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【Objective】This study is aimed to develop a comprehensive approach based on gas chromatography-mass spectrometry（GC-MS）combined with multivariate resolution and multivariate data analysis to achieve rapid and accurate identification of commonly encountered aged vegetable oils and animal fats in forensic science, particularly for samples with complex fatty acid compositions post-degradation that are difficult to distinguish through traditional spectral comparison.【Method】Firstly, the heuristic evolving latent projection (HELP) method was employed to resolve complex overlapping peaks acquired by GC-MS, enabling the separation and extraction of pure chromatograms and mass spectra of individual chemical components in vegetable oils and animal fats. Subsequently, two unsupervised learning methods, hierarchical cluster analysis (HCA) and principal component analysis（PCA), were applied to reduce the dimensionality and perform cluster analysis of GC-MS data from 13 different types of aged vegetable oils and animal fats (aged at 60℃ for 36 d) attached to five different carriers, with the aim of exploring differences among species. Furthermore, orthogonal partial least squares-discriminant analysis（OPLS-DA), a supervised learning method, was utilized for rapid identification of the geographical origin and brand of the fat and oil samples.【Result】The analyzed results via HCA and PCA indicated that this approach effectively differentiated the species categories of aged vegetable oils and animal fats. However, limitations were observed in further distinguishing fats and oils from different regions or brands. In contrast, the OPLS-DA model demonstrated higher classification accuracy, successfully achieving rapid and accurate identification of aged vegetable oils and animal fats from various regions or brands.【Conclusion】This study provides an efficient and accurate technical solution for the identification of aged vegetable oils and animal fats in forensic science through the integration of GC-MS with HELP multivariate resolution techniques, as well as HCA, PCA, and OPLS-DA analytical methods. This approach effectively addresses the complexities associated with oil degradation and decay, enabling rapid and precise differentiation of oils from different regions or brands. [ABSTRACT FROM AUTHOR]

The huge amount of citrus peel produced worldwide represents an economic burden for society. However, this agricultural by-product is a rich source of natural molecules, potentially endowed with interesting pharmacological activities. In this regard, we decided to investigate if the polymethoxyflavones contained in citrus peel waste could be exploited as novel vasorelaxant agents. A hydroalcoholic blond orange (Citrus sinensis) peel extract, obtained by ultrasonication, was partitioned in dichloromethane. Column chromatography allowed for the isolation of four polymethoxyflavones, namely, scutellarein tetramethyl ether, nobiletin, tangeretin, and sinensetin, identified by nuclear magnetic resonance (NMR) spectroscopy and UPLC-HRMS/MS and confirmed by multivariate curve resolution of NMR fractional spectra. The four molecules showed interesting in vitro vasorelaxant activity, at least, in part, due to the blockade of smooth muscle CaV1.2 channels. Molecular modeling and docking analysis elucidated the binding mode of the polymethoxyflavones at the homology model of the rat CaV1.2c subunit and provided the structural basis to rationalise the highest activity of scutellarein tetramethyl ether in the set and the dramatic effect of the additional methoxy group occurring in nobiletin and sinensetin. In conclusion, citrus peel can be considered a freely available, valuable source of vasoactive compounds worthy of pharmaceutical and/or nutraceutical exploitation. [ABSTRACT FROM AUTHOR]

Pure component decomposition problems in chemometrics can be classified into rank‐regular and rank‐deficient problems. Rank‐deficient problems are characterized by a spectral data matrix that has a lower rank than the number of chemical species. However, it is possible that there exists rank‐regular factorization of the spectral data matrix, but none of these solutions can be interpreted chemically, and only a solution of the MCR problem with rank deficiency is chemically meaningful. Then we say that the underlying problem suffers from a hidden rank deficiency. In this paper, MCR problems with hidden rank deficiency are introduced and analyzed with several examples for problems of rank 2 and rank 3. The area of feasible solutions is determined with the help of additional constraints on the solution. [ABSTRACT FROM AUTHOR]

Berries are a rich source of natural antioxidant compounds, which are essential to profile, as they add to their nutritional value. However, the complexity of the matrix and the structural diversity of these compounds pose challenges in extraction and chromatographic separation. By relying on multivariate curve resolution alternating least squares (MCR-ALS) ability to extract components from complex spectral mixtures, our study evaluates the contributions of various extraction techniques to interference, extractability, and quantifying different groups of overlapping compounds using liquid chromatography diode array detection (LC-DAD) data. Additionally, the combination of these methods extends its applicability to evaluate polyphenol degradation in stored berry smoothies, where evolving factor analysis (EFA) is also used to elucidate degradation products. Results indicate that among the extraction techniques, ultrasonication-assisted extraction employing 1% formic acid in methanol demonstrated superior extractability and selectivity for the different phenolic compound groups, compared with both pressurized liquid extraction and centrifugation of the fresh berry smoothie. Employing MCR-ALS on the LC-DAD data enabled reliable estimation of total amounts of compound classes with high spectral overlaps. Degradation studies revealed significant temperature-dependent effects on anthocyanins, with at least 50% degradation after 7 months of storage at room temperature, while refrigeration and freezing maintained fair stability for at least 12 months. The EFA model estimated phenolic derivatives as the main possible degradation products. These findings enhance the reliability of quantifying polyphenolic compounds and understanding their stability during the storage of berry products. [ABSTRACT FROM AUTHOR]

Gas chromatography-mass spectrometry (GC/MS) due to high sensitivity and qualifying the volatile compounds is one of the most practical methods for the analysis of essential oils. Accurate identification of trace components and complete separation of overlapped and embedded peaks are difficult to achieve even if precise conditions are imposed on the chromatographic separation process. In this study, the essential oil of Lamium amplexicaule L. (L. amplexicaule) after extraction by the Clevenger apparatus, was analyzed by GC/MS. This study focuses on the characterization of the trace and co-eluted components of essential oils in the mentioned species using chemometric methods. Advanced multivariate curve resolution (MCR) methods were used to overcome the problem of background, baseline offset and overlapping peaks and recognition of the noises from the trace components in GC/MS. The analysis of GC/MS data without chemometric methods revealed that eighteen components exist in the L. amplexicaule essential oil. It is noteworthy that, by a combination of MCR with GC/MS method, this number was extended to more than twenty-five. Using chemometric tools and methods, components with a percent higher than 0.01%, were identified from noises and other overlapped peaks were resolved for 85.56% of the total relative content of the L. amplexicaule essential oil. The most important volatile constituents were identified as hexahydrofarnesyl acetone, spathulenol, caryophyllene oxide, hexadecanoic acid and trans-phytol respectively.

