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BACKGROUND: Recent interest has been focused on the application of multivariate curve resolution-alternating least-squares (MCR-ALS) to systems involving the measurement of first-order and non-bilinear second-order data. The latter pose important challenges to bilinear decomposition models, due to the phenomenon of rotational ambiguity in the solutions, even under the application of the full set of chemical constraints that is usually employed in MCR-ALS calibration. RESULTS: After the analysis of several simulated and experimental datasets, important conclusions regarding the role of the selectivity patterns in the constituent spectra have been drawn concerning the achievement of the second-order advantage. Theoretical considerations based on the calculation of the areas of feasible solutions helped to support the observations regarding the predictive ability of MCR- ALS in the various datasets. SIGNIFICANCE: The understanding of the impact of rotational ambiguity in obtaining the second-order advantage with both first-order and non-bilinear second-order data is of paramount importance in the future development of analytical protocols of complex samples.

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The identification of baijiu vintage is crucial for quality assessment and economic value determination. However, its complex composition and multifaceted influences pose significant technical challenges, necessitating research into its aging mechanisms and the development of related identification methods. This study utilized Chemometrics in conjunction with GC × GC-TOFMS for Baijiu Vintage identification. Data compression achieved a reduction of over 1000-fold without compromising key information, enabling analysis on many samples and get their changing regular in a big matrix by MCR. Subsequently, MCR-ALS facilitated the extraction of physical and chemical meaningful information related to baijiu vintage. Key MCR principal components suitable for qualitative and quantitative assessments were selected using CARS-PLS. The regression model demonstrated errors of less than one year. Furthermore, a PLS-DA model provided 30 MCR principal components as potential markers. The research results provide technical support for baijiu vintage identification and lay the groundwork for studying the changing patterns of flavor compounds in baijiu.

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BACKGROUND: the chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). Recently, an alternative procedure was described based on the estimation of image moments for each data matrix and subsequent application of multiple linear regression after suitable variable selection. RESULTS: The analysis of both simulated and experimental data leads to the conclusion that the image moment method, although can cope with chromatographic lack of reproducibility across injections, it only performs well in the absence of uncalibrated interferents. MCR-ALS, on the other hand, provides good analytical results in all studied situations, whether the test samples contain uncalibrated interferents or not. SIGNIFICANCE: The results are useful to assess the real usefulness of newly proposed methodologies for second-order calibration in the case of chromatographic-spectral data sets, especially when samples contain unexpected chemical constituents.

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The evolutionary behavior is examined for heterogeneously distributed hyperspectral images of a simulated biological tissue sample comprising lipid-like and protein-like components during the aging process. Taking a simple planar average of a spectral image loses useful information about the spatially resolved nature of the data. In contrast, multivariate curve resolution (MCR) analysis of a spectral image at a given stage of aging produces a set of loadings of major component groups. Each loading represents the combined spectral contributions of a mixture of similar but not identical constituents (i.e., lipid-like and protein-like components). Temporal analysis of individual component groups using two-dimensional correlation spectroscopy (2D-COS) and MCR provides much-streamlined results without interferences from the overlapped contributions. Grouping of data into separate components also allows for the effective comparison of the parallel processes of lipid oxidation and protein denaturation involving a number of constituents using the heterocomponent 2D-COS analysis. The complex interplays of lipid constituents and protein secondary structures during the tissue aging process are unambiguously highlighted. The possibility of extending this approach to a much more general form of applications using a moving window analysis is also discussed.

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BACKGROUND: the chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). When baseline contributions occur in the data, the background profile retrieved with MCR-ALS may show abnormal lumps or negative dips at the position of the remaining component peaks. RESULTS: The phenomenon is shown to be due to remaining rotational ambiguity in the obtained profiles, as confirmed by the estimation of the boundaries of the range of feasible bilinear profiles. To avoid the abnormal features in the retrieved profile, a new background interpolation constraint is proposed and described in detail. Both simulated and experimental data are employed to support the need of the new MCR-ALS constraint. In the latter case, the estimated analyte concentrations agreed with those previously reported. SIGNIFICANCE: The developed procedure helps to reduce the extent of rotational ambiguity in the solution and to better interpret the results on physicochemical grounds.

