Superpixels meet essential spectra for fast Raman hyperspectral microimaging.

In the context of spectral unmixing, essential information corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix which are indispensable to reproduce the full data matrix in a convex linear way. Essential information has recently been shown accessible on-the-fly via a decomposition of the measured spectra in the Fourier domain and has opened new perspectives for fast Raman hyperspectral microimaging. In addition, when some spatial prior is available about the sample, such as the existence of homogeneous objects in the image, further acceleration for the data acquisition procedure can be achieved by using superpixels. The expected gain in acquisition time is shown to be around three order of magnitude on simulated and real data with very limited distortions of the estimated spectrum of each object composing the images.

Assessment of Essential Information in the Fourier Domain to Accelerate Raman Hyperspectral Microimaging.

In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix. In recent works, the assessment of EI has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on the principal component analysis to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach to extract EI in the Fourier domain (EIFD). Spectral information is transformed into Fourier coefficients, and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of a few selected harmonics. Because the coordinate system of a phasor plot does not depend on the data themselves, the evaluation of the essentiality of the information carried by each spectrum can be achieved individually and independently from the others. As a result, time-consuming operations like Raman spectral imaging can be significantly accelerated exploiting a chemometric-driven (i.e., based on the EI content of a spectral pixel) procedure for data acquisition and targeted sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating a potential 50-fold acceleration of Raman acquisition.

Selection of essential spectra to improve the multivariate curve resolution of minor compounds in complex pharmaceutical formulations.

Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low sources of variance are present in complex samples, as for minor (low-concentrated) chemical compounds in pharmaceutical formulations. In this work, it was shown how the reduction of hyperspectral imaging data matrices through the selection of essential spectra can be crucial for the analysis of complex unknown pharmaceutical formulation applying Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Results were obtained on simulated datasets and on real FT-IR and Raman hyperspectral images of both genuine and falsified tablets. When simulating the presence of minor compounds, different situations were investigated considering the presence of single pixels of pure composition as well as binary and ternary mixtures. The comparison of the results obtained applying MCR-ALS on the reduced data matrices with those obtained on the full matrices revealed unequivocal: more accurate decomposition could be achieved when only essential spectra were analyzed. Indeed, when analyzing the full dataset, MCR-ALS failed resolving minor compounds even though pure spectra were provided as initial estimation, as shown for Raman hyperspectral imaging data obtained on a medicine sample containing 7 chemical compounds. In contrast, when considering the reduced dataset, all minor contributions (down to 1 pixel over 17,956) were successfully unmixed. The same conclusion could be drawn from the results obtained analysing FT-IR hyperspectral imaging data of a falsified medicine.

New perspective for the in-field analysis of cannabis samples using handheld near-infrared spectroscopy: A case study focusing on the determination of Δ9-tetrahydrocannabinol.

The aim of the present study was to explore the feasibility of applying near-infrared (NIR) spectroscopy for the quantitative analysis of Δ9-tetrahydrocannabinol (THC) in cannabis products using handheld devices. A preliminary study was conducted on different physical forms (entire, ground and sieved) of cannabis inflorescences in order to evaluate the impact of sample homogeneity on THC content predictions. Since entire cannabis inflorescences represent the most common types of samples found in both the pharmaceutical and illicit markets, they have been considered priority analytical targets. Two handheld NIR spectrophotometers (a low-cost device and a mid-cost device) were used to perform the analyses and their predictive performance was compared. Six partial least square (PLS) models based on reference data obtained by UHPLC-UV were built. The importance of the technical features of the spectrophotometer for quantitative applications was highlighted. The mid-cost system outperformed the low-cost system in terms of predictive performance, especially when analyzing entire cannabis inflorescences. In contrast, for the more homogeneous forms, the results were comparable. The mid-cost system was selected as the best-suited spectrophotometer for this application. The number of cannabis inflorescence samples was augmented with new real samples, and a chemometric model based on machine learning ensemble algorithms was developed to predict the concentration of THC in those samples. Good predictive performance was obtained with a root mean squared error of prediction of 1.75 % (w/w). The Bland-Altman method was then used to compare the NIR predictions to the quantitative results obtained by UHPLC-UV and to evaluate the degree of accordance between the two analytical techniques. Each result fell within the established limits of agreement, demonstrating the feasibility of this chemometric model for analytical purposes. Finally, resin samples were investigated by both NIR devices. Two PLS models were built by using a sample set of 45 samples. When the analytical performances were compared, the mid-cost spectrophotometer significantly outperformed the low-cost device for prediction accuracy and reproducibility.

Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations.

Hyperspectral imaging has been widely used for different kinds of applications and many chemometric tools have been developed to help identifying chemical compounds. However, most of those tools rely on factorial decomposition techniques that can be challenging for large data sets and/or in the presence of minor compounds. The present study proposes a pixel-based identification (PBI) approach that allows readily identifying spectral signatures in Raman hyperspectral imaging data. This strategy is based on the identification of essential spectral pixels (ESP), which can be found by convex hull calculation. As the corresponding set of spectra is largely reduced and encompasses the purest spectral signatures, direct database matching and identification can be reliably and rapidly performed. The efficiency of PBI was evaluated on both known and unknown samples, considering genuine and falsified pharmaceutical tablets. We showed that it is possible to analyze a wide variety of pharmaceutical formulations of increasing complexity (from 5 to 0.1% (w/w) of polymorphic impurity detection) for medium (150 x 150 pixels) and big (1000 x 1000 pixels) map sizes in less than 2 min. Moreover, in the case of falsified medicines, it is demonstrated that the proposed approach allows the identification of all compounds, found in very different proportions and, sometimes, in trace amounts. Furthermore, the relevant spectral signatures for which no match is found in the reference database can be identified at a later stage and the nature of the corresponding compounds further investigated. Overall, the provided results show that Raman hyperspectral imaging combined with PBI enables rapid and reliable spectral identification of complex pharmaceutical formulations.