The development of in situ techniques to quantitatively characterize the heterogeneous reactions is essential for understanding physicochemical processes in aqueous phase. In this work, a new approach coupling in situ UV–vis spectroscopy with a two‐step algorithm strategy is developed to quantitatively monitor heterogeneous reactions in a compact closed‐loop incorporation. The algorithm involves the inverse adding‐doubling method for light scattering correction and the multivariate curve resolution‐alternating least squares (MCR‐ALS) method for spectral deconvolution. Innovatively, theoretical spectral simulations are employed to connect MCR‐ALS solutions with chemical molecular structural evolution without prior information for reference spectra. As a model case study, the aqueous adsorption kinetics of bisphenol A onto polyamide microparticles are successfully quantified in a one‐step UV–vis spectroscopic measurement. The practical applicability of this approach is confirmed by rapidly screening a superior adsorbent from commercial materials for antibiotic wastewater adsorption treatment. The demonstrated capabilities are expected to extend beyond monitoring adsorption systems to other heterogeneous reactions, significantly advancing UV–vis spectroscopic techniques toward practical integration into automated experimental platforms for probing aqueous chemical processes and beyond. [ABSTRACT FROM AUTHOR]

Modern society's keen regard for aesthetics made hair products an integral part of a multi‐billion‐dollar cosmetic industry. Hair care products (e.g., shampoos and conditioners) and chemical treatments (e.g., bleaching and permanent waving) result in various effects on the morphological attributes of hair. Generally, water adsorbed on the hair surface is known to significantly dictate the hair's mechanical characteristics (smoothness and friction), and hair's macroscopic wettability has been commonly used to indicate its surface properties. However, an approach to selectively characterize the hydration water in the hair surface is required to accurately understand the intermolecular events between the hair and its vicinal water. In this paper, we successfully obtained the infrared (IR) absorption spectra of the hydration water of human hair. We employed the multivariate curve resolution‐alternating least square (MCR‐ALS) method to separate the hydration and bulk water spectra from the whole spectra. Comparing the IR spectra of the hydration water of chemically untreated and bleached hair samples, we conclude that water molecules form strong hydrogen bonds with the bleached hair surface due to the destruction of the hair's hydrophobic outer layer and the consequent formation of hydrophilic residues. [ABSTRACT FROM AUTHOR]

Objective: Hair ageing is accompanied by hair fibres becoming irregularly shaped causing them to stick out in irregular directions or have more curliness and being spread out. This is believed to be due to changes within the hair fibre structure which occur with ageing, and one of the causes of these changes could be an increase in the number of protein carbonyl groups present in the hair. The aim of this study is to investigate the internal denaturation of hair related to protein carbonyls in attempt to gain new insight into age‐related changes that occur in hair. Methods: The degree of carbonylation of the hair structural protein as determined by fluorescent labelling and Western blotting analysis was used to investigate the primary structure of hair protein. The amount of helix, a common conformation in the secondary structure of proteins, in hair in groups of women with different ages was also analysed using infrared microscopy coupled with multivariate curve resolution (MCR). From the results of this, an image of the two‐dimensional distribution of the α‐helices was generated for the hair taken from each age group. Also, high‐pressure differential scanning calorimetry (HPDSC) of the hair in water was performed on the hair taken from each age group to determine the peak temperature of endothermic effect and the enthalpy of denaturation. Results: We found that the amino group content in hair proteins decreased and Type II keratin, one of the subunits of intermediate filament, was more carbonylated with age. The results of the MCR indicated eight separate components, including components of the secondary structure of proteins, such as α helices and β sheets. Two‐dimensional images of the hair cross‐sections revealed that the presence of α helices decreased with age. In addition, data from the HPDSC showed that the enthalpy associated with the denaturing temperature also significantly decreased with age. Conclusion: These results suggest that there is a negative correlation between age and structural integrity of the helix segment in intermediate filament. The results of this study also show that there is a positive correlation between age‐related hair denaturation and protein carbonyls. [ABSTRACT FROM AUTHOR]

Gas chromatography-mass spectrometry (GC/MS) due to high sensitivity for quantitying volatile compounds is one of the most practical methods for the analysis of essential oils. Accurate identification of trace components and complete separation of overlapped and embedded peaks are difficult to achieve even if precise conditions are imposed on the chromatographic separation process. In this study, the essential oil of Lamium amplexicaule L. (L. amplexicaule) after extraction by the Clevenger apparatus, was analyzed by GC/MS. This study focuses on the characterization of the trace and co-eluted components of essential oils in the mentioned species using chemometric methods. Advanced multivariate curve resolution (MCR) methods were used to overcome the problem of background, baseline offset and overlapping peaks, and recognition of the noises from the trace components in GC/MS. The analysis of GC/MS data without chemometric methods revealed that eighteen components exist in the L. amplexicaule essential oil. It is noteworthy that, by a combination of MCR with GC/MS method, this number was extended to more than twenty-five. Using chemometric tools and methods, components with a percent age higher than 0.01% were identified from noises, and other overlapped peaks were resolved for 85.56% of the total relative content of the L. amplexicaule essential oil. The most important volatile constituents were identified as hexahydrofarnesyl acetone, spathulenol, caryophyllene oxide, hexadecanoic acid and trans-phytol, respectively. [ABSTRACT FROM AUTHOR]

