A novel multivariate curve resolution-alternating least squares (MCR-ALS) methodology for application in hyperspectral Raman imaging analysis

Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy

In this work, a new model with broad utility for quantitative spectroscopy development is reported. A primary objective of this work is to create a novel modeling procedure that may allow for higher automation of the model development process. The fundamental concept is simple yet powerful even for complex spectra and is employed with no additional preprocessing. This approach is applicable for several types of spectroscopic data to develop regression models that have similar or greater quality than the current methods. The key modeling steps are a matrix transformation and subsequent feature selection process that are collectively referred to as iterative regression of corrective baselines (IRCB). The transformed matrix (Xtransform) is a linearized form of the original X data set. Features from Xtransform that are predictive of Y can be ranked and selected by ordinary least-squares regression. The best features (rows of Xtransform) are linear depictions of Y that can be utilized to develop regression models with several machine learning models. The IRCB workflow is first detailed by using a case study of Fourier transform infrared (FTIR) spectroscopy for prepared solutions of a three-component mixture. Next, IRCB is applied and compared to benchmark results for the 2006 "Chimiométrie" near-infrared spectroscopy (NIR) soil composition challenge and Raman measurements of a simulated nuclear waste slurry.

Toy building bricks as a potential source of microplastics and nanoplastics

Microplastics and nanoplastics have become noteworthy contaminants, affecting not only outdoor ecosystems but also making a notable impact within indoor environments. The release of microplastics and nanoplastics from commonly used plastic items remains a concern, and the characterisation of these contaminants is still challenging. This study focused on evaluating the microplastics and nanoplastics produced from plastic building bricks. Using Raman spectroscopy and correlation analysis, the plastic material used to manufacture building blocks was determined to be either acrylonitrile butadiene styrene (correlation value of 0.77) or polycarbonate (correlation value of 0.96). A principal component analysis (PCA) algorithm was optimised for improved detection of the debris particles released. Some challenges in microplastic analysis, such as the interference from the colourants in the building block materials, was explored and discussed. Combining Raman results with scanning electron microscopy - energy-dispersive X-ray spectroscopy, we found the scratches on the building blocks to be a significant source of contamination, estimated several thousand microplastics and several hundred thousand nanoplastics were generated per mm2 following simulated play activities. The potential exposure to microplastics and nanoplastics during play poses risks associated with the ingestion and inhalation of these minute plastic particles.

Heterotypic Seeding Generates Mixed Amyloid Polymorphs

Aggregation of the amyloid β (Aβ) peptide into fibrils represents one of the major biochemical pathways underlying the development of Alzheimer's disease (AD). Extensive studies have been carried out to understand the role of fibrillar seeds on the overall kinetics of amyloid aggregation. However, the precise effect of seeds that are structurally or sequentially different from Aβ on the structure of the resulting amyloid aggregates is yet to be fully understood. In this work, we use nanoscale infrared spectroscopy to probe the spectral facets of individual aggregates formed by aggregating Aβ42 with antiparallel fibrillar seeds of Aβ (16-22) and E22Q Aβ (1-40) Dutch mutant and demonstrate that Aβ can form heterotypic or mixed polymorphs that deviate significantly from its expected parallel cross β structure. We further show that formation of heterotypic aggregates is not limited to coaggregation of Aβ and its isomers, and that the former can form heterotypic fibrils with alpha synuclein and brain protein lysates. These findings highlight the complexity of Aβ aggregation in AD and underscore the need to explore how Aβ interacts with other brain components, which is crucial for developing better therapeutic strategies for AD.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

Label-free detection and mapping of graphene oxide in single HeLa cells based on MCR-Raman spectroscopy

