinal cross-sections, immersion-fixed in 4% (w/v) PFA and cryoprotected, were placed on salinized slides and air-dried prior to direct Raman microscopic analysis at three excitation wavelengths, 785 nm, 633 nm, and 514 nm.
Raman spectra of each of the photoreceptor inner and outer segments (PIS, POS) and of the outer nuclear layer (ONL) of the retina acquired at 785 nm were dominated by vibrational features characteristic of proteins and lipids. There was a clear difference between the inner and outer domains in the spectroscopic regions, amide I and III, known to be sensitive to protein conformation. The spectra recorded with 633 nm excitation mirrored those observed at 785 nm excitation for the amide I region, but with an additional pattern of bands in the spectra of the PIS region, attributed to cytochrome c. The same features were even more enhanced in spectra recorded with 514 nm excitation. A significant nucleotide contribution was observed in the spectra recorded for the ONL at all three excitation wavelengths. A Raman map was constructed of the major spectral components found in the retinal outer segments, as predicted by principal component analysis of the data acquired using 633 nm excitation. Comparison of the Raman map with its histological counterpart revealed a strong correlation between the two images.
It has been demonstrated that Raman spectroscopy offers a unique insight into the biochemical composition of the light-sensing cells of the retina following the application of standard histological protocols. The present study points to the considerable promise of Raman microscopy as a component-specific probe of retinal tissue.
Alpha-tocopherol (aT), the predominant form of vitamin E in mammals, is thought to prevent oxidation of polyunsaturated fatty acids. In the lung, aT is perceived to be accumulated in alveolar type II cells and secreted together with surfactant into the epithelial lining fluid. Conventionally, determination of aT and related compounds requires extraction with organic solvents. This study describes a new method to determine and image the distribution of aT and related compounds within cells and tissue sections using the light-scattering technique of Raman microscopy to enable high spatial as well as spectral resolution. This study compared the nondestructive analysis by Raman microscopy of vitamin E, in particular aT, in biological samples with data obtained using conventional HPLC analysis. Raman spectra were acquired at spatial resolutions of 2-0.8 microm. Multivariate analysis techniques were used for analyses and construction of corresponding maps showing the distribution of aT, alpha-tocopherol quinone (aTQ), and other constituents (hemes, proteins, DNA, and surfactant lipids). A combination of images enabled identification of colocalized constituents (heme/aTQ and aT/surfactant lipids). Our data demonstrate the ability of Raman microscopy to discriminate between different tocopherols and oxidation products in biological specimens without sample destruction. By enabling the visualization of lipid-protein interactions, Raman microscopy offers a novel method of investigating biological characterization of lipid-soluble compounds, including those that may be embedded in biological membranes such as aT.
Standard Reference Materials SRMs 2241 through 2243 are certified spectroscopic standards intended for the correction of the relative intensity of Raman spectra obtained with instruments employing laser excitation wavelengths of 785 nm, 532 nm, or 488 nm/514.5 nm. These SRMs each consist of an optical glass that emits a broadband luminescence spectrum when illuminated with the Raman excitation laser. The shape of the luminescence spectrum is described by a polynomial expression that relates the relative spectral intensity to the Raman shift with units in wavenumber (cm(-1)). This polynomial, together with a measurement of the luminescence spectrum of the standard, can be used to determine the spectral intensity-response correction, which is unique to each Raman system. The resulting instrument intensity-response correction may then be used to obtain Raman spectra that are corrected for a number of, but not all, instrument-dependent artifacts. Peak area ratios of the intensity-corrected Raman spectrum of cyclohexane are presented as an example of a methodology to validate the spectral intensity calibration process and to illustrate variations that can occur in this measurement.