The interaction of microcroplastics (MP) with dissolved organic matter, especially humic substances, is of great importance in understanding the behavior of microplastics in aquatic ecosystems. Surface modification by humic substances plays an essential role in transport and interaction of MP with abiotic and biotic components. Previous studies on the interaction between MP and humic substances were largely based on a model compound, humic acid (Sigma‐Aldrich). In our work, we therefore investigated the interaction of natural organic matter (NOM) sampled from a German surface water with low‐density polyethylene particles (LDPE). High‐pressure size exclusion chromatography (HPSEC) and UV/vis absorption and fluorescence spectroscopy were used to characterize the incubation solutions after modifications due to the presence of LDPE, and Raman spectroscopy was used to characterize the incubated microplastics. While the studies of the solutions generally showed only very small effects, Raman spectroscopic studies allowed clear evidence of the binding of humic fractions to MP. The comparison of the incubation of NOM and a lignite fulvic acid which also was tested further showed that specific signatures of the humic substances used could be detected by Raman spectroscopy. This provides an elegant opportunity to conduct broader studies on this issue in the future. [ABSTRACT FROM AUTHOR]

In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR‐ALS) algorithm for analyzing three‐dimensional (3D) 1H‐MRSI data of the prostate in prostate cancer (PCa) patients. MCR‐ALS generates relative intensities of components representing spectral profiles derived from a large training set of patients, providing an interpretable model. Our objectives were to classify magnetic resonance (MR) spectra, differentiating tumor lesions from benign tissue, and to assess PCa aggressiveness. We included multicenter 3D 1H‐MRSI data from 106 PCa patients across eight centers. The patient cohort was divided into a training set (N = 63) and an independent test set (N = 43). Singular value decomposition determined that MR spectra were optimally represented by five components. The profiles of these components were extracted from the training set by MCR‐ALS and assigned to specific tissue types. Using these components, MCR‐ALS was applied to the test set for a quantitative analysis to discriminate tumor lesions from benign tissue and to assess tumor aggressiveness. Relative intensity maps of the components were reconstructed and compared with histopathology reports. The quantitative analysis demonstrated a significant separation between tumor and benign voxels (t‐test, p < 0.001). This result was achieved including voxels with low‐quality MR spectra. A receiver operating characteristic analysis of the relative intensity of the tumor component revealed that low‐ and high‐risk tumor lesions could be distinguished with an area under the curve of 0.88. Maps of this component properly identified the extent of tumor lesions. Our study demonstrated that MCR‐ALS analysis of 1H‐MRSI of the prostate can reliably identify tumor lesions and assess their aggressiveness. It handled multicenter data with minimal preprocessing and without using prior knowledge or quality control. These findings indicate that MCR‐ALS can serve as an automated tool to assess the presence, extent, and aggressiveness of tumor lesions in the prostate, enhancing diagnostic capabilities and treatment planning of PCa patients. [ABSTRACT FROM AUTHOR]

Data-independent acquisition–all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples. [ABSTRACT FROM AUTHOR]

1,4-dihydropyridines (1,4-DHPs) are widely recognized as highly effective L-type calcium channel blockers with significant therapeutic benefits in the treatment of cardiovascular disorders. 1,4-DHPs can also target T-type calcium channels, making them promising drug candidates for neurological conditions. When exposed to light, all 1,4-DHPs tend to easily degrade, leading to an oxidation product derived from the aromatization of the dihydropyridine ring. Herein, the elaboration of a quantitative structure–property relationships (QSPR) model was carried out by correlating the light sensitivity of structurally different 1,4-DHPs with theoretical molecular descriptors. Photodegradation experiments were performed by exposing the drugs to a Xenon lamp following the ICH rules. The degradation was monitored by spectrophotometry, and experimental data were elaborated by Multivariate Curve Resolution (MCR) methodologies to assess the kinetic rates. The results were confirmed by the HPLC-DAD method. PaDEL-Descriptor software was used to calculate molecular descriptors and fingerprints related to the chemical structures. Seventeen of the 1875 molecular descriptors were selected and correlated to the photodegradation rate by means of the Ordinary Least Squares (OLS) algorithm. The chemometric model is useful to predict the photosensitivity of other 1,4-DHP derivatives with a very low relative error percentage of 5.03% and represents an effective tool to design new analogs characterized by higher photostability. [ABSTRACT FROM AUTHOR]

This Insight Note follows a series of three previous insight notes on X‐ray photoelectron spectroscopy image analysis that focused on the importance of analyzing the raw data, the use of summary statistics, and principal component analysis (PCA). The same X‐ray photoelectron spectroscopy image data set was analyzed in all three notes. We now show an analysis of this same data set using multivariate curve resolution (MCR). MCR is a widely used exploratory data analysis method. Because of MCR's nonnegativity constraints, it has the important advantage of producing factors that look like real spectra. That is, both its scores and loadings are positive, so its results are often more interpretable than those from PCA. The requirements for preprocessing data are also, in general, lower for MCR compared with PCA. To help determine the number of factors that best describe the data set, a series of MCR models with different numbers of factors was created. Based on the chemical reasonableness of its factors, a two‐factor model was selected. Scores plots/images show the regions of the image that correspond to these two factors. [ABSTRACT FROM AUTHOR]

This article presents a study on the photodegradation of beta-blocker drugs, with a specific focus on propranolol. The researchers used UV spectra to analyze the susceptibility of the drugs to degradation and employed chemometric approaches to determine the number of chemical components present in the samples. The study found that propranolol showed significant degradation and the formation of new products, possibly due to its unique chemical structure. The results provide valuable insights into the photodegradation mechanisms of beta-blocker drugs. The article also references other studies on photodegradation and stability analysis of pharmaceutical drugs. [Extracted from the article]

Letrozole is one of the most prescribed drugs for the treatment of breast cancer in post-menopausal women, and it is endowed with selective peripheral aromatase inhibitory activity. The efficacy of this drug is also a consequence of its long-lasting activity, likely due to its metabolic stability. The reactivity of cyano groups in the letrozole structure could, however, lead to chemical derivatives still endowed with residual biological activity. Herein, the chemical degradation process of the drug was studied by coupling multivariate curve resolution and spectrophotometric methodologies in order to assess a detailed kinetic profile. Three main derivatives were identified after drug exposure to different degradation conditions, consisting of acid-base and oxidative environments and stressing light. Molecular docking confirmed the capability of these compounds to accommodate into the active site of the enzyme, suggesting that the sustained inhibitory activity of letrozole may be at least in part attributed to the degradation compounds.

