Spectrometer calibration protocol for Raman spectra recorded with different excitation wavelengths

New Protocol for Twinning of Raman Devices Toward a Raman Intensity Harmonization

The use of Raman spectroscopy has rapidly been on the rise across a great number of industries where comparability, reproducibility, and reliability of the data are of paramount importance. However, controlling the intensity of the Raman signal depends on a large number of factors such as the wavelength of the laser light, the optical components of each device, or the number of molecules in the illuminated volume. For this reason, in this study, a new protocol has been applied to twin Raman devices to achieve a conversion of the signal between them, by pairing the intensity response of the units using a reference sample. The new reference material is a homogenous dispersion of a 0.5 wt% anatase (titanium dioxide, or TiO2) in an epoxy resin matrix, with deviations <2.5% in Raman intensity across the reference material. The proposed protocol for Raman-twinned devices takes a well-defined approach that leads to obtaining a correction factor that relates the differences in the signal intensity between the two Raman devices, in order to obtain the same Raman intensity counts. The performance of the proposed method was evaluated based on the data from the devices, which presented the most common user cases: twinning Raman devices of the non-confocal same model for two different wavelengths; and twinning confocal and non-confocal devices. The results obtained show that the protocol has worked for both of the Raman twinning cases, allowing the Raman intensity harmonization of Raman spectra between two different devices.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Calibration of Dual-Channel Raman Spectrometer via Optical Frequency Comb

The portable Raman spectrometer boasts portability, rapid analysis, and high flexibility. It stands as a crucial and powerful technical tool for analyzing the chemical composition of samples, whether biological or non-biological, across diverse fields. To improve the resolution of grating spectrometers and ensure a wide spectral range, many spectrometer systems have been designed with double-grating structures. However, the impact of external forces, such as installation deviations and inevitable collisions, may cause differences between the actual state of the internal spectrometer components and their theoretical values. Therefore, spectrometers must be calibrated to establish the relationship between the wavelength and the pixel positions. The characteristic peaks of commonly used calibration substances are primarily distributed in the 200-2000 cm-1 range. The distribution of characteristic peaks in other wavenumber ranges is sparse, especially for spectrometers with double-channel spectral structures and wide spectral ranges. This uneven distribution of spectral peaks generates significant errors in the polynomial fitting results used to calibrate spectrometers. Therefore, to satisfy the calibration requirements of a dual-channel portable Raman spectrometer with a wide spectral range, this study designed a calibration method based on an optical frequency comb, which generates dense and uniform comb-like spectral signals at equal intervals. The method was verified experimentally and compared to the traditional calibration method of using a mercury-argon lamp. The results showed that the error bandwidth of the calibration results of the proposed method was significantly smaller than that of the mercury-argon lamp method, thus demonstrating a substantial improvement in the calibration accuracy.

Investigating the effect of different pre-treatment methods on Raman spectra recorded with different excitation wavelengths

Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω4 correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.

Identification of bacteria in mixed infection from urinary tract of patient's samples using Raman analysis of dried droplets

Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis - linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample.

Improved Wavelength Calibration by Modeling the Spectrometer

Wavelength calibration is a necessary first step for a range of applications in spectroscopy. The relationship between wavelength and pixel position on the array detector is approximately governed by a low-order polynomial and traditional wavelength calibration involves first-, second-, and third-order polynomial fitting to the pixel positions of spectral lines from a well known reference lamp such as neon. However, these methods lose accuracy for bands outside of the outermost spectral line in the reference spectrum. We propose a fast and robust wavelength calibration routine based on modeling the optical system that is the spectrometer. For spectral bands within the range of spectral lines of the lamp, we report similar accuracy to second- and third-order fitting. For bands that lie outside of the range of spectral lines, we report an accuracy 12-121 times greater than that of third-order fitting and 2.5-6 times more accurate than second-order fitting. The algorithm is developed for both reflection and transmission spectrometers and tested for both cases. Compared with similar algorithms in the literature that use the physical model of the spectrometer, we search over more physical parameters in shorter time, and obtain superior accuracy. A secondary contribution in this paper is the introduction of new evaluation methods for wavelength accuracy that are superior to traditional evaluation.

Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma

Early diagnosis of esophageal squamous cell carcinoma (ESCC), a common malignant tumor with a low overall survival rate due to metastasis and recurrence, is critical for effective treatment and improved prognosis. Raman spectroscopy, an advanced detection technology for esophageal cancer, was developed to improve diagnosis sensitivity, specificity, and accuracy. This study proposed a novel, effective, and noninvasive Raman spectroscopy technique to differentiate and classify ESCC cell lines. Seven ESCC cell lines and tissues of an ESCC patient with staging of T3N1M0 and T3N2M0 at low and high differentiation levels were investigated through Raman spectroscopy. Raman spectral data analysis was performed with four machine learning algorithms, namely principal components analysis (PCA)- linear discriminant analysis (LDA), PCA-eXtreme gradient boosting (XGB), PCA- support vector machine (SVM), and PCA- (LDA, XGB, SVM)-stacked Gradient Boosting Machine (GBM). Four machine learning algorithms were able to classifiy ESCC cell subtypes from normal esophageal cells. The PCA-XGB model achieved an overall predictive accuracy of 85% for classifying ESCC and adjacent tissues. Moreover, an overall predictive accuracy of 90.3% was achieved in distinguishing low differentiation and high differentiation ESCC tissues with the same stage when PCA-LDA, XGM, and SVM models were combined. This study illustrated the Raman spectral traits of ESCC cell lines and esophageal tissues related to clinical pathological diagnosis. Future studies should investigate the role of Raman spectral features in ESCC pathogenesis.

Phenotypic Differentiation of Autotrophic and Heterotrophic Bacterial Cells Using Raman-D2O Labeling

Carbon cycling is one of the major biogeochemical processes driven by bacteria. Autotrophic bacteria convert carbon dioxide (CO₂) into organic compounds that are used by heterotrophs. Mixotrophic bacteria can employ both autotrophy and heterotrophy for growth. The characterization of the lifestyle of individual cells is essential to understand the microbial activity and thus reveal the implication of bacteria in the carbon flux. In this study, we used groundwater bacteria to investigate the potential of Raman-D₂O labeling in combination with chemometrics to identify the carbon assimilation strategies of bacteria. Classification models were built using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Autotrophs assimilated a significantly higher amount (mean C-D ratio between 16.63 and 21.69%) of deuterium than heterotrophs. The C-D signal only provides information about the activity since it appears in the Raman-silent region, where no interference with the taxonomic information is expected. The classification between autotrophs and heterotrophs achieved an overall accuracy of 96.3%. In the validation step with an independent dataset containing species not included in the model, the PCA-LDA model achieved 100% accuracy. This demonstrated that the C-D signal contributed to the identification of autotrophic and heterotrophic bacterial cells. This work reports a robust, rapid, and nondestructive approach for the identification of single cells based on their carbon acquisition strategies. The present study foresees the potential of Raman-D₂O labeling as a promising method for automated discrimination of in situ functional activities of bacteria in environmental systems.

Bolstering fitness via CO2 fixation and organic carbon uptake: mixotrophs in modern groundwater

Current understanding of organic carbon inputs into ecosystems lacking photosynthetic primary production is predicated on data and inferences derived almost entirely from metagenomic analyses. The elevated abundances of putative chemolithoautotrophs in groundwaters suggest that dark CO₂ fixation is an integral component of subsurface trophic webs. To understand the impact of autotrophically fixed carbon, the flux of CO₂-derived carbon through various populations of subsurface microbiota must first be resolved, both quantitatively and temporally. Here we implement novel Stable Isotope Cluster Analysis to render a time-resolved and quantitative evaluation of ¹³CO₂-derived carbon flow through a groundwater community in microcosms stimulated with reduced sulfur compounds. We demonstrate that mixotrophs, not strict autotrophs, were the most abundant active organisms in groundwater microcosms. Species of Hydrogenophaga, Polaromonas, Dechloromonas, and other metabolically versatile mixotrophs drove the production and remineralization of organic carbon. Their activity facilitated the replacement of 43% and 80% of total microbial carbon stores in the groundwater microcosms with ¹³C in just 21 and 70 days, respectively. The mixotrophs employed different strategies for satisfying their carbon requirements by balancing CO₂ fixation and uptake of available organic compounds. These different strategies might provide fitness under nutrient-limited conditions, explaining the great abundances of mixotrophs in other oligotrophic habitats, such as the upper ocean and boreal lakes.

