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

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.

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.

Comparison of Vibrational Spectroscopic Techniques for Quantification of Water in Natural Deep Eutectic Solvents

Vibrational spectroscopic techniques, i.e., attenuated total reflectance infrared (ATR-IR), near infrared spectroscopy (NIRS) and Raman spectroscopy (RS), coupled with Partial Least Squares Regression (PLSR), were evaluated as cost-effective label-free and reagent-free tools to monitor water content in Levulinic Acid/L-Proline (LALP) (2:1, mol/mol) Natural Deep Eutectic Solvent (NADES). ATR-IR delivered the best outcome of Root Mean Squared Error (RMSE) of Cross-Validation (CV) = 0.27% added water concentration, RMSE of Prediction (P) = 0.27% added water concentration and mean % relative error = 2.59%. Two NIRS instruments (benchtop and handheld) were also compared during the study, respectively yielding RMSECV = 0.35% added water concentration, RMSEP = 0.56% added water concentration and mean % relative error = 5.13% added water concentration, and RMECV = 0.36% added water concentration, RMSEP = 0.68% added water concentration and mean % relative error = 6.23%. RS analysis performed in quartz cuvettes enabled accurate water quantification with RMECV = 0.43% added water concentration, RMSEP = 0.67% added water concentration and mean % relative error = 6.75%. While the vibrational spectroscopic techniques studied have shown high performance in relation to reliable determination of water concentration, their accuracy is most likely related to their sensitivity to detect the LALP compounds in the NADES. For instance, whereas ATR-IR spectra display strong features from water, Levulinic Acid and L-Proline that contribute to the PLSR predictive models constructed, NIRS and RS spectra are respectively dominated by either water or LALP compounds, representing partial molecular information and moderate accuracy compared to ATR-IR. However, while ATR-IR instruments are common in chemistry and physics laboratories, making the technique readily transferable to water quantification in NADES, Raman spectroscopy offers promising potential for future development for in situ, sample withdrawal-free analysis for high throughput and online monitoring.

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).

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.

Portable through Bottle SORS for the Authentication of Extra Virgin Olive Oil

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil; with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.

In Situ Water Quantification in Natural Deep Eutectic Solvents Using Portable Raman Spectroscopy

Raman spectroscopy is a label-free, non-destructive, non-invasive analytical tool that provides insight into the molecular composition of samples with minimum or no sample preparation. The increased availability of commercial portable Raman devices presents a potentially easy and convenient analytical solution for day-to-day analysis in laboratories and production lines. However, their performance for highly specific and sensitive analysis applications has not been extensively evaluated. This study performs a direct comparison of such a commercially available, portable Raman system, with a research grade Raman microscope system for the analysis of water content of Natural Deep Eutectic Solvents (NADES). NADES are renewable, biodegradable and easily tunable “green” solvents, outcompeting existing organic solvents for applications in extraction from biomass, biocatalysis, and nanoparticle synthesis. Water content in NADES is, however, a critical parameter, affecting their properties, optimal use and extraction efficiency. In the present study, portable Raman spectroscopy coupled with Partial Least Squares Regression (PLSR) is investigated for rapid determination of water content in NADES samples in situ, i.e., directly in glassware. Three NADES systems, namely Betaine Glycerol (BG), Choline Chloride Glycerol (CCG) and Glucose Glycerol (GG), containing a range of water concentrations between 0% (w/w) and 28.5% (w/w), were studied. The results are directly compared with previously published studies of the same systems, using a research grade Raman microscope. PLSR results demonstrate the reliability of the analysis, surrendering R² values above 0.99. Root Mean Square Errors Prediction (RMSEP) of 0.6805%, 0.9859% and 1.2907% w/w were found for respectively unknown CCG, BG and GG samples using the portable device compared to 0.4715%, 0.3437% and 0.7409% w/w previously obtained by analysis in quartz cuvettes with a Raman confocal microscope. Despite the relatively higher values of RMSEP observed, the comparison of the percentage of relative errors in the predicted concentration highlights that, overall, the portable device delivers accuracy below 5%. Ultimately, it has been demonstrated that portable Raman spectroscopy enables accurate quantification of water in NADES directly through glass vials without the requirement for sample withdrawal. Such compact instruments provide solvent and consumable free analysis for rapid analysis directly in laboratories and for non-expert users. Portable Raman is a promising approach for high throughput monitoring of water content in NADES that can support the development of new analytical protocols in the field of green chemistry in research and development laboratories but also in the industry as a routine quality control tool.

