Multivariate Curve Resolution-alternating Least Squares Research Articles

A novel triple templates molecularly imprinted biosensor assisted by first derivatives of second-order hydrodynamic differential normal pulse voltametric data and multi-way calibration methods for simultaneous biosensing of penicillin, tetracycline and amoxicillin in dairy products: A novel multi-disciplinary study

In this work, a screen printed carbon electrode (SPCE) was used as a platform to fabricate a novel electrochemical biosensor assisted by the first derivatives of the second-order hydrodynamic differential normal pulse voltammetric (HDNPV) data modeled by multivariate curve resolution alternating least squares (MCR-ALS) and parallel factor analysis applied to shift invariant amplitude spectra (PARASIAS) for simultaneous determination of penicillin (PC), tetracycline (TC), and amoxicillin (AC) in dairy products. The SPCE was modified with poly(3,4-ethylenedioxythiophene) (PEDOT) by electropolymerization of the EDOT. Triple templates molecularly imprinted polymers (TTMIPs) were synthesized by using acrylic acid as the functional monomer, and TC, PC, and AC as the template molecules, and epichlorohydrin as a crosslinking agent. The TTMIPs were mixed with multi-walled carbon nanotubes-graphene-ionic liquid (MWCNTs-GR-IL), and drop-casted onto the surface of PEDOT/SPCE. The first derivatives of individual HDNPV responses of the TTMIPs-MWCNTs-GR-IL/PEDOT/SPCE to TC, PC and AC were univariately calibrated, and their linear ranges were used to develop second-order calibration models using MCR-ALS and PARASIAS whose performances were validated by application of them to a validation set with the aim of choosing the best model to assist the biosensor for the analysis of yogurt, milk, and yogurt drink as real samples. The biosensor assisted by MCR-ALS showed a better performance than PARASIAS which forced us to make a final decision on choosing it to assist the biosensor for the analysis of real matrices. The methodology developed in this work was benefited from TTMIIPs, good structure of the biosensor, and three-way calibration models which helped us to develop a novel electroanalytical method for simultaneous determination of TC (0.1–9 pM), PC (1–10 pM), and AC (0.35–8 pM) whose performance in analysing real matrices was comparable with the HPLC-UV as the reference method.

Quantitative Mass Spectrometry Imaging Using Multivariate Curve Resolution and Deep Learning: A Case Study

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.

Multivariate curve Resolution-Alternating least squares coupled with Raman microspectroscopy: new insights into the kinetic response of primary oral squamous carcinoma cells to cisplatin.

Raman MicroSpectroscopy (RMS) is a powerful label-free tool to probe the effects of drugs at a cellular/subcellular level. It is important, however, to be able to extract relevant biochemical and kinetic spectroscopic signatures of the specific cellular responses. In the present study, a combination of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis (PCA) is used to analyse the RMS data for the example of exposure of primary Oral Squamous Carcinoma Cells (OSCC) to the chemotherapeutic agent cisplatin. Dosing regimens were established by cytotoxicity assays, and the effects of the drug on cellular spectral profiles were monitored from 16 to 72 hours post-exposure using an apoptosis assay, to establish the relative populations of viable (V), early (EA) and late apoptotic/dead (LA/D) cells after the drug treatment. Based on a kinetic model of the progression from V > EA > D, MCR-ALS regression analysis of the RMS responses was able to extract spectral profiles associated with each stage of the cellular responses, enabling a quantitative comparison of the response rates for the respective drug treatments. Moreover, PCA was used to compare the spectral profiles of the viable cells exposed to the drug. Spectral differences were highlighted in the early stages (16 hours exposure), indicative of the initial cellular response to the drug treatment, and also in the late stages (48-72 hours exposure), representing the cell death pathway. The study demonstrates that RMS coupled with multivariate analysis can be used to quantitatively monitor the progression of cellular responses to different drugs, towards future applications for label-free, in vitro, pre-clinical screening.

Understanding C-H activation in light alkanes over Cu-MOR zeolites by coupling advanced spectroscopy and temperature-programmed reduction experiments.

