Interpretable One-Class Classification of Raman Spectra Using Prediction Bands Estimated by Wavelet Regression.

Previously, we introduced a novel one-class classification (OCC) concept for spectra. It uses as acceptance space for genuine spectra of the target chemical, a prediction band in the wavelengths' space. As a decision rule, test spectra falling substantially outside this band are rejected as noncomplying with the target, and their deviations are documented in the wavelengths' space. This band-based OCC concept was applied to smooth signals like near-infrared (NIR) spectra. A regression model based on a smoothed principal component (PC) representation of the training spectra was used to predict unseen trajectories of future spectra. The boundaries of the most central predicted trajectories were chosen as critical trajectories. We now propose a methodology to construct a similar band-based one-class classifier for Raman spectra, which are sharper and noisier than NIR spectra. The spectra are transformed by a composition of wavelet and principal component (wPC) expansions instead of just a PC expansion in the previous methodology for NIR spectra. Wavelets can capture sharp features of Raman signals and provide a framework to efficiently denoise them. A multinormal prediction model is then used to derive predictions of future wPC scores of unseen spectra. These predicted wPC scores are then backtransformed to obtain predictions of future trajectories of unseen spectra in the wavelengths' space, whose most central region defines the acceptance band or space. This band-based one-class classifier successfully classified the first derivatives of real pharmaceutical Raman spectra, while enjoying the advantage of documenting deviations from the critical trajectories in the wavelengths' space and hence is more interpretable.

A new alternative tool to analyse glycosylation in pharmaceutical proteins based on infrared spectroscopy combined with nonlinear support vector regression.

Almost 60% of commercialized pharmaceutical proteins are glycosylated. Glycosylation is considered a critical quality attribute, as it affects the stability, bioactivity and safety of proteins. Hence, the development of analytical methods to characterise the composition and structure of glycoproteins is crucial. Currently, existing methods are time-consuming, expensive, and require significant sample preparation steps, which can alter the robustness of the analyses. In this work, we suggest the use of a fast, direct, and simple Fourier transform infrared spectroscopy (FT-IR) combined with a chemometric strategy to address this challenge. In this context, a database of FT-IR spectra of glycoproteins was built, and the glycoproteins were characterised by reference methods (MALDI-TOF, LC-ESI-QTOF and LC-FLR-MS) to estimate the mass ratio between carbohydrates and proteins and determine the composition in monosaccharides. The FT-IR spectra were processed first by Partial Least Squares Regression (PLSR), one of the most used regression algorithms in spectroscopy and secondly by Support Vector Regression (SVR). SVR has emerged in recent years and is now considered a powerful alternative to PLSR, thanks to its ability to flexibly model nonlinear relationships. The results provide clear evidence of the efficiency of the combination of FT-IR spectroscopy, and SVR modelling to characterise glycosylation in therapeutic proteins. The SVR models showed better predictive performances than the PLSR models in terms of RMSECV, RMSEP, R2CV, R2Pred and RPD. This tool offers several potential applications, such as comparing the glycosylation of a biosimilar and the original molecule, monitoring batch-to-batch homogeneity, and in-process control.

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, R2CV.

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.

Development of a sensitive MEKC-LIF method for synthetic cathinones analysis.

Synthetic cathinones are phenylalkylamine compounds related to natural cathinone from Catha edulis leaves. Due to their sympathomimetic effects comparable to common illicit drugs, these substances are mainly drugs of abuse and constitute the second most frequently seized group of new psychoactive substances. In order to ensure their regulation and to promote public health, reliable analytical tools are required to track these substances. In the present study, we developed a CE hyphenated to laser-induced fluorescence detection method to demonstrate its suitability to perform fast and cost-effective synthetic cathinones analysis. Fourteen compounds including isobaric compounds and position isomers were selected to encompass the large panel of chemical structures. To separate the FITC-labeled analytes (presenting the same negative charge and close mass to charge ratios), MEKC separation mode was selected. Method selectivity was not suitable using common surfactants. In this context, alkyl polyethylene glycol ether surfactants were successfully used as neutral surfactant to overcome this analytical challenge. The effect of surfactant nature on separation performances and migration behaviors of the analytes was also studied. Optimal BGE composition included 75 mM borate buffer at pH 9.3 and 0.4 mM of C12E10 surfactant. Final MEKC separation conditions were proposed to analyze a large panel of synthetic cathinones. This method helped to reach a sensitivity with LOD from 0.1 to 0.4 nM (pg/mL order).

Poplar-Root Knot Nematode Interaction: A Model for Perennial Woody Species.