Axially slanted laser illumination scheme for direct and accurate Raman spectroscopic determination of gemcitabine concentration in freeze-dried gemcitabine injection powder housed in a glass container

When Raman spectroscopy is employed for a direct in situ determination of ingredient concentration for a product stored in a glass container, minimization of the interfering glass background in the collected spectrum is demanding to secure a more accurate analysis. To meet this request, an axially slanted illumination (ASI) scheme slantingly irradiating laser on the headspace side of a glass container and positioning a detector beneath the container was demonstrated in this study. This ASI scheme was basically designed to increase the distance between the laser illumination spot and detector location to minimize the number of glass photons reaching the detector. The analytical utility of the scheme was evaluated for the determination of gemcitabine concentration (42.9-58.2 wt%) in the gemcitabine injection powder housed in a glass container. Using the ASI scheme, the spectral features of the gemcitabine powder became distinct with only a weak underlying glass background signal. For comparative purpose, when an axially perpendicular offset (APO) scheme perpendicularly irradiating laser on the side wall where the sample was filled was used, the magnitude of glass background was higher, and the most intense gemcitabine peak was largely buried in the glass peak. The accuracy for determination of gemcitabine concentration using the ASI scheme was superior with an error of 0.20 wt%, while 0.33 wt% with employing the APO scheme. Overall, this study demonstrates that the ASI scheme is a potentially versatile Raman spectroscopic tool for fast non-sampling analysis of other products stored in a glass container.

Comparison of Raman and attenuated total reflectance (ATR) infrared spectroscopy for water quantification in natural deep eutectic solvent

Natural deep eutectic solvents (NADES) are ionic solutions, of great interest for extraction from biomass, biocatalysis, and nanoparticle synthesis. They are easily synthesised and eco-friendly, have low volatility and high dissolution power, and are biodegradable. However, water content in NADES is a critical parameter, affecting their optimal use and extraction efficiency. Vibrational spectroscopic techniques are rapid, label-free, non-destructive, non-invasive, and cost-effective analytical tools that can probe the molecular composition of samples. A direct comparison between a previous study using attenuated total reflectance infrared (ATR-IR) spectroscopy for water quantification in NADES and the same investigation performed with Raman spectroscopy is presently reported. Three NADES systems, namely betaine-glycerol (BG), choline chloride-glycerol (CCG), and glucose-glycerol (GG), containing a range of water concentrations between 0% (w/w) and 40% (w/w), have been analysed with Raman spectroscopy coupled to partial least squares regression multivariate analysis. The values of root mean square error of cross-validation (RMSECV) obtained from analysis performed on the pre-processed spectra over the full spectral range (150-3750 cm^-1) are respectively 0.2966% (w/w), 0.4703% (w/w), and 0.2351% (w/w) for BG, GG, and CCG. While the direct comparison to previous ATR-IR results shows essentially similar outcomes for BG, the RMSECV is 33.14% lower and 65.84% lower for CG and CCG. Furthermore, mean relative errors obtained with Raman spectroscopy, and calculated from a set of samples used as independent samples, were 1.452% (w/w), 1.175% (w/w), and 1.188% (w/w). Ultimately, Raman spectroscopy delivered performances for quantification of water in NADES with similar accuracy to ATR-IR. The present demonstration clearly highlights the potential of Raman spectroscopy to support the development of new analytical protocols in the field of green chemistry.

Non-Invasive Identification of Nutrient Components in Grain

Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification

Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use this large amount of information to create models that can predict the state of new samples. We study here linear models, whose separation coefficients can be used to interpret which bands are contributing to the discrimination, and compare the performance of principal component analysis coupled with linear discriminant analysis (PCA/LDA), with regularized logistic regression (Lasso). By applying these methods to single-cell measurements for the detection of macrophage activation, we found that PCA/LDA yields poorer performance in classification compared to Lasso, and underestimates the required sample size to reach stable models. Direct use of Lasso (without PCA) also yields more stable models, and provides sparse separation vectors that directly contain the Raman bands most relevant to classification. To further evaluate these sparse vectors, we apply Lasso to a well-defined case where protein synthesis is inhibited, and show that the separating features are consistent with RNA accumulation and protein levels depletion. Surprisingly, when features are selected purely in terms of their classification power (Lasso), they consist mostly of side bands, while typical strong Raman peaks are not present in the discrimination vector. We propose that this occurs because large Raman bands are representative of a wide variety of intracellular molecules and are therefore less suited for accurate classification.

