A small-window moving average-based fully automated baseline estimation method for Raman spectra

Use of microscale heterogeneity in samples for spectral factorization-A strategy to build robust prediction models for nondestructive analyses

Nondestructive spectroscopic analysis is widely used to evaluate food composition. However, distinguishing analytes of interest from other compounds remains challenging. Since most foods are heterogeneous when viewed under a microscope, we hypothesized that spectra measured at microscopic points would be "purer" than spectra acquired from a larger area. By coupling this data with nonnegative matrix factorization (NMF), the analytes of interest can be separated. This preliminary study discusses the quantification of glucose in mixtures of different sugars. Samples were made by mixing glucose with other powders in different ratios and Raman spectra were measured at 200 micro-points for each sample. NMF was applied to factorize the mixed spectra into spectra of pure compounds and their concentrations, leading to the accurate quantification of glucose, while eliminating the effects of other compounds. While this study targets simple powders, separation of analytes using microscale heterogeneity is applicable for measuring more complex foods.

Unveiling the effect of CaCl2 on amyloid β aggregation via supercritical angle Raman and fluorescence spectroscopy and microscopy

Amyloid β aggregation is an important factor in Alzheimer's disease. Since calcium homeostasis plays an important role in amyloid β aggregation, it is crucial to study the interaction between calcium ions and amyloid β directly at the surface of the lipid membrane. With supercritical angle techniques, the signal of interest at the surface is easily separated from the bulk solution, making them a powerful tool for aggregation study. In this work, the influence of calcium ions on amyloid β aggregation over different aggregation time periods is investigated with supercritical angle Raman and fluorescence spectroscopy and microscopy. Note that calcium ions have a larger influence on amyloid β1-42 than on the 40 amino acid variant. We found that a small layer of calcium ions significantly protects the lipid membrane against the protein insertion process.

MURDA: Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains for Medical Diagnosis Assistance

Artificial intelligence combined with Raman spectroscopy for disease diagnosis is on the rise. However, these methods require a large amount of annotated spectral data for modeling to achieve high diagnostic accuracy. Annotating labels consumes significant medical resources and time. To reduce dependence on labeled medical data resources, we propose a method called Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains (MURDA). It transfers knowledge learned from source domain data sets of different diseases to an unlabeled target domain data set. Compared to knowledge transfer from a single source domain, knowledge from multiple disease source domains provides more generalized knowledge. Considering the diversity of autoimmune diseases and the limited sample size, we apply MURDA to assist in the medical diagnosis of autoimmune diseases. Additionally, we propose a Double-Branch Multiscale Convolutional Self-Attention (DMCS) feature extractor that is more suitable for spectral data feature extraction. On three sets of serum Raman spectroscopy data sets for autoimmune diseases, the multisource domain adaptation diagnostic accuracy of MURDA was superior to traditional single source and multisource models, with accuracy rates of 73.6%, 83.4%, and 82.9%, respectively. Compared with pure source tasks without domain adaptation, it improved by 15.1%, 36%, and 21.6%, respectively. This demonstrates the effectiveness of Raman spectroscopy combined with MURDA in diagnosing autoimmune diseases. We investigated the important decision dependency peaks in migration tasks, providing assistance for future research on artificial intelligence combined with Raman spectroscopy for diagnosing autoimmune diseases. Furthermore, to validate the effectiveness and generalization performance of MURDA, we conducted experiments on the publicly available RRUFF data set, exploring the application of multisource unsupervised domain adaptation in more Raman spectroscopy scenarios.

Spectral intensity drift correction of Spark Mapping Analysis for large-size metal materials

BACKGROUND

Spectral intensity drift is a frequent issue in analytical processes, especially in long time excitation scanning for large size metal materials, which can significantly adversely impact the accuracy and stability of analysis results. Spectral intensity drift correction is the process of preprocessing spectral data using mathematical algorithms in order to facilitate the subsequent qualitative and quantitative analysis of spectra, especially in combination with stoichiometric methods. Up to now, spectral intensity drift correction within prolonged excitation has not been reported yet.

RESULTS

We propose an intensity drift correction method for element content of large-size samples using the Spark Mapping Analysis for Large Samples (SMALS) technique. By considering the row-by-row and column-by-column mapping modes of the SMALS, this includes curve fitting baseline correction for in-row and in-column correction, as well as total average value correction for inter-row and inter-column correction. The final measurement values are derived by coupling rows with columns. The careful implementation of correction steps can enhance baseline correction performance, effectively reducing measurement errors a drift errors. Application of this method to characterize the cross and longitudinal sections of an oversized steel billet indicates high agreement with composition distribution obtained by micro-beam X-ray fluorescence (μ-XRF). The corrected longitudinal and cross-sectional data also exhibit strong alignment. Comparison of statistical analysis results pre- and post-correction demonstrates significant improvements in the clarity of elements segregation pattern.

SIGNIFICANCE

This intensity drift correction method not only enhances the spectral quality but also improves the accuracy and robustness of quantitative and qualitative spectral analysis. This study contributes to establishing a robust foundation for component characterization of large-size metal materials using the SMALS technique. The novel spectral intensity correction method shows theoretical significance and practical value for large-scale, long-duration excitation scanning analysis.

