Simultaneous measurement of two enzyme activities using infrared spectroscopy: A comparative evaluation of PARAFAC, TUCKER and N-PLS modeling

Multiway Analysis Reveals Hydrophobicity as the Sole Determinant of Dynamic Peptide-Acetonitrile-Water Association Behavior

Multiway analysis, a class of techniques developed for the purpose of studying multi-dimensional multivariate data, has been applied to study the dynamical structure of the first solvation layer of Ace-Gly-X-Gly-Nme peptides (where X is any amino acid) perturbed with the increase in concentrations of acetonitrile. Separate MD simulations of each peptide were carried out in five different concentrations of acetonitrile. Association of peptide, water, and acetonitrile atoms was quantified in terms of the relative abundance of Delaunay tetrahedra whose vertices could be centered on either the peptide, acetonitrile, or water atoms. A three-way data set comprising nine types of Delaunay tetrahedra in the first dimension, five concentrations of acetonitrile in the second dimension, and 26 different peptides in the third dimension was subjected to two different multiway methods viz., the constrained PARAFAC and the unconstrained Tucker3 analysis. The results unequivocally show that the dynamic peptide-acetonitrile-water association behavior could be solely explained by the hydrophobicity of the central amino acid. The study also demonstrates the utility of multiway analysis for the integration and interpretation of large number of separate MD simulations.

Monophenolase assay using excitation-emission matrix fluorescence and ELMAN neural network assisted by whale optimization algorithm

Tyrosinase plays a vital role for melanogenesis and inherently involves both monophenolase activity and diphenolase activity. Monophenolase catalyzes hydroxylation of tyrosine to l-DOPA (L-3,4-dihydroxyphenylalanine). Real-time monophenolase assay method is of outstanding interest for both scientific research and industrial application. A combined strategy of three-dimensional excitation-emission matrix (EEM) fluorescence spectra and artificial neural network was developed to determine monophenolase activity. A quantitation system for tyrosine in presence of l-DOPA was designed based on ELMAN neural network. Principal component analysis (PCA) was conducted to reduce the dimensionality of fluorescence spectra. Four principal components was used as input variables. Whale optimization algorithm (WOA) was implemented to optimize the initial weights and threshold network. Real-time concentration of tyrosine in monophenolase reaction was monitored to calculate the initial velocity for tyrosine consumption. The exclusive monophenolase activity without interference from diphenolase reaction was determined. Limit of detection (LOD) for monophenolase assay is 0.0113 U mL^-1. Using the proposed method, enzyme kinetics for monophenolase was investigate. K_m was calculated as 14.16 μM. Inhibitor for monophenolase was screened by using model molecule kojic acid with IC₅₀ of 3.49 μM. The assay method exhibited a promising prospect to characterize the kinetics and inhibitor of monophenolase.

Three-way analysis of pH-UV absorbance dataset for the determination of paracetamol and its pKa value in presence of excipients

A three-way analysis method, parallel factor analysis (PARAFAC) model was applied to the pH-absorbance dataset for the simultaneous determination of paracetamol and its acid-base dissociation constant in presence of excipient interference in a syrup formulation without using chemical pretreatment or chromatographic separation step. The UV spectroscopic data matrices of calibration set, validation and unknown samples were obtained from the absorbance measurements at the five different pH media, considering conjugate acid/base properties of the related drug. Their pH-absorbance data matrices were arranged as a cubic data array (wavelength x sample x pH) (425x52x5). Three-way array of pH-absorbance dataset was decomposed into a trilinear set of spectral, pH and relative concentration profiles of paracetamol and excipients in the commercial syrup using PARAFAC model. In the PARAFAC implementation, paracetamol in the commercial syrup formulation and its pKa value were simultaneously predicted from the relative concentration and pH profiles, respectively. In the method validation step of this study, the performance of PARAFAC model was checked by analyzing the validation samples in terms of selectivity, sensitivity, accuracy and precision of the method. The determination results of paracetamol and its pKa value provided from PARAFAC application were compared to those obtained by a newly developed ultra-performance liquid chromatography (UPLC) method, in terms of simplicity, applicability, interpretability with low cost and short analysis time.