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Magnetic resonance microimaging (MRµI) is an outstanding technique for studying water transfers in millimetric bio-based materials in a non-destructive and non-invasive manner. However, depending on the composition of the material, monitoring and quantification of these transfers can be very complex, and hence reliable image processing and analysis tools are necessary. In this study, a combination of MRµI and multivariate curve resolution-alternating least squares (MCR-ALS) is proposed to monitor the water ingress into a potato starch extruded blend containing 20% glycerol that was shown to have interesting properties for biomedical, textile, and food applications. In this work, the main purpose of MCR is to provide spectral signatures and distribution maps of the components involved in the water uptake process that occurs over time with various kinetics. This approach allowed the description of the system evolution at a global (image) and a local (pixel) level, hence, permitted the resolution of two waterfronts, at two different times into the blend that could not be resolved by any other mathematical processing method usually used in magnetic resonance imaging (MRI). The results were supplemented by scanning electron microscopy (SEM) observations in order to interpret these two waterfronts in a biological and physico-chemical point of view.

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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.

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The addition of water is used to past by internal post-curing of hardening cement. Hydration and curing of cementitious are widely identified by non-destructive 1H nuclear magnetic resonance (NMR) measurements of transverse relaxation time and self-diffusion. However, those non-destructive analytical methodologies do not give a truly chemical characterization of the cement matrix during the hydration and curing process. Indeed, the NMR studies only the water dynamics of hydrating cement with internal post-curing. Recent research indicated chemometrics coupled with Raman spectroscopy allows for a better understanding of chemical processes. Recent advances in computing gave industries and research centers the opportunity to generate cost effective data. In this work, an original method is presented, which uses both a data analysis and a non-invasive, non-destructive Raman monitoring of the hydration reaction of a Portland cement. Data was then analyzed by means of chemometrics methods (principal components analysis (PCA), independent components analysis (ICA), and multivariate curve resolution-alternated least-squares (MCR-ALS) with SIMPLe-to-use Interactive Self-modelling Mixture Analysi (SIMPLISMA) and Orthogonal Projection Approach (OP initialization). Results were compared to the ones obtained with thermogravimetric analysis of this cement paste. Besides the consistency of results from both analytical measurements, chemometrics coupled to Raman spectroscopy accurately revealed the details of the setting without any samples collection. The acquisition frequency allowed a proper identification of the occurrence of each of the various phases involved in the hydration and setting process.

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By using Raman microspectroscopy, it aims to elucidate the cellular variations caused by the combination drug of γ-secretase inhibitor (DAPT) and cisplatin in osteosarcoma (OS) cells. Illustrated by the obtained results of spectral analysis, the intracellular composition significantly changed after combined drug actions compared to the solo DAPT treatment, indicating the synergistic effect of DAPT combined with cisplatin on OS cells. Meanwhile, multivariate curve resolution-alternating least squares (MCR-ALS) algorithm was utilized to address the biochemical constitution changes in all investigated groups including the untreated (UT), DAPT (40D) and combined drug (40D + 20C) treated cells. K-means cluster and univariate imaging were both utilized to visualize the changes in subcellular morphology and biochemical distribution. The presented study provides a unique understanding on the cellular responses to DAPT combined with cisplatin from the natural biochemical perspectives, and laids an experimental foundation for exploring the therapeutic strategies of other combined anticancer drugs in cancer cell model.

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Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake and subsequent cellular responses using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and tailored kinetic constraints, based on a system of ordinary differential equations. Advantages of and challenges to the methodology were evaluated using simulated Raman spectral data sets and real Raman spectra acquired from A549 and Calu-1 human lung cells inoculated with doxorubicin, in vitro. The results suggest a dependency of the outcome on the system of equations used, and the importance of the temporal resolution of the data set to enable the use of complex equations. Nevertheless, the use of tailored kinetic constraints during MCR-ALS allowed a more comprehensive modelling of the system, enabling the elucidation of not only the time-dependent concentration profiles and spectral features of the drug binding and cellular responses, but also an accurate computation of the kinetic constants.

