Noninvasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

Rapid screening and multicomponent quantifications of active components of oral syrup over-the-counter medications by Raman and UV-visible spectroscopy and multivariate regression analysis

Over-the-counter medications (OTCMs) are frequently recommended as a first-line treatment for common ailments, diseases, and illnesses. Oral liquid dosage forms are advantageous for rapid absorption with no dissolution time and are easier for pediatric and geriatric consumers to swallow. The production of these medicines by pharmaceutical industry makes them readily available to the public. Although the US Food and Drug Administration (FDA) provides strict guidelines to drug manufacturers of these products; the risk of counterfeiting is a global issue. This can lead to several adverse effects and health issues. Here, we report a fast screening and quality assurance method using Raman and UV-visible spectroscopy combined with Principal Component Analysis (PCA) and Partial-Least-Square (PLS) regression of commonly used OTCM oral syrups. PLS regressions of UV-visible absorption spectra were used for multicomponent quantifications of the active component (acetaminophen, guaifenesin, dextromethorphan HBr, and phenylephrine HCl) concentrations of OTMCs in flavored (sugar or sugar-free) oral syrups. Raman and UV-visible spectral responses varied based on the type and concentration of the active component analyzed. PCA of the spectral data provided pattern recognition of the oral syrup OTCM. The developed PLS method demonstrated good linearity with an R2 > 0.9784 and high sensitivity with a low detection limit of 0.02 mg/mL for acetaminophen and guaifenesin. Moreover, the simultaneous quantification of concentrations of all active components by the described method yielded good accuracies ranging from 88 to 94%. This study provides an example of the benefits of the combined use of Raman and UV-vis spectral profiling, PCA, and PLS regression for the quality analysis of oral syrups OTCM providing multicomponent quantification of active components with no need for sample extraction. The reported method can be easily adapted and scaled for online detection analysis used in the drug manufacturing industry, both in-situ and field analysis, and for the quality control of syrups OTCM by regulatory agencies and quality control officers.

Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte

There is increasing interest in the application of Raman spectroscopy in a medical setting, ranging from supporting real-time clinical decisions e.g. surgical margins to assisting pathologists with disease classification. However, there remain a number of barriers for adoption in the medical setting due to the increased complexity of probing highly heterogeneous, dynamic biological materials. This inherent challenge can also limit the deployment of higher level analytical approaches such as Artificial Intelligence (AI) including convolutional neural networks (CNN), as there is a lack of a ground truth required for training purposes i.e. in complex clinical samples. Principal component analysis (PCA) is an unsupervised data reduction approach (orthogonal linear transformation) that has been used extensively in spectroscopy for 30+ years, due to its capability to simplify analysis of complex spectroscopic data. However, due to PCA being unsupervised features will inherently appear mixed and their rank may vary between experiments. Here we propose Guided PCA (GPCA), a simple approach that allows PCA to be guided with spectral data to ensure a consistent rank of a key target moiety by the inclusion of a reference (guiding) spectrum to the data set. This simplifies analysis, increases robustness of PCA analysis and improves quantification and the limits of detection and decreases RMSE.

Two-layer reconstruction of Raman spectra in diffusive media based on an analytical model in the time domain

We derive and validate an analytical model that describes the migration of Raman scattered photons in two-layer diffusive media, based on the diffusion equation in the time domain. The model is derived under a heuristic approximation that background optical properties are identical on the excitation and Raman emission wavelengths. Methods for the reconstruction of two-layer Raman spectra have been developed, tested in computer simulations and validated on tissue-mimicking phantom measurements data. Effects of different parameters were studied in simulations, showing that the thickness of the top layer and number of detected photon counts have the most significant impact on the reconstruction. The concept of quantitative, mathematically rigorous reconstruction using the proposed model was finally proven on experimental measurements, by successfully separating the spectra of silicone and calcium carbonate (calcite) layers, showing the potential for further development and eventual application in clinical diagnostics.

Label-Free Quantification of Nanoencapsulated Piperonyl Esters in Cosmetic Hydrogels Using Raman Spectroscopy

Raman spectroscopy is a well-established technique for the molecular characterisation of samples and does not require extensive pre-analytical processing for complex cosmetic products. As an illustration of its potential, this study investigates the quantitative performance of Raman spectroscopy coupled with partial least squares regression (PLSR) for the analysis of Alginate nanoencapsulated Piperonyl Esters (ANC-PE) incorporated into a hydrogel. A total of 96 ANC-PE samples covering a 0.4% w/w-8.3% w/w PE concentration range have been prepared and analysed. Despite the complex formulation of the sample, the spectral features of the PE can be detected and used to quantify the concentrations. Using a leave-K-out cross-validation approach, samples were divided into a training set (n = 64) and a test set, samples that were previously unknown to the PLSR model (n = 32). The root mean square error of cross-validation (RMSECV) and prediction (RMSEP) was evaluated to be 0.142% (w/w PE) and 0.148% (w/w PE), respectively. The accuracy of the prediction model was further evaluated by the percent relative error calculated from the predicted concentration compared to the true value, yielding values of 3.58% for the training set and 3.67% for the test set. The outcome of the analysis demonstrated the analytical power of Raman to obtain label-free, non-destructive quantification of the active cosmetic ingredient, presently PE, in complex formulations, holding promise for future analytical quality control (AQC) applications in the cosmetics industry with rapid and consumable-free analysis.