Compositional assessment of bone by Raman spectroscopy

Raman spectroscopy (RS) is used to analyze the physiochemical properties of bone because it is non-destructive and requires minimal sample preparation. With over two decades of research involving measurements of mineral-to-matrix ratio, type-B carbonate substitution, crystallinity, and other compositional characteristics of the bone matrix by RS, there are multiple methods to acquire Raman signals from bone, to process those signals, and to determine peak ratios including sub-peak ratios as well as the full-width at half maximum of the most prominent Raman peak, which is nu1 phosphate (ν1PO4). Selecting which methods to use is not always clear. Herein, we describe the components of RS instruments and how they influence the quality of Raman spectra acquired from bone because signal-to-noise of the acquisition and the accompanying background fluorescence dictate the pre-processing of the Raman spectra. We also describe common methods and challenges in preparing acquired spectra for the determination of matrix properties of bone. This article also serves to provide guidance for the analysis of bone by RS with examples of how methods for pre-processing the Raman signals and for determining properties of bone composition affect RS sensitivity to potential differences between experimental groups. Attention is also given to deconvolution methods that are used to ascertain sub-peak ratios of the amide I band as a way to assess characteristics of collagen type I. We provide suggestions and recommendations on the application of RS to bone with the goal of improving reproducibility across studies and solidify RS as a valuable technique in the field of bone research.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Biochemical Analysis of Leukocytes after In Vitro and In Vivo Activation with Bacterial and Fungal Pathogens Using Raman Spectroscopy

Biochemical information from activated leukocytes provide valuable diagnostic information. In this study, Raman spectroscopy was applied as a label-free analytical technique to characterize the activation pattern of leukocyte subpopulations in an in vitro infection model. Neutrophils, monocytes, and lymphocytes were isolated from healthy volunteers and stimulated with heat-inactivated clinical isolates of Candida albicans, Staphylococcus aureus, and Klebsiella pneumoniae. Binary classification models could identify the presence of infection for monocytes and lymphocytes, classify the type of infection as bacterial or fungal for neutrophils, monocytes, and lymphocytes and distinguish the cause of infection as Gram-negative or Gram-positive bacteria in the monocyte subpopulation. Changes in single-cell Raman spectra, upon leukocyte stimulation, can be explained with biochemical changes due to the leukocyte’s specific reaction to each type of pathogen. Raman spectra of leukocytes from the in vitro infection model were compared with spectra from leukocytes of patients with infection (DRKS-ID: DRKS00006265) with the same pathogen groups, and a good agreement was revealed. Our study elucidates the potential of Raman spectroscopy-based single-cell analysis for the differentiation of circulating leukocyte subtypes and identification of the infection by probing the molecular phenotype of those cells.

Raman Stable Isotope Probing of Bacteria in Visible and Deep UV-Ranges

Raman stable isotope probing (Raman-SIP) is an excellent technique that can be used to access the overall metabolism of microorganisms. Recent studies have mainly used an excitation wavelength in the visible range to characterize isotopically labeled bacteria. In this work, we used UV resonance Raman spectroscopy (UVRR) to evaluate the spectral red-shifts caused by the uptake of isotopes (¹³C, ¹⁵N, ²H(D) and ¹⁸O) in E. coli cells. Moreover, we present a new approach based on the extraction of labeled DNA in combination with UVRR to identify metabolically active cells. The proof-of-principle study on E. coli revealed heterogeneities in the Raman features of both the bacterial cells and the extracted DNA after labeling with ¹³C, ¹⁵N, and D. The wavelength of choice for studying ¹⁸O- and deuterium-labeled cells is 532 nm is, while ¹³C-labeled cells can be investigated with visible and deep UV wavelengths. However, ¹⁵N-labeled cells are best studied at the excitation wavelength of 244 nm since nucleic acids are in resonance at this wavelength. These results highlight the potential of the presented approach to identify active bacterial cells. This work can serve as a basis for the development of new techniques for the rapid and efficient detection of active bacteria cells without the need for a cultivation step.

Noise Sources and Requirements for Confocal Raman Spectrometers in Biosensor Applications

Raman spectroscopy probes the biochemical composition of samples in a non-destructive, non-invasive and label-free fashion yielding specific information on a molecular level. Nevertheless, the Raman effect is very weak. The detection of all inelastically scattered photons with highest efficiency is therefore crucial as well as the identification of all noise sources present in the system. Here we provide a study for performance comparison and assessment of different spectrometers for confocal Raman spectroscopy in biosensor applications. A low-cost, home-built Raman spectrometer with a complementary metal-oxide-semiconductor (CMOS) camera, a middle price-class mini charge-coupled device (CCD) Raman spectrometer and a laboratory grade confocal Raman system with a deeply cooled CCD detector are compared. It is often overlooked that the sample itself is the most important "optical" component in a Raman spectrometer and its properties contribute most significantly to the signal-to-noise ratio. For this purpose, different representative samples: a crystalline silicon wafer, a polypropylene sample and E. coli bacteria were measured under similar conditions using the three confocal Raman spectrometers. We show that biosensor applications do not in every case profit from the most expensive equipment. Finally, a small Raman database of three different bacteria species is set up with the middle price-class mini CCD Raman spectrometer in order to demonstrate the potential of a compact setup for pathogen discrimination.