The modification of proteins by nonenzymatic glycation leading to accumulation of advanced glycation end products (AGEs) is a well-established phenomenon of aging. In the eyes of elderly patients, these adducts have been observed in retinal pigment epithelium (RPE), particularly within the underlying pentalaminar substrate known as Bruch's membrane. AGEs have also been localized to age-related subcellular deposits (drusen and basal laminar deposits) and are thought to play a pathogenic role in progression of the major sight-threatening condition known as age-related macular degeneration (AMD). The current study has quantified AGEs in Bruch's membrane from postmortem eyes and established age-related correlations. In particular, we investigated the potential of confocal Raman microscopy to identify and quantify AGEs in Bruch's membrane in a nondestructive, analytical fashion. Bruch's membrane and the innermost layers of the underlying choroid (BM-Ch) were dissected from fresh postmortem eye-cups (n=56). AGE adducts were quantified from homogenized tissue using reverse-phase HPLC and GC/MS in combination with immunohistochemistry. For parallel Raman analysis, BM-Ch was flat-mounted on slides and evaluated using a Raman confocal microscope and spectra analyzed by a range of statistical approaches. Quantitative analysis showed that the AGEs pentosidine, carboxymethyllysine (CML), and carboxyethyllysine (CEL) occurred at significantly higher levels in BM-Ch with age (P<0.05-0.01). Defined Raman spectral "fingerprints" were identified for various AGEs and these were observed in the clinical samples using confocal Raman microscopy. The Raman data set successfully modeled AGEs and not only provided quantitative data that compared with conventional analytical approaches, but also provided new and complementary information via a nondestructive approach with high spatial resolution. It was shown that the Raman approach could be used to predict chronological age of the clinical samples (P<0.001) and a difference in the Raman spectra between genders was highly significant (P<0.000001). With further development, this Raman-based approach has the potential for noninvasive examination of AGE adducts in living eyes and ultimately to assess their precise pathogenic role in age-related diseases.
To characterize the Raman spectra of porcine inner retinal layers, specifically, the inner nuclear, inner plexiform, ganglion cell, and nerve fiber layers.
Raman microscopy was employed at three excitation wavelengths, 785, 633, and 514 nm to measure Raman spectra in a high resolution grid across the inner layers of 4% paraformaldehyde cryoprotected porcine retina. Multivariate statistics were used to summarize the principal spectral signals within those layers and to map the distribution of each of those signals.
The detected Raman scattering was dominated by a signal characteristic of the protein population present in each layer. As expected, a significant nucleotide contribution was observed in the inner nuclear layer, while the inner plexiform layer displayed a minor contribution from fatty acid based lipid, which would be characteristic of the axonal and synaptic connection resident in this layer. Blood vessels were readily characterized by their distinct heme-derived spectral signature, which increased at 633 and 514 nm excitation compared to 785 nm. Discrete isolated nucleotide signals were identified in the ganglion cell layer, while the nerve fiber layer exhibited a homogenous profile, which is indicative of its broadly uniform axonal and cytoplasmic Muller cell components.
The present study demonstrated the potential of Raman microscopy as a tool to study the biochemical composition of pathologically normal retina. Specifically, the method allowed a unique method of analyzing the network of neurons involved in relaying information from the photoreceptor population to the ganglion cell derived nerve fiber layer. The study has demonstrated the ability of Raman microscopy to generate simultaneously information on a range of specific biochemical entities within the stratified normal retina.
Many scientists have a passing familiarity with Raman spectroscopy and those of us who have tried using it, say 15 years ago, to identify chemical groups were probably disappointed. At the time, a long recording time and poor signal/noise ratios did not inspire strong recommendations for chemical analysis. This was disappointing because there was always a deep-seated feeling that here was a technique that could be capable of detailed chemical analysis, indeed chemical imaging. How things have changed! The more widespread use of infrared lasers, CCD detectors and signal processing using powerful PCs have all reduced the recording times from around one hour to about 30 seconds. The general convenience of the technique is bringing it to the attention of a wider audience of chemists and materials scientists.
This volume by McCreery is another in the excellent Wiley series of monographs on Analytical Chemistry. These have provided a very high standard, indeed a `flagship' of authority and quality in this broad area. The treatment given here will capture the attention of both the novice who wants to find out how Raman spectroscopy works, and the expert practitioner who requires some original sources of information. It will be very useful to all researchers who use, or wish to use, the Raman technique. The introductory sections really do highlight the differences to be expected between modern Raman techniques and the `rival' techniques of near infrared (NIR) and Fourier transform infrared (FTIR) spectroscopies. The treatment is a no-nonsense, pragmatic approach, with comparable spectra for the techniques, warts and all! Later sections then go on to give a rigorous analysis of signal levels, signal/noise ratios and practical details of the lasers, detectors and software that are best suited to specific needs and applications. There are several extensions of Raman spectroscopy that have been made possible by improved software and instrumentation in recent years, but probably the ability to form images is the one that will capture a large share of devotees.