GO is a 2D nanomaterial that has attracted attention in many industries in recent years, such as the chemical industry, electronics or medicine. Due to its unique properties such as strength, hydrophilicity and large specific surface area with the possibility of functionalization, GO is a particularly attractive material in biomedicine as a candidate for use in targeted drug delivery. In such a case, we need information on whether graphene oxide penetrates into cells and whether we are able to detect and monitor GO in these cells during and also after the treatment to evaluate possible degradation process of GO and its interaction within the cell compartements. This work introduces the Raman spectroscopy as label-free detection method showing the advantages of combining Raman spectroscopy with MCR (Multivariate Curve Resolution) analysis for advanced detection of GO in cervical cancer (HeLa) cells. Our synthesized GO is characterized firstly by AFM, SEM and Raman spectroscopy and then MCR-Raman spectroscopy is used to detect internalized GO in individual HeLa cells. Moreover, by using our methodology, distribution of GO as well as its chemical stability inside the cell for up to six months is investigated without using any additional labeling or tracing the GO. Thus, MCR-Raman spectroscopy may become a new analytical tool in preclinical and clinical applications of graphene-based nanotheranostics.

Measurement of chemical penetration in skin using Stimulated Raman scattering microscopy and multivariate curve resolution - alternating least squares

The mechanistic understanding of skin penetration underpins the design, efficacy and risk assessment of many high-value products including functional personal care products, topical and transdermal drugs. Stimulated Raman scattering (SRS) microscopy, a label free chemical imaging tool, combines molecular spectroscopy with submicron spatial information to map the distribution of chemicals as they penetrate the skin. However, the quantification of penetration is hampered by significant interference from Raman signals of skin constituents. This study reports a method for disentangling exogeneous contributions and measuring their permeation profile through human skin combining SRS measurements with chemometrics. We investigated the spectral decomposition capability of multivariate curve resolution - alternating least squares (MCR-ALS) using hyperspectral SRS images of skin dosed with 4-cyanophenol. By performing MCR-ALS on the fingerprint region spectral data, the distribution of 4-cyanophenol in skin was estimated in an attempt to quantify the amount permeated at different depths. The reconstructed distribution was compared with the experimental mapping of CN, a strong vibrational peak in 4-cyanophenol where the skin is spectroscopically silent. The similarity between MCR-ALS resolved and experimental distribution in skin dosed for 4 h was 0.79 which improved to 0.91 for skin dosed for 1 h. The correlation was observed to be lower for deeper layers of skin where SRS signal intensity is low which is an indication of low sensitivity of SRS. This work is the first demonstration, to the best of our knowledge, of combining SRS imaging technique with spectral unmixing methods for direct observation and mapping of the chemical penetration and distribution in biological tissues.

Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis

BACKGROUND

Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development.

AIM

To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters.

METHODS

Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences.

RESULTS

Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples.

CONCLUSION

Raman hyperspectral imaging with machine learning can be used to investigate, interpret, and classify different data collection parameters, sample preparation methods, and enzyme immobilization supports, providing crucial insight into enzyme immobilization process development.

Raman imaging for the analysis of silicone microplastics and nanoplastics released from a kitchen sealant

Plastic products are used ubiquitously and can potentially release microplastics and nanoplastics into the environment, for example, products such as the silicone sealant used in kitchens. It is important to develop an effective method to monitor these emerging contaminants, as reported herein. By using advanced Raman imaging to characterize microplastics and nanoplastics from hundreds of spectra in a scanning spectrum matrix and not from a single spectrum or peak, the signal-to-noise ratio can be significantly increased, from a statistical point of view. The diffraction of the laser spot usually constrains the imaging resolution (such as at ∼300 nm), which is also pushed to the limit in this report by shrinking the scanning pixel size down to ∼50 nm to capture and image small nanoplastics effectively. To this end, image reconstruction is developed to successfully pick up the meaningful Raman signal and intentionally avoid the noise. The results indicate that the silicone sealant in a kitchen can release a significant amount of microplastics and nanoplastics. Overall, advanced Raman imaging can be employed to characterize the microplastics and even nanoplastics that are smaller than the diffraction limit of the laser via Raman imaging and image reconstruction toward deconvolution.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Comparative Analysis of G-Layers in Bast Fiber and Xylem Cell Walls in Flax Using Raman Spectroscopy