The combined use of antihypertensive drugs in the treatment of hypertension and prevention of heart attacks has become a very important approach in health care. Valsartan (VAL) and Amlodipine (AML) are two kinds of antihypertensive agents that are used as exforge in the treatment of cardiovascular disease. Clarifying the mechanism and kinetics associated with the release of these drugs is of great importance to establishing an efficient drug delivery system. The aim of this study is to use Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in the analysis of the in vitro kinetic-spectrophotometric data of simultaneous release of AML and VAL from the poly-(acrylic acid-co-2-hydroxyethyl methacrylate) cross-linked by butanediol dimethacrylate P(AA-co-HEMA)-BDMA at pH = 5.5 and 37 °C to obtain the kinetic profiles. Successful loading of drugs on P(AA-co-HEMA)-BDMA was confirmed by investigating the FT-IR spectrums of polymers with and without the loaded drugs. Various mathematical models were exploited to fit the release profile of the drugs. Based on the obtained values for the correlation coefficient (R2), the release kinetics of both drugs match the Korsmeyer-Peppas model. [ABSTRACT FROM AUTHOR]

Purpose: Although the characterization of the chemical and spatial distribution of compounds within a pharmaceutical tablet is still not a routine task, applying Raman spectroscopy with data analysis methods gives the possibility to obtain in-depth information on tablet quality rapidly. However, constraints such as analysis time, laser intensity, and spot size influence the quality of acquired spectra resulting in low signal-to-noise ratio spectra. Therefore, this study proposes a method to characterize a solid heterogeneous pharmaceutical product (e.g., a tablet) based on the product's Raman chemical map. Methods: In this work, surface Raman data were acquired using a simple and rapid method. An algorithm based on the hierarchical application of multivariate curve resolution with log-likelihood maximization combined with principal component analysis was used to blind identify the compounds and create a chemical map. Results: Although the direct application of multivariate curve resolution algorithms did not allow a complete tablet characterization, the hierarchical application enabled individual compounds acquired from the mixed spectra to be identified and their chemical distribution in the tablet to be mapped without the use of external references. Results were successfully benchmarked against the EDXS analysis. Conclusions: Innovations in multivariate methods could help overcome challenges and constraints in data acquisition. This method was, for example, found to be more robust against the presence of spectral outliers. It is promising for 3D analysis of real and complex samples. [ABSTRACT FROM AUTHOR]

Multivariate Curve Resolution (MCR) is a multivariate analysis procedure commonly used to analyze spectroscopic data providing the number of components coexisting in a chemical system, the pure spectra of the components as well as their concentration profiles. Usually, this procedure relies on the existence of distinct systematic variability among spectra of the different samples, which is provided by different sources of variation associated to differences in samples origin, composition, physical chemical treatment, etc. In solid-state NMR, MCR has been also used as a post-processing method for spectral denoising or editing based on a given NMR property. In this type of use, the variability is induced by the incrementation of a given parameter in the pulse-sequence, which encodes the separation property in the acquired spectra. In this article we further explore the idea of using a specific pulse sequence to induce a controlled variability in the 13C solid-state NMR spectra and then apply MCR to separate the pure spectra of the components according to the properties associated to the induced variability. We build upon a previous study of sugarcane bagasse where a series of 13C solid-state NMR spectra acquired with the Torchia-T1 CPMAS pulse sequence, with varying relaxation periods, was combined with different sample treatments, to estimate individual 13C solid-state NMR spectra of different molecular components (cellulose, xylan and lignin). Using the same pulse sequence, we show other application examples to demonstrate the potentiality, parameter optimization and/or establish the limitations of the procedure. As a first proof of principle, we apply the approach to commercial semicrystalline medium density polyethylene (MDPE) and polyether ether ketone (PEEK) providing the estimation of the individual 13C ssNMR spectra of the polymer chains in the amorphous (short T1) and crystalline (long T1) domains. The analysis also provided the relative intensities of each estimated pure spectra, which are related to the characteristic T1 decays of the amorphous and crystalline domain fractions. We also apply the analysis to isotactic poly (1-butene) (iPB-I) as an example in which the induced T1 variability occurs due to the mobility difference between the polymer backbone and side-chains. A jack-knifing procedure and a student t text allow us to stablish the minimum number of spectra and the range of relaxation periods that need to be used to achieve a precise estimation of the individual pure spectra and their relative intensities. A detail discussion about possible drawbacks, applications to more complex systems, and potential extensions to other type of induced variability are also presented.

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. [ABSTRACT FROM AUTHOR]

Optical spectroscopic analysis of the chemical composition of milk in its natural state is complicated by a complex colloidal structure, represented by differently sized fat and protein particles. The classical techniques of molecular spectroscopy in the visible, near-, and mid-infrared ranges carry only bulk chemical information about a sample, which usually undergoes a destructive preparation stage. The combination of Raman spectroscopy with confocal microscopy provides a unique opportunity to obtain a vibrational spectrum at any single point of the sample volume. In this study, scanning confocal Raman microscopy was applied for the first time to investigate the chemical microstructure of milk using samples of various compositions. The obtained hyperspectral images of selected planes in milk samples are represented by three-dimensional data arrays. Chemometric data analysis, in particular the method of multivariate curve resolution, has been used to extract the chemical information from complex partially overlaid spectral responses. The results obtained show the spatial distribution of the main chemical components, i.e., fat, protein, and lactose, in the milk samples under study using intuitive graphical maps. The proposed experimental and data analysis method can be used in an advanced chemical analysis of natural milk and products on its basis. [ABSTRACT FROM AUTHOR]