The direct activation of methane to methanol (MTM) proceeds through a chemical-looping process over Cu-oxo sites in zeolites. Herein, we extend the overall understanding of oxidation reactions over metal-oxo sites and C-H activation reactions by pinpointing the evolution of Cu species during reduction. To do so, a set of temperature-programmed reduction experiments were performed with CH4, C2H6, and CO. With a temperature ramp, the Cu reduction could be accelerated to detect changes in Cu speciation that are normally not detected due to the slow CH4 adsorption/interaction during MTM (∼200 °C). To follow the Cu-speciation with the three reductants, X-ray absorption spectroscopy (XAS), UV-vis and FT-IR spectroscopy were applied. Multivariate curve resolution alternating least-square (MCR-ALS) analysis was used to resolve the time-dependent concentration profiles of pure Cu components in the X-ray absorption near edge structure (XANES) spectra. Within the large datasets, as many as six different CuII and CuI components were found. Close correlations were found between the XANES-derived CuII to CuI reduction, CH4 consumption, and CO2 production. A reducibility-activity relationship was also observed for the Cu-MOR zeolites. Extended X-ray absorption fine structure (EXAFS) spectra for the pure Cu components were furthermore obtained with MCR-ALS analysis. With wavelet transform (WT) analysis of the EXAFS spectra, we were able to resolve the atomic speciation at different radial distances from Cu (up to about 4 Å). These results indicate that all the CuII components consist of multimeric CuII-oxo sites, albeit with different Cu-Cu distances.

Non-destructive diagnostic testing of cardiac myxoma by serum confocal Raman microspectroscopy combined with multivariate analysis.

The symptoms of cardiac myxoma (CM) mainly occur when the tumor is growing, and the diagnosis is determined by clinical presentation. Unfortunately, there is no evidence that specific blood tests are useful in CM diagnosis. Raman spectroscopy (RS) has emerged as a promising auxiliary diagnostic tool because of its ability to simultaneously detect multiple molecular features without labelling. The objective of this study was to identify spectral markers for CM, one of the most common benign cardiac tumors with insidious onset and rapid progression. In this study, a preliminary analysis was conducted based on serum Raman spectra to obtain the spectral differences between CM patients (CM group) and healthy control subjects (normal group). Principal component analysis-linear discriminant analysis (PCA-LDA) was constructed to highlight the differences in the distribution of biochemical components among the groups according to the obtained spectral information. Principal component analysis was combined with a support vector machine model (PCA-SVM) based on three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)) to resolve spectral variations between all study groups. The results showed that CM patients had lower serum levels of phenylalanine and carotenoid than those in the normal group, and increased levels of fatty acids. The resulting Raman data was used in a multivariate analysis to determine the Raman range that could be used for CM diagnosis. Also, the chemical interpretation of the spectral results obtained is further presented in the discussion section based on the multivariate curve resolution-alternating least squares (MCR-ALS) method. These results suggest that RS can be used as an adjunct and promising tool for CM diagnosis, and that vibrations in the fingerprint region can be used as spectral markers for the disease under study.

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.

Modeling changes in chocolate during production and storage by ATR-FT-IR spectroscopy and MCR-ALS hybrid soft and hard modeling

Industrially tempered dark chocolate processed under different conditions was analyzed using Attenuated Total Reflection-Fourier Transform-Infrared spectroscopy (ATR-FT-IR). Spectra were collected during the cooling process right after tempering using two distinct machines and machine operational modes and over short and long storage (1 day and two months, respectively). To interpret the spectra collected, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis was used. MCR-ALS incorporated a dedicated implementation of hard modeling constraints for the elucidation of the spectral and concentration profiles of chocolate contributions during the monitored process with the aim of reducing ambiguity in the data interpretation. Thus, the spectral signature of the amorphous form of chocolate was constrained to follow a Gaussian shape, in line with previously reported works and with measurements observed at the beginning of the cooling process. Additionally, hard modeling constraints were applied to the concentration profiles linked to the cooling process by implementing the Avrami kinetic model, often postulated to describe thermally induced fat crystal transformations. The application of the hard modeling constraints eased the generation of interpretable solutions and resulted in concentration profiles and spectral signatures that were defining much better the crystal state of the chocolate samples. A two-component system explained the studied procedures, with a contribution S1 representing the highly crystalline state of the chocolate fats and S2 representative of the amorphous/less stable state. In general, we were able to track the transition from a less ordered to a highly ordered crystal state of the fats during the cooling stage of chocolate. After short storage, the chocolates had contributions of both S1 and S2 profiles. After long storage, the S1 crystalline form was the most dominant interpreted as the reorganization of the triglyceride acyl chains towards a thermodynamically favorable state. The latter was observed for all analyzed chocolates, implying that, regardless the tempering process or temper regime tested, the fats in chocolates reach the same high crystalline state after maturing.