Model-Based Optimization of Laser Excitation and Detection Improves Spectral Contrast in Noninvasive Diffuse Raman Spectroscopy

Spatially offset Raman spectroscopy (SORS) is a powerful technique for subsurface molecular analysis of optically turbid samples. Numerical modeling of light propagation has been used to investigate opportunities for improving spectral contrast and signal to noise ratio when imaging regions of interest located 0-4.5 mm below the surface in polymer bulk material. Two- and three-dimensional modeling results demonstrate that when analyzing a certain region of interest (ROI) of finite lateral dimensions below the sample surface, offsetting both the laser source and detector in opposite directions from the central point of the ROI can increase the spectral contrast as compared to conventional SORS approach where the detector or the laser source is maintained at the central point (centered SORS). The outlined modeling results have been validated experimentally using a bulk polymer sample with a trans-stilbene ROI (cylinder) below the sample surface. The results show that modeling of the spatial configurations of laser excitation and detection points can be used to optimize the instrument configuration to achieve significant improvements (up to 2.25-fold) in performance over the conventional centered SORS. Such optimal solutions can then be implemented, for example, using robust fiber optic probes, moveable optics, or flexible spatial light modulator instruments for specific applications.

Through-container quantitative analysis of hand sanitizers using spatially offset Raman spectroscopy

The COVID-19 pandemic created an increased demand for hygiene supplies such as hand sanitizers. In response, a large number of new domestic or imported hand sanitizer products entered the US market. Some of these products were later found to be out of specification. Here, to quickly assess the quality of the hand sanitizer products, a quantitative, through-container screening method was developed for rapid and non-destructive screening. Using spatially offset Raman spectroscopy (SORS) and support vector regression (SVR), active ingredients (e.g., type of alcohol) of 173 commercial and in-house products were identified and quantified regardless of the container material or opacity. Alcohol content in hand sanitizer formulations were predicted with high accuracy [Formula: see text] using SVR and [Formula: see text] of the substandard test samples were identified. In sum, a SORS-SVR method was developed and used for testing medical countermeasures used against COVID-19, demonstrating a potential for high-volume testing during public health threats.

Vibrational spectroscopy and chemometrics in GSR: review and current trend

Background

This review represents many significant methods of chemometrics applied as data assessment methods originated by many hyphenated analytical techniques containing their application since its origin to today.

Main body of the abstract

The study has been divided into many parts, which contain many multivariate regression methods. The main aim of this study is to investigate the chemometrics tools used in GSR (gunshot residue) or forensic ballistics.

Short conclusion

As a final point, the end of part of this review deals with the applicability of chemometric methods in forensic ballistics. We select to give an elaborate description of many significant tools established with their algorithm in admire of utilizing and accepting them by researchers not very aware with chemometrics.

Pseudo-3D Subsurface Imaging of Pharmaceutical Solid Dosage Forms Using Micro-spatially Offset Low-Frequency Raman Spectroscopy

A new combinatory Raman subtechnique of low-frequency and micro-spatially offset Raman spectroscopy (denoted micro-SOLFRS) is demonstrated via analysis of pharmaceutical solid dosage forms. A variety of different (multilayer/multicomponent) model systems comprising celecoxib, α-lactose (the anhydrous and monohydrate form), and polyvinylpyrrolidone (PVP) were probed to test the potency of this newly developed technique to, for example, provide qualitative and quantitative information on surface and subsurface layer characteristics, including their thicknesses as well as enable monitoring of surface-driven solid-state form transformations. A simultaneous collection of low- and, the more commonly used, mid-frequency data enabled a direct comparison between these spectral regions, where the low-frequency domain (hence, micro-SOLFRS) proved superior for every respective analysis carried out herein.

Thermal Decomposition of Cocaine and Methamphetamine Investigated by Infrared Spectroscopy and Quantum Chemical Simulations

Examination of thermal decomposition of street samples of cocaine and methamphetamine shows that typical products detected in previous studies are accompanied by a wide palette of simple volatile compounds easily detectable by spectral techniques. These molecules increase smoke toxicity and their spectral detection can be potentially used for identification of drug samples by well-controlled laboratory thermolysis in temperature progression. In our study, street samples of cocaine and methamphetamine have been thermolyzed under vacuum over the temperature range of 350-650 °C. The volatile products (CO, HCN, CH₄, C₂H₄, etc.) have been monitored by high-resolution Fourier-transform infrared (FTIR) spectrometry in this temperature range. The decomposition mechanism has been additionally examined theoretically by quantum-chemical calculations for the highest temperature achieved experimentally in our study and beyond. Prior to analysis, the street samples have also been characterized by FTIR, Raman spectroscopy, energy-dispersive X-ray spectroscopy, and melting point determination.