An imine synthesis was investigated in a nearly isothermal oscillating segmented flow microreactor at different temperatures using non-invasive Raman spectroscopy. Multivariate curve resolution provided a calibration-free approach for obtaining kinetic parameters. The two different multivariate curve resolution approaches, soft and hard modeling, were applied and contrasted, leading to similar results. Taking heat and mass balance into account, the proposed kinetic model was applied for a model-based scale-up prediction. Finally, the reaction was performed in a 0.5 L semi-batch reactor, followed by in-line Raman spectroscopy and off-line gas chromatography analysis. The successful scale-up was demonstrated with a good agreement between measured and predicted concentration profiles. Highlights: • Oscillation segmented flow reactor with inline Raman spectroscopy. • Multivariate Curve Resolution with hard and soft constraints. • High quality kinetic model for scale-up predictions. Graphical abstract [ABSTRACT FROM AUTHOR]

The conventional method for analyzing fatty acid is gas chromatography (GC) with polar capillary columns. However, owing to the high cost and the sensitivity of these columns to the presence of water and oxygen, these columns are not the best choice for separation. Also, analyzing long‐chain acids (>C28) with polar columns is impossible. On the other hand, complete separation with nonpolar columns is not possible for some saturated, unsaturated and long‐chain fatty acids. Therefore, in this study, with the help of chemometric methods, a method was developed using GC/mass spectrometry (MS) with a nonpolar column to resolve the peaks to completely separate and accurately identify and quantify fatty acids. Using this method, the fatty acid profiles of the seed oils of Sesamum indicum L, Nigella sativa, Pimpinella anisum, Linum asitatissimum L, Silybum marianum and Amygdalus communis L. var. Amara and var. Dulcis were identified. Through applying the multivariate curve resolution method after GC/MS, the C‐18 fatty acids such as α‐linolenic, linoleic, oleic and stearic acids were separated and quantified. Also, the total percentages of identified fatty acids increased by 1–6% after resolving overlapping peaks. Finally, the obtained percentages of saturated and unsaturated fatty acids were confirmed by reference reports. [ABSTRACT FROM AUTHOR]

In the present contribution, a novel non-targeted volatilomic study based on headspace GC-IMS (HS-GC-IMS) was developed for the authentication and geographical origin discrimination of saffron. In this regard, multivariate curve resolution-alternating least squares (MCR-ALS) was employed to recover the pure GC elution and IMS profiles of saffron metabolites. Iranian saffron samples from seven important areas were analyzed by HS-GC-IMS. The resulting second-order GC-IMS datasets were organized in a augmented matrix and processed using MCR-ALS with various constraints. The MCR-ALS resolved GC profiles were analyzed by different pattern recognition techniques; principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and data driven-soft independent modeling of class analogy (DD-SIMCA). The saffron samples were assigned to their seven geographical origins with an accuracy of 89.0 %. Additionally, four adulterants (style, safflower, madder and calendula) were reliably detected with over 94.0 % accuracy. In this context, GC-IMS substantially outperformed the commonly used FT-NIR spectroscopy approach. • A non-targeted GC-IMS volatilomic approach is proposed for saffron authentication. • MCR-ALS is developed for exploiting pure GC-IMS profiles of saffron metabolites. • PLS-DA is used for geographical discrimination of saffron from seven regions. • DD-SIMCA is utilized for detection of the adulterated saffron samples. • GC-IMS outperformed FT-NIR for saffron authentication. [ABSTRACT FROM AUTHOR]

[Display omitted] • Visible-near infrared hyperspectral imaging was proposed to detect breast cancer. • Multivariate curve resolution was extracted pure profiles of healthy and cancerous tissue. • Partial least square-discriminant analysis was discriminated healthy from cancerous samples. • Hyperspectral imaging is a reliable and non-invasive technique for breast cancer detection. This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400–950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients. [ABSTRACT FROM AUTHOR]

The plant cell wall, composed mainly of polysaccharides, lignin, and structural proteins, supports the architecture, mechanics, and functions of plants. Developing appropriate chemical imaging methods to study spatiotemporal changes of cell wall structural components at the microscopic level is important for understanding plant growth and senescence. In this study, tobacco (Nicotiana tabacum L.), a widely cultivated economic crop and model plant, was selected as the research object. Based on Raman confocal imaging combined with a multivariate curve resolution model, a label-free, in situ , high-throughput and high specificity imaging method for cellulose, high methylated pectin, and low methylated pectin in tobacco leaf cell wall was established to study their microscopic spatiotemporal changes during leaf growth and senescence (flue-curing) processes. The results based on the proposed method revealed that cellulose and pectin levels in the midrib cell wall gradually increased as the leaves matured, from appeared mainly at the cell corners and middle lamella respectively, to appeared in the cell corners, middle lamella, and cell wall. The same trend was observed in the lateral vein cell walls, where cellulose and pectin levels gradually increased. During the flue-curing process, cellulose and highly methylated pectin degraded. The proposed chemical imaging method is expected to provide a label-free, in situ , and high-throughput cell imaging technique for investigating the microscopic spatiotemporal distribution of the main structural components of the leaf cell wall. [Display omitted] • An accurate imaging method for leaf cell wall of model plant tobacco is proposed. • Based on Raman confocal microscopy combined with multivariate curve resolution model. • The method has the advantages of label-free, high throughput, and high specificity. • The growth and flue-curing processes of model plant tobacco is studied by the method. • The spatiotemporal changes in cellulose and pectin are imaged. [ABSTRACT FROM AUTHOR]