Monitoring glucose levels in urine using FTIR spectroscopy combined with univariate and multivariate statistical methods

The development of novel platforms for non-invasive continuous glucose monitoring applied in the screening and monitoring of diabetes is crucial to improve diabetes surveillance systems. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy of urine can be an alternative as a sustainable, label-free, fast, non-invasive, and highly sensitive analysis to detect changes in urine promoted by diabetes and insulin treatment. In this study, we used ATR-FTIR to evaluate the urinary components of non-diabetic (ND), diabetic (D), and diabetic insulin-treated (D + I) rats. As expected, insulin treatment was capable to revert changes in glycemia, 24-h urine collection volume, urine creatinine, urea, and glucose excretion promoted by diabetes. Several differences in the urine spectra of ND, D, and D + I were observed, with urea, creatinine, and glucose analytes being related to these changes. Principal components analysis (PCA) scores plots allowed for the discrimination of ND and D + I from D with an accuracy of ∼ 99 %. The PCA loadings associated with PC1 confirmed the importance of urea and glucose vibrational modes for this discrimination. Univariate analysis of second derivative spectra showed a high correlation (r: 0.865, p < 0.0001) between the height of 1074 cm-1 vibrational mode with urinary glucose concentration. In order to estimate the amount of glucose present in the infrared spectra from urine, multivariate curve resolution-alternating least square (MCR-ALS) was applied and a higher predicted concentration of glucose in the urine was observed with a correlation of 78.9 % compared to urinary glucose concentration assessed using enzyme assays. In summary, ATR-FTIR combined with univariate and multivariate chemometric analyses provides an innovative, non-invasive, and sustainable approach to diabetes surveillance.

On-line Raman spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) to investigate the synthesis mechanism of 3,5-diamino-1,2,4-triazole (DAT)

The precise and accurate synthesis mechanism of typical energetic materials (EMs) intermediate is extremely important for the optimization of synthesis technology of EMs. In this research, on-line Raman spectroscopy technique combined with multivariate curve resolution-alternating least squares(MCR-ALS) method was proposed and used to investigate the synthesis mechanism of EMs intermediate (3,5-diamino-1,2,4-triazole, DAT). Initially, on-line Raman spectroscopy was applied to collect the Raman spectral data of DAT synthesis process. Secondly, principal component analysis (PCA), coupled with singular value decomposition (SVD) were used to determine the number of component of the reaction system and the components was 5. Thirdly, MCR-ALS was used to extract the pure Raman spectra and concentration curves of each substance of DAT synthesis process. During the MCR-ALS operation, evolving factor analysis (EFA) was choose to acquire the initial concentration estimation for MCR-ALS. Several constraints were selected to apply to ALS optimization including non-negative, closure, equality and correlation constraint. And the correlation coefficient between the Raman spectra and the actual Raman spectra of the hydrazine hydrochloride, dicyandiamide and DAT was calculated, their correlation coefficient R2 were 0.9522, 0.9446, 0.9908 respectively which showed a good data fit of MCR-ALS method. Finally, according to the results of MCR-ALS analysis, the structure of the synthetic intermediates was successfully deduced and the mechanism of DAT synthesis was proposed. Hence, a precise and comprehensive method for analyzing the DAT synthesis reaction mechanism is proposed, which is helpful to provide a new idea for the analysis of the synthesis reaction mechanism of energetic materials.

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.

Detection of bacterial spoilage during wine alcoholic fermentation using ATR-MIR and MCR-ALS

A new methodology is proposed to describe the evolution of the main chemical compounds of grape must during wine alcoholic fermentation using Attenuated Total Reflectance Mid Infrared (ATR-MIR) spectra in combination with Multivariate Curve Resolution Alternating Least-Squares (MCR-ALS). In addition, we have developed a process control strategy to detect differences between fermentations running under Normal Operation Conditions (NOC) and fermentations intentionally spoiled with lactic acid bacteria at the beginning of alcoholic fermentation (MLF) to promote deviations from NOC. MCR-ALS models on these data showed a good data fit (R 2 = 99.95% and lack of fit = 2.31%). It was possible to resolve the spectral profiles of relevant molecules involved in the alcoholic fermentation process, including the one related to bacterial spoilage (lactic acid). MSPC charts were built based on the concentration profiles obtained from the MCR-ALS models and using T 2 and Q statistics. Spoiled wines showed off-limit values for T 2 after 96 hours, making it possible to detect lactic acid bacteria spoilage in early stages of alcoholic fermentation. Thus, the use of ATR-MIR spectra and MCR-ALS analysis shows a great potential for a rapid control of the state of the alcoholic fermentation process, making it possible to early detect the appearance of undesired molecules during the process, which allows the winemaker to apply corrective measures and obtain a good final product. • ATR-MIR and MCR-ALS allow resolving main compounds involved in wine fermentation. • T 2 and Q MSPC charts are built using MCR-ALS concentration profiles. • The concept of ‘Inverse’ MSPC charts' is introduced. • Early detection of undesired molecules related to bacterial spoilage is achieved.