In conclusion, McCreery gives a very well balanced and authoritative account of Raman microscopy in all its different variants. He repeats this very effectively for its application via fibre-optic probes, and the examples that he has selected are clear, simple and useful. The section on surface enhanced Raman spectroscopy (SERS) is a model of clarity and one that I will use in future lectures. I can recommend this volume without hesitation.
P J Dobson
Raman spectra are reported for oxygenated and deoxygenated haemoglobin contained within a single red blood cell in vivo using excitation wavelengths of 488, 514, 568 and 632.8 nm. The peak assigned in previous work to ν4 is observed at 1376 cm−1 in oxygenated cells and 1356 cm−1 in deoxygenated cells with the results from 488 nm excitation consistent with earlier Raman studies on isolated haem proteins. Exciting the cells with 514 nm radiation revealed two bands appearing in this region at 1372 and 1356 cm−1 in the oxygenated state, whereas in the deoxygenated state only one band at 1356 cm−1 is observed. At 632.8 nm excitation bands in the ν4 region appeared at 1367 and 1365 cm−1 in the oxygenated and deoxygenated states, respectively. Our results clearly show that the enhancement of bands in the vicinity of ν4 within single erythrocytes is influenced by the excitation wavelength. Furthermore, many other bands observed in oxygenated erythrocytes using 632.8 nm excitation were dramatically enhanced compared with the bands observed with other excitation wavelengths. Ruling out other explanations, it is hypothesized that the enhancement observed at 632.8 nm results from excitonic coupling between aligned porphyrins. The high concentration of haemoglobin in a single cell enables the porphyrins to be in close proximity to permit charge transfer between the haem moieties. The high signal-to-noise ratio and excellent reproducibility obtained using Raman water immersion microspectroscopy on single erythrocytes in vivo shows potential as a diagnostic tool for a variety of haemopathies. However, judicious choice of the excitation wavelength is a prerequisite especially if the technique is applied to diagnose oxidation status within erythrocytes. Copyright © 2002 John Wiley & Sons, Ltd.
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We have previously shown that Raman spectroscopy can be used for chemical analysis of intact human coronary artery atherosclerotic lesions ex vivo without tissue homogenization or extraction. Here, we report the chemical analysis of individual cellular and extracellular components of atherosclerotic lesions in different stages of disease progression in situ using Raman microspectroscopy.
Thirty-five coronary artery samples were taken from 16 explanted transplant recipient hearts, and thin sections were prepared. Using a high-resolution confocal Raman microspectrometer system with an 830-nm laser light, high signal-to-noise Raman spectra were obtained from the following morphologic structures: internal and external elastic lamina, collagen fibers, fat, foam cells, smooth muscle cells, necrotic core, beta-carotene, cholesterol crystals, and calcium mineralizations. Their Raman spectra were modeled by using a linear combination of basis Raman spectra from the major biochemicals present in arterial tissue, including collagen, elastin, actin, myosin, tropomyosin, cholesterol monohydrate, cholesterol linoleate, phosphatidyl choline, triolein, calcium hydroxyapatite, calcium carbonate, and beta-carotene.
The results show that the various morphologic structures have characteristic Raman spectra, which vary little from structure to structure and from artery to artery. The biochemical model described the spectrum of each morphologic structure quite well, indicating that the most essential biochemical components were included in the model. Furthermore, the biochemical composition of each structure, indicated by the fit contributions of the biochemical basis spectra of the morphologic structure spectrum, was very consistent.
The Raman spectra of various morphologic structures in normal and atherosclerotic coronary artery may be used as basis spectra in a linear combination model to analyze the morphologic composition of atherosclerotic coronary artery lesions.
To review and synthesize information concerning the pathogenesis of age-related macular degeneration (AMD).
Review of the English-language literature.
