Method for Removing Spectral Contaminants to Improve Analysis of Raman Imaging Data

Versatile Confocal Raman Imaging Microscope Built from Off-the-Shelf Opto-Mechanical Components

Confocal Raman microscopic (CRM) imaging has evolved to become a key tool for spatially resolved, compositional analysis and imaging, down to the μm-scale, and nowadays one may choose between numerous commercial instruments. That notwithstanding, situations may arise which exclude the use of a commercial instrument, e.g., if the analysis involves toxic or radioactive samples/environments; one may not wish to render an expensive instrument unusable for other uses, due to contamination. Therefore, custom-designed CRM instrumentation-being adaptable to hazardous conditions and providing operational flexibility-may be beneficial. Here, we describe a CRM setup, which is constructed nearly in its entirety from off-the-shelf optomechanical and optical components. The original aim was to develop a CRM suitable for the investigation of samples exposed to tritium. For increased flexibility, the CRM system incorporates optical fiber coupling to both the Raman excitation laser and the spectrometer. Lateral raster scans and axial profiling of samples are facilitated by the use of a motorized xyz-translation assembly. Besides the description of the construction and alignment of the CRM system, we also provide (i) the experimental evaluation of system performance (such as, e.g., spatial resolution) and (ii) examples of Raman raster maps and axial profiles of selected thin-film samples (such as, e.g., graphene sheets).

Raman imaging: An indispensable technique to comprehend the functionalization of lignocellulosic material

With the increasing demands on sustainability in the material science and engineering landscape, the use of wood, a renewable and biodegradable material, for new material development has drawn increasing attentions in the materials science community. To promote the development of new wood-based materials, it is critical to understanding not only wood's hierarchical structure from molecule to macroscale level, but also the interactions of wood with other materials and chemicals upon modification and functionalization. In this review, we discuss the recent advances in the Raman imaging technique, a new approach that combines spectroscopy and microscopy, in wood characterization and structural evolution monitoring during functionalization. We introduce the principles of Raman spectroscopy and common Raman instrumentations. We survey the use of traditional Raman spectroscopy for lignocellulosic material characterizations including cellulose crystallinity determination, holocellulose discrimination, and lignin substructure evaluation. We briefly review the recent studies on wood property enhancement and functional wood-based material development through wood modification including thermal treatment, acetylation, furfurylation, methacrylation, delignification. Subsequently, we highlight the use of the Raman imaging for visualization, spatial and temporal distribution of wood cell wall structure, as well as the microstructure evolution upon functionalization. Finally, we discuss the future prospects of the field.

Investigating kitchen sponge-derived microplastics and nanoplastics with Raman imaging and multivariate analysis

Microplastics can be found almost everywhere, including in our kitchens. The challenge is how to characterise them, particularly for the small ones (<1 μm), referred to as nanoplastics, when they are mixed with larger particles and other components. Herewith we advance Raman imaging to characterise microplastics and nanoplastics released from a dish sponge that we use every day to clean our cookware and eating utensils. The scanning electron microscopy result shows significantly different structures of the soft and hard layers of the sponge, with the hard layer being more likely to shed particles. By scanning the sample surface to generate a spectrum matrix, Raman imaging can significantly improve signal-noise-ratio, compared with individual Raman spectra. Through mapping the characteristic peaks from the matrix that contains hundreds, even thousands of Raman spectra, it is confirmed that the particles released from the soft and hard layers of the sponge are mainly Nylon PA6 and polyethylene terephthalate, respectively. Using principal component analysis (PCA) to decode the spectrum matrix further enhances the signal-noise ratio, which enables mapping the whole set of the spectrum, rather than the selected peaks. By optimising the Raman scanning parameters, the PCA-Raman imaging is able to reliably capture and visualise microplastics and nanoplastics released from both sides of the dish sponge, including a plastic-surrounding-sand composite structure. Overall, PCA-Raman imaging is a holistic and effective approach to characterising miniature plastic particles.

Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics

Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexities involved in the end-to-end Raman analytics pipeline: baseline removal, cosmic noise elimination, and extraction of trusted spectral signatures and abundance maps. Elimination of cosmic noise is the bottleneck in the entire Raman analytics pipeline. Unless this issue is addressed, the realization of autonomous Raman analytics is impractical. Here, we present a learner-predictor strategy-based "automated hyperspectral Raman analysis framework" to rapidly fingerprint the molecular variations in the hyperspectral 2D/3D Raman dataset. We introduce the spectrum angle mapper (SAM) technique to eradicate the cosmic noise from the hyperspectral Raman dataset. The learner-predictor strategy eludes the necessity of human inference, and analytics can be done in autonomous mode. The learner owns the ability to learn; it automatically eliminates the baseline and cosmic noise from the Raman dataset, extracts the predominant spectral signatures, and renders the respective abundance maps. In a nutshell, the learner precisely learned the spectral features space during the hyperspectral Raman analysis. Afterward, the learned spectral features space was translated into a neural network (LNN) model. In the predictor, machine-learned intelligence (LNN) is utilized to predict the alternate batch specimen's abundance maps in real time. The qualitative/quantitative evaluation of abundance maps implicitly lays the foundation for monitoring the offline/inline industrial qualitative/quantitative quality control (QA/QC) process. The present strategy is best suited for 2D/3D/four-dimensional (4D) hyperspectral Raman spectroscopic techniques. The proposed ML framework is intuitive because it obviates human intelligence, sophisticated computational hardware, and solely a personal computer is enough for the end-to-end pipeline.

Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms

Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.

Pipeline for the removal of hardware related artifacts and background noise for Raman spectroscopy

Raman spectroscopy is a real-time, non-contact, and non-destructive technique able to obtain information about the composition of materials, chemicals, and mixtures. It uses the energy transfer properties of molecules to detect the composition of matter. Raman spectroscopy is mainly used in the chemical field because background fluorescence and instrumental noise affect biological (in vitro and in vivo) measurements. In this method, we describe how hardware related artifacts and fluorescence background can be corrected without affecting signal of the measurement. First, we applied manual correction for cosmic ray spikes, followed by automated correction to reduce fluorescence and hardware related artifacts based on a partial 5^th degree polynomial fitting and Tophat correction. Along with this manuscript we provide a MatLab^Ⓡ script for the automated correction of Raman spectra.•

“Polynomial_Tophat_background_subtraction _methods.m” offers an automated method for the removal of hardware related artifacts and fluorescence signals in Raman spectra.

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“Polynomial_Tophat_background_subtraction _methods.m” provides a modifiable MatLab file adjustable for multipurpose spectroscopy analysis.

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We offer a standardized method for Raman spectra processing suitable for biological and chemical applications for modular confocal Raman spectroscopes.

Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes

The intelligence to synchronously identify multiple spectral signatures in a lithium-ion battery electrode (LIB) would facilitate the usage of analytical technique for inline quality control and product development. Here, we present an analytical framework (AF) to automatically identify the existing spectral signatures in the hyperspectral Raman dataset of LIB electrodes. The AF is entirely automated and requires fewer or almost no human assistance. The end-to-end pipeline of AF own the following features; (i) intelligently pre-processing the hyperspectral Raman dataset to eliminate the cosmic noise and baseline, (ii) extract all the reliable spectral signatures from the hyperspectral dataset and assign the class labels, (iii) training a neural network (NN) on to the precisely “labelled” spectral signature, and finally, examined the interoperability/reusability of already trained NN on to the newly measured dataset taken from the same LIB specimen or completely different LIB specimen for inline real-time analytics. Furthermore, we demonstrate that it is possible to quantitatively assess the capacity degradation of LIB via a capacity retention coefficient that can be calculated by comparing the LMO signatures extracted by the analytical framework (AF). The present approach is suited for real-time vibrational spectroscopy based industrial applications; multicomponent chemical reactions, chromatographic, spectroscopic mixtures, and environmental monitoring.

Confocal Raman spectroscopy: Evaluation of a non-invasive technique for the detection of topically applied ketorolac tromethamine in vitro and in vivo

Current information about the pharmacokinetics of an ocular drug can only be achieved by invasive sampling. However, confocal Raman spectroscopy bears the potential to quantify drug concentrations non-invasively. In this project, we evaluated the detection and quantification of ocular ketorolac tromethamine levels with confocal Raman spectroscopy after topical administration. Confocal Raman spectroscopy and high-performance liquid chromatography (HPLC) were compared in terms of sensitivity of detection. Enucleated pig eyes were treated with different concentrations of ketorolac. Hereafter, ketorolac concentrations in the aqueous humor of pig eyes were analyzed by confocal Raman spectroscopy and HPLC. Subsequently, twelve rabbits were treated with Acular™ for four weeks. At several time points, ketorolac concentrations in aqueous humor of the rabbits were measured by confocal Raman spectroscopy followed by drawing an aqueous humor sample for HPLC analysis. In ketorolac treated pig eyes, both ex vivo Raman spectroscopy as well as HPLC were able to detect ketorolac in a broad concentration range. However, in vivo confocal Raman spectroscopy in rabbits was unable to detect ketorolac in contrast to HPLC. To conclude, confocal Raman spectroscopy has the capacity to detect ketorolac tromethamine in vitro, but currently lacks sensitivity for in vivo detection.

Temperature dependent separation of immiscible polymer blend in a melted state

The density and the spectral fingerprint of a compounded blend or composite vary widely depending on the type of the components and their composition. However, the currently used polymer separation techniques, such as density-based and optical sorting systems are not suitable for recovering these materials fully due to the physical-chemical bonding between the components. The application of a novel separation principle creates the opportunity to enrich the blend fractions to neat, homogeneous zones in a melted state by utilising centrifugal force. In this study three different types of plastics: high density polyethylene, polystyrene and polyethylene terephthalate were deeply investigated in order to understand the separability of their blends as a function of rotation time and melt temperature. The results showed that the separation of polymer mixtures and blends depends strongly on the viscosity and bulk density at a given temperature, and the initial particle size also has a significant impact.

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The analysis does not usually require extensive sample preparation; structural and chemical information can be obtained without labeling. However, each Raman image contains thousands of spectra; this raises difficulties when extracting hidden information, especially for components with similar chemical structures. This work introduces a multivariate analysis to address this issue. The protocol establishes a general method to visualize the main components, including lignin, cellulose, and hemicellulose within the plant cell wall. In this protocol, procedures for sample preparation, spectral acquisition, and data processing are described. It is highly dependent upon operator skill at sample preparation and data analysis. By using this approach, a Raman investigation can be performed by a non-specialist user to acquire high-quality data and meaningful results for plant cell wall analysis.