Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data

Protonation of palmitic acid embedded in DPPC lipid bilayers obscures detection of ripple phase by FTIR spectroscopy

The transformation of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) lipid bilayers from the gel (Lβ') to the fluid (Lα) phase involves an intermediate ripple (Pβ') phase forming a few degrees below the main transition temperature (Tm). While the exact cause of bilayer rippling is still debated, the presence of amphiphilic molecules, pH, and lipid bilayer architecture are all known to influence (pre)transition behavior. In particular, fatty acid chains interact with hydrophobic lipid tails, while the carboxylic groups simultaneously participate in proton transfer with interfacial water in the polar lipid region which is controlled by the pH of the surrounding aqueous medium. The molecular-level variations in the DPPC ripple phase in the presence of 2% palmitic acid (PA) were studied at pH levels 4.0, 7.3, and 9.1, where PA is fully protonated, partially protonated, or fully deprotonated. Bilayer thermotropic behavior was investigated by differential scanning calorimetry (DSC) and Fourier-transform infrared (FTIR) spectroscopy which agreed in their characterization of (pre)transition at pH of 9.1, but not at pH 4.0 and especially not at 7.3. Owing to the different insertion depths of protonated and deprotonated PA, along with the ability of protonated PA to undergo flip-flop in the bilayer, these two forms of PA show a different hydration pattern in the interfacial water layer. Finally, these results demonstrated the hitherto undiscovered potential of FTIR spectroscopy in the detection of the events occurring at the surface of lipid bilayers that obscure the low-cooperativity phase transition explored in this work.

Serum Raman spectroscopy: Evaluation of tumour load variations in experimental carcinogenesis

Several serum Raman spectroscopy (RS) studies have demonstrated its potential as an oral cancer screening tool. This study investigates influence of low tumour load (LTL) and high tumour load (HTL) on serum RS using hamster buccal pouch model of experimental oral carcinogenesis. Sera of untreated control, LTL, and HTL groups at week intervals during malignant transformation were employed. Serum Raman spectra were subjected to multivariate analyses-principal component analysis, principal component-based linear discriminant analysis (for stratification of study groups), and multivariate curve resolution-alternating least squares (MCR-ALS) (to comprehend biomolecular differences). Multivariate analysis revealed misclassifications between LTL and HTL at all week intervals. MCR-ALS components showed statistically significant abundances between control versus LTL and control versus HTL, but could not discern LTL and HTL. MCR-ALS components exhibited spectral mixtures of proteins, lipids, heme and nucleic acids. Thus, these findings support use of serum RS as a screening tool as varying tumour load is not a confounding factor influencing the technique.

An optimized methodology for the determination of multiclass organic ultraviolet sunscreens and metabolites in human milk through chromatographic and chemometric resolution

Organic UV filters (UVFS) are used to mitigate the dermal effects associated with health risks from UV radiation, making them essential in personal care products. UVFS are frequently identified in environmental samples due to their high lipophilicity and persistence, underscoring the urgency of comprehensive assessments and regulatory measures aimed at safeguarding ecosystems and human health. The present study reports a multiclass analytical method for determining 16 UV sunscreens and metabolites in breast milk based on an ultrasound-assisted-dispersive liquid-liquid micro-extraction (UA-DLLME) with further chromatographic and chemometric resolution. The experimental conditions of the UA-DLLME were optimized through the implementation of the Design of Experiment tools. To model the responses, least-squares and artificial neural network methodologies were implemented. The optimal conditions were found by employing the desirability function. The samples were analyzed through reverse-phase liquid chromatographic separation, UV diode array, and fast-scanning fluorescence detection. The chromatographic analysis enabled the resolution of 16 analytes in a total time of 13.0 min. Multivariate curve resolution-alternating least-square (MCR-ALS) modelling was implemented to resolve analytes that were not fully resolved and to determine analytes that coeluted with endogenous components of the breast milk samples. An enrichment factor of 5-fold concentration was obtained with this methodology, reaching recoveries between 65 % and 105 % for 13 multiclass UV sunscreens and metabolites in breast milk samples with RSD % and REP % lower than 12 %.