Plant root-knot nematode (RKN) interaction studies are performed on several host plant models. Though RKN interact with trees, no perennial woody model has been explored so far. Here, we show that poplar (Populus tremula × P. alba) grown in vitro is susceptible to Meloidogyne incognita, allowing this nematode to penetrate, to induce feeding sites, and to successfully complete its life cycle. Quantitative reverse transcription-polymerase chain reaction analysis was performed to study changes in poplar gene expression in galls compared with noninfected roots. Three genes (expansin A, histone 3.1, and asparagine synthase), selected as gall development marker genes, followed, during poplar-nematode interaction, a similar expression pattern to what was described for other plant hosts. Downregulation of four genes implicated in the monolignol biosynthesis pathway was evidenced in galls, suggesting a shift in the phenolic profile within galls developed on poplar roots. Raman microspectroscopy demonstrated that cell walls of giant cells were not lignified but mainly composed of pectin and cellulose. The data presented here suggest that RKN exercise conserved strategies to reproduce and to invade perennial plant species and that poplar is a suitable model host to study specific traits of tree-nematode interactions.

Comprehensive analysis of vitamin D3 impurities in oily drug products using supercritical fluid chromatography-mass spectrometry.

Vitamin D3, an essential micronutrient, often requires supplementation via medicines or food supplements, which necessitate quality control (QC). This study presents the development of a method for detecting and quantifying seven impurities of vitamin D3 in oily drug products using supercritical fluid chromatography-mass spectrometry (SFC-MS). Targeted impurities include two esters of vitamin D3 and five non-esters including four that are isobaric to vitamin D3. Firstly, a screening study highlighted the Torus 1-AA column and acetonitrile modifier as adequate for the separation, followed by optimization of the SFC conditions. Secondly, make-up solvent composition and MS settings were optimized to reach high sensitivity. For both the separation and MS response, the screening design of experiments proved useful. Lastly, a fast saponification and liquid-liquid extraction method was developed, enabling efficient sample cleanup and impurities recovery from the complex oily matrix. The SFC-MS method suitability was assessed in two validation studies. The first study employed the ICH Q2 guideline for impurity limit test to demonstrate method specificity and establish a limit of detection (LOD) and a limit of quantification (LOQ) at 0.2% and 0.5%, respectively, for ester impurities. The second study conducted a comprehensive quantitative assessment for three non-ester impurities using a total error approach, determining method validity through accuracy profiles. The validated method exhibited reliable performance across impurity concentrations from 0.1% to 2.0%, with estimated LODs ranging from 2 to 7 ng/mL. This study further promotes SFC-MS as a valuable, versatile, and green tool for routine pharmaceutical QC.

Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets.

Portable near-infrared (NIR) spectrophotometers have emerged as valuable tools for identifying substandard and falsified pharmaceuticals (SFPs). Integration of these devices with chemometric and machine learning models enhances their ability to provide quantitative chemical insights. However, different NIR spectrophotometer models vary in resolution, sensitivity, and responses to environmental factors such as temperature and humidity, necessitating instrument-specific libraries that hinder the wider adoption of NIR technology. This study addresses these challenges and seeks to establish a robust approach to promote the use of NIR technology in post-market pharmaceutical analysis. We developed support vector machine and partial least squares regression models based on binary mixtures of lab-made ciprofloxacin and microcrystalline cellulose, then applied the models to ciprofloxacin dosage forms that were assayed with high performance liquid chromatography (HPLC). A receiver operating characteristic (ROC) analysis was performed to set spectrophotometer independent NIR metrics to evaluate ciprofloxacin dosage forms as "meets standard," "needs HPLC assay," or "fails standard." Over 200 ciprofloxacin tablets representing 50 different brands were evaluated using spectra acquired from three types of NIR spectrophotometer with 85% of the prediction agreeing with HPLC testing. This study shows that non-brand-specific predictive models can be applied across multiple spectrophotometers for rapid screening of the conformity of pharmaceutical active ingredients to regulatory standard.

Improvement of pixel classification by the simultaneous use of spectral and spatial information in the framework of spectroscopic imaging.

Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification.

Optimization of silver nanoparticles synthesis by chemical reduction to enhance SERS quantitative performances: Early characterization using the quality by design approach.