Monitoring Deuterium Uptake in Single Bacterial Cells via Two-Dimensional Raman Correlation Spectroscopy

Raman-stable isotope labeling using heavy water (Raman-D₂O) is attracting great interest as a fast technique with various applications ranging from the identification of pathogens in medical samples to the determination of microbial activity in the environment. Despite its widespread applications, little is known about the fundamental processes of hydrogen-deuterium (H/D) exchange, which are crucial for understanding molecular interactions in microorganisms. By combining two-dimensional (2D) correlation spectroscopy and Raman deuterium labeling, we have investigated H/D exchange in bacterial cells under time dependence. Most C-H stretching signals decreased in intensity over time, prior to the formation of the C-D stretching vibration signals. The intensity of the C-D signal gradually increased over time, and the shape of the C-D signal was more uniform after longer incubation times. Deuterium uptake showed high variability between the bacterial genera and mainly led to an observable labeling of methylene and methyl groups. Thus, the C-D signal encompassed a combination of symmetric and antisymmetric CD₂ and CD₃ stretching vibrations, depending on the bacterial genera. The present study allowed for the determination of the sequential order of deuterium incorporation into the functional groups of proteins, lipids, and nucleic acids and hence understanding the process of biomolecule synthesis and the growth strategies of different bacterial taxa. We present the combination of Raman-D₂O labeling and 2D correlation spectroscopy as a promising approach to gain a fundamental understanding of molecular interactions in biological systems.

Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.

Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.

Adaptive Hit-Quality Index for Raman Spectrum Identification

The recognition capability of the identification system using Raman spectroscopy is increasing with the demands in the field. Among the various approaches that determine the identity of a target, signal correlation using a moving window is one of the most effective and intuitive methods. In this paper, we report a new correlation method that is robust to spectral intensity variations. Using the peak distribution of a given spectrum, this method adaptively determines meaningful spectral regions for the identification target. Three commercial Raman spectrometer and a 14 033 library were included in the study, which was used for a library-based chemical discrimination test and mixed material analysis experiments. According to the identification experimental results, the proposed method correctly identified all of the spectra and maintained a mean correlation score above 0.95 while maintaining the correlation score of nontarget materials as low as possible.

Towards Raman spectroscopy of urine as screening tool

For the screening purposes urine is an especially attractive biofluid, since it offers easy and noninvasive sample collection and provides a snapshot of the whole metabolic status of the organism, which may change under different pathological conditions. Raman spectroscopy (RS) has the potential to monitor these changes and utilize them for disease diagnostics. The current study utilizes mouse models aiming to compare the feasibility of the urine based RS combined with chemometrics for diagnosing kidney diseases directly influencing urine composition and respiratory tract diseases having no direct connection to urine formation. The diagnostic models for included diseases were built using principal component analysis with linear discriminant analysis and validated with a leave-one-mouse-out cross-validation approach. Considering kidney disorders, the accuracy of 100% was obtained in discrimination between sick and healthy mice, as well as between two different kidney diseases. For asthma and invasive pulmonary aspergillosis achieved accuracies were noticeably lower, being, respectively, 77.27% and 78.57%. In conclusion, our results suggest that RS of urine samples not only provides a solution for a rapid, sensitive and noninvasive diagnosis of kidney disorders, but also holds some promises for the screening of nonurinary tract diseases.

Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives

This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.

Continuous-wave coherent Raman spectroscopy for improving the accuracy of Raman shifts

Raman spectroscopy is an appealing technique that probes molecular vibrations in a wide variety of materials with virtually no sample preparation. However, accurate and reliable Raman measurements are still a challenge and require more robust and practical calibration methods. We demonstrate the implementation of a simple low-cost continuous-wave (cw) stimulated Raman spectroscopy scheme for accurate and high-resolution spectroscopy. We perform shot noise-limited cw stimulated Raman scattering as well as cw coherent anti-Stokes Raman scattering on polystyrene samples. Our method enables accurate determination of Raman shifts with an uncertainty below 0.1  cm^-1. The setup is used for the characterization of reference materials required for the calibration of Raman spectrometers. Compared with existing standards, we provide an order of magnitude improvement of the uncertainty of Raman energy shifts in a polystyrene reference material.