In the present contribution, a novel approach based on multivariate curve resolution and deep learning (DL) is proposed for quantitative mass spectrometry imaging (MSI) as a potent technique for identifying different compounds and creating their distribution maps in biological tissues without need for sample preparation. As a case study, chlordecone as a carcinogenic pesticide was quantitatively determined in mouse liver using matrix-assisted laser desorption ionization-MSI (MALDI-MSI). For this purpose, data from seven standard spots containing 0 to 20 picomoles of chlordecone and four unknown tissues from the mouse livers infected with chlordecone for 1, 5, and 10 days were analyzed using a convolutional neural network (CNN). To solve the lack of sufficient data for CNN model training, each pixel was considered as a sample, the designed CNN models were trained by pixels in training sets, and their corresponding amounts of chlordecone were obtained by multivariate curve resolution-alternating least-squares (MCR-ALS). The trained models were then externally evaluated using calibration pixels in test sets for 1, 5, and 10 days of exposure, respectively. Prediction R2 for all three data sets ranged from 0.93 to 0.96, which was superior to support vector machine (SVM) and partial least-squares (PLS). The trained CNN models were finally used to predict the amount of chlordecone in mouse liver tissues, and their results were compared with MALDI-MSI and GC-MS methods, which were comparable. Inspection of the results confirmed the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Three-dimensional reconstruction in brightfield microscopy is challenging since a 2D image includes from in-focus and out-of-focus light which removes the details of the specimen's structures. To overcome this problem, many techniques exist, but these generally require an appropriate model of Point Spread Function (PSF). Here, we propose a new images restoration method based on the application of Multivariate Curve Resolution (MCR) algorithms to a stack of brightfield microscopy images to achieve 3D reconstruction without the need for PSF. The method is based on a statistical reconstruction approach using a self-modelling mixture analysis. The MCR-ALS (ALS for Alternating Least Square) algorithm under non-negativity constraints, Wiener, Richardson–Lucy, and blind deconvolution algorithms were applied to silica microbeads and red blood cells images. The MCR analysis produces restored images that show informative structures which are not noticeable in the initial images, and this demonstrates its capability for the multiplane reconstruction of the amplitude of 3D objects. In comparison with 3D deconvolution methods based on a set of No Reference Images Quality Metrics (NR-IQMs) that are Standard Deviation, ENTROPY Average Gradient, and Auto Correlation, our method presents better values of these metrics, showing that it can be used as an alternative to 3D deconvolution methods. [ABSTRACT FROM AUTHOR]

Given a nonnegative matrix factorization, R, and a factorization rank, r, exact nonnegative matrix factorization (exact NMF) decomposes R as the product of two nonnegative matrices, C and S with r columns, such as R = CS⊤. A central research topic in the literature is the conditions under which such a decomposition is unique/identifiable up to trivial ambiguities. In this paper, we focus on partial identifiability, that is, the uniqueness of a subset of columns of C and S. We start our investigations with the data-based uniqueness (DBU) theorem from the chemometrics literature. The DBU theorem analyzes all feasible solutions of exact NMF and relies on sparsity conditions on C and S. We provide a mathematically rigorous theorem of a recently published restricted version of the DBU theorem, relying only on simple sparsity and algebraic conditions: it applies to a particular solution of exact NMF (as opposed to all feasible solutions) and allows us to guarantee the partial uniqueness of a single column of C or S. Second, based on a geometric interpretation of the restricted DBU theorem, we obtain a new partial identifiability result. This geometric interpretation also leads us to another partial identifiability result in the case r = 3. Third, we show how partial identifiability results can be used sequentially to guarantee the identifiability of more columns of C and S. We illustrate these results on several examples, including one from the chemometrics literature. [ABSTRACT FROM AUTHOR]

In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components. [ABSTRACT FROM AUTHOR]

Letrozole is one of the most prescribed drugs for the treatment of breast cancer in post-menopausal women, and it is endowed with selective peripheral aromatase inhibitory activity. The efficacy of this drug is also a consequence of its long-lasting activity, likely due to its metabolic stability. The reactivity of cyano groups in the letrozole structure could, however, lead to chemical derivatives still endowed with residual biological activity. Herein, the chemical degradation process of the drug was studied by coupling multivariate curve resolution and spectrophotometric methodologies in order to assess a detailed kinetic profile. Three main derivatives were identified after drug exposure to different degradation conditions, consisting of acid-base and oxidative environments and stressing light. Molecular docking confirmed the capability of these compounds to accommodate into the active site of the enzyme, suggesting that the sustained inhibitory activity of letrozole may be at least in part attributed to the degradation compounds. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop a device based on thin-layer chromatography coupled with surface-enhanced Raman spectroscopy (TLC-SERS) to analyze sulfamethoxazole (SMX) and trimethoprim (TMP) in commercial milk samples using chemometric tools. Samples were eluted in TLC plates, and a central composite design (CCD) of two factors was performed to optimize the gold nanoparticle dispersion on TLC plates for SERS, aiming at the detection of both drugs at concentrations close to their maximum residual limits (MRLs). Following the optimization, hyperspectral images from the SERS were captured of the TLC plates. Multivariate curve resolution (MCR-ALS) and independent component analysis (ICA) chemometric techniques were used to extract the signals of the analytes. All the samples presented recovery values of 81–128% for TMP. The quantification of SMX was not possible due to SERS suppression by an interferent. However, it was possible to detect SMX at a concentration of two times the MRL (8.0 × 10−7 mol·L−1). The results demonstrate that the TLC-SERS device is a potential tool for the quantification of TMP and the detection of SMX in milk. [ABSTRACT FROM AUTHOR]

Melamine is a chemical compound generating a very characteristic Raman signature. This component is fraudulently added to milk to artificially increase its nitrogen content (and thereby its apparent protein content). In this paper, we evaluate the ability of a specific wide‐field Fourier‐based Raman imaging system to identify melamine. The melamine is studied in powder form or diluted and dried out on SERS (surface‐enhanced Raman spectroscopy) Cu2O‐Ag substrate, which enhances the Raman signature. In both forms, we show that the spatial content of the information is an asset to characterize the samples; on SERS, we demonstrate the inhomogeneity of the deposition, and in powder form, we identify melamine as individual grain mixed with milk powder. The specific wide‐field imaging technology features much higher laser excitation with lower local intensity than traditional point to point Raman measurement, thereby reducing the acquisition time for full data sets. [ABSTRACT FROM AUTHOR]

In 1985 Borgen and Kowalski published a geometry-based mathematical approach in order to determine the set of feasible solutions of the multivariate curve resolution problem for chemical systems with three species. Twenty years later Rajkó and István devised an algorithm for the analytical derivation of the feasible regions. They showed that the precise boundary of the solution set is piecewise representable in terms of analytical expressions for the boundary curve. This paper generalizes the approach for finding analytical boundary curves by means of duality arguments, provides the precise functional form of the curves in detail, shows how to determine the contact change values and suggests improved analytical expressions which can numerically be evaluated in a stable way. Additionally, it offers detailed instructions for an algorithmic solution and provides the underlying analysis. A program code is presented which generates a piecewise functional representation of the boundary curve. [ABSTRACT FROM AUTHOR]