Surface-enhanced Raman scattering (SERS) is a vibrational widely used technique thanks to its multiple advantages such as its high specificity and sensitivity. The Raman signal exaltation comes from the use of metallic nanoparticles (Nps) acting as antennas by amplifying the Raman scattering. Controlling the Nps synthesis is a major point for the implementation of SERS in routine analysis and especially in quantitative applications. Effectively, nature, size and shape of these Nps considerably influence the SERS response intensity and repeatability. The Lee-Meisel protocol is the most common synthesis route used by the SERS community due to the low cost, rapidity and ease of manufacturing. However, this process leads to a significant heterogeneity in terms of particle size and shape. In this context, this study aimed to synthesize repeatable and homogeneous silver nanoparticles (AgNps) by chemical reduction. The Quality by Design strategy from quality target product profile to early characterization design was considered to optimize this reaction. The first step of this strategy aimed to highlight critical parameters by the means of an early characterization design. Based on an Ishikawa diagram, five process parameters were studied: the reaction volume as categorical variable and the temperature, the time of reaction, the trisodium citrate concentration and pH as continuous variables. A D-Optimal design of 35 conditions was performed. Three critical quality attributes were selected to maximize the SERS intensity, minimize the variation coefficient on SERS intensities and the polydispersity index of the AgNps. Considering these factors, it appeared that concentration, pH and time of reaction were identified as having a critical impact on the Nps formation and can then be considered for the further optimization step.

Prevalence of Poor Quality Ciprofloxacin and Metronidazole Tablets in Three Cities in Cameroon.

Quality is one of the essential components of medicines and needs to be ensured to preserve the population's health. This can be achieved through post-marketing quality control of medicines and is one of the most important duties of national regulatory authorities. In collaboration with the Cameroonian National Drug Quality Control and Valuation Laboratory, the decision was made to initiate a prevalence study to assess the quality of antiinfective medicines in Cameroon. A total of 150 samples of ciprofloxacin tablets and 142 samples of metronidazole tablets were collected from 76 licensed pharmacies and 75 informal vendors in three cities in Cameroon using a random strategy wherever possible and a mystery shopper approach. Three tests were carried out on each of the samples. Visual inspection allowed to find two falsified samples (0.7%) due to lack of information about the manufacturing company, and five more samples (1.7%) were deemed to be substandard due to flaws in the product. An additional 13 samples (4.5%) failed disintegration testing, and six (2.1%) others failed high-performance liquid chromatography assay testing due to insufficient active pharmaceutical ingredient (API) content. All samples were found to contain some API. A prevalence of 7.9% substandard or falsified (SF) medicines was found. Moreover, the prevalence of outlets selling SF medicines was greater in the informal sector (26.7%) than in the formal sector (2.6%). Although the prevalence of SF medicines found was low, efforts need to be made by national regulatory authorities to monitor the pharmaceutical market more closely.

In-Field Implementation of Near-Infrared Quantitative Methods for Analysis of Medicines in Tropical Environments.

Near-infrared (NIR) spectroscopy is actually a well-established technique that demonstrates its performance in the frame of detection of poor-quality medicines. The use of low-cost handheld NIR spectrophotometers in low-resource contexts can allow an inexpensive and more rapid detection compared to laboratory methods. Considering these points, it was decided to develop, validate, and transfer methods for the quantification of ciprofloxacin and metronidazole tablet samples using a NIR handheld spectrophotometer in transmission mode (NIR-M-T1) coupled to chemometrics such as partial least squares regression (PLSR) algorithm. All of the models were validated with the total error approach using an accuracy profile as a decision tool, with ±10% specifications and a risk α set at 5%. Quantitative PLSR models were first validated in Belgium, which is a temperate oceanic climate zone. Second, they were transferred to Cameroon, a tropical climate zone, where issues regarding the prediction of new validation series with the initial models were highlighted. Two augmentation strategies were then envisaged to make the predictive models robust to environmental conditions, incorporating the potential variability linked to environmental effects in the initial calibration sets. The resulting models were then used for in-field analysis of ciprofloxacin and metronidazole tablet samples collected in three cities in Cameroon. The contents results obtained for each sample with the two strategies were close and not statistically different. Nevertheless, the first one is easier to implement and the second is the best regarding model diagnostic measures and accuracy profiles. Two samples were found to be noncompliant in terms of content, and these results were confirmed using high-performance liquid chromatography taken as the reference method.

Usefulness of medicine screening tools in the frame of pharmaceutical post-marketing surveillance.