Analyzing Raman spectroscopic data

This chapter is a short introduction into the data analysis pipeline, which is typically utilized to analyze Raman spectra. We empathized in the chapter that this data analysis pipeline must be tailored to the specific application of interest. Nevertheless, the tailored data analysis pipeline consists always of the same general procedures applied sequentially. The utilized procedures correct for artefacts, standardize the measured spectral data and translate the spectroscopic signals into higher level information. These computational procedures can be arranged into separate groups namely data pre-treatment, pre-processing and modeling. Thereby the pre-treatment aims to correct for non-sample-dependent artefacts, like cosmic spikes and contributions of the measurement device. The block of procedures, which needs to be applied next, is called pre-processing. This group consists of smoothing, baseline correction, normalization and dimension reduction. Thereafter, the analysis model is constructed and the performance of the models is evaluated. Every data analysis pipeline should be composed of procedures of these three groups and we describe every group in this chapter. After the description of data pre-treatment, pre-processing and modeling, we summarized trends in the analysis of Raman spectra namely model transfer approaches and data fusion. At the end of the chapter we tried to condense the whole chapter into guidelines for the analysis of Raman spectra.

Sample-Size Planning for Multivariate Data: A Raman-Spectroscopy-Based Example

The goal of sample-size planning (SSP) is to determine the number of measurements needed for statistical analysis. This SSP is necessary to achieve robust and significant results with a minimal number of measurements that need to be collected. SSP is a common procedure for univariate measurements, whereas for multivariate measurements, like spectra or time traces, no general sample-size-planning method exists. Sample-size planning becomes more important for biospectroscopic data because the data generation is time-consuming and costly. Additionally, ethical reasons do not allow the use of unnecessary samples and the measurement of unnecessary spectra. In this paper, a general sample-size-planning algorithm is presented that is based on learning curves. The learning curve quantifies the improvement of a classifier for an increasing training-set size. These curves are fitted by the inverse-power law, and the parameters of this fit can be utilized to predict the necessary training-set size. Sample-size planning is demonstrated for a biospectroscopic task of differentiating six different bacterial species, including Escherichia coli, Klebsiella terrigena, Pseudomonas stutzeri, Listeria innocua, Staphylococcus warneri, and Staphylococcus cohnii, on the basis of their Raman spectra. Thereby, we estimate the required number of Raman spectra and biological replicates to train a classification model, which consists of principal-component analysis (PCA) combined with linear-discriminant analysis (LDA). The presented algorithm revealed that 142 Raman spectra per species and seven biological replicates are needed for the above-mentioned biospectroscopic-classification task. Even though it was not demonstrated, the learning-curve-based sample-size-planning algorithm can be applied to any multivariate data and in particular to biospectroscopic-classification tasks.

Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications

The chemometric analysis of Raman spectra of biological materials is hampered by spectral variations due to the instrumental setup that overlay the subtle biological changes of interest. Thus, an established statistical model may fail when applied to Raman spectra of samples acquired with a different device. Therefore, model transfer strategies are essential. Herein we report a model transfer approach based on extended multiplicative signal correction (EMSC). As opposed to existing model transfer methods, the EMSC based approach does not require group information on the secondary data sets, thus no extra measurements are required. The proposed model-transfer approach is a preprocessing procedure and can be combined with any method for regression and classification. The performance of EMSC as a model transfer method was demonstrated with a data set of Raman spectra of three Bacillus bacteria spore species ( B. mycoides, B. subtilis, and B. thuringiensis), which were acquired on four Raman spectrometers. A three-group classification by partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV) was performed. The mean sensitivities of the prediction on the independent device were considerably improved by the EMSC method. Besides the mean sensitivity, the model transferability was additionally benchmarked by the newly defined numeric markers: (1) relative Pearson's correlation coefficient and (2) relative Fisher's discriminant ratio. We show that these markers have led to consistent conclusions compared to the mean sensitivity of the classification. The advantage of our defined markers is that the evaluation is more effective and objective, because it is independent of the classification models.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

Raman spectrum identification based on the correlation score using the weighted segmental hit quality index

In this paper, we consider a novel method for identification of Raman spectra recorded on different instruments with different wavelengths.