A STATISTICAL APPROACH BASED ON THE TOTAL ERROR CONCEPT FOR VALIDATION THE BIOANALYTICAL METHOD: APPLICATION TO THE SPECTROPHOTOMETRIC DETERMINATION OF TRACES AMOUNT OF ACETAMINOPHEN IN HUMAN PLASMA

Objective: The use of the classical approach of analytical validation, in practice or in the literature, is common. However, statistical verification, that looks separately the two errors (such as bias and precision) to make a decision, presents a risk to declare that an analytical method is valid while it is not, or conversely. To minimize this risk, a new approach based on the concept of total error was proposed. Methods: This approach proposes a calculation the two sided tolerance interval by combining the two errors; bias and precision, in order to examine the validity of an analytical and bioanalytical method at each concentration level. In this paper, we aim to demonstrate the applicability and simplicity of the both methods based on the total error approach: accuracy profile and uncertainty profile. This study will be illustrated by validation case of a spectrophotometric method for the determination of trace amounts of acetaminophen in human plasma. Results: After the introduction of the correction coefficient which is worth 1.16, the results obtained with accuracy profile approach show clearly that the bioanalytical method is valid over a concentrations range of [100.34- 500] µg mL-1 since the upper and lower 90%-expectation tolerance limits have fallen within the two acceptance limits of ± 20%. The same results found using the uncertainty profile approach because the "two - sided 66.7%-content, 90% -confidence tolerance intervals "are found within two acceptance limits of ± 20% over the range of [170; 500] µm mL-1. Conclusion: The excellence of the total error approach was showen since it enables successfully to validate the analytical procedure as well the calculation of the measurement uncertainty at each concentration level

AN INNOVATIVE STRATEGY BASED ON UNCERTAINTY PROFILE FOR THE VALIDATION OF MICROBIOLOGICAL METHODS FOR COUNTING ENTEROBACTERIACEAE IN FOODS

Objective: A new and powerful statistical approach known as the uncertainty profile concept has been suggested for both testing the validity and making easy and straightforward interpretation of results obtained during the validation of an analytical method. The main goal of this paper is to confirm the applicability of this new strategy for the validation of a commercial kit, microbiological method, for the enumeration of the Enterobacteriaceae in foods and the estimate of the measurement uncertainty by using the newly provided formula and without referring to any additional experiments.Methods: An innovative formula to assess the uncertainty by using validation data and without recourse to other additional experiments was proposed. The uncertainty was evaluated through the two-sided β-content, γ-confidence tolerance interval, which is computed with three manners: the Mee's approach, the Generalized Pivotal Confidence, and the Modified Large Simple procedureResults: After the use of the three chemometric method of calculation of tolerance intervals, the obtained results with uncertainty profile show without doubt that the enumeration method is valid over the range of target values given that the upper and the lower 66.7 %-content, 90 %-confidence tolerance limits have fallen within the two acceptance limits of±0.25 Log unit. If the β is stretched to 80 %-content, 90 %-confidence, the three computed tolerance intervals lead to different decisions.Conclusion: we have demonstrated the ability of the uncertainty profile to be used for testing the validity of enumeration method which represents the first application of an uncertainty profile to food microbiological methods, and provides good estimations of the uncertainty measurements for each concentration level.Keywords: Validation, Uncertainty profile, β-content-γ-confidence tolerance interval, Uncertainty measurement, Microbiological metho

The synergic approach between machine learning, chemometrics, and NIR hyperspectral imagery for a real-time, reliable, and accurate prediction of mass loss in cement samples

Alternative and non-destructive analytical methods that predict analyte concentration accurately and immediately in a specific matrix are becoming vital in the analytical chemistry domain. Here, a new innovative and rapid method of predicting mass loss of cement samples based on a combination of Machine Learning (ML) and the emerging technique called Hyperspectral Imaging (HSI) is presented. The method has proved its reliability and accuracy by providing a predictive ML model, with satisfactory best validation scores recorded using partial least squared regression, with a reported ratio of performance to inter-quartile distance and root mean squared error of 12,89 and 0.337, respectively. Moreover, the possibility of optimizing and boosting the performance of the method by optimizing the predictive model performance has been suggested. Therefore, a features selection approach was conducted to disqualify non-relevant wavelengths and stress only relevant ones in order to make them the only contributors to a final optimized model. The best selected features subset was composed of 28 wavelengths out of 121, found by applying genetic algorithm combined to partial least squares regression as a feature selection method, on spectra preprocessed consecutively by the first-order savitzky-golay derivative calculated with 7-point quadratic SG filter, and multiplicative scatter correction method. The overall results show the possibility of combining HSI and ML for fast monitoring of water content in cement samples

QSAR modeling, molecular docking and molecular dynamic simulation of phosphorus-substituted quinoline derivatives as topoisomerase I inhibitors

As they facilitate the cleavage of single and double stranded DNA to relax supercoils, unwind catenanes, and condense chromosomes in eukaryotic cells, Topoisomerase plays crucial roles in cellular reproduction and DNA organization. Because the unrepaired single and double stranded DNA breaks