Raman developmental markers in root cell walls are associated with lodging tendency in tef.

MAIN CONCLUSION: Using Raman micro-spectroscopy on tef roots, we could monitor cell wall maturation in lines with varied genetic lodging tendency. We describe the developing cell wall composition in root endodermis and cylinder tissue. Tef [Eragrostis tef (Zucc.) Trotter] is an important staple crop in Ethiopia and Eritrea, producing gluten-free and protein-rich grains. However, this crop is not adapted to modern farming practices due to high lodging susceptibility, which prevents the application of mechanical harvest. Lodging describes the displacement of roots (root lodging) or fracture of culms (stem lodging), forcing plants to bend or fall from their vertical position, causing significant yield losses. In this study, we aimed to understand the microstructural properties of crown roots, underlining tef tolerance/susceptibility to lodging. We analyzed plants at 5 and 10 weeks after emergence and compared trellised to lodged plants. Root cross sections from different tef genotypes were characterized by scanning electron microscopy, micro-computed tomography, and Raman micro-spectroscopy. Lodging susceptible genotypes exhibited early tissue maturation, including developed aerenchyma, intensive lignification, and lignin with high levels of crosslinks. A comparison between trellised and lodged plants suggested that lodging itself does not affect the histology of root tissue. Furthermore, cell wall composition along plant maturation was typical to each of the tested genotypes independently of trellising. Our results suggest that it is possible to select lines that exhibit slow maturation of crown roots. Such lines are predicted to show reduction in lodging and facilitate mechanical harvest.

A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness.

In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR-ALS) algorithm for analyzing three-dimensional (3D) 1 H-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 1 H-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 1 H-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.

Investigating the quality of extraction and quantification of bioactive compounds in berries through liquid chromatography and multivariate curve resolution.

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.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion.

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.

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.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach.

Data-independent acquisition (DIA) mode in liquid chromatography (LC) high-resolution mass spectrometry (HRMS) has emerged as a powerful strategy in untargeted metabolomics for detecting a broad range of metabolites. However, the use of this approach also represents a challenge in the analysis of the large datasets generated. The regions of interest (ROI) multivariate curve resolution (MCR) approach can help in the identification and characterization of unknown metabolites in their mixtures by linking their MS1 and MS2 DIA spectral signals. In this study, it is proposed for the first time the analysis of MS1 and MS2 DIA signals in positive and negative electrospray ionization modes simultaneously to increase the coverage of possible metabolites present in biological systems. In this work, this approach has been tested for the detection and identification of the amino acids present in a standard mixture solution and in fish embryo samples. The ROIMCR analysis allowed for the identification of all amino acids present in the analyzed mixtures in both positive and negative modes. The methodology allowed for the direct linking and correspondence between the MS signals in their different acquisition modes. Overall, this approach confirmed the advantages and possibilities of performing the proposed ROIMCR simultaneous analysis of mass spectrometry signals in their differing acquisition modes in untargeted metabolomics studies.

Gas chromatography/mass spectrometry with chemometric methods for the determination of fatty acid profiles in herbal oils.

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.

A multivariate curve resolution-alternating least squares (MCR-ALS) technology assisted 1 H-NMR methodology for multi-component quantitation of Trichosanthis Pericarpium injection.

INTRODUCTION: Trichosanthis Pericarpium injection (TPI) is a traditional Chinese medicine preparation obtained from Trichosanthis Pericarpium by extraction, purification and sterilisation. It contains amino acids, alkaloids, nucleotides and other components. Existing quantitative methods only analyse a few components in injections, so this study intends to develop a method for comprehensive analysis of TPI components. OBJECTIVE: To develop a method for quantification of components in TPI by multivariate curve resolution-alternating least squares (MCR-ALS) assisted proton nuclear magnetic resonance (1 H-NMR). METHODS: A 1 H-NMR method was developed for the quantification of components in TPI. For components with independent signals, 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP) was used as an internal standard to calculate the component contents. For components with overlapping signals, the method of MCR-ALS was used. RESULTS: A total of 36 components were identified in TPI, of which 33 were quantified. Methodological validation results showed that the developed 1 H-NMR method has good linearity, accuracy, precision, robustness and specificity. CONCLUSION: The use of 1 H-NMR provides a reliable and universal method for the TPI components identification and quantification. Also, it can be used as a powerful tool for analysing the contents in a complex mixture as a quality control measure.

Analysis of Milk Microstructure Using Raman Hyperspectral Imaging.

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.

Age-related hair denaturation related to protein carbonyls.

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 ß 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.