Extraction of components from individual refinery streams (e.g., reformates and alkylates) in finished gasoline was undertaken using Raman spectroscopy to characterize the chemical content of the finished product. Modified alternating least squares (MALS) was used for separating Raman spectroscopic data sets of the finished product into its pure individual components. The advantages of MALS over alternating least squares (ALS) for multicomponent resolution are highlighted in this study using three Raman spectroscopic data sets which provide a suitable benchmark for comparing the performance of these two methods. MALS is superior to ALS in terms of accuracy and can better resolve components than ALS, and it is also more robust toward collinear data. Finally, components near the noise level usually cannot be extracted by ALS because of instability when inverting the covariance structure which inflates the noise present in the data. However, these same components can be extracted by MALS due to the stabilization of the least squares regression with respect to the matrix inversion using modified techniques from ridge regression. [ABSTRACT FROM AUTHOR]

The widespread application of mid-infrared (MIR) spectroscopy for process monitoring is currently limited by the poor transmission of MIR light through fibre optics. In this work, the performance of a novel and robust MIR spectrometer has been evaluated for practical deployment in a pilot plant or production environment. The spectrometer utilises a Sagnac interferometer design containing no moving parts and is directly attached to an attenuated total reflectance probe, eliminating the need for fibre optics. The quantitative performance of the spectrometer for the in situ analysis of ternary solvent mixtures was assessed. The predictions obtained by partial least squares were accurate (root mean square error of prediction of < 1 % w/w) and comparable to those of a benchmark Michelson-based spectrometer with a fibre-coupled probe, which is more amenable to process development in a laboratory or pilot plant. Calibration transfer between the two spectrometers was performed using the spectral space transformation method to mimic the scenario of the scale-up of a process from the laboratory to pilot scale or from a pilot plant to production scale, where the two different MIR instruments might be deployed. The ability to perform in situ reaction monitoring with the robust Sagnac-based spectrometer was then demonstrated. Spectra acquired during an esterification reaction were resolved using multivariate curve resolution, to produce concentration profiles of each component. These results demonstrate the suitability of this rugged spectrometer for quantitative in situ monitoring of liquid processes, opening up new opportunities for process monitoring in the MIR region. [Display omitted] • A rugged mid-infrared spectrometer was evaluated for in situ analysis of liquids. • Quantitative analysis of ternary solvent mixtures was demonstrated. • Successfully transferred calibration models between mid-infrared instruments. • Reaction concentration profiles were obtained using multivariate curve resolution. [ABSTRACT FROM AUTHOR]

Peak Group Analysis (PGA) is a multivariate curve resolution technique that attempts to extract single pure component spectra from time series of spectral mixture data. It requires that the mixture spectra consist of relatively sharp peaks, as is typical in IR and Raman spectroscopy. PGA aims to construct from individual peaks the associated pure component spectra in the form of nonnegative linear combinations of the right singular vectors of the spectral data matrix. This work presents an automated PGA (autoPGA) that starts with upstream peak detection applied to time series of spectra, combining different window-based peak detection techniques with balanced peak acceptance criteria and peak grouping to deal with repeated detections. The next step is a single-spectrum-oriented PGA analysis. This is followed by a downstream correlation analysis to identify pure component spectra that occur multiple times. AutoPGA provides a complete pure component factorization of the matrix of measured data. The algorithm is applied to FT-IR data sets on various rhodium carbonyl complexes and from an equilibrium of iridium complexes. • The Automated Peak Group Analysis (autoPGA) is an MCR algorithm for vibrational spectra analysis. • AutoPGA detects peaks, finds associated pure component spectra and performs a correlation analysis. • AutoPGA is stable for noisy, experimental data and is implemented in FACPACK with a new GUI. is presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • First use of ToF-SIMS with PCA and MCR for forensic ink analysis. • Clear differentiation of six blue pen inks via advanced statistical methods. • Non-destructive identification of pen strokes in forgery cases. • MCR provides exact pen stroke ID based on unique spectral signals. • Detailed spatial analysis of pen strokes using large-area ToF-SIMS imaging. This study investigates the use of time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate statistical analysis for the identification of blue pen strokes in document forgery cases. Strokes of six commercially available blue pens were analysed using ToF-SIMS, and the data were interpreted using principal component analysis (PCA) and multivariate curve resolution (MCR). PCA effectively reduced the dimensionality of the complex ToF-SIMS data, enabling clear differentiation of the blue pen inks based on the obtained PCA scores. The first three principal components accounted for most of the variance, with PCA score plots showing distinct clusters of samples corresponding to the spectra of each pen. PCA analysis of the large-area ToF-SIMS images further revealed some characteristic spatial patterns and variations across the pen strokes; however, this analysis was less useful for the identification process. On the other hand, MCR provided a more chemically intuitive analysis by extracting pure component spectra from the complex mixtures. Each MCR factor was individually associated with a specific pen, allowing for exact identification of the pen stroke made. MCR score images displayed clear correlations with individual pen strokes, and the associated loadings identified signals at particular m / z values that were characteristic of each pen. This study shows that an efficient method for non-destructive forensic analysis of blue pen strokes can be achieved by combining ToF-SIMS with PCA and MCR. This analytical method provides reliable identification, which is crucial for forgery detection without requiring any sample pretreatment, i.e. the samples (pen strokes) were used just as they were made on paper. [ABSTRACT FROM AUTHOR]