Detection and Quantification of Tire Particles in Sediments Using a Combination of Simultaneous Thermal Analysis, Fourier Transform Infra-Red, and Parallel Factor Analysis

Detection and quantification of tread wear particles in the environment have been a challenge owing to lack of a robust method. This study investigated the applicability of a combination of Simultaneous Thermal Analysis (STA), Fourier Transform Infra-Red (FTIR), and Parallel Factor Analysis (PARAFAC) in the detection and quantification of tire particles from formulated sediments. FTIR spectral data were obtained by heating 20 samples in STA. Among the 20 samples, 12 were tire granules in formulated sediments (TGIS) containing 1%, 2%, 5%, and 10% by mass of tire granules, while the remaining eight contained 0.5, 1, 2.5, and 5 mg of tire granules only (TGO). The PARAFAC models decomposed the trilinear data into three components. Tire rubber materials in tire granules (RM) and a combination of water and carbon dioxide were the components identified in all samples. The linear regression analysis of score values from the PARAFAC models showed that the RM quantity predicted were comparable to measured values in both TGIS and TGO. Decomposing the overlying components in the spectral data into different components, and predicting unknown quantity in both sample types, the method proves robust in identifying and quantifying tire particles from sediments.

Evaluation of Kluyveromyces marxianus endo-polygalacturonase activity through ATR-FTIR

The endo-polygalacturonase enzyme (endoPG: EC 3.2.1.15) plays an important role in the fruit juice and wine industries, so the development of new tools for the quantitative and qualitative analysis of its enzymatic action is necessary. In this work, we report the development of a simple, fast and practical method that did not use any chemical reagent to identify and evaluate the action of the endoPG enzyme, produced by the yeast Kluyveromyces marxianus CCT3172, using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy combined with principal component analysis-linear discriminant analysis (PCA-LDA). This method evaluated the action of the endoPG enzyme on the polygalacturonic acid (PGA) substrate at 5 different times (0, 10, 15, 20 and 30 minutes), and at each time interval the samples were analyzed by ATR-FTIR. It was demonstrated that there was clear segregation between the samples that were and that were not subjected to the action of the endoPG enzyme, and it was also possible to distinguish the samples that were subjected to different incubation times with the enzyme. Through PCA-LDA it was possible to obtain wavelengths that are biomarkers for this enzymatic reaction and the observed changes as a function of hydrolysis duration were found to be in agreement with the breakdown of the glycosidic chain (1011 cm^-1-CH-O- CH stretching) of PGA and release of oligosaccharides (1078 cm^-1 C-OH elongation). The activity of the endoPG enzyme and the release of galacturonic acid were verified by the dinitrosalicylic acid (DNS) method in all samples. The efficacy of an automatic classifier using a principal component analysis-linear discriminant classifier (PCA-LDC) was evaluated to diagnose the action of the endoPG enzyme. The results showed an accuracy of 100% for the identification of the endoPG enzyme action and from 91.67% to 100% for classification according to the hydrolysis duration in which PGA was exposed to endoPG. The present study indicates that this methodology may be a new approach for the qualitative evaluation of the endoPG enzyme with the potential to be used in laboratories and industries.

Simultaneous quantification of multiple endogenous biothiols in cancer cells based on a multi-signal fluorescent probe

The feature information was extracted from the 3D spectrum by the TM method, and the simultaneous quantification of intracellular thiols was realized.

The application of a Tchebichef moment method to the quantitative analysis of multiple compounds based on three-dimensional HPLC fingerprint spectra

A Tchebichef moment method was proposed and used to successfully quantify four compounds based on 3D HPLC-PAD spectra.