OBJECTIF: Le vieillissement du cheveu s'accompagne d'une irrégularité de la forme des fibres capillaires, ce qui les fait ressortir dans des directions irrégulières ou les rend plus bouclées, et d'un étalement de celles-ci. On pense que cela est dû à des changements dans la structure des fibres capillaires qui surviennent avec l'âge, et l'une des causes de ces changements pourrait être une augmentation du nombre de protéines carbonyles présentes dans le cheveu. L'objectif de cette étude est d'étudier la dénaturation interne du cheveu liée aux protéines carbonyles afin d'obtenir de nouvelles informations sur les changements liés à l'âge qui surviennent dans le cheveu. MÉTHODES: Le degré de carbonylation de la protéine structurelle du cheveu, déterminé par marquage par fluorescence et analyse par western blotting, a été utilisé pour étudier la structure primaire de la protéine des cheveux. La quantité d'hélices, conformation fréquente de la structure secondaire des protéines, dans les cheveux de groupes de femmes d'âges différents, a également été analysée par microscopie infrarouge couplée à une résolution multivariée de courbes (RCM). À partir des résultats, une image de la distribution bidimensionnelle des hélices alpha a été générée pour les cheveux prélevés dans chaque groupe d'âge. De plus, une calorimétrie différentielle à balayage haute pression (HPDSC) des cheveux dans l'eau a été effectuée sur les cheveux prélevés dans chaque groupe d'âge pour déterminer la température maximale de l'effet endothermique et l'enthalpie de la dénaturation. RÉSULTATS: Nous avons constaté que la teneur en groupe amino dans les protéines capillaires diminuait et que la kératine de type II, l'une des sous-unités du filament intermédiaire, était plus carbonylée avec l'âge. Les résultats de la RCM ont mis en évidence huit composants distincts, y compris des composants de la structure secondaire des protéines, tels que les hélices α et les feuillets ß. Des images bidimensionnelles des coupes transversales de cheveux ont révélé que la présence d'hélices alpha diminuait avec l'âge. En outre, les données de l'HPDSC ont montré que l'enthalpie associée à la température de dénaturation diminuait également de manière significative avec l'âge. CONCLUSION: Ces résultats suggèrent qu'il existe une corrélation négative entre l'âge et l'intégrité structurelle du segment hélicoïdal dans le filament intermédiaire. Les résultats de cette étude montrent également qu'il existe une corrélation positive entre la dénaturation du cheveu liée à l'âge et les protéines carbonyles.

Interaction of letrozole and its degradation products with aromatase: chemometric assessment of kinetics and structure-based binding validation.

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.

Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra.

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.

Analysis of Vicinal Water in Soft Contact Lenses Using a Combination of Infrared Absorption Spectroscopy and Multivariate Curve Resolution.

In this paper, we propose a new spectroscopic method to explore the behavior of molecules near polymeric molecular networks of water-containing soft materials such as hydrogels. We demonstrate the analysis of hydrogen bonding states of water in the vicinity of hydrogels (soft contact lenses). In this method, we apply force to hydrated contact lenses to deform them and to modulate the ratio between the signals from bulk and vicinal regions. We then collect spectra at different forces. Finally, we extracted the spectra of the vicinal region using the multivariate curve resolution-alternating least square (MCR-ALS) method. We report the hydration states depending on the chemical structures of hydrogels constituting the contact lenses.

Chemometric Assisted UV-Spectrophotometric Methods Using Multivariate Curve Resolution Alternating Least Squares and Partial Least Squares Regression for Determination of Beta-Antagonists in Formulated Products: Evaluation of the Ecological Impact.

In this study, UV-spectrophotometry coupled with chemometrics has been utilized to enhance the sustainability of quality control analysis of beta antagonists. First, we developed and optimized two eco-friendly chemometric-assisted methods without preliminary separation utilizing (1) multivariate curve resolution alternating least squares (MCR-ALS) and (2) well-established partial least squares regression (PLSR) multivariate calibration for the resolution and quantification of the most commonly prescribed beta antagonists in active pharmaceutical ingredients or commercial pharmaceutical products. The performance of the two proposed chemometric methods was computed and compared. Second, a comprehensive qualitative and quantitative evaluation of the eco-friendliness of the developed methods was performed utilizing the following greenness assessment tools: Green Analytical Procedure Index (GAPI), Analytical Eco-scale assessment (AES) tool, Raynie and Driver's assessment tool and Analytical GREEnness Metric (AGREE). The models showed satisfactory recovery with a range from 99.83% to 101.12% for MCR-ALS and from 99.66% to 101.54% for PLSR. The optimized models were employed for green analysis of the investigated beta-blockers in single or co-formulated formulations without prior separation. The predictivity of the proposed MCR-ALS and the well-established PLSR method were very comparable. Nevertheless, the MCR-ALS method has the ability to recover the pure spectra of the studied analytes and the interferences as well. The proposed chemometric methods are fast, precise and do not need any sample pretreatment. In addition, they can be used as a benign substitute for the traditional methods used for the analysis of the investigated drugs in pharmaceutical products without harmful impacts on human health and the environment. They also provide advantages in terms of low solvent usage, reduced energy consumption and short analysis time, making them a safe and sustainable approach for quality control analysis.