Non-invasive detection technologies of solid foreign matter and their applications to lyophilized pharmaceutical products: A review

Good Manufacturing Practice Regulations, under the Food and Drug Administration (FDA), stipulate that all pharmaceutical products must be free of any contaminants, including, namely, any foreign solid objects. Lyophilization is a common manufacturing method that consists of several steps where foreign materials may enter the product. The presence of unintended particles in freeze drying, which will herein be referred to under the term 'Lyophilization', is of great concern to the authorities responsible for drug safety and effectiveness. In the pharmaceutical industry, presently, the inspection of lyophilized products for foreign matter particulates relies on visual inspection where only the outer surface of the lyophilized cake is visible. This review is motivated by the need for new control strategies for foreign matter (FM) detection in lyophilized products; more specifically, it assesses the reliability of non-destructive technologies for FM detection in dried samples. Emerging technologies applied in other industries, such as various types of spectroscopies and imaging (e.g. chemical, X-ray, ultrasound, thermal and terahertz), are evaluated based on compatibility with the intended application, with identification of the possible technical challenges.

Influence of interfering co-appearing container peaks on the accuracy of direct quantitative Raman measurement of a sample in a plastic container

The axially perpendicular offset (APO) scheme was previously demonstrated as a versatile scheme able to minimize or eliminate the glass background in the direct and non-sampling Raman measurement of an ethanol sample housed in a glass bottle. Alternatively, when directly analyzing a sample housed in a plastic container, another typical container yielding strong Raman peaks itself, the Raman peaks of both the container and the housed sample are unavoidably present together in a collected spectrum. Therefore, a crucial issue to investigate under this situation is how the magnitude of the co-appearing container peaks influences the accuracy for quantitative analysis of the housed sample. For the evaluation, a non-sampling Raman spectroscopic measurement of the urea concentration in a urea gel housed in a circular polypropylene (PP) container was attempted by employing two axially perpendicular offset (APO) schemes with detection windows of different sizes (25.4 and 10.0 mm, referred to as the wide-window APO (WW-APO) and narrow-window APO (NW-APO), respectively), and transmission and back-scattering schemes incorporating a 25.4 mm detection window. The intensity ratios between the container and urea peaks in the collected spectra were different depending on the adopted measurement scheme. The intensity ratio was greatest (smallest container peak) in the NW-APO measurement due to the narrowed detection window, making the generated container Raman photons at the side-wall less detectable to the bottom-positioned detector. A spectral acquisition scheme allowing the maximal suppression of the container peaks, while still maintaining the sample features, was a key requirement to secure an accurate measurement of the sample concentration. In addition, a Monte Carlo simulation was used to visualize the distributions of the container and urea photons inside the sample-housed container.

Non-invasive identification of potato varieties and prediction of the origin of tuber cultivation using spatially offset Raman spectroscopy

High starch content, simplicity of cultivation, and high productivity make potatoes (Solanum tuberosum) a staple in the diet of people around the world. On average, potatoes are composed of 83% water and 12% carbohydrates, and the remaining 4% includes proteins, vitamins, and other trace elements. These proportions vary depending on the type of potato and location where they were cultivated. At the same time, the chemical composition determines the nutritional value of potato tubers and can be proved using various wet chemistry and spectroscopic methods. For instance, gravity measurements, as well as several different colorimetric assays, can be used to investigate the starch content. However, these approaches are indirect, often destructive, and time- and labor-consuming. This study reports on the use of Raman spectroscopy (RS) for completely non-invasive and non-destructive assessment of nutrient content of potato tubers. We also show that RS can be used to identify nine different potato varieties, as well as determine the origin of their cultivation. The portable nature of Raman-based identification of potato offers the possibility to perform such analysis directly upon potato harvesting to enable quick quality evaluation. Graphical abstract.