Comparison of different analysis methods applied in archaeological soil analysis with chemometric techniques in the interpretation of past human activity: A case study from the Yassıtepe Höyük in İzmir, Turkey

In this study, human activities carried out in the past of the Yassıtepe Höyük, one of the most important Bronze Age settlements in Western Anatolia, were tried to be determined as a result of examining the data obtained from 108 anthropogenic soil samples (89 from the on-site; 19 from the off-site) taken from different points with chemometric modeling techniques. According to the moisture, pH, and total organic matter analyzes, the mean results were determined as 6.50 % ± 0.21, 8.44 ± 0.28, and 7.42 % ± 0.80 %, respectively. Some elements such as calcium, phosphorus, iron, sodium, potassium, magnesium, and sulfur are determined mean as 119548.15 ± 30680.23, 3497.41 ± 1494.37, 29414.82 ± 5221.90, 5148.15 ± 1072.29, 15962.96 ± 3391.98, 10737.96 ± 1917.15, and 1138.89 ± 420.47 mg L−1, respectively, by elemental analysis. The highest mean phosphorus ratio is reached in the M17 building (5105.00 ± 2886.98 mg L−1) and the IIB2-2 phase of this building had the highest mean phosphorus ratio (8432.50 ± 2199.75 mg L−1). According to the enrichment factor (EF) analysis, lead, arsenic, strontium, phosphorus, calcium, barium, potassium, beryllium, and sulfur elements were found to accumulate in the soil samples of the on-site. To determine which analysis techniques gave more meaningful results, 11 principal component analysis (PCA) models were created by using at least binary combinations of these analysis techniques, and the best PCA model was selected by evaluating their statistical values of the number of factors reached, the eigenvalue, and the number of samples outside of explained variance. The PCA model 6, which has the highest factor number of 7 and includes the results of total organic matter and elemental analysis, is chosen as the most successful model. In addition, it was seen that multivariate curve resolution-alternating least squares (MCR-ALS) data, which determined the distribution of the variables in the archaeological area, supported the EF and PCA data. According to the results, it is determined that the Yassıtepe Höyük and its surroundings have been exposed to intense human activity in the past and there has been an agricultural village life where shellfish have been consumed a lot due to high calcium rates with phosphorus. The large amount of waste belonging to various shellfish obtained from the archaeological studies carried out in the area confirms this result.

Analysis of longitudinal metabolomic data using multivariate curve resolution-alternating least squares and pathway analysis

Extraction of meaningful biological information from longitudinal metabolomic studies is a major challenge and typically involves multivariate analysis and dimensional reduction methods for data visualization such as Principal Component Analysis or Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Besides, a variety of computational tools have been developed to identify changes in metabolic pathways including functional analysis and pathway analysis. In this work, the joint analysis of results from MCR-ALS and metabolic pathway analysis is proposed to facilitate the interpretation of dynamic changes in longitudinal metabolomic data. The strategy is based on the use of MCR-ALS to remove unstructured random variation in the raw data, thus facilitating the interpretation of dynamic changes observed by metabolic pathway analysis over time. A simulated data set representing dynamic longitudinal changes in the intensities of a subset of metabolites from three metabolic pathways was initially used to test the applicability of MCR-ALS to support pathway analysis for detecting pathway perturbations. Then, the strategy is applied to real data acquired for the analysis of changes during CD8+ T cell activation. Results obtained show that MCR-ALS facilitates the interpretation of longitudinal metabolomic profiles in multivariate data sets by identifying metabolic pathways associated with each detected dynamic component.