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept.

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a "coffee ring". MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R2CV.

Do Spectra Live in the Matrix? A Brief Tutorial on Applications of Factor Analysis to Resolving Spectral Datasets of Mixtures.

In spite of a rapid growth of data processing software, that has allowed for a huge advancement in many fields of chemistry, some research issues still remain problematic. A standard example of a troublesome challenge is the analysis of multi-component mixtures. The classical approach to such a problem consists of separating each component from a sample and performing individual measurements. The advent of computers, however, gave rise to a relatively new domain of data processing - chemometry - focused on decomposing signal recorded for the sample rather than the sample itself. Regrettably, still a very few chemometric methods are practically used in everyday laboratory routines. The Authors believe that a brief 'user-friendly' guide-like article on several 'flagship' algorithms of chemometrics may, at least partly, stimulate an increased interest in the use of these techniques among researchers specializing in many fields of chemistry. In the paper, five different techniques of factor analysis are used for the analysis of a three-component system of fluorophores. These algorithms, applied on the excitation-emission spectra, recorded for the 'unknown' mixture, allowed to unambiguously determine its composition without the need for physical separation of the components. An example of using chemometric methods for physical chemistry research is also provided. For each presented technique of the data analysis, a short description of its theoretical background followed by an example of its practical performance is given. In addition, the Reader is supplemented with a basic information on matrix algebra, detailed experimental 'recipes', reference specialist literature and ready-to-use MATLAB codes.

Characterization of Green Paints in Ming and Qianlong Dynasties' Lin'xi Pavilion by Complimentary Techniques.

During conservation of the painted ceiling decoration of Lin'xi Pavilion in the Forbidden City, two distinct paint campaigns were isolated as a unique case study into architectural paint materials during both the Ming and Qing dynasties. Paint samples and cross sections from both paint generations were analyzed with SEM-EDX, time of flight-secondary ion mass spectrometry (ToF-SIMS), XRD, FTIR, and Raman spectroscopies. Similar organic and inorganic materials characteristic of these time periods were identified. The pigments of interest found in both paint generations were botallackite and atacamite polymorphs. This suggests a shift from natural mineral sources to synthetic copper-based pigments for these larger architectural projects.

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.

Analysis of Carcinogenic Polycyclic Aromatic Hydrocarbons in the Complex Background of the Petroleum Fluorescence Using Multivariate Curve Resolution Alternating Least Square and Total Synchronous Fluorescence Spectroscopic Technique.

Multivariate curve resolution alternating least square (MCR-ALS) analysis allows the simultaneous retrieval of pure concentration and spectral profiles for each of the analysed chemical components from the composite spectrum even in the presence of unknown interferences. Total synchronous fluorescence spectroscopy (TSFS), a multidimensional fluorescence technique that describes the variation of synchronous fluorescence profile acquired as a function of increasing offset, has become a useful analytical technique. Suitably arranged TSFS data set can be easily processed using MCR-ALS and thereby a simple and sensitive analytical tool could be developed. The present work successfully used the combination of the MCR-ALS and TSFS to analyse the three carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs) namely Benzo[a]Pyrene, Chrysene and Pyrene in the presence of complex fluorescence background originated from petroleum product. MCR-ALS assisted TSFS can be used for the routine analyses of these carcinogenic PAHs to ensure the quality of water and other samples belonging to different part of the ecosystem.

Direct optical detection of cell density and viability of mammalian cells by means of UV/VIS spectroscopy.

The critical process parameters cell density and viability during mammalian cell cultivation are assessed by UV/VIS spectroscopy in combination with multivariate data analytical methods. This direct optical detection technique uses a commercial optical probe to acquire spectra in a label-free way without signal enhancement. For the cultivation, an inverse cultivation protocol is applied, which simulates the exponential growth phase by exponentially replacing cells and metabolites of a growing Chinese hamster ovary cell batch with fresh medium. For the simulation of the death phase, a batch of growing cells is progressively replaced by a batch with completely starved cells. Thus, the most important parts of an industrial batch cultivation are easily imitated. The cell viability was determined by the well-established method partial least squares regression (PLS). To further improve process knowledge, the viability has been determined from the spectra based on a multivariate curve resolution (MCR) model. With this approach, the progress of the cultivations can be continuously monitored solely based on an UV/VIS sensor. Thus, the monitoring of critical process parameters is possible inline within a mammalian cell cultivation process, especially the viable cell density. In addition, the beginning of cell death can be detected by this method which allows us to determine the cell viability with acceptable error. The combination of inline UV/VIS spectroscopy with multivariate curve resolution generates additional process knowledge complementary to PLS and is considered a suitable process analytical tool for monitoring industrial cultivation processes.