Pesticide Identification Using Surface-Enhanced Raman Spectroscopy and Density Functional Theory Calculations: From Structural Insights to On-Site Detection

Pesticides play an important role in conventional agriculture. Yet, their harmful effects on the environment are becoming increasingly apparent. The occurrence of pesticides is hence being monitored worldwide. For fast, easy, yet sensitive identification, surface-enhanced Raman spectroscopy (SERS) is a powerful tool. In this study, a method is introduced that may be amended to in-field detection of pesticides. Gold and silver nanoparticles were synthesized, size-tailored, and characterized. The herbicide paraquat and the fungicide thiram served as model compounds. The preparation yielded reproducible SERS spectra. Using quantum chemical computation, Raman and SERS spectra were calculated and analyzed. The interpretation of vibrational modes in combination with SERS enhancement and attenuation allowed us to identify compound-specific bands. The assignment was interpreted in terms of the orientation of paraquat and thiram on the gold and silver nanoparticle surfaces. Paraquat preferred a co-planar arrangement parallel to the gold nanoparticle surface and a head-on orientation on the silver nanoparticle. For thiram, breaking of the disulfide bond was recognized, such that interaction with the surface occurred via the sulfur atoms. Successful detection of the pesticides after recollection from vegetable leaves demonstrated the method's applicability for pesticide identification.

Predicting Silicate Glass Geochemistry Using Raman Spectroscopy and Supervised Machine Learning: Partial Least Square Applications to Amorphous Raman Spectra

Here, Raman spectroscopy is used to develop a univariate partial least squares (PLS) calibration capable of quantifying geochemistry in synthetic and natural silicate glass samples. The calibration yields eight oxide-specific models that allow predictions of silicon dioxide (SiO2), sodium oxide (Na2O), potassium oxide (K2O), calcium oxide (CaO), titanium dioxide (TiO2), aluminum oxide (Al2O3), ferrous oxide (FeOT), and magnesium oxide (MgO) (wt%) in glasses spanning a wide range of compositions, while also providing correlation-coefficient matrices that highlight the importance of specific Raman channels in the regression of a particular oxide. The PLS suite is trained on 48 of the 69 total glasses, and tested against 21 validation samples (i.e., held out of training). Trends in root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP) model accuracy metrics are investigated to uncover the efficacy of utilizing multivariate analysis for such Raman data and are contextualized against recently produced strategies. The technique yields an average root mean of calibration (∼2.4 wt%), cross-validation (∼ 2.9 wt%), prediction (∼ 2.6 wt%), and normalized variance (∼ 28%). Raman band positional shifts are also mapped against underlying chemical variations; with major influences arising primarily as a function of overall oxidation state and silica concentration: via ferric cation (Fe3+)/ferrous cation (Fe2+) ratios and SiO2 (wt%). The algorithm is further validated preliminarily against a separate external set of 11 natural basaltic glasses to unravel the limitations of the synthetic models on natural samples, and to determine the suitability of "universal" Raman-model applications in scenarios where prior chemical contextualization of the target sample is possible. This study represents the first time Raman spectra of amorphous silicates have been paired with PLS, offering a foundation for future improvements utilizing these systems.

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.

Fully automated F-wave corridor extraction and analysis algorithm for F-wave analyses and MUNE studies

F-waves are used in motor unit number estimation (MUNE) studies, which require rapid dedicated software to perform calculations. The aim of this study is to define a mathematical method for a fully automated F-wave extraction algorithm to perform F-wave and MUNE studies while performing baseline corrections without distorting traces. Ten recordings from each class, such as healthy controls, polio patients and ALS patients, were included. Submaximal stimuli were applied to the median and ulnar nerves to record 300 traces from the abductor pollicis brevis and abductor digiti minimi muscles. The autocorrelation function and the signal of sum of all traces were used to find the location for the maximum amplitude of the F-waves. F-waves were revealed by using a cutting window. Linear line estimation was preferred for baseline corrections because it did not cause any distortion in the traces. The algorithm automatically revealed F-waves from all 30 recordings in accordance with the locations marked by a neurophysiologist. The execution of the algorithm was less than 2 (usually < 1) minutes when 300 traces were analyzed. Mean sMUP amplitudes and MUNE values are important for differentiating healthy controls from patients. Moreover, F-wave parameters belonging to polio patients on whom there was a relatively low number of studies conducted were also evaluated.

Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra

Spectroscopic peak parameters are important since they provide information about the analyte under study. Besides obtaining these parameters, peak fitting also resolves overlapped peaks. Thus, the obtained parameters should permit the construction of a higher-resolution version of the original spectrum. However, peak fitting is not an easy task due to computational reasons and because the true nature of the analyte is often unknown. These difficulties are major impediments when large hyperspectral data sets need to be processed rapidly, such as for manufacturing process control. We have developed a novel and relatively fast two-part algorithm to perform peak fitting and resolution enhancement on such data sets. In the first part of the algorithm, estimates of the total number of bands and their parameters were obtained from a representative spectrum in the data set, using a combination of techniques. Starting with these parameter estimates, all the spectra were then iteratively and rapidly fitted with Gaussian bands, exploiting intrinsic features of the Gaussian distribution with vector operations. The best fits for each spectrum were retained. By reducing the obtained bandwidths and commensurately increasing their amplitudes, high-resolution spectra were constructed that greatly improved correlation-based analyses. We tested the performance of the algorithm on synthetic spectra to confirm that this method could recover the ground truth correlations between highly overlapped peaks. To assess effective peak resolution, the method was applied to low-resolution spectra of glucose and compared to results from high-resolution spectra. We then processed a larger spectral data set from mammalian cells, fixed with methanol or air drying, to demonstrate the resolution enhancement of the algorithm on complex spectra and the effects of resolution-enhanced spectra on two-dimensional correlation spectroscopy and principal component analyses. The results indicated that the algorithm would allow users to obtain high-resolution spectra relatively fast and permit the recovery of important aspects of the data's intrinsic correlation structure.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Novel Compton suppression equipment in γ-ray spectrometry with list mode data acquisition

A novel Compton suppression device has been developed at the Radiation and Nuclear Safety Authority of Finland to improve the sensitivity of measurements in the Gamma Laboratory. It utilizes γ-γ anticoincidence, but operates as a full coincidence system. Specific software has been developed for the sorting, visualization and analysis of list mode data produced by the multi-detector list-mode devices in the Gamma Laboratory. By utilizing the software, the coincidence data can be accessed and the true-coincidence losses of photo-peaks of multiple gamma-ray-emitting nuclides restored in the analysis. This simplifies data analysis and further increases the sensitivity of the device in low count-rate gamma spectrometry. A detailed Geant4 simulation model of the device was developed and used to optimize the device as well as to support calibrations and complex analysis tasks. The setup has been integrated to current laboratory information management system used in the Gamma Laboratory. Compton suppression reduces the continuous background seen by the high-purity germanium detector by a factor of 3-10, in addition to comparable reduction in the Compton continuum of any peaks in the spectrum. A comparison with the results of conventional gamma-ray spectrometry is presented.

Assessing the quality of stored red blood cells using handheld Spatially Offset Raman spectroscopy with multisource correlation analysis

In this work we employ Spatially Offset Raman Spectroscopy (SORS) to non-invasively identify storage-related changes in red blood cell concentrate (RCC) in-situ within standard plastic transfusion bags. To validate the measurements, we set up a parallel study comparing both bioanalytical data (obtained by blood-gas analysis, hematology analysis and spectrophotometric assays), and Raman spectrometry data from the same blood samples. We then employ Multisource Correlation Analysis (MuSCA) to correlate the different types of data in RCC. Our analysis confirmed a strong correlation of glucose, methemoglobin and oxyhemoglobin with their respective bioassay values in RCC units. Finally, by combining MuSCA with k-means clustering, we assessed changes in all Raman wavenumbers during cold storage in both RCC Raman data from the current study and parallel RCC supernatant Raman data previously acquired from the same units. Direct RCC quality monitoring during storage, would help to establish a basis for improved inventory management of blood products in blood banks and hospitals based on analytical data.

Bearing Fault Diagnosis Based on an Enhanced Image Representation Method of Vibration Signal and Conditional Super Token Transformer

Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is an advanced deconvolution method, which can effectively inhibit the interference of background noise and distinguish the fault period by calculating the multipoint kurtosis values. However, multipoint kurtosis (MKurt) could lead to misjudgment since it is sensitive to spurious noise spikes. Considering that L-kurtosis has good robustness with noise, this paper proposes a multipoint envelope L-kurtosis (MELkurt) method for establishing the temporal features. Then, an enhanced image representation method of vibration signals is proposed by employing the Gramian Angular Difference Field (GADF) method to convert the MELkurt series into images. Furthermore, to effectively learn and extract the features of GADF images, this paper develops a deep learning method named Conditional Super Token Transformer (CSTT) by incorporating the Super Token Transformer block, Super Token Mixer module, and Conditional Positional Encoding mechanism into Vision Transformer appropriately. Transfer learning is introduced to enhance the diagnostic accuracy and generalization capability of the designed CSTT. Consequently, a novel bearing fault diagnosis framework is established based on the presented enhanced image representation and CSTT. The proposed method is compared with Vision Transformer and some CNN-based models to verify the recognition effect by two experimental datasets. The results show that MELkurt significantly improves the fault feature enhancement ability with superior noise robustness to kurtosis, and the proposed CSTT achieves the highest diagnostic accuracy and stability.