Probing Oxidation-Driven Amorphized Surfaces in a Ta(110) Film for Superconducting Qubit

Recent advances in superconducting qubit technology have led to significant progress in quantum computing, but the challenge of achieving a long coherence time remains. Despite the excellent lifetime performance that tantalum (Ta) based qubits have demonstrated to date, the majority of superconducting qubit systems, including Ta-based qubits, are generally believed to have uncontrolled surface oxidation as the primary source of the two-level system loss in two-dimensional transmon qubits. Therefore, atomic-scale insight into the surface oxidation process is needed to make progress toward a practical quantum processor. In this study, the surface oxidation mechanism of native Ta films and its potential impact on the lifetime of superconducting qubits were investigated using advanced scanning transmission electron microscopy (STEM) techniques combined with density functional theory calculations. The results suggest an atomistic model of the oxidized Ta(110) surface, showing that oxygen atoms tend to penetrate the Ta surface and accumulate between the two outermost Ta atomic planes; oxygen accumulation at the level exceeding a 1:1 O/Ta ratio drives disordering and, eventually, the formation of an amorphous Ta2O5 phase. In addition, we discuss how the formation of a noninsulating ordered TaO1-δ (δ < 0.1) suboxide layer could further contribute to the losses of superconducting qubits. Subsurface oxidation leads to charge redistribution and electric polarization, potentially causing quasiparticle loss and decreased current-carrying capacity, thus affecting superconducting qubit coherence. The findings enhance the comprehension of the realistic factors that might influence the performance of superconducting qubits, thus providing valuable guidance for the development of future quantum computing hardware.

UV-Vis absorption spectrophotometry and LC-DAD-MS-ESI(+)-ESI(-) coupled to chemometrics analysis of the monitoring of sulfamethoxazole degradation by chlorination, photodegradation, and chlorination/photodegradation

Sulfamethoxazole (SMX) is one of the most widely used antibiotics worldwide and has been detected at high concentrations in wastewater treatment plant effluents and river waters. In this study, the SMX degradation process combining the simultaneous chlorine oxidation and UV photodegradation is assessed and compared with both photodegradation and chlorine oxidation processes individually. Photodegradation and Chlorine/UV tests were performed using Suntest CPS equipment. Different experimental techniques, including UV-Visible spectrophotometry and liquid chromatography coupled to a diode array detector and positive and negative ionization mass spectrometry (LC-DAD-MS-ESI(+)-ESI(-)), were used to evaluate the degradation reaction of SMX. All the analytical data generated have been processed with the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method to monitor, resolve, and identify the several transformation products generated during the studied degradation processes. A new data fusion analysis strategy is proposed to examine the three processes simultaneously (with only photodegradation, only chlorination, and simultaneous chlorination+photodegradation). Combined with the analysis of different analytical techniques individually (spectrophotometry, LC-DAD, and LC-MS), the fusion of all generated data improved the description of the degradation processes. Detection using DAD allowed a better correspondence among the species monitored spectrophotometrically (UV-Vis) with those analyzed chromatographically. On the other side, detection using MS in both positive and negative acquisition modes allowed resolving a larger number of chemical compounds (specially SMX degradation subproducts) that could not be detected by UV-Vis spectrometry. The results obtained permitted the comparison of the effects produced by the three different degradation processes.