The negative consequences of Substandard and falsified (SF) medicines are widely documented nowadays and there is still an urgent need to find them in more efficient ways. Several screening tools have been developed for this purpose recently. In this study, three screening tools were used on 292 samples of ciprofloxacin and metronidazole collected in Cameroon. Each sample was then analyzed by HPLC and disintegration tests. Seven additional samples from the nitro-imidazole (secnidazole, ornidazole, tinidazole) and the fluoroquinolone (levofloxacin, ofloxacin, norfloxacin, moxifloxacin) families were analyzed to mimic falsified medicines. Placebo samples that contained only inert excipients were also tested to mimic falsified samples without active pharmaceutical ingredient (API). The three screening tools implemented were: a simplified visual inspection checklist, a low-cost handheld near infrared (NIR) spectrophotometer and paper analytical devices (PADs). Overall, 61.1% of the samples that failed disintegration and assay tests also failed the visual inspection checklist test. For the handheld NIR, one-class classifier models were built to detect the presence of ciprofloxacin and metronidazole, respectively. The APIs were correctly identified in all the samples with sensitivities and specificities of 100%. However, the importance of a representative and up-to-date spectral database was underlined by comparing models built with different calibration set spanning different variability spaces. The PADs were used only on ciprofloxacin samples and detected the API in all samples in which the presence of ciprofloxacin was confirmed by HPLC. However, these PADs were not specific to ciprofloxacin since they reacted like ciprofloxacin to other fluoroquinolone compounds. The advantages and drawbacks of each screening tool were highlighted. They are promising means in the frame of early detection of SF medicines and they can increase the speed of decision about SF medicines in the context of pharmaceutical post-marketing surveillance.

Emerging analytical techniques for pharmaceutical quality control: Where are we in 2022?

Quality control is a fundamental and critical activity in the pharmaceutical industry that guarantees the quality of medicines. QC analyses are currently performed using several well-known techniques, mainly liquid and gas chromatography. However, current trends are focused on the development of new techniques to reduce analysis time and cost, to improve the performances and decrease ecological footprint. In this context, analytical scientists developed and studied emerging technologies based on spectroscopy and chromatography. The present review aims to give an overview of the recent development of vibrational spectroscopy, supercritical fluid chromatography and multi-dimensional chromatography. Selected emerging techniques are discussed using SWOT analysis and published pharmaceutical QC applications are discussed.

Critical review on recent trends in cannabinoid determination on cannabis herbal samples: From chromatographic to vibrational spectroscopic techniques.

Cannabis has been at the center of scientific attention for some years now. Since its pharmacological potential has been highlighted, cannabis has become a hot topic in research laboratories, leading to the publication of many scientific studies. Focusing on analytical chemistry, an enormous number of analytical methods for cannabinoid (CNB) determination have been published, involving various techniques. However, no globally accepted reference method for CNB determination has yet been chosen. This review aims to identify very recent analytical methods developed to analyze phytocannabinoids in cannabis herbal samples. For certain techniques, stagnation in terms of employed operational conditions can be observed. In this context, a reference method of analysis should be proposed and accepted worldwide to standardize CNB determination. In contrast, for other techniques, we are witnessing a scientific ferment, which is resulting in the development of new interesting analytical options. In this regard, particular focus has been given to these niche techniques, which are now emerging in the analytical panorama of cannabis analysis, offering new important perspectives for the future of cannabis testing. Supercritical fluid chromatography and infrared spectroscopy showed tangible advantages when applied to CNB determination in herbal samples.

Generic SFC-MS methodology for the quality control of vitamin D3 oily formulations.

Vitamin D3 is a key micronutrient whose intakes are inadequate for most populations worldwide. Supplementation with medicines or food supplements is commonly prescribed to correct this imbalance and the quality of these products must be ensured. In this context, a generic methodology for the assay of vitamin D3 in oily formulations is proposed using supercritical fluid chromatography coupled to mass spectrometry (SFC-MS). It is in line with green analytical chemistry principles and combines the use of i) a fast and robust analytical method (4.0 min analysis time) ii) an easy sample preparation compatible with high throughput analysis ("dilute-and-shoot" approach) and iii) a relevant control strategy. Seventeen products from multiple manufacturers and encompassing a large content range were evaluated in this study. They were classified in four groups to streamline their processing considering the use of a matrix-matched calibration procedure. Matrix effect was thoroughly studied and was found to be low (99-106%), stable intra/inter-series and comparable between the different groups and types of matrices. The implemented control strategy was based on a three-level system suitability tests (SST). Level 1 SST: resolution of the critical pair that was above 1.5 for all analysis series. Level 2 SST: evaluation of the adequacy of the calibration for a QC sample in terms of recovery that was between 97% and 104% with a variability between 1% and 2%. Level 3 SST: method trueness that was between 95% and 102%. Sample analysis highlighted differences in types of products and dosage forms. This is the first study to propose a complete strategy for the quality control of vitamin D3 oily formulations and should prove useful in QC laboratories.