[Display omitted] • NIR spectra to monitor the ripening process in soft cheeses. • MCR-ALS for data compression, enhancing LDA, QDA, and BP-ANN modeling. • MCR-ALS modeling with QDA and BP-ANN for discriminating between two types of soft cheeses. • MCR-ALS scores modeled with LDA to differentiate among various weeks of ripening. In this study, near-infrared (NIR) spectra were employed to monitor the ripening process of two kinds of soft cheese produced in the Extremadura region of Spain, manufactured by two different producers, "Torta del Casar" and "Queso de la Serena". Spectra were collected from the interior of the cheeses and the rind and analysed using appropriate chemometric techniques to distinguish between the two varieties and among different weeks of the maturation process. Different chemometric tools, including multivariate curve resolution with alternating least-squares (MCR-ALS), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and feed-forward artificial neural networks (FF-ANN), were utilised, resulting in outstanding discrimination outcomes with sensitivity, precision, specificity, and accuracy reaching values c.a. 1.00 in optimal scenarios. More comprehensive information was acquired from the rind spectra analysis, indicating that the sampling process can be performed without disturbing the cheese in a non-destructive way. Remarkably, the capability to distinguish between various weeks of ripening for both cheeses could enable manufacturers to produce market-ready products earlier than the typically established timeline. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal stability of 7 vegetable oils studied using Raman spectroscopy. • Combined Raman data with machine learning techniques like PCA and MCR. • Oils richer in polyunsaturated fatty acids showed delayed degradation onset. • Contrasting thermal degradation profiles resolved across different oil types. • Feasible solution bands calculated to account for rotational ambiguity in MCR. This study investigates the thermal stability of seven commonly consumed vegetable oils (canola, corn, frying, sesame, soybean, olive, and sunflower) using Raman spectroscopy and chemometrics. Thermal treatment during cooking can induce degradation reactions impacting oil quality, with oxidation being a major concern. Raman spectra were acquired during controlled heating from 25 °C to 205 °C. Principal component analysis revealed distinct degradation patterns among oils based on spectral variations. Multivariate curve resolution resolved compositional changes, indicating degradation of heat-sensitive species and formation of oxidation products. Calculating feasible solution bands accounted for rotational ambiguity effects. Contrasting behaviors were attributed to differences in fatty acid compositions and natural antioxidant levels. Oils richer in polyunsaturated fatty acids like soybean exhibited delayed degradation onset compared to olive oil. Sesame and commercial frying oil demonstrated superior heat resistance. This integrated approach enables robust comparative analysis of oil thermal stabilities for optimizing high-temperature food processing while preserving quality. [ABSTRACT FROM AUTHOR]

Aqueous‐organic redox flow batteries (RFBs) have gained considerable interest in recent years, given their potential for an economically viable energy storage at large scale. This, however, strongly depends on both the robustness of the underlying electrolyte chemistry against molecular decomposition reactions as well as the device's operation. With regard to this, the presented study focuses on the use of in situ IR spectroscopy in combination with a multivariate curve resolution approach to gain insight into both the molecular structures of the active materials present within the electrolyte as well as crucial electrolyte state parameters, represented by the electrolyte's state of charge (SOC) and state of health (SOH). To demonstrate the general applicability of the approach, methyl viologen (MV) and bis(3‐trimethylammonium)propyl viologen (BTMAPV) are chosen, as viologens are frequently used as negolytes in aqueous‐organic RFBs. The study's findings highlight the impact of in situ spectroscopy and spectral deconvolution tools on the precision of the obtainable SOC and SOH values. Furthermore, the study indicates the occurrence of multiple viologen dimers, which possibly influence the electrolyte lifetime and charging characteristics. [ABSTRACT FROM AUTHOR]

The mechanism of production of extracellular vesicles (EVs) and their molecular contents are of great interest due to their diverse roles in biological systems and are far from being completely understood. Even though cellular cargo releases mediated by EVs have been demonstrated in several cases, their role in secondary metabolite production and release remains elusive. In this study, this aspect is investigated in detail using Raman microspectroscopic imaging. Considerable evidence is provided to suggest that the release of antibiotic penicillin by the filamentous fungus Penicillium chrysogenum involves EVs. Further, the study also reveals morphological modifications of the fungal body during biogenesis, changes in cell composition at the locus of biogenesis, and major molecular contents of the released EVs. The results suggest a possible general role of EVs in the release of antibiotics from the producing organisms. [ABSTRACT FROM AUTHOR]

Mixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with time-resolved, high-throughput or chromatography-coupled setups. Deconvolution and interpretation of the resulting datasets, however, are nontrivial when neither the scattering components nor the way in which they evolve are known a priori. To address this issue, the REGALS method (regularized alternating least squares) is introduced, which incorporates simple expectations about the data as prior knowledge, and utilizes parameterization and regularization to provide robust deconvolution solutions. The restraints used by REGALS are general properties such as smoothness of profiles and maximum dimensions of species, making it well suited for exploring datasets with unknown species. Here, REGALS is applied to the analysis of experimental data from four types of SAXS experiment: anion-exchange (AEX) coupled SAXS, ligand titration, time-resolved mixing and time-resolved temperature jump. Based on its performance with these challenging datasets, it is anticipated that REGALS will be a valuable addition to the SAXS analysis toolkit and enable new experiments. The software is implemented in both MATLAB and Python and is available freely as an open-source software package.

A powerful methodology, based on multivariate curve resolution alternating least squares (MCR-ALS) with quadrilinearity constraints, is proposed to handle complex and incomplete four-way atmospheric data sets, providing concise and easy interpretable results. Changes in air quality by nitrogen dioxide (NO2), ozone (O3) and particular matter (PM10) in eight sampling stations located in Barcelona metropolitan area and other parts of Catalonia during the COVID-19 lockdown (2020) with respect to previous years (2018 and 2019) are investigated using such methodology. MCR-ALS simultaneous analysis of the 3 contaminants among the 8 stations and for the 3 years allows the evaluation of potential correlations among the pollutants even when having missing data blocks. NO2 and PM10 show correlated profiles due to similar pollution sources (traffic and industry), evidencing a decrease in 2019 and 2020 due to traffic restriction policies and COVID-19 lockdown, especially noticeable in the most transited urban areas (i.e., Vall d’Hebron, Granollers and Gràcia). Ozone evidences an opposed inter-annual trend, showing higher amounts in 2019 and 2020 respect to 2018 due to the decreased titration effect, more significant in rural areas (Begur) and in the control site (Obserbatori Fabra). [ABSTRACT FROM AUTHOR]