Quantitation of active pharmaceutical ingredient through the packaging using Raman handheld spectrophotometers: A comparison study

Handheld Raman spectroscopy is actually booming. Recent devices improvements aim at addressing the usual Raman spectroscopy issues: fluorescence with shifted-excitation Raman difference spectroscopy (SERDS), poor sensitivity with surface enhanced Raman scattering (SERS) and information only about the sample surface with spatially offset Raman spectroscopy (SORS). While qualitative performances of handheld devices are generally well established, the quantitative analysis of pharmaceutical samples remains challenging. The aim of this study was to compare the quantitative performances of three commercially available handheld Raman spectroscopy devices. Two of them (TruScan and IDRaman mini) are equipped with a 785 nm laser wavelength and operate in a conventional backscattering mode. The IDRaman has the Orbital Raster Scanning (ORS) option to increase the analyzed surface. The third device (Resolve) operates with an 830 nm laser wavelength both in backscattering and in SORS modes. The comparative study was carried out on ibuprofen-mannitol-microcrystalline cellulose ternary mixtures. The concentration of ibuprofen ranged from 24 to 52% (w/w) while the proportions of the two excipients were varied to avoid cross-correlation as much as possible. Analyses were performed either directly through a glass vial or with the glass vial in an opaque polypropylene flask, using a validated FT-NIR spectroscopy method as a reference method. Chemometric analyses were carried out with the Partial Least Squares Regression (PLS-R) algorithm. The quantitative models were validated using the total error approach and the ICH Q2 (R1) guidelines with ± 15% as acceptance limits.

Non-invasive In Vivo Imaging of Cancer Using Surface-Enhanced Spatially Offset Raman Spectroscopy (SESORS)

Rationale: The goal of imaging tumors at depth with high sensitivity and specificity represents a significant challenge in the field of biomedical optical imaging. 'Surface enhanced Raman scattering' (SERS) nanoparticles (NPs) have been employed as image contrast agents and can be used to specifically target cells in vivo. By tracking their unique "fingerprint" spectra, it becomes possible to determine their precise location. However, while the detection of SERS NPs is very sensitive and specific, conventional Raman spectroscopy imaging devices are limited in their inability to probe through tissue depths of more than a few millimetres, due to scattering and absorption of photons by biological tissues. Here, we combine the use of "Spatially Offset Raman spectroscopy" (SORS) with that of "surface-enhanced resonance Raman spectroscopy" (SERRS) in a technique known as "surface enhanced spatially offset resonance Raman spectroscopy" (SESO(R)RS) to image deep-seated glioblastoma multiforme (GBM) tumors in vivo in mice through the intact skull. Methods: A SORS imaging system was built in-house. Proof of concept SORS imaging was achieved using a PTFE-skull-tissue phantom. Imaging of GBMs in the RCAS-PDGF/N-tva transgenic mouse model was achieved through the use of gold nanostars functionalized with a resonant Raman reporter to create SERRS nanostars. These were then encapsulated in a thin silica shell and functionalized with a cyclic-RGDyK peptide to yield integrin-targeting SERRS nanostars. Non-invasive in vivo SORS image acquisition of the integrin-targeted nanostars was then performed in living mice under general anesthesia. Conventional non-SORS imaging was used as a direct comparison. Results: Using a low power density laser, GBMs were imaged via SESORRS in mice (n = 5) and confirmed using MRI and histopathology. The results demonstrate that via utilization of the SORS approach, it is possible to acquire clear and distinct Raman spectra from deep-seated GBMs in mice in vivo through the skull. SESORRS images generated using classical least squares outlined the tumors with high precision as confirmed via MRI and histology. Unlike SESORRS, conventional Raman imaging of the same areas did not provide a clear delineation of the tumor. Conclusion: To the best of our knowledge this is the first report of in vivo SESO(R)RS imaging. In a relevant brain tumor mouse model we demonstrate that this technique can overcome the limitations of conventional Raman imaging with regards to penetration depth. This work therefore represents a significant step forward in the potential clinical translation of SERRS nanoparticles for high precision cancer imaging.