Kernelizing: A way to increase accuracy in trilinear decomposition analysis of multiexponential signals

The unmixing of multiexponential decay signals into monoexponential components using soft modelling approaches is a challenging task due to the strong correlation and complete window overlap of the profiles. To solve this problem, slicing methodologies, such as PowerSlicing, tensorize the original data matrix into a three-way data array that can be decomposed based on trilinear models providing unique solutions. Satisfactory results have been reported for different types of data, e.g., nuclear magnetic resonance or time-resolved fluorescence spectra. However, when decay signals are described by only a few sampling (time) points, a significant degradation of the results can be observed in terms of accuracy and precision of the recovered profiles. In this work, we propose a methodology called Kernelizing that provides a more efficient way to tensorize data matrices of multiexponential decays. Kernelizing relies on the invariance of exponential decays, i.e., when convolving a monoexponential decaying function with any positive function of finite width (hereafter called "kernel"), the shape of the decay (determined by the characteristic decay constant) remains unchanged and only the preexponential factor varies. The way preexponential factors are affected across the sample and time modes is linear, and it only depends on the kernel used. Thus, using kernels of different shapes, a set of convolved curves can be obtained for every sample, and a three-way data array generated, for which the modes are sample, time and kernelizing effect. This three-way array can be afterwards analyzed by a trilinear decomposition method, such as PARAFAC-ALS, to resolve the underlying monoexponential profiles. To validate this new approach and assess its performance, we applied Kernelizing to simulated datasets, real time-resolved fluorescence spectra collected on mixtures of fluorophores and fluorescence-lifetime imaging microscopy data. When the measured multiexponential decays feature few sampling points (down to fifteen), more accurate trilinear model estimates are obtained than when using slicing methodologies.

A novel Raman spectroscopic method for detecting traces of blood on an interfering substrate

Traces of body fluids discovered at a crime scene are a primary source of DNA evidence. Raman spectroscopy is a promising universal technique for identifying biological stains for forensic purposes. The advantages of this method include the ability to work with trace amounts, high chemical specificity, no need for sample preparation and the nondestructive nature. However, common substrate interference limits the practical application of this novel technology. To overcome this limitation, two approaches called "Reducing a spectrum complexity" (RSC) and "Multivariate curve resolution combined with the additions method" (MCRAD) were investigated for detecting bloodstains on several common substrates. In the latter approach, the experimental spectra were "titrated" numerically with a known spectrum of a targeted component. The advantages and disadvantages of both methods for practical forensics were evaluated. In addition, a hierarchical approach to reduce the possibility of false positives was suggested.

Deciphering phase evolution in complex metal oxide thin films via high-throughput materials synthesis and characterization

Discovery of structure-property relationships in thin film alloys of complex metal oxides enabled by high-throughput materials synthesis and characterization facilities is demonstrated here with a case-study. Thin films of binary transition metal oxides (Ti-Zn) are prepared by pulsed laser deposition with continuously varying Ti:Zn ratio, creating combinatorial samples for exploration of the properties of this material family. The atomic structure and electronic properties are probed by spatially resolved techniques including x-ray absorption near edge structures (XANES) and x-ray fluorescence (XRF) at the Ti and Zn K-edge, x-ray diffraction, and spectroscopic ellipsometry. The observed properties as a function of Ti:Zn ratio are resolved into mixtures of five distinguishable phases by deploying multivariate curve resolution analysis on the XANES spectral series, under constraints set by results from the other characterization techniques. First-principles computations based on density function theory connect the observed properties of each distinct phase with structural and spectral characteristics of crystalline polymorphs of Ti-Zn oxide. Continuous tuning of the optical absorption edge as a function of Ti:Zn ratio, including the unusual observation of negative optical bowing, exemplifies a functional property of the film correlated to the phase evolution.

Coupling of spectrometric, chromatographic, and chemometric analysis in the investigation of the photodegradation of sulfamethoxazole