Gold-coated silver nanowires for long lifetime AFM-TERS probes

Tip-enhanced Raman scattering (TERS) microscopy is an advanced technique for investigation at the nanoscale because of its excellent properties, such as its label-free functionality, non-invasiveness, and ability to simultaneously provide topographic and chemical information. The probe plays a crucial role in TERS technique performance. Widely used AFM-TERS probes fabricated with metal deposition suffer from relatively low reproductivity as well as limited mapping and storage lifetime. To solve the reproducibility issue, silver nanowire (AgNW)-based TERS probes were developed, which, thanks to the high homogeneity of the liquid-phase synthesis of AgNW, can achieve high TERS performance with excellent probe reproductivity, but still present short lifetime due to probe oxidation. In this work, a simple Au coating method is proposed to overcome the limited lifetime and improve the performance of the AgNW-based TERS probe. For the Au-coating, different [Au]/[Ag] molar ratios were investigated. The TERS performance was evaluated in terms of changes in the enhancement factor (EF) and signal-to-noise ratio through multiple mappings and the storage lifetime in air. The Au-coated AgNWs exhibited higher EF than pristine AgNWs and galvanically replaced AgNWs with no remarkable difference between the two molar ratios tested. However, for longer scanning time and multiple mappings, the probes obtained with low Au concentration showed much longer-term stability and maintained a high EF. Furthermore, the Au-coated AgNW probes were found to possess a longer storage lifetime in air, allowing for long and multiple TERS mappings with one single probe.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

Practical considerations for quantitative and reproducible measurements with stimulated Raman scattering microscopy

This tutorial review presents the most important practical considerations for sample preparation, instrument set-up, image acquisition and data analysis to obtain reproducible SRS measurements.

Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning

We have recently developed a low-cost spark-induced breakdown spectroscopy (SIBS) instrument for in-situ analysis of toxic metal aerosol particles that we call TARTA (toxic-metal aerosol real time analyzer). In this work, we applied machine learning methods to improve the quantitative analysis of elemental mass concentrations measured by this instrument. Specifically, we applied least absolute shrinkage and selection operator (LASSO), partial least squares (PLS) regression, principal component regression (PCR), and support vector regression (SVR) to develop multivariate calibration models for 13 metals (e.g., Cr, Cu, Mn, Fe, Zn, Co, Al, K, Be, Hg, Cd, Pb, and Ni), some of which are included on the US EPA hazardous air pollutants (HAPS) list. The calibration performance, adjusted coefficient of determination (R2) and normalized root mean square error (RMSE), and limit of detection (LOD) of the proposed models were compared to those of univariate calibration models for each analyte. Our results suggest that machine learning models tend to have better prediction accuracy and lower LODs than conventional univariate calibration, of which the LASSO approach performs the best with R2 > 0.8 and LODs of 40-170 ng m-3 at a sampling time of 30 min and a flow rate of 15 l min -1. We then assessed the applicability of the LASSO model for quantifying elemental concentrations in mixtures of these metals, serving as independent validation datasets. Ultimately, the LASSO model developed in this work is a very promising machine learning approach for quantifying mass concentration of metals in aerosol particles using TARTA.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy

Skin cancer is currently the most common type of cancer with millions of cases diagnosed worldwide yearly. The current gold standard for clinical diagnosis of skin cancer is an invasive and relatively time-consuming procedure, consisting of visual examination followed by biopsy collection and histopathological analysis. Raman spectroscopy has been shown to efficiently aid the non-invasive diagnosis of skin cancer when probing the surface of the skin. In this study, we employ a recent development of Raman spectroscopy (Spatially Offset Raman Spectroscopy, SORS) which is able to look deeper in tissue and create a deep layer biochemical profile of the skin in areas where cancer lesions subtly evolve. After optimizing the measurement parameters on skin tissue phantoms, we then adopted SORS on human skin tissue from different anatomical areas to investigate the contribution of the different skin layers to the recorded Raman signal. Our results show that using a diffuse beam with zero offset to probe a sampling volume where the lesion is typically included (surface to epidermis-dermis junction), provides the optimum signal-to-noise ratio (SNR) and may be employed in future skin cancer screening applications.

Design of a confocal micro-Raman spectroscopy system and research on microplastics detection

Traditional micro-Raman spectroscopy technology has the disadvantages of a weak signal and low signal-to-noise ratio. To fix these issues, a cost-effective and rigorous design method is proposed in this paper, whereby a confocal micro-Raman spectroscopy system is designed and built, and a low-cost reflector and high-pass filter are introduced into the Raman signal-receiving module. The Raman light incident is fully perpendicular to the coupling lens by adjusting the reflection angle of the mirror, making the focus of the coupling lens highly conjugate with the focus of the microscope objective, to enhance the intensity of the Raman signal and improve the signal-to-noise ratio. In order to better apply this technology to the detection and study of microplastics in offshore sediments, a reflective illumination light path is used to avoid the visual interference caused by the capillary structure and opacity of the glass cellulose filter membrane. The detection and analysis of the microplastics on the glass cellulose filter membrane have been carried out by the confocal micro-Raman system designed, which is low cost and capable of obtaining good detection results and meeting the requirements of microplastics detection. The system designed in this paper is expected to be applied to the research and development of Raman detection equipment for microplastics in marine sediments, which is beneficial to promote the development of marine microplastic monitoring technology in the world.

Non-invasive monitoring of red blood cells during cold storage using handheld Raman spectroscopy

BACKGROUND

The current best practices allow for the red blood cells (RBCs) to be stored for prolonged periods in blood banks worldwide. However, due to the individual-related variability in donated blood and RBCs continual degradation within transfusion bags, the quality of stored blood varies considerably. There is currently no method for assessing the blood product quality without compromising the sterility of the unit. This study demonstrates the feasibility of monitoring storage lesion of RBCs in situ while maintaining sterility using an optical approach.