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Overlapping fingerprints are often found at crime scenes, but only individual fingerprints separated from each other are admissible as evidence in court. Fingerprint components differ slightly among individuals, and thus their fluorescence spectra also differ from each other. Therefore, the separation of overlapping fingerprints using the difference of the fluorescence spectrum was performed with a hyperspectral imager. Hyperspectral data (HSD) of overlapping fingerprints were recorded under UV LED excitation. Principal component analysis (PCA) and multivariate curve resolution-alternating least squares (MCR-ALS) were applied to the HSD to determine the optimal method for obtaining high-contrast images of individual fingerprints. The results suggested that MCR-ALS combined with PCA-based initialization is capable of separating overlapping fingerprints into individual fingerprints. In this study, a method for separating overlapping fingerprints without initial parameters was proposed.

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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.

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

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Microplastics (MPs) contamination is ubiquitous in environmental matrices worldwide. Moreover these pollutants can be ingested by organisms and transported to organs via the circulatory system. Although efficient methods for the analysis of MPs derived from environment matrices and organisms' tissue samples have been developed after special sample pre-treatment, there remains a need for an optimised approach allowing direct identification and visualisation these MPs in real environmental matrices and organismal samples. Herein, we firstly used a multivariate curve resolution-alternating least squares (MCR-ALS) analysis of Raman hyperspectral imaging data to direct identification and visualisation of MPs in a complex serum background. Four common MPs types including polyethylene (PE), polystyrene (PS), polypropylene (PP) and polyethylene terephthalate (PET) were identified and visualised either individually or in mixtures within spiked samples at an 8-µm spatial resolution. Moreover, Raman imaging based on MCR-ALS was successfully applied in fish faeces biological samples and environmental sand samples for in situ MPs identification directly without washing or removal of organic matter. The current results demonstrate Raman imaging based on MCR-ALS as a novel imaging approach for direct identification and visualisation of MPs, through extraction of MPs' chemical spectra within a complicated biological or environmental background whilst eliminating overlapping Raman bands and fluorescence interference.

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Cocoa butter (CB) is an ingredient traditionally used in the manufacturing of chocolates, but its availability is decreasing due to its scarcity and high cost. For this reason, other vegetable oils, known as cocoa butter equivalents (CBE), are used to replace CB partially or wholly. In the present work, two Peruvian vegetable oils, coconut oil (CNO) and sacha inchi oil (SIO), are proposed as novel CBEs. Confocal Raman microscopy (CRM) was used for the chemical differentiation and polymorphism of these oils with CB based on their Raman spectra. To analyze their miscibility, two types of blends were prepared: CB with CNO, and CB with SIO. Both were prepared at 5 different concentrations (5%, 15%, 25%, 35%, and 45%). Raman mapping was used to obtain the chemical maps of the blends and analyze their miscibility through distribution maps, histograms and relative standard deviation (RSD). These values were obtained with multivariate curve resolution-alternating least squares. The results show that both vegetable oils are miscible with CB at high concentrations: 45% for CNO and 35% for SIO. At low concentrations, their miscibility decreases. This shows that it is possible to consider these vegetable oils as novel CBEs in the manufacturing of chocolates.

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Multi-way calibration based on second-order data constitutes a revolutionary milestone for analytical applications. However, most classical chemometric models assume that these data fulfil the property of low rank bilinearity, which cannot be accomplished by all instrumental methods. Indeed, various techniques are able to generate non-bilinear data, which are all potentially useful for the development of novel second-order calibration methodologies. However, the achievement of the second-order advantage in these cases may be severely limited, since methods for comprehensive modelling of non-bilinear second-order data remain only partially explored. In this research, the analytical performance of three well-known second-order models, namely non-bilinear rank annihilation (NBRA), unfolded partial least-squares with residual bilinearization (U-PLS-RBL) and multivariate curve resolution - alternating least-squares (MCR-ALS) is systematically assessed through sets of simulated and experimental non-bilinear second-order data, involving one analyte and one interferent. Although it is not possible to establish a single strategy to model any type of non-bilinear second-order data with the studied methods, each approach may lead to successful predictions under certain circumstances. It is shown that the prediction capacity is severely affected by data properties such as the level of instrumental noise, the rank of the response matrices and the signal selectivity pattern of the analyte.