A workflow is proposed for the study of the photodegradation process of the sulfamethoxazole (SMX) based on the combination of different experimental techniques, including liquid chromatography, mass spectrometry, UV-Visible spectrophotometry, and the treatment of all the analytical data with advanced chemometric methods. SMX, which is one of the most widely used antibiotics worldwide and has been found at remarkable concentrations in various rivers and effluents over all Europe, was degraded in the laboratory under a controlled source of UV radiation, which simulates the environmental solar radiation (Suntest). Kinetic monitoring of the photodegradation process was performed using UV-Visible spectrophotometric measurements and by further Liquid Chromatography with Diode Array Detector and Mass Spectrometry analysis (LC-DAD-MS). Additionally, the acid-base properties were also investigated to see how the pH can affect the speciation of this substance during the photodegradation process. Based on the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) application, the proposed chemometric method coped with the large amounts of data generated by the different analytical techniques used to monitor the evolution of the photodegradation process. Their simultaneous analysis involved applying a data fusion strategy and an advanced MCR-ALS constrained analysis, which allowed and improved the description of the complete degradation process, detecting the different species of the reaction, and identifying the possible transformation products formed. A total number of six species were resolved in the degradation process of SMX. In addition to the initial SMX, a second species corresponded to a conformational isomer, and the other four species represented different photoproducts, which have also been identified. Furthermore, three different acid-base species of SMX were obtained, and their pK_a values were estimated.

Introducing ATR-FTIR Spectroscopy through Analysis of Acetaminophen Drugs: Practical Lessons for Interdisciplinary and Progressive Learning for Undergraduate Students

Infrared (IR) spectroscopy is a vibrational spectroscopic technique useful in chemical, pharmaceutical, and forensic sciences. It is essential to identify chemicals for reasons spanning from scientific research and academic practices to quality control in companies. However, in some university degrees, graduate students do not get the proficiency to optimize the experimental parameters to obtain the best IR spectra; to correlate the IR spectral bands with the molecular vibrations (chemical elucidation); to have some criteria for any substance identification (especially relevant in quality control to recognize counterfeit); and to apply chemometrics for comparing, visualizing, and classifying the IR spectra. This work presents an experimental laboratory practice for an introductory teaching of the IR instrumental conditions in the identification of substances based on visual spectra comparison and statistical analysis and matching. Then, the selected IR conditions are applied to different commercial drugs, in the solid state or in solution, mostly composed of acetaminophen. Finally, the students apply chemometrics analysis to the IR data. This practice was designed for the training in a chemistry subject for undergraduate students of the chemistry, pharmacy, or forensics degrees, among others related to science, medical, food, or technological sciences.

Application of machine learning and laser optical-acoustic spectroscopy to study the profile of exhaled air volatile markers of acute myocardial infarction

Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its effectiveness. However, despite this, discovering more operative methods of this disease detection is underway. From this point of view, the application of exhaled air analysis for a similar diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target group included 30 patients with primary myocardial infarction. The control group included 42 healthy volunteers. The 'LaserBreeze' laser gas analyzer (Special Technologies Ltd, Russia), based on the dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser source, had been used. The pattern recognition approach was applied in the same manner for the set of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most informative spectral range 2.900 ± 0.125µm. The created predictive model based on the set of absorption coefficients provided 0.86 of the mean values of both the sensitivity and specificity when linear support vector machine (SVM) combined with principal component analysis was used. The created predictive model based on using six volatile AMI markers (C₅H₁₂, N₂O, NO₂, C₂H₄, CO, CO₂) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used.

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.

Magnetic resonance spectroscopy for the study of cns malignancies

Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.

3D and 4D Image Fusion: Coping with Differences in Spectroscopic Modes among Hyperspectral Images

Image fusion is often oriented to solve differences in spatial scale and orientation among different spectroscopic platforms. However, an additional problem arises when the nature of the spectroscopic information differs in dimensionality as well. Indeed, most imaging systems, e.g., Raman, IR, MS, etc., allow acquisition of 3D images, with a linear spectrum per pixel, but new platforms have emerged, such as the recent excitation-emission fluorescence imaging platforms that provide 4D images, with a 2D spectral landscape per pixel. A proper 3D/4D image fusion needs to take into account the difference in the dimension of the spectral information and in the underlying models of both measurements (bilinear for 3D images and trilinear for 4D images). This work solves this image fusion problem through a new dedicated variant of the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm for multiset analysis based on the incorporation of a hybrid bilinear/trilinear model that can handle the image fused structure preserving the natural behavior of the 3D and 4D imaging techniques coupled. The example is illustrated on the fusion of real 3D Raman and 4D fluorescence images recorded on cross sections of rice leaf samples.