Optical Properties of Meloxicam in the Far-Infrared Spectral Region

One of the most commonly used nonsteroidal anti-inflammatory active pharmaceutical ingredient called Meloxicam has been characterized spectroscopically both by Terahertz (THz) time domain spectroscopy (THz-TDS) and by Fourier Transform Infrared (FTIR) spectroscopy in far-IR regions of electromagnetic spectrum; 0.2 THz to 20 THz. While many relatively sharp features are observed in the far-IR range between 2 THz to 20 THz as expected for being an organic substance, very distinct and relatively strong absorption bands are also observed at 1.00, 1.66, 2.07 and 2.57 THz in the THz range. These well separated, defined, and fairly strong spectral features can be used for discrimination and quantification of Meloxicam in drug analysis. Frequency dependent refractive index of the drug was determined in a range of 0.2 THz and 2.7 THz, where an almost constant index was observed with an average index of 1.75. Powder XRD, and solid-state Density Functional Theory (SS-DFT) calculations were utilized to determine the crystalline form of the Meloxicam sample in its enolic crystalline form. Single molecule DFT calculations were also performed in all four possible structures of Meloxicam. In addition, the capability of THz waves transmission through common packaging materials is demonstrated for possibility of future on-site analysis. The results suggest that drug analysis will be possible to perform not only at every stage of manufacturing without destruction but also directly at the shelf of a market after development of portable THz technologies.

Axially perpendicular offset scheme for obtaining Raman spectra of housed samples in glass bottles with minimized glass-peak background

An axially perpendicular offset (APO) scheme based on an axially perpendicular geometrical arrangement of laser illumination and photon detection is proposed as a versatile tool for the minimization of the glass background in direct measurements of Raman spectra of samples housed in glass bottles.

Correcting transmission losses in short-wave infrared spatially offset Raman spectroscopy measurements to enable reduced fluorescence through-barrier detection

Spatially offset Raman spectroscopy (SORS) is a proven technique for sub-surface detection. SORS is able to separate Raman signals from a container and its contents, thereby demonstrating application to through-barrier detection for defence and security. Whilst SORS has been demonstrated to reduce fluorescence from the barrier (or surface), fluorescence from the sample (or sub-surface) can still be problematic for some materials when using Raman excitation wavelengths typical in commercially available instrumentation (e.g. 785 nm). Previous work has demonstrated that short-wave infrared (SWIR) excited SORS (e.g. 1064 nm) can reduce fluorescence from the sample and barrier, thereby providing the potential to detect a wider range of materials through a wider range of barriers. In this paper we highlight an additional challenge for detection through some plastic container materials (e.g. high density polyethylene (HDPE) and other opaque plastics) that absorb and scatter both incident and Raman scattered photons in the SWIR band, leading to distortion of the resultant SORS spectrum. The existence of this effect and its impact is explored, along with a potential solution to overcome this challenge that uses multi-wavelength Raman excitation to avoid the detrimental HDPE absorption region.

Through-container, extremely low concentration detection of multiple chemical markers of counterfeit alcohol using a handheld SORS device

Major food adulteration incidents occur with alarming frequency and are episodic, with the latest incident, involving the adulteration of meat from 21 producers in Brazil supplied to 60 other countries, reinforcing this view. Food fraud and counterfeiting involves all types of foods, feed, beverages, and packaging, with the potential for serious health, as well as significant economic and social impacts. In the spirit drinks sector, counterfeiters often ‘recycle’ used genuine packaging, or employ good quality simulants. To prove that suspect products are non-authentic ideally requires accurate, sensitive, analysis of the complex chemical composition while still in its packaging. This has yet to be achieved. Here, we have developed handheld spatially offset Raman spectroscopy (SORS) for the first time in a food or beverage product, and demonstrate the potential for rapid in situ through-container analysis; achieving unequivocal detection of multiple chemical markers known for their use in the adulteration and counterfeiting of Scotch whisky, and other spirit drinks. We demonstrate that it is possible to detect a total of 10 denaturants/additives in extremely low concentrations without any contact with the sample; discriminate between and within multiple well-known Scotch whisky brands, and detect methanol concentrations well below the maximum human tolerable level.

DMD-based software-configurable spatially-offset Raman spectroscopy for spectral depth-profiling of optically turbid samples

Spectral depth-profiling of optically turbid samples is of high interest to a broad range of applications. We present a method for measuring spatially-offset Raman spectroscopy (SORS) over a range of length scales by incorporating a digital micro-mirror device (DMD) into a sample-conjugate plane in the detection optical path. The DMD can be arbitrarily programmed to collect/reject light at spatial positions in the 2D sample-conjugate plane, allowing spatially offset Raman measurements. We demonstrate several detection geometries, including annular and simultaneous multi-offset modalities, for both macro- and micro-SORS measurements, all on the same instrument. Compared to other SORS modalities, DMD-based SORS provides more flexibility with only minimal additional experimental complexity for subsurface Raman collection.