STUDY DESIGN AND METHODS

A handheld spatially offset Raman spectroscopy (RS) device was employed to non-invasively monitor hemolysis and metabolic changes in 12 red cell concentrate (RCC) units within standard sealed transfusion bags over 7 weeks of cold storage. The donated blood was analyzed in parallel by biochemical (chemical analysis, spectrophotometry, hematology analysis) and RS measurements, which were then correlated through multisource correlation analysis.

RESULTS

Raman bands of lactate (857 cm^-1 ), glucose (787 cm^-1 ), and hemolysis (1003 cm^-1 ) were found to correlate strongly with bioanalytical data over the length of storage, with correlation values 0.98 (95% confidence interval [CI]: 0.86-1.00; p = .0001), 0.95 (95% CI: 0.71-0.99; p = .0008) and 0.97 (95% CI: 0.79-1.00; p = .0004) respectively.

DISCUSSION

This study demonstrates the potential of collecting information on the clinical quality of blood units without breaching the sterility using Raman technology. This could significantly benefit quality control of RCC units, patient safety and inventory management in blood banks and hospitals.

An improved PD-AsLS method for baseline estimation in EDXRF analysis

Baseline correction is an important step in energy-dispersive X-ray fluorescence analysis. The asymmetric least squares method (AsLS), adaptive iteratively reweighted penalized least squares method (airPLS), and asymmetrically reweighted penalized least squares method (arPLS) are widely used to automatically select the data points for the baseline. Considering the parametric sensitivity of the aforementioned methods and the statistical characteristics of the X-ray energy spectrum, this paper proposes an asymmetrically reweighted penalized least squares method based on the Poisson distribution (PD-AsLS) to automatically correct the baseline of X-ray spectra. Monte Carlo (MC) simulation is used to obtain the background spectrum, and PD-AsLS is used to estimate the baseline of the background. The relative error and the absolute error between the simulated background and PD-AsLS estimated background are used to determine the accuracy of PD-AsLS. The correlation coefficient (COR) and the root mean square error (RMSE) between the estmated baseline and the real baseline are calculated, and results of PD-AsLS are compared with results of three other classical methods (arPLS, airPLS and AsLS) to evaluate the reliability of PD-AsLS. The results of PD-AsLS show that the COR is above 0.95 and RMSE is less than 6. The stability and the practicability of PD-AsLS are also evaluated in experiments. A sample is measured five time to get its X-ray energy spectra, and the coefficient of variation (CV) of the estimated baseline is smaller than that of measured spectra. Experiments show that PD-AsLS can estimate baselines better than arPLS without any overestimation. Those results indicate that PD-AsLS can reliably estimate the baselines of X-ray spectra and effectively suppress the statistical fluctuation.

Augmented Two-Dimensional Correlation Spectroscopy for the Joint Analysis of Correlated Changes in Spectroscopic and Disparate Sources

Here, we present an augmented form of two-dimensional correlation spectroscopy, that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. The integration is affected by augmenting every spectrum in a hyperspectral data set with relevant non-spectroscopic data to permit two-dimensional correlation analysis^(2D-COS) of the ensemble of augmented spectra. A k-means clustering is then applied to the results of the perturbation domain decomposition to determine which Raman peaks cluster with any of the non-spectroscopic data. We introduce and explain the method with the aid of synthetic spectra and synthetic non-spectroscopic data. We then demonstrate this approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells and parallel bioassays of hormone mRNA expression and C-peptide levels in spent medium. These pancreatic endocrine cells generally contain insulin or glucagon. Insulin has disulfide bonds that produce Raman scattering near 513 cm^-1, but no tryptophan. For insulin-positive cells, we found that the application of multisource correlation analysis revealed a high correlation between insulin mRNA and Raman scattering in the disulfide region. In contrast, glucagon has no disulfide bonds but does contain tryptophan. For glucagon-positive cells, we also observed a high correlation between glucagon mRNA and tryptophan Raman scattering (∼757 cm^-1). We conclude with a discussion of methods to enhance spectral resolution and its effects on the performance of multisource correlation analysis.

Granadaene Photobleaching Reduces the Virulence and Increases Antimicrobial Susceptibility of Streptococcus agalactiae

Streptococcus agalactiae, also known as Group B Streptococcus (GBS), is increasingly recognized as a major cause of soft tissue and invasive diseases in the elderly and diabetic populations. Antibiotics like penicillin are used with great frequency to treat these infections, although antimicrobial resistance is increasing among GBS strains and underlines a need for alternative methods not reliant on traditional antibiotics. GBS granadaene pigment is related to the hemolysin/cytolysin of GBS, which is critical for the pathogenesis of GBS diseases. Here, we show that photobleaching granadaene dampens the hemolytic activity of GBS. Furthermore, photobleaching of this antioxidant was found to increase GBS susceptibility to killing by reactive oxygen species like hydrogen peroxide. Treatment with light was also shown to affect GBS membrane permeability and contribute to increased susceptibility to the cell membrane-targeting antibiotic daptomycin. Overall, our study demonstrates dual effects of photobleaching on the virulence and antimicrobial susceptibility of GBS and suggests a novel approach for the treatment of GBS infection.