μXRF Mapping as a Powerful Technique for Investigating Metal Objects from the Archaeological Site of Ferento (Central Italy)

This research concerns the application of micro X-ray fluorescence (µXRF) mapping to the investigation of a group of selected metal objects from the archaeological site of Ferento, a Roman and then medieval town in Central Italy. Specifically, attention was focused on two test pits, named IV and V, in which metal objects were found, mainly pertaining to the medieval period and never investigated before the present work from a compositional point of view. The potentiality of µXRF mapping was tested through a Bruker Tornado M4 equipped with an Rh tube, operating at 50 kV, 500 μA, and spot 25 μm obtained with polycapillary optics. Principal component analysis (PCA) and multivariate curve resolution (MCR) were used for processing the X-ray fluorescence spectra. The results showed that the investigated items are characterized by different compositions in terms of chemical elements. Three little wheels are made of lead, while the fibulae are made of copper-based alloys with varying amounts of tin, zinc, and lead. Only one ring is iron-based, and the other objects, namely a spatula and an applique, are also made of copper-based alloys, but with different relative amounts of the main elements. In two objects, traces of gold were found, suggesting the precious character of these pieces. MCR analysis was demonstrated to be particularly useful to confirm the presence of trace elements, such as gold, as it could differentiate the signals related to minor elements from those due to major chemical elements.

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T₂ -filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.

Cellular localization of tolyporphins, unusual tetrapyrroles, in a microbial photosynthetic community determined using hyperspectral confocal fluorescence microscopy

The cyanobacterial culture HT-58-2, composed of a filamentous cyanobacterium and accompanying community bacteria, produces chlorophyll a as well as the tetrapyrrole macrocycles known as tolyporphins. Almost all known tolyporphins (A-M except K) contain a dioxobacteriochlorin chromophore and exhibit an absorption spectrum somewhat similar to that of chlorophyll a. Here, hyperspectral confocal fluorescence microscopy was employed to noninvasively probe the locale of tolyporphins within live cells under various growth conditions (media, illumination, culture age). Cultures grown in nitrate-depleted media (BG-11₀ vs. nitrate-rich, BG-11) are known to increase the production of tolyporphins by orders of magnitude (rivaling that of chlorophyll a) over a period of 30-45 days. Multivariate curve resolution (MCR) was applied to an image set containing images from each condition to obtain pure component spectra of the endogenous pigments. The relative abundances of these components were then calculated for individual pixels in each image in the entire set, and 3D-volume renderings were obtained. At 30 days in media with or without nitrate, the chlorophyll a and phycobilisomes (combined phycocyanin and phycobilin components) co-localize in the filament outer cytoplasmic region. Tolyporphins localize in a distinct peripheral pattern in cells grown in BG-11₀ versus a diffuse pattern (mimicking the chlorophyll a localization) upon growth in BG-11. In BG-11₀, distinct puncta of tolyporphins were commonly found at the septa between cells and at the end of filaments. This work quantifies the relative abundance and envelope localization of tolyporphins in single cells, and illustrates the ability to identify novel tetrapyrroles in the presence of chlorophyll a in a photosynthetic microorganism within a non-axenic culture.