A New Frontier for Nondestructive Spatial Analysis of Pharmaceutical Solid Dosage Forms: Spatially Offset Low-Frequency Raman Spectroscopy

A new Raman subtechnique, spatially offset low-frequency Raman spectroscopy (SOLFRS), is demonstrated via an analysis of pharmaceutical solid dosage forms. Several different model systems comprised of celecoxib (a popular anti-inflammatory drug), α-lactose anhydrous stable form, α-lactose monohydrate, and polyvinylpyrrolidone (PVP) were used to represent tangible scenarios for the application of SOLFRS. Additionally, the challenges and limitations were highlighted in relation to its real-time use, and potential solutions to address them were also provided. Lastly, the future directions for this new variation of Raman spectroscopic technique were briefly discussed, including its potential for broader application in pharmaceutical analysis and other research fields.

Infrared spectroscopy and spectroscopic imaging in forensic science

Infrared spectroscopy and spectroscopic imaging, are robust, label free and inherently non-destructive methods with a high chemical specificity and sensitivity that are frequently employed in forensic science research and practices. This review aims to discuss the applications and recent developments of these methodologies in this field. Furthermore, the use of recently emerged Fourier transform infrared (FT-IR) spectroscopic imaging in transmission, external reflection and Attenuated Total Reflection (ATR) modes are summarised with relevance and potential for forensic science applications. This spectroscopic imaging approach provides the opportunity to obtain the chemical composition of fingermarks and information about possible contaminants deposited at a crime scene. Research that demonstrates the great potential of these techniques for analysis of fingerprint residues, explosive materials and counterfeit drugs will be reviewed. The implications of this research for the examination of different materials are considered, along with an outlook of possible future research avenues for the application of vibrational spectroscopic methods to the analysis of forensic samples.

A novel approach for subsurface through-skin analysis of salmon using spatially offset Raman spectroscopy (SORS)

In the present study, the possibility of employing spatially offset Raman spectroscopy (SORS) in the qualitative and quantitative characterization of quality parameters of salmon through the skin has been explored. A laboratory-based SORS setup comprising an 830 nm laser was employed, and intact samples and model samples made of salmon tissue constituents were used to investigate the penetration of Raman signals through the dark and light part of salmon skin. Intact salmon samples with both dark and light skin were measured at different spatial offsets. When using spatial offsets in the range of 5-6 mm, the results clearly show that information regarding fatty acid composition and carotenoid content could be obtained from both dark and light parts of the skin. Similar information could not be obtained using conventional backscattering Raman spectroscopy. Model samples of ground salmon spiked with either solutions of carotenoids or a range of vegetable oils were also measured, and at a spatial offset of 5 mm, a clear relationship between Raman carotenoid band intensities and carotenoid concentrations in the model samples was revealed. In addition, high correlations for the estimation of iodine values (i.e., fatty acid unsaturation) could be obtained for SORS measurements through light and dark parts of the salmon skin. A crude estimate suggested that information from around 5 mm beneath the surface area of the salmon skin could be obtained. The choice of a laser line in the near-infrared region is a major prerequisite for successful through-skin analysis of salmon. This feasibility study could pave the way for future Raman analysis of intact salmon.

Non-invasive identification of incoming raw pharmaceutical materials using Spatially Offset Raman Spectroscopy

A new approach to verification of incoming raw materials through packaging in pharmaceutical manufacturing is proposed and demonstrated. The method is based around Spatially Offset Raman Spectroscopy (SORS) and permits a rapid chemical identity analysis of incoming materials to satisfy regulatory requirements but without the need to open the packaging. This dramatically increases the throughput of incoming raw materials into the pharmaceutical manufacturing chain and eliminates the need for a chemically safe sampling environment required for invasive inspection methods. Since the inspection is non-invasive the safety of the operators is ensured and the integrity of inspected material is not compromised by preventing exposure to the ambient atmosphere and cross contamination. The experiments presented here demonstrate the ability to accurately identify common pharmaceutical materials, typically in under 10s acquisition time, through a range of frequently used packaging, including translucent plastic and paper sacks and coloured glass bottles, which can be challenging for conventional Raman spectroscopy as well as other optical spectroscopy methods. With the exception of metallic containers and cardboard drums all the tested packaging materials proved to be amenable to this technique. This demonstrates the viability of this new rapid verification method for non-invasive materials identification in pharmaceutical manufacture.