SERS-Active Pattern in Silver-Ion-Exchanged Glass Drawn by Infrared Nanosecond Laser

The irradiation of silver-to-sodium ion-exchanged glass with 1.06-μm nanosecond laser pulses of mJ-range energy results in the formation of silver nanoparticles under the glass surface. Following chemical removal of ~25-nm glass layer reveals a pattern of nanoparticles capable of surface enhancement of Raman scattering (SERS). The pattern formed when laser pulses are more than half-overlapped provides up to ~105 enhancement and uniform SERS signal distribution, while the decrease of the pulse overlap results in an order of magnitude higher but less uniform enhancement.

Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas-chromatography mass-spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.

Crystallographic Characterisation of Ultra-Thin, or Amorphous Transparent Conducting Oxides-The Case for Raman Spectroscopy

The electronic and optical properties of transparent conducting oxides (TCOs) are closely linked to their crystallographic structure on a macroscopic (grain sizes) and microscopic (bond structure) level. With the increasing drive towards using reduced film thicknesses in devices and growing interest in amorphous TCOs such as n-type InGaZnO4 (IGZO), ZnSnO3 (ZTO), p-type CuxCrO2, or ZnRh2O4, the task of gaining in-depth knowledge on their crystal structure by conventional X-ray diffraction-based measurements are becoming increasingly difficult. We demonstrate the use of a focal shift based background subtraction technique for Raman spectroscopy specifically developed for the case of transparent thin films on amorphous substrates. Using this technique we demonstrate, for a variety of TCOs CuO, a-ZTO, ZnO:Al), how changes in local vibrational modes reflect changes in the composition of the TCO and consequently their electronic properties.

Investigation of Laser Power Output and Its Effect on Raman Spectrum for Marine Metal Corrosion Cleaning

The relationship between the laser power output and its effect on Raman spectrum is investigated for a laser cleaning application of marine metal corrosion processing. First, an image feature based on a corrosion degree evaluation is implemented before cleaning. The image features include texture coarseness, texture entropy, texture intensity, texture contrast, the texture’s cluster degree, and texture homogeneity. To decrease the image feature dimension for a convenient application, the Analytic Hierarchy Process (AHP) method is used to estimate the weight of each feature. Then the linear weighted sum of image features can be computed to get only one evaluation result. Second, a series of laser power outputs are implemented for the cleaning application under a typical corrosion degree. Then the analysis results of Raman spectrum can be obtained. The analyzed spectrum results include the corrosion components and their contents. Lastly, the relationship between laser power output and Raman spectrum under a typical initial corrosion degree can be constructed. This research study can build the prediction result of the cleaning effect map for the workpiece and guide the secondary processing of metal surface cleaning.

Optimization and quantification of the systematic effects of a rolling circle filter for spectral pre-processing

Spectral pre-processing, especially baseline approximation, is a crucial part in quantitative spectroscopic applications, such as Raman or FTIR spectroscopy. Filters used for this task need to be optimized for their application, in order to achieve a sufficient baseline approximation while minimizing the distortion of the spectral lines. We propose a combined method that optimizes a rolling circle filter and quantifies the residual systematic influence on the spectral lines by a Monte Carlo approach that simulates and subsequently analyses spectra with known line properties and known maximum baseline curvature.

Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples

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.

Deposits in the lungs of Kwädąy Dän Ts'ìnchį man: Characterization by a combination of analytical microscopical methods

OBJECTIVES

The approximately 250 years old remains of the Kwädąy Dän Ts'ìnchį man were found in a glacier in Canada. Studying the state of preservation of the corpse, we observed black deposits in his lung. Following this observation we wanted to determine: (1) location of the deposits in the lung tissue, (2) composition and origins of the deposits.

METHODS

By light microscopy (LM) and transmission electron microscopy (TEM), we studied the deposits in the Kwädąy Dän Ts'ìnchį man' s lung and compared it with distribution of anthracotic deposits in contemporary samples from the David Harwick Pathology Centre (DHPC). To determine chemical composition of the inclusions we used Raman spectroscopy. Scanning electron microscopy and elemental mapping was used for determine the chemical elements.

RESULTS

The histopathological identification of anthracosis in the Kwädąy Dän Ts'ìnchį man's lung allowed us to distinguish crushed parenchyma from conducting airway tissue and identification of particles using LM and TEM. Crystal particles were found using TEM. Ordered carbonaceous material (graphene and graphite), disordered carbonaceous material (soot) and what might be minerals (likely conglomerates) were found with Raman spectrometry. Gold and lead particles in the lung were discovered with scanning electron microscopy and elemental mapping.

CONCLUSIONS

Presence of soot particles in anthracotic areas in the Kwädąy Dän Ts'ìnchį man's lung probably were due to an inhalation of particles in open fires. Gold and lead particles are most likely of an environmental origin and may have been inhaled and could have impacted his health and his Champagne and Aishihik First Nations (CAFN) contemporaries.