Photochemical multivariate curve resolution models for the investigation of photochromic systems under continuous irradiation

We propose a multivariate curve resolution approach for the investigation of photochromic systems using UV-Visible spectroscopy. The incorporation of photochemical hard-models as constraints in multivariate curve resolution alternating least squares (MCR-ALS) allows extracting reaction quantum yields in situations where a complete knowledge of the system is not available. We apply this approach to the study of the photochromism of CMTE (cis-1,2-dicyano-1,2-bis(2,4,5-trimethyl-3-thienyl)ethene) under continuous monochromatic irradiation. The mechanism, involving 3 species and 2 reversible reactions, is written and translated into a kinetic constraint that can be applied to the concentration profiles within ALS. First, ambiguity of the solution obtained for photochemical model(s) is calculated and discussed for single set analysis. Multiset analysis is then proposed combining data obtained under different irradiation wavelengths to provide more reliable results. Finally, the photochemical reactivity of CMTE is widely unraveled, and some description of the mechanism observed under irradiation at 365 nm is given.

Discrimination of constructed air samples using multivariate analysis of full scan membrane introduction mass spectrometry (MIMS) data

RATIONALE

Volatile and semi-volatile organic compounds (S/VOCs) are important atmospheric pollutants affecting both human and environmental health. They are directly measured as an unresolved mixture using membrane introduction mass spectrometry (MIMS). We apply chemometric techniques to discriminate, classify, and apportion air samples from a variety of sources.

METHODS

Full scan mass spectra of lab-constructed air samples were obtained using a polydimethylsiloxane membrane interface and an electron ionization ion trap mass spectrometer. Normalized full scan spectra were analyzed using principal component analysis (PCA), cluster analysis, and k-nearest neighbours (kNN) for sample discrimination and classification. Multivariate curve resolution (MCR) was used to extract pure component contributions. Similar techniques were applied to VOC mixtures sampled from different woodsmoke emissions and from the headspace above aqueous hydrocarbon solutions.

RESULTS

PCA successfully discriminated 32 constructed VOC mixtures from nearly 300 air samples, with cluster analysis showing similar results. Further, kNN classification (k = 1) correctly classified all but one test set sample, and MCR successfully identified the pure compounds used to construct the VOC mixtures. Real-world samples resulting from the combustion of different wood species and those associated with water contaminated with different commercial hydrocarbon products were similarly discriminated by PCA.

CONCLUSIONS

Chemometric techniques have been evaluated using full scan MIMS spectra with a series of VOC mixtures of known composition containing known compounds, and successfully applied to samples with known sources, but unknown molecular composition. These techniques have application to source identification and apportionment in real-world environmental samples impacted by atmospheric pollutants.

Photolysis study of octyl p-methoxycinnamate loaded microemulsion by molecular fluorescence and chemometric approach

Octyl p-methoxycinnamate (OMC) is one of the most widely used sunscreen agents. However, the efficiency of OMC as UV filter over time is affected due to the formation of the cis-isomer which presents a markedly lower extinction coefficient (ε_cis=12,600L mol^-1cm^-1 at 291nm) than the original trans-isomer (ε_trans=24,000L mol^-1cm^-1 at 310nm). In this work, a novel carrier for OMC based on an oil-in-water microemulsion is proposed in order to improve the photostability of this sunscreen. The formulation was composed of 29.2% (w/w) of a 3:1 mixture of ethanol (co-surfactant) and decaethylene glycol mono-dodecyl ether (surfactant), 1.5% (w/w) of oleic acid (oil phase) and 69.2% (w/w) of water. This microemulsion was prepared in a simple way, under moderate stirring at 25°C and using acceptable, biocompatible and accessible materials for topical use. OMC was incorporated in the vehicle at a final concentration of 5.0% (w/w), taking into account the maximum permitted levels established by international norms. Then, a photolysis study of the loaded formulation was performed using a continuous flow system. The direct photolysis was monitored over time by molecular fluorescence. The recorded spectra data between 370 y 490nm were analyzed by multivariate curve resolution-alternating least squares algorithm. The kinetic rate constants corresponding to the photolysis of the trans-OMC were calculated from the concentration profiles, resulting in 0.0049s^-1 for the trans-OMC loaded microemulsion and 0.0131s^-1 for the trans-OMC in aqueous media. These results demonstrate a higher photostability of the trans-OMC when loaded in the proposed vehicle with respect to the free trans-OMC in aqueous media.