Development of a prototype device for near real-time surface-enhanced Raman scattering monitoring of biological samples.

With the fast growth of bioanalytical surface-enhanced Raman scattering (SERS), analytical methods have had to adapt to the complex nature of biological samples. In particular, interfering species and protein adsorption onto the SERS substrates have been addressed by sample preparation steps, such as precipitation or extraction, and by smart SERS substrate functionalisation. These additional handling steps however result in irreversible sample alteration, which in turn prevents sample monitoring over time. A new methodology, that enables near real-time, non-invasive and non-destructive SERS monitoring of biological samples, is therefore proposed. It combines solid SERS substrates, benefitting from liquid immersion resistance for extended periods of time, with an original protein filtering device and an on-field detection by means of a handheld Raman analyser. The protein removal device aims at avoiding protein surface fouling on the SERS substrate. It consists of an ultracentrifugation membrane fixed under a cell culture insert for multi-well plates. The inside of the insert is dedicated to containing biological samples. The solid SERS substrate and a simple medium, without any protein, are placed under the insert. By carefully selecting the membrane molecular weight cutoff, selective diffusion of small analytes through the device could be achieved whereas larger proteins were retained inside the insert. Non-invasive SERS spectral acquisition was then carried out through the bottom of the multi-well plate. The diffusion of a SERS probe, 2-mercaptopyridine, and of a neurotransmitter having a less intense SERS signal, serotonin, were first successfully monitored with the device. Then, the latter was applied to distinguish between subclones of cancerous cells through differences in metabolite production. This promising methodology showed a high level of versatility, together with the capability to reduce cellular stress and contamination hazards.

Evaluation of the analytical performances of two Raman handheld spectrophotometers for pharmaceutical solid dosage form quantitation.

This paper addresses the issue of pharmaceutical solid dosage form quantitation using handheld Raman spectrophotometers. The two spectrophotometers used are designed with different technologies: one allows getting a more representative sampling with the Orbital Raster Scanning technology and the other one allows setting acquisition parameters. The goal was to evaluate which technology could provide the best analytical results. Several parameters were optimized to get the lowest prediction error in the end. The main objective of this study was to evaluate if this kind of instrument would be able to identify substandard medicines. For that purpose, two case-study were explored. At first, a full ICH Q2 (R1) compliant validation was performed for moderate Raman scatterer active pharmaceutical ingredient (API) in a specific formulation. It was successfully validated in the ±15% relative total error acceptance limits, with a RMSEP of 0.85% (w/w). Subsequently, it was interesting to evaluate the influence of excipients when the API is a high Raman scatterer. For that purpose, a multi-formulation model was developed and successfully validated with a RMSEP of 2.98% (w/w) in the best case. These two studies showed that thanks to the optimization of acquisition parameters, Raman handheld spectrophotometers methods were validated for two different case-study and could be applied to identify substandard medicines.

Effect of the functionalisation agent on the surface-enhanced Raman scattering (SERS) spectrum: Case study of pyridine derivatives.

Nowadays, the use of functionalised surface-enhanced Raman scattering (SERS) substrates has become common. These surface modifying agents notably act as Raman reporters, as sensors of biological processes (pH, redox probes) or to increase the sensitivity and/or the specificity of SERS detections. However, the effects of the functionalisation agents are deeply examined in very few studies, even though they can affect the aggregation behaviour of the SERS substrate. Moreover, depending on their concentration and on the pH, their spectral signature can be modified and they can even degrade if stored inappropriately. In this context, this paper aims at emphasising the importance of the different aspects previously listed in the selection of a functionalisation agent. Pyridine derivatives were picked out to highlight these parameters, as some of these compounds are commonly used to be grafted onto SERS substrates. Two widespread syntheses of nanoparticles were selected as SERS substrates: citrate-reduced gold and silver nanoparticles. The surface of the nanoparticles was functionalised with several pyridine derivatives at different concentrations and in several solvents. It was observed that the molecules under study had a concentration-dependent effect on nanoparticle aggregation. A stability study was furthermore conducted in order to determine the best preservation conditions of the grafting solutions. In conclusion, this paper shines a light on the relevance of the investigation of the too-often neglected behaviour of the surface modifying agents. Before their application in SERS analyses, parameters such as the label concentration should therefore be included in an experimental design to optimise the sample preparation.

Comparison of hyperspectral imaging techniques for the elucidation of falsified medicines composition.

Hyperspectral imaging has shown a high potential to analyze falsifications of solid pharmaceutical products since the last decade. Thanks to the non-destructive, ecological and non-invasive properties, it is a preferred technique for these kinds of applications. Moreover, thanks to the spectroscopic properties, it is possible to detect as well organic compounds as inorganic compounds in a single analysis. Therefore, we recommend using it as second-line laboratory analysis technique. Raman microscopy and Fourier Transform Infrared (FT-IR) microscopy are two interesting techniques that are complementary. In this study, the potential of the two hyperspectral imaging techniques is evaluated to elucidate the composition of falsified antimalarial tablets. Hyperspectral data are analyzed by Multivariate Curve Resolution-Alternating Least Square (MCR-ALS). The results obtained from this study show that Raman hyperspectral imaging seems to be more suited to detect low dosed compounds possibly due to a smallest sampling volume. It has been also possible to link formulations of falsified samples of two different brands.