Fused deposition modeling 3D printing of solid oral dosage forms containing amorphous solid dispersions: How to elucidate drug dissolution mechanisms through surface spectral analysis techniques?

Many active principles belong to the second class of the Biopharmaceutics Classification System due to their low aqueous solubility. Elaboration of new solid oral forms by hot-melt extrusion and fused deposition modeling appears as a promising tool to increase the dissolution rate of these drugs. Indeed, hot-melt extrusion allows the amorphisation of drugs and forms with complex geometries are built by 3D printing. Therefore, the goal of this work is to enhance the dissolution rate of poorly soluble drugs using hot-melt extrusion coupled with fused deposition modeling. Four formulations containing Affinisol® 15LV, Kollidon® VA64 and a challenging amount of itraconazole (25 % (wt.)) were successfully printed into forms of 20, 50 and 80 % infill densities. Differential scanning calorimetry analysis has shown that itraconazole remained amorphous during 52 weeks. The drug release rate was highly improved compared to itraconazole in a crystalline form. The dissolution rate was influenced by the infill density and the polymer composition of printed forms which could modify respectively the surface to volume ratio and the distribution of the components in the printed forms. One formulation printed with 20 % infill density even had a solubility profile similar to that of Sporanox®, the commercialized drug product in Belgium.

Optimizing the soft independent modeling of class analogy (SIMCA) using statistical prediction regions.

The ultimate goal of a one-class classifier like the "rigorous" soft independent modeling of class analogy (SIMCA) is to predict with a certain confidence probability, the conformity of future objects with a given reference class. However, the SIMCA model, as currently implemented often suffers from an undercoverage problem, meaning that its observed sensitivity often falls far below the desired theoretical confidence probability, hence undermining its intended use as a predictive tool. To overcome the issue, the most reported strategy in the literature, involves incrementing the nominal confidence probability until the desired sensitivity is obtained in cross-validation. This article proposes a statistical prediction interval-based strategy as an alternative strategy to properly overcome this undercoverage issue. The strategy uses the concept of predictive distributions sensu stricto to construct statistical prediction regions for the metrics. Firstly, a procedure based on goodness-of-fit criteria is used to select the best-fitting family of probability models for each metric or its monotonic transformation, among several plausible candidate families of right-skewed probability distributions for positive random variables, including the gamma and the lognormal families. Secondly, assuming the best-fitting distribution, a generalized linear model is fitted to each metric data using the Bayesian method. This method enables to conveniently estimate uncertainties about the parameters of the selected distribution. Propagating these uncertainties to the best-fitting probability model of the metric enables to derive its so-called posterior predictive distribution, which is then used to set its critical limit. Overall, the evaluation of the proposed approach on a diversity of real datasets shows that it yields unbiased and more accurate sensitivities than existing methods which are not based on predictive densities. It can even yield better specificities than the strategy that attempts to improve sensitivities of existing methods by "optimizing" the type 1 error, especially in low sample sizes' contexts.

Development and validation of an integrated microfluidic device with an in-line Surface Enhanced Raman Spectroscopy (SERS) detection of glyphosate in drinking water.

Glyphosate, also known as N-(phosphonomethyl)glycine, is one of the most widely used herbicides in the world. However, the controversy surrounding the toxicity of glyphosate and its main breakdown product, aminomethylphosphonic acid (AMPA), remains a serious public concern. Therefore, there is a clear need to develop a rapid, sensitive and automated alternative method for the quantification of glyphosate and AMPA. In this context, surface enhanced Raman spectroscopy (SERS) coupled with a microfluidic system for the determination of glyphosate in tap water was developed, optimized and validated. The design of the microfluidic configuration for this application was built constructed to integrate the synthesis of the SERS substrate through to the detection of the analyte. To optimize the microfluidic setup, a design of experiments approach was used to maximize the SERS signal of glyphosate. Subsequently, an approach based on the European guideline document SANTE/11312/2021 was used to validate the method in the range of 78-480 µg/L using the normalized band intensities. The limit of detection and quantification obtained for glyphosate were 40 and 78 µg/L, respectively. Recoveries were in the range 76-117%, while repeatability and intra-day reproducibility were ≤17%. Finally, the method was also tested for the determination of AMPA in tap water matrix and for the simultaneous detection of AMPA and glyphosate.

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.