Multiple adulterants detection in turmeric powder using Vis-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques

In the present contribution, visible and short wavelengths of near infrared hyperspectral imaging (Vis-SWNIR-HSI) combined with different chemometric techniques is proposed as a novel technique for turmeric authentication and multiple adulterants (corn flour, rice flour, starch, wheat flour, and zedoary) detection. In this regard, twenty-three samples of turmeric were collected as whole rhizomes or powdered from seven countries and then their VIS-SWNIR hyperspectral images were obtained in 400–1000 nm using SPECIM IQ HSI device. Two multivariate resolution techniques of multivariate curve resolution-alternating least squares (MCR-ALS) and mean-field independent component analysis (MF-ICA) were used to extract pure spatial and spectral profiles of the components, and their results were compared by projecting their solutions in the area of feasible solutions (AFSs). Then, the distribution maps of turmeric component obtained using MCR-ALS and MF-ICA were used for authentication. Principal component analysis (PCA) was used to find the pattern of authentic samples using their distribution maps. Additionally, data-driven soft independent modeling of class analogy (DD-SIMCA) was employed to find boundary between authentic and adulterated turmeric samples. On this matter, good class modelling results with sensitivity of 95 % and specificity of 100 % were obtained. Finally, partial least squares-discriminant analysis (PLS-DA) was utilized for discrimination of the of adulternats and their in binary and multi-class classification modes. On this matter, PLS-DA accuracies were acceptable for binary and mixed samples which confirmed the validity of the proposed method.

Sensitive and selective simultaneous biosensing of nandrolone and testosterone as two anabolic steroids by a novel biosensor assisted by second-order calibration

Recently, our research group have focused on an interesting project in which a novel dual template molecularly imprinted (DTMIP) biosensor was fabricated and assisted by second-order differential pulse voltammetric (DPV) data for simultaneous determination of nandrolone decanoate (ND) and testosterone decanoate (TS). An indium tin oxide (ITO) was modified with multiwalled carbon nanotubes-graphene-ionic liquid (MWCNT-Gr-IL) and then, the fullerene C60 was casted onto the surface of MWCNT-Gr-IL/ITO and electrochemically reduced. Finally, DTMIPs were electrosynthesized by electropolymerization of 4-aminobenzoic acid (ABA) as monomer with ND and TS as template molecules to obtain the final structure of the biosensor (DTMIP/C60/MWCNT-Gr-IL/ITO). Structure of the biosensor was electrochemically and microscopically characterized. The ND and TS generated two severely overlapped DPVs at the surface of the biosensor which forced us to assist the biosensor with three-way calibration by second-order DPV data to simultaneous determine them. Two second-order algorithms including multivariate curve resolution alternating least squares (MCR-ALS) and parallel factor analysis2 (PARAFAC2) were used to build second-order calibration models and evaluation of their performance in the analysis of synthetic samples showed more superiority of the MCR-ALS than PARAFC2 which motivated us to select PARAFC2 for the analysis of urine samples as real cases. Application of the biosensor assisted by PARAFC2 for the analysis of urine samples towards simultaneous determination of ND and TS was successful.

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

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. 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). 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. 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. 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.

Appraisal of Ni Species Supported on Porous MgO during Ethanol Steam Reforming by XAS

AbstractOperando time‐resolved X‐ray absorption spectroscopy (XAS) was used to determine nickel species supported on porous MgO during the ethanol steam reforming reaction. The Principal Component Analysis (PCA) of the nickel K‐edge XAS spectra revealed Ni(OH)2, NiMgO, and metallic Ni during the activation and reaction of the catalysts. The Multivariate Curve Resolution‐Alternating Least Squares (MCR‐ALS) method for the time‐resolved XAS spectra data permitted to calculate the concentration of species during the activation and reaction steps and to determine the formation of NiMgO and nickel reduction degree. The samples prepared with surfactants, Ni10MgO‐F127 and Ni10MgO‐P123, showed less nickel reduction (less than 30 %) but were more active and stable than the reference Ni10MgO catalyst, which reduced 52 %. It happened due to the smaller particle size and a larger specific area of the porous supports. The samples synthesized without Pluronic deactivated, and ethanol dehydrogenation became predominant instead of the ethanol steam reforming.