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For the first time, alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) was applied to analyse complex gas chromatography-mass spectrometric (GC-MS) data with severe baseline drifts, serious co-elution peaks and slight retention time shifts for the simultaneous identification and quantification of polycyclic aromatic hydrocarbons (PAHs) in aerosols. It was also compared with the classic multivariate curve resolution-alternating least-squares (MCR-ALS) and the GC-MS-based external standard method. In validation samples, average recoveries of five PAHs were within the range from (96.2 ± 6.8)% to (106.5 ± 4.1)% for ATLD-MCR, near to the results of MCR-ALS ((98.0 ± 1.5)% to (106.7 ± 4.3)%). In aerosol samples, the concentrations of pyrene provided by ATLD-MCR were not significantly different from those of MCR-ALS. The other four PAHs including chrysene, benzo[a]anthracene, fluoranthene and benzo[b]fluoranthene were not detected by ATLD-MCR and the GC-MS-based external standard method. The results of figures of merit further demonstrated that ATLD-MCR achieved high sensitivities (8.9 × 104 to 1.7 × 106 mAU ml µg-1) and low limits of detection (0.003 to 0.087 µg ml-1), which were better than or similar to MCR-ALS, presenting a great choice to deal with complex GC-MS data for the simultaneous determination of targeted PAHs in aerosols.

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The aim of this study is to investigate the ability of Time-Domain Nuclear Magnetic Resonance (TD-NMR) combined with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis to detect changes in hydration properties of nineteen genotypes of Arabidopsis (Arabidopsis thaliana) seeds during the imbibition process. The Hybrid hard and Soft modelling version of MCR-ALS (HS-MCR) applied to raw TD-NMR data allowed the introduction of kinetic models to elucidate underlying biological mechanisms. The imbibition process of all investigated hydrated Arabidopsis seeds could be described with a kinetic model based on two consecutive first-order reactions related to an initial absorption of water from the bulk around the seed and a posteriori hydration of the internal seed tissues, respectively. Good data fit was achieved (LOF % = 0.98 and r2% = 99.9), indicating that the hypothesis of the selected kinetic model was correct. An interpretation of the mucilage characteristics of the studied Arabidopsis seeds was also provided. The presented methodology offers a novel and general strategy to describe in a comprehensive way the kinetic process of plant tissue hydration in a screening objective. This work also proves the potential of the MCR methods to analyse raw TD-NMR signals as alternative to the controversial and time-consuming pre-processing techniques of this kind of data, known to be an ill-conditioned and ill-posed problem.

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The interaction of acenaphthene (ACN), a widespread environmental pollutant, with bovine serum albumin (BSA) was explored using spectroscopic methods, molecular modeling and chemometric approaches. The multivariate curve resolution-alternating least squares (MCR-ALS) analysis decomposed the overlapped excitation-emission matrix (EEM) spectra of mixture of ACN and BSA successfully and extracted spectral profiles of pure BSA, ACN and BSA-ACN complex. Based on fluorescence quenching analysis, ACN quenched the inherent fluorescence of BSA remarkably via a static mechanism. The obtained value of binding constant (Kb = 3.82 × 105 L mol-1) revealed a high binding affinity of ACN to BSA which facilitates its distribution by blood circulation system. Furthermore, the binding parameters values revealed that one binding site in BSA was involved in BSA-ACN complex. FT-IR, UV-Vis and CD spectra showed that the conformation of BSA was altered in presence of ACN slightly. Molecular docking simulation suggested that ACN was located in the IA region of BSA and the main interactions between ACN and BSA, are van der Waals forces. The obtained results provide some insight into interactions between ACN and serum albumins at the molecular level.

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The possibility of building an interference-free calibration with first-order instrumental data with multivariate curve resolution-alternating least-squares (MCR-ALS) has been a recent topic of interest. When the protocols were successful, MCR-ALS proved to be suitable for the extraction of chemically meaningful information from first-order calibration datasets, even in the presence of unexpected species, i.e., constituents present in the test samples but absent in the calibration set. This may represent an interesting advantage over classical first-order models, e.g. partial least-squares regression (PLS). However, the predictive capacity of MCR-ALS models can be severely affected by rotational ambiguity (RA), which is usually present in first-order datasets when interferents occur, and has not been always characterized in the published analytical protocols. The aim of this report is to discuss important issues regarding MCR-ALS modelling of first-order data for a calibration scenario with a single analyte and one interferent through simulated and experimental data. Specifically, the question of when and why MCR-ALS allows one to build interference-free calibration models with first-order data is studied in terms of signal overlapping, extent of RA, and especially the role of ALS initialization procedures in prediction performance. The aim is to alert analytical chemists that interference-free MCR-ALS with first-order data may not always be successful.