Five concepts relevant to the cell biology of AMD are as follows: (1) AMD involves aging changes plus additional pathological changes (ie, AMD is not just an aging change); (2) in aging and AMD, oxidative stress causes retinal pigment epithelial (RPE) and, possibly, choriocapillaris injury; (3) in AMD (and perhaps in aging), RPE and, possibly, choriocapillaris injury results in a chronic inflammatory response within the Bruch membrane and the choroid; (4) in AMD, RPE and, possibly, choriocapillaris injury and inflammation lead to formation of an abnormal extracellular matrix (ECM), which causes altered diffusion of nutrients to the retina and RPE, possibly precipitating further RPE and retinal damage; and (5) the abnormal ECM results in altered RPE-choriocapillaris behavior leading ultimately to atrophy of the retina, RPE, and choriocapillaris and/or choroidal new vessel growth. In this sequence of events, both the environment and multiple genes can alter a patient's susceptibility to AMD. Implicit in this characterization of AMD pathogenesis is the concept that there is linear progression from one stage of the disease to the next. This assumption may be incorrect, and different biochemical pathways leading to geographic atrophy and/or choroidal new vessels may operate simultaneously.
Better knowledge of AMD cell biology will lead to better treatments for AMD at all stages of the disease. Many unanswered questions regarding AMD pathogenesis remain. Multiple animal models and in vitro models of specific aspects of AMD are needed to make rapid progress in developing effective therapies for different stages of the disease.
Long-lived proteins undergo age-related postsynthetic modifications by glycation and advanced glycation end products (AGEs), which destabilize them by altering their conformation and charge. It was accidentally discovered that ornithine (orn) increased with age in acid hydrolyzates of human skin collagen and lens crystallins which led us to investigate the source of orn. Here, we detected such modifications of orn in these proteins. Acid hydrolysis of arginine (arg)-base AGE standards produced orn at different yields. The data provide unequivocal evidence for the in vivo formation of orn and its own AGEs in aging proteins, and suggest that arg-based AGEs serve as precursors of orn.
To investigate whether age-related macular degeneration (AMD) is associated with the development of myocardial infarction (MI) among elderly Americans.
Population-based cross-sectional and cohort study.
Five percent random sample of 2000 to 2003 Medicare enrollees.
The cross-sectional study included the first 2-year (2000 and 2001) enrollees who were aged > or =65 years (n = 1,519,086). The cohort study included only baseline MI-free enrollees (n = 1445677).
Chronic conditions (AMD and type, history of MI, hypertension, and diabetes) were defined based on any occurrence of relevant International Classification of Diseases 9 codes in relevant diagnosis fields of the baseline Medicare claim files. A total of 56611 incident MI cases were identified from the follow-up data (2002 and 2003).
Baseline mean age was 76 years, with 60% women and 88% whites. The prevalence of neovascular AMD was 2.2% (2.3% in women vs. 1.7% in men and 2.3% in whites vs. 1.2% in blacks; P<0.01 for both gender and race differences). The prevalence of nonneovascular AMD was 8.8% (9.9% in women vs. 7.3% in men and 9.5% in whites vs. 4.3% in blacks; P<0.01 for both gender and race differences). Baseline age-, gender-, and race-adjusted prevalences of hypertension, diabetes, and history of MI were 75%, 33%, and 5.00%, respectively, in the neovascular AMD group. In contrast, they were 73%, 27%, and 4.68% in the nonneovascular AMD group, and 65%, 25%, and 4.54% in the non-AMD group (P<0.01 for comparing the prevalence in neovascular and nonneovascular AMD vs. non-AMD groups). Prospectively, baseline age-, gender-, race-, hypertension-, and diabetes-adjusted 2-year incident odds ratios and 95% confidence intervals of MI associated with AMD are 1.19 (1.16-1.22) for all persons with AMD, 1.26 (1.20-1.33) for neovascular AMD, and 1.18 (1.14-1.21) for nonneovascular AMD.
AMD is associated with older age, female gender, being white, and having a history of MI, hypertension, and diabetes. Furthermore, presence of AMD, especially neovascular AMD, is prospectively associated with a higher risk of incident MI. These findings, if confirmed by other studies that control for smoking and other lifestyle covariables, suggest the possibility of shared common antecedents between MI and AMD.
- J R Beattie
- S J Bell
- B W Moss
J. R. Beattie, S. J. Bell, B. W. Moss, Lipids 2004, 39, 407.
Maillard Reaction: Chemistry at the Interface of Nutrition
- D R Sell
D. R. Sell, Maillard Reaction: Chemistry at the Interface of Nutrition,
Aging, and Disease, vol. 1043. Wiley-Blackwell: New York, 2005
pp. 118.
- T G Spiro
T. G. Spiro, Biological Applications of Raman Spectroscopy, vol. 1,
John Wiley & Sons: New York, 1986.
- McCreery