Toward Better Understanding of EBPR Systems via Linking Raman-Based Phenotypic Profiling with Phylogenetic Diversity

This study reports a proof-of concept study to demonstrate the novel approach of phenotyping microbial communities in enhanced biological phosphorus removal (EBPR) systems using single cell Raman microspectroscopy and link it with phylogentic structures. We use hierarchical clustering analysis (HCA) of single-cell Raman spectral fingerprints and intracellular polymer signatures to separate and classify the functionally relevant populations in EBPR systems, namely polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), as well as other microbial populations. We then investigated the link between Raman-based community phenotyping and 16S rRNA gene-based phylogenetic characterization of four lab-scale EBPR systems with varying solid retention time (SRT) to gain insights into possible genotype-function relationships. Combined and simultaneous phylogenetic and phenotypic evaluation of EBPR ecosystems revealed SRT-dependent phylogenetic and phenotypic characteristics of the PAOs and GAOs, and their association with EBPR performance. The phenotypic diversity and plasticity of PAO populations, which otherwise could not be obtained with phylogenetic analysis alone, showed complex but potentially crucial association with EBPR process stability.

An Automated Baseline Correction Method Based on Iterative Morphological Operations

Raman spectra usually suffer from baseline drift caused by fluorescence or other reasons. Therefore, baseline correction is a necessary and crucial step that must be performed before subsequent processing and analysis of Raman spectra. An automated baseline correction method based on iterative morphological operations is proposed in this work. The method can adaptively determine the structuring element first and then gradually remove the spectral peaks during iteration to get an estimated baseline. Experiments on simulated data and real-world Raman data show that the proposed method is accurate, fast, and flexible for handling different kinds of baselines in various practical situations. The comparison of the proposed method with some state-of-the-art baseline correction methods demonstrates its advantages over the existing methods in terms of accuracy, adaptability, and flexibility. Although only Raman spectra are investigated in this paper, the proposed method is hopefully to be used for the baseline correction of other analytical instrumental signals, such as IR spectra and chromatograms.

Raman spectroscopy of stored red blood cell concentrate within sealed transfusion blood bags

Spectral information relevant to the quality of stored blood can be obtained in situ through sealed blood transfusion bags using a commercially available instrument.

Empirical Factors Affecting the Quality of Non-Negative Matrix Factorization of Mammalian Cell Raman Spectra

Mammalian cells contain various macromolecules that can be investigated non-invasively with Raman spectroscopy. The particular mixture of major macromolecules present in a cell being probed are reflected in the measured Raman spectra. Determining macromolecular identities and estimating their concentrations from these mixture Raman spectra can distinguish cell types and otherwise enable biological research. However, the application of canonical multivariate methods, such as principal component analysis (PCA), to perform spectral unmixing yields mathematical solutions that can be difficult to interpret. Non-negative matrix factorization (NNMF) improves the interpretability of unmixed macromolecular components, but can be difficult to apply because ambiguities produced by overlapping Raman bands permit multiple solutions. Furthermore, theoretically sound methods can be difficult to implement in practice. Here we examined the effects of a number of empirical approaches on the quality of NNMF results. These approaches were evaluated on simulated mammalian cell Raman hyperspectra and the results were used to develop an enhanced procedure for implementing NNMF. We demonstrated the utility of this procedure using a Raman hyperspectral data set measured from human islet cells to recover the spectra of insulin and glucagon. This was compared to the relatively inferior PCA of these data.

Background Subtraction of Raman Spectra Based on Iterative Polynomial Smoothing

In this paper, a novel background subtraction algorithm is presented that can automatically recover Raman signal. This algorithm is based on an iterative polynomial smoothing method that highly reduces the need for experience and a priori knowledge. First, a polynomial filter is applied to smooth the input spectrum (the input spectrum is just an original spectrum at the first iteration). The output curve of the filter divides the original spectrum into two parts, top and bottom. Second, a proportion is calculated between the lowest point of the signal in the bottom part and the highest point of the signal in the top part. The proportion is a key index that decides whether to go into a new iteration. If a new iteration is needed, the minimum value between the output curve and the original spectrum forms a new curve that goes into the same filter in the first step and continues as another iteration until no more iteration is needed to finally get the background of the original spectrum. Results from the simulation experiments not only show that the iterative polynomial smoothing algorithm achieves good performance, processing time, cost, and accuracy of recovery, but also prove that the algorithm adapts to different background types and a large signal-to-noise ratio range. Furthermore, real measured Raman spectra of organic mixtures and non-organic samples are used to demonstrate the application of the algorithm.

Using Raman spectroscopy to assess hemoglobin oxygenation in red blood cell concentrate: an objective proxy for morphological index to gauge the quality of stored blood?

A relationship has been found between hemoglobin oxygenation of stored red blood cells (measured using Raman spectroscopy) and a morphological index.