A colorimetric assay and MCR-ALS analysis of the peroxidase-like activity of poly (N-phenylglycine) functionalized with polyethylene glycol (PNPG-PEG) nanozyme for the determination of dopamine

This report describes, for the first time, the coupling of UV–visible spectroscopy with multivariate curve resolution-alternative least-squares (MCR-ALS) algorithm to study peroxidase-like catalytic reaction of polyethylene glycol-functionalized poly (N-phenyl glycine) (PNPG-PEG) as an efficient and intrinsic peroxidase mimic activity (PMA) class of conducting organic polymer for selective detection of dopamine (DA) in the PNPG-PEG + TMB + H2O2 reaction system. PNPG-PEG was produced by means of a chemical route using ammonium persulphate (APS) as an oxidant agent of N-phenyl glycine monomer. The chemical composition, morphology, and thermal behavior of PNPG-PEG were examined by various instrumental techniques. PNPG-PEG exhibited significant peroxidase-mimic activity to catalyze the oxidation 3,3′,5,5′- tetramethylbenzidine (TMB) substrate to oxidized TMB (oxTMB). The qualitative and quantitative determination of the oxidized TMB can easily be detected by the naked-eye and the recorded UV–vis absorbance spectra at 652 nm, respectively. Owing to the superior peroxidase-mimic activity of PNPG-PEG, the colorimetric detection of dopamine was successfully achieved at pH 4.0. Under optimal conditions, acceptable linear dependency was recorded in the concentration range of 5.1–125.0 μM, with a limit of detection (LOD) and limit of quantification (LOQ) equal to 4.6 μM and 13.8 μM (S/N = 3), respectively. Furthermore, this colorimetric assay was successfully used for quantitative analysis of dopamine in fetal bovine serum (FBS) and horse serum (HS) samples with recoveries in the range of 97–105% and 100–122%, respectively. After resolving the bilinear data matrix using MCR-ALS, three chemical components were found for different concentrations and pure spectral profiles. Based on the resolved profiles, the presence of free, slightly penetrated, and majorly penetrated TMB molecules entering the polymeric structure can be easily detected using MCR-ALS as an available statical method without any complex separation instruments. This peroxidase mimetic nanozyme as a visual, simple, low-cost, sensitive, and reproducible colorimetric platform can provide great potential applications in the monitoring and diagnosis of dopamine-related diseases.

The trilinear constraint adapted to solve data with strong patterns of outlying observations or missing values

The possibility to perform trilinear decompositions of data sets has the clear advantage of providing unique solutions. Excitation-emission fluorescence matrices (EEM) are the best known paradigm of chemical measurements providing a trilinear structure associated with the configuration of excitation, emission and sample modes. Chemometric tools, such as Parallel Factor Analysis (PARAFAC) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) with trilinear constraint, assist in solving the mixture analysis problem by exploiting the trilinear behavior of the EEM measurements. However, the spectroscopic nature of EEM measurements makes that no emission signal can be recorded below the current excitation wavelength, generating a strong and systematic pattern of outlier (zero observations) in EEM data that challenges the classical analysis by MCR-ALS or PARAFAC. Several approaches have been proposed to deal with this problem, such as the identification of outlying values below the excitation wavelength and, thus, the use of data imputation in PARAFAC, but they show severe limitations when systematic outlying data patterns occur. In this paper, we propose a new implementation of the trilinear constraint in MCR-ALS algorithm to cope with EEM measurements where a strongly patterned of outlying data is present. This approach preserves the trilinear property and does not require any data imputation step to replace the outlying observations. Its performance is tested on simulated data, controlled pharmaceutical mixtures and hyperspectral images of a plant tissue (HSI). It should be noted that the approach proposed is applicable to EEM data, where a systematic pattern of outlying observations exist, but can be generalized to the treatment of any trilinear data set with a strong pattern of missing values.

On a black hole effect in bilinear curve resolution based on least squares

AbstractLeast squares‐based estimations lay behind most chemometric methodologies. Their properties, though, have been extensively studied mainly in the domain of regression, in relation to which the effect of well‐known deleterious factors (like object leverage or data distributions deviating from ideal conditions) on the accuracy of the prediction of an external response variable has been thoroughly assessed. Conversely, much less attention has been paid to what these factors might yield in alternative scenarios, where least squares approaches are still utilised, yet the objectives of data modelling may be very different. As an example, one can think of multivariate curve resolution (MCR) problems which are usually addressed by means of multivariate curve resolution‐alternating least squares (MCR‐ALS). In this respect, this article wants to offer a perspective on the basic principles of MCR‐ALS from the regression point of view. In particular, the following critical aspects will be highlighted: (i) in the presence of minor components, if the number of analysed data points is too large, the leverage of those that may be essential for a MCR‐ALS resolution might become too low for guaranteeing its correctness, and (ii) in order to overcome this black hole effect and improve the accuracy of the MCR‐ALS output, data pruning can be exploited. More in detail, this communication will provide a practical illustration of such aspects in the field of hyperspectral imaging where even single experimental runs may lead to the generation of massive amounts of spectral recordings.