Recent Advances in Solid-State Analysis of Pharmaceuticals

Current analytical techniques for characterizing solid-state pharmaceuticals include powder x-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, infrared spectroscopy, Raman spectroscopy, electron microscopy and nuclear magnetic resonance. Powder x-ray diffraction and differential scanning calorimetry are mainstream techniques but they lack spatial resolution. Scanning electron microscopy and micro-Raman spectroscopy provide good chemical and optical characterization but they are not capable of analysing very small nanoparticles. Transmission electron microscopy and nano-thermal analysis can provide explicit characterization of nanoparticles but they are invasive. Nuclear magnetic resonance offers good spatial resolution but its use is mainly limited by poor sensitivity and high costs. In view of the many challenges posed by existing methods, new and novel techniques are being continually researched and developed to cater to the growing number of solid formulations in the pipeline and in the market. Some of the recent advances attained in the solid-state analysis of pharmaceutical are summarized in this review article.

Raman spectroscopy in pharmaceutical product design

Almost 100 years after the discovery of the Raman scattering phenomenon, related analytical techniques have emerged as important tools in biomedical sciences. Raman spectroscopy and microscopy are frontier, non-invasive analytical techniques amenable for diverse biomedical areas, ranging from molecular-based drug discovery, design of innovative drug delivery systems and quality control of finished products. This review presents concise accounts of various conventional and emerging Raman instrumentations including associated hyphenated tools of pharmaceutical interest. Moreover, relevant application cases of Raman spectroscopy in early and late phase pharmaceutical development, process analysis and micro-structural analysis of drug delivery systems are introduced. Finally, potential areas of future advancement and application of Raman spectroscopic techniques are discussed.

Raman imaging of drug delivery systems

This review article includes an introduction to the principals of Raman spectroscopy, an outline of the experimental systems used for Raman imaging and the associated important considerations and limitations of this method. Common spectral analysis methods are briefly described and examples of interesting published studies which utilised Raman imaging of pharmaceutical and biomedical devices are discussed, along with summary tables of the literature at this point in time.

In Situ Identification of Semen Stains on Common Substrates via Raman Spectroscopy,

The spectroscopic identification of body fluids in situ is a major objective in forensic science. This approach offers the confirmatory, nondestructive, rapid, and on-scene identification of various body fluids. Although Raman spectroscopy has shown tremendous promise toward this goal in prior proof-of-concept experiments, a significant challenge which still remains is substrate interference. Here, an approach for detecting semen stains in situ on various substrates using Raman spectroscopy is explored. Simulated semen evidence was prepared on skin, glass, and various fabrics. Raman data were accumulated from stains without any pretreatment using a common confocal mapping spectrometer using 785 nm laser excitation. The results demonstrate that the spectroscopic interferences encountered by substrates can be reduced and eliminated using a combination of existing subtraction techniques and chemometric models. Heterogeneous substrates proved most challenging, however, automatic subtraction treatment, and location of fluid hotspots was able to elucidate a clear spectroscopic signature of semen in every instance.

Identification of anisodamine tablets by Raman and near-infrared spectroscopy with chemometrics

Vibrational spectroscopy including Raman and near-infrared (NIR) spectroscopy has become an attractive tool for pharmaceutical analysis. In this study, effective calibration models for the identification of anisodamine tablet and its counterfeit and the distinguishment of manufacturing plants, based on Raman and NIR spectroscopy, were built, respectively. Anisodamine counterfeit tablets were identified by Raman spectroscopy with correlation coefficient method, and the results showed that the predictive accuracy was 100%. The genuine anisodamine tablets from 5 different manufacturing plants were distinguished by NIR spectroscopy using partial least squares discriminant analysis (PLS-DA) models based on interval principal component analysis (iPCA) method. And the results showed the recognition rate and rejection rate were 100% respectively. In conclusion, Raman spectroscopy and NIR spectroscopy combined with chemometrics are feasible and potential tools for rapid pharmaceutical tablet discrimination.

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.