The characterization of plant species using first-derivative fluorescence spectra

Plants are one of the most important parts of the ecological system and demand a reliable method for accurate classification. In this study, the first-derivative fluorescence spectral curves (FDFSCs) based on laser-induced fluorescence technology were proposed for the characterization of plant species. The measurement system is mainly composed of a spectrometer, an excitation light source (the two excitation wavelengths are 460 and 556 nm, respectively), and an intensified charge-coupled device camera. FDFSCs were calculated from the deviation between the fluorescence values at each wavelength, plus and minus one band, divided by the wavelength range. Principal component analysis was utilized to analyze the FDFSCs by extracting the main attributes and reducing the dimensionality of variables. A support vector machine was used to evaluate FDFSC performance for the identification of plant species. Plant species that are difficult to distinguished by the naked eye, can be identified effectively using the proposed FDFSCs. For the 556 nm and 460 nm excitation wavelengths, the overall identification rates of the six plant species evaluated were 93.3% and 91.7%, respectively. Experimental results demonstrated that the combination of the FDFSCs with multivariate analysis could provide a simple and reliable method for the characterization of plant species.

Label-free biomarkers of human embryonic stem cell differentiation to hepatocytes

Four different label-free, minimally invasive, live single cell analysis techniques were applied in a quantitative comparison, to characterize embryonic stem cells and the hepatocytes into which they were differentiated. Atomic force microscopy measures the cell's mechanical properties, Raman spectroscopy measures its chemical properties, and dielectrophoresis measures the membrane's capacitance. They were able to assign cell type of individual cells with accuracies of 91% (atomic force microscopy), 95.5% (Raman spectroscopy), and 72% (dielectrophoresis). In addition, stimulated Raman scattering (SRS) microscopy was able to easily identify hepatocytes in images by the presence of lipid droplets. These techniques, used either independently or in combination, offer label-free methods to study individual living cells. Although these minimally invasive biomarkers can be applied to sense phenotypical or environmental changes to cells, these techniques have most potential in human stem cell therapies where the use of traditional biomarkers is best avoided. Destructive assays consume valuable stem cells and do not characterize the cells which go on to be used in therapies; whereas immunolabeling risks altering cell behavior. It was suggested how these four minimally invasive methods could be applied to cell culture, and how they could in future be combined into one microfluidic chip for cell sorting. © 2016 International Society for Advancement of Cytometry.

Label-free identification and characterization of living human primary and secondary tumour cells

We used three label-free minimally invasive methods to characterize individual cells derived from primary and secondary tumours from the same patient, and of the same type – colorectal. Raman spectroscopy distinguished cells by their biochemical 'fingerprint' in a vibrational spectrum with 100% accuracy, and revealed that the primary cell line contains more lipids and alpha-helix proteins, whereas the secondary cell line contains more porphyrins and beta-sheet proteins. Stimulated Raman scattering (SRS) microscopy distinguished cells in chemically-specific images of CH2 bonds which revealed lipid droplets in secondary tumour cells. Atomic force microscopy (AFM) was used to distinguish cells with 80% accuracy by measuring their elasticity – secondary tumour cells (SW620) are around 3 times softer than primary ones (SW480). As well as characterizing the physical and biochemical differences between cell lines in vitro, these techniques offer three novel methods which could potentially be used for diagnosis – to assign a tumour as primary or secondary.

Condensing Raman spectrum for single-cell phenotype analysis

Background

In recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc.

Results

In this work, we have proposed an approach called rDisc to discretize the original Raman spectrum into only a few (usually less than 20) representative peaks (Raman shifts). The approach has advantages in removing noises, and condensing the original spectrum. In particular, effective signal processing procedures were designed to eliminate noise, utilising wavelet transform denoising, baseline correction, and signal normalization. In the discretizing process, representative peaks were selected to signicantly decrease the Raman data size. More importantly, the selected peaks are chosen as suitable to serve as key biological markers to differentiate species and other cellular features. Additionally, the classication performance of discretized spectra was found to be comparable to full spectrum having more than 1000 Raman shifts. Overall, the discretized spectrum needs about 5storage space of a full spectrum and the processing speed is considerably faster. This makes rDisc clearly superior to other methods for single-cell classication.

Development and integration of block operations for data invariant automation of digital preprocessing and analysis of biological and biomedical Raman spectra

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

Fully automated decomposition of Raman spectra into individual Pearson's type VII distributions applied to biological and biomedical samples

Rapid technological advances have made the acquisition of large numbers of spectra not only feasible, but also routine. As a result, a significant research effort is focused on semi-automated and fully automated spectral processing techniques. However, the need to provide initial estimates of the number of peaks, their band shapes, and the initial parameters of these bands presents an obstacle to the full automation of peak fitting and its incorporation into fully automated spectral-preprocessing workflows. Moreover, the sensitivity of peak-fit routines to initial parameter settings and the resultant variations in solution quality further impede user-free operation. We have developed a technique to perform fully automated peak fitting on fully automated preconditioned spectra-specifically, baseline-corrected and smoothed spectra that are free of cosmic-ray-induced spikes. Briefly, the tallest peak in a spectrum is located and a Gaussian peak-fit is performed. The fitted peak is then subtracted from the spectrum, and the procedure is repeated until the entire spectrum has been processed. In second and third passes, all the peaks in the spectrum are fitted concurrently, but are fitted to a Pearson Type VII model using the parameters for the model established in the prior pass. The technique is applied to a synthetic spectrum with several peaks, some of which have substantial overlap, to test the ability of the method to recover the correct number of peaks, their true shape, and their appropriate parameters. Finally the method is tested on measured Raman spectra collected from human embryonic stem cells and samples of red blood cells.