In a response to gravitropic stress, G-layers (gelatinous layers) were deposited in xylem cell walls of tilted flax plants. G-layers were produced in both tension wood (upper side) as expected but were also observed in opposite wood (lower side). Raman spectral profiles were acquired for xylem G-layers from the tension and opposite side as well as from the G-layer of bast fibers grown under non-tilted conditions. Statistical analysis by principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) clearly distinguished bast fiber G-layers from xylem G-layers. Discriminating bands were observed for cellulose (380–1150–1376 cm–1), hemicelluloses (517–1094–1126–1452 cm–1) and aromatics (1270–1599–1658 cm–1). PCA did not allow separation of G-layers from tension/opposite-wood sides. In contrast, the two types of xylem G-layers could be incompletely discriminated through PLS-DA. Overall, the results suggested that while the architecture (polymer spatial distribution) of bast fibers G-layers and xylem G-layers are similar, they should be considered as belonging to a different cell wall layer category based upon ontogenetical and chemical composition parameters.

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a "coffee ring". MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R²_CV.

A packaged food internal Raman signal separation method based on spatially offset Raman spectroscopy combined with FastICA

Raman spectroscopy attempts to reflect food quality by characterizing molecular vibration and rotation. However, the blocking of optical signals by packaging materials and the interference of the optical signal generated by the packaging itself make the detection of internal food quality without destroying packaging highly difficult. In this regard, this paper proposes a novel packaged food internal signal separation based on spatially offset Raman spectroscopy (SORS) coupled with improved fast independent component analysis (FastICA). Firstly, the Raman scattering image of the packaged food with offset laser incident point was obtained. Then, the movable quadratic mean of information entropy was used to select the observation feature region of the image. Thirdly, the main independents decomposed by the optimized FastICA method were identified by spectral attenuation characteristics of the SORS peak signal. Finally, the non-negativity of the separated signal was ensured by baseline recognition and correction. The effectiveness of this method was verified by refactoring the similarity between the signal and the reference signal by testing three different packaging and four internal materials under standard experimental conditions. The applicability of the method was proved by the internal signal separation of three packaged foods on sale. The experimental results indicate that the proposed method can separate the Raman signal of packaged food and can be used as a pretreatment method and auxiliary analysis means for the detection of packaged food.

In situ Raman spectroscopy for real time detection of cysteine

Cysteine serves a wide range of important biological and chemical functions and may have an association to neurodegenerative disease and cancer. Rapid, accurate analytical methods for cysteine detection are thus highly desirable. In this work, we report an investigation into the utility of in situ Raman spectroscopy as a Process Analytical Technology (PAT) for real time monitoring of cysteine. Cysteine concentrations are tracked in real time using Raman spectroscopy across a range of pharmaceutically-relevant concentrations, demonstrating the capability of Raman spectroscopy detection for in situ cysteine monitoring. The concentration range over which this analytical methodology can be applied is successfully established. As such, the results herein serve as a proof-of-principle investigation to demonstrate and evaluate the capabilities of a real time Raman spectroscopic approach for in situ cysteine detection, thus informing the range of important chemical and biological processes to which this approach can be applied. To the best of our knowledge, this is the first report of in situ Raman spectroscopy for real time monitoring of dynamically changing cysteine process concentrations.

Leaf microscopy applications in photosynthesis research: identifying the gaps

Leaf imaging via microscopy has provided critical insights into research on photosynthesis at multiple junctures, from the early understanding of the role of stomata, through elucidating C4 photosynthesis via Kranz anatomy and chloroplast arrangement in single cells, to detailed explorations of diffusion pathways and light utilization gradients within leaves. In recent decades, the original two-dimensional (2D) explorations have begun to be visualized in three-dimensional (3D) space, revising our understanding of structure-function relationships between internal leaf anatomy and photosynthesis. In particular, advancing new technologies and analyses are providing fresh insight into the relationship between leaf cellular components and improving the ability to model net carbon fixation, water use efficiency, and metabolite turnover rate in leaves. While ground-breaking developments in imaging tools and techniques have expanded our knowledge of leaf 3D structure via high-resolution 3D and time-series images, there is a growing need for more in vivo imaging as well as metabolite imaging. However, these advances necessitate further improvement in microscopy sciences to overcome the unique challenges a green leaf poses. In this review, we discuss the available tools, techniques, challenges, and gaps for efficient in vivo leaf 3D imaging, as well as innovations to overcome these difficulties.

Process monitoring of polysaccharide deketalization for vaccine bioconjugation development using in situ analytical methodology

Pneumococcal conjugate vaccines (PCVs) are formed by bioconjugation of a carrier protein to the purified capsular polysaccharide (Ps) from multiple serological strains of Streptococcus pneumoniae. The associated bioconjugation chemistry relies on initial selective modifications to the Ps backbone structure. Among these modifications, removal of a ketal functional group, termed deketalization, is one that is important for pharmaceutical PCV production. Herein, we report a process monitoring investigation into the deketalization of a polysaccharide relevant to PCV process development. We have applied process analytical technology (PAT) for in situ process monitoring to study the deketalization reaction in real time. We find that in situ FTIR spectroscopy elucidates multiple classes of reaction kinetics, one of which correlates strongly with the deketalization reaction of interest. This PAT approach to real time reaction monitoring offers the possibility of improved process monitoring in the pharmaceutical production of PCVs. To our knowledge, this report represents the first PAT investigation into Ps deketalization. Our findings suggest that broader application of PAT to the chemical modifications associated with PCV bioconjugation, as well as other pharmaceutically relevant bioconjugation processes, carries the power to enhance process understanding, control, and efficiency through real time process monitoring.

Machine Learning and Chemical Imaging to Elucidate Enzyme Immobilization for Biocatalysis

Biocatalysis has rapidly become an essential tool in the scientific and industrial communities for the development of efficient, safe, and sustainable chemical syntheses. Immobilization of the biocatalyst, typically an engineered enzyme, offers significant advantages, including increased enzyme stability and control, resistance to environmental change, and enhanced reusability. Determination and optimization of the spatial and chemical distribution of immobilized enzymes are critical for proper functionality; however, analytical methods currently employed for doing so are frequently inadequate. Machine learning, in the form of multivariate curve resolution, with Raman hyperspectral imaging is presented herein as a potential method for investigating the spatial and chemical distribution of evolved pantothenate kinase immobilized onto two diverse, microporous resins. An exhaustive analysis indicates that this method can successfully resolve, both spatially and spectrally, all chemical species involved in enzyme immobilization, including the enzyme, both resins, and other key components. Quantitation of the spatial coverage of immobilized enzymes, a key parameter used for process development, was accomplished. Optimal analytical parameters were determined by the evaluation of different excitation wavelengths. Exploratory chemometric approaches, including principal component analysis, were utilized to investigate the chemical species embedded within the data sets and their relationships. The totality of this information can be utilized for an enhanced understanding of enzyme immobilization processes and can allow for the further implementation of biocatalysis within the scientific and pharmaceutical communities.

MCR-ALS with sample insertion constraint to enhance the sensitivity of surface-enhanced Raman scattering detection

The multivariate curve resolution-alternative least squares (MCR-ALS) algorithm was modified with sample insertion constraint to deconvolute the overlapping peaks in SERS spectra. The developed method was evaluated by the spectral data simulated using a Gaussian distribution function to generate two independent peaks corresponding to a capping agent and an analyte. The spectra were generated with different overlapping levels and various intensity ratios of the analyte to the capping agent. By using MCR-ALS with the sample insertion constraint, the peak of the capping agent was completely excluded to obtain a calibration model of the analyte with R² > 0.95 under all conditions. Furthermore, our developed method was later applied to a real SERS measurement to quantify carbofuran (analyte) using the azo-coupling reaction with p-ATP (capping agent) on silver nanoparticles as a SERS substrate. A calibration model of derivative carbofuran phenol was generated with R² = 0.99 and LOD = 28.19 ppm. To assess the performance of the calibration model, the model was used to estimate the concentration of carbofuran in an external validation set. It was found that the RMSE of prediction was only 2.109 with a promising R² = 0.97.

Application of MCR-ALS with EFA on FT-IR spectra of lipid bilayers in the assessment of phase transition temperatures: Potential for discernment of coupled events

Temperature-dependent transmission FT-IR spectroscopy and DSC measurements were conducted on lipid multibilayers constituted from 1,2-dipalmitoyl-sn-glycero-3-phosphocholine. Lipid multibilayers made from 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine, which do not form a ripple phase, were examined as a reference. Spectra were analyzed using multivariate curve resolution technique with alternating least squares and evolving factor analysis (MCR-ALS with EFA) and lipid phase transition temperatures were determined. Polar parts of lipid molecules exert greater response on a ripple phase formation than non-polar ones. However, vibrational signatures of hydrocarbon chains with intramolecular origins display certain qualitative differences that pave the way for future work oriented on uncoupling the events that drive ripple phase formation.

Raman hyperspectral imaging with multivariate analysis for investigating enzyme immobilization

Directed enzyme evolution has led to significant application of biocatalysis for improved chemical transformations throughout the scientific and industrial communities. Biocatalytic reactions utilizing evolved enzymes immobilized within microporous supports have realized unique advantages, including notably higher enzyme stability, higher enzyme load, enzyme reusability, and efficient product-enzyme separation. To date, limited analytical methodology is available to discern the spatial and chemical distribution of immobilized enzymes, in which techniques for surface visualization, enzyme stability, or activity are instead employed. New analytical tools to investigate enzyme immobilization are therefore needed. In this work, development, application, and evaluation of an analytical methodology to study enzyme immobilization is presented. Specifically, Raman hyperspectral imaging with principal component analysis, a multivariate method, is demonstrated for the first time to investigate evolved enzymes immobilized onto microporous supports for biocatalysis. Herein we demonstrate the ability to spatially and spectrally resolve evolved pantothenate kinase (PanK) immobilized onto two commercially-available, chemically-diverse porous resins. This analytical methodology is able to chemically distinguish evolved enzyme, resin, and chemical species pertinent to immobilization. As such, a new analytical approach to study immobilized biocatalysts is demonstrated, offering potential wide application for analysis of protein or biomolecule immobilization.

Effect of molten sodium nitrate on the decomposition pathways of hydrated magnesium hydroxycarbonate to magnesium oxide probed by in situ total scattering

The effect of NaNO₃ and its physical state on the thermal decomposition pathways of hydrated magnesium hydroxycarbonate (hydromagnesite, HM) towards MgO was examined by in situ total scattering. Pair distribution function (PDF) analysis of these data allowed us to probe the structural evolution of pristine and NaNO₃-promoted HM. A multivariate curve resolution alternating least squares (MCR-ALS) analysis identified the intermediate phases and their evolution upon the decomposition of both precursors to MgO. The total scattering results are discussed in relation with thermogravimetric measurements coupled with off-gas analysis. MgO is obtained from pristine HM (N₂, 10 °C min^-1) through an amorphous magnesium carbonate intermediate (AMC), formed after the partial removal of water of crystallization from HM. The decomposition continues via a gradual release of water (due to dehydration and dehydroxylation) and, in the last step, via decarbonation, leading to crystalline MgO. The presence of molten NaNO₃ alters the decomposition pathways of HM, proceeding now through AMC and crystalline MgCO₃. These results demonstrate that molten NaNO₃ facilitates the release of water (from both water of crystallization and through dehydroxylation) and decarbonation, and promotes the crystallization of MgCO₃ and MgO in comparison to pristine HM. MgO formed from the pristine HM precursor shows a smaller average crystallite size than NaNO₃-promoted HM and preserves the initial nano-plate-like morphology of HM. NaNO₃-promoted HM was decomposed to MgO that is characterized by a larger average crystallite size and irregular morphology. Additionally, in situ SEM allowed visualization of the morphological evolution of HM promoted with NaNO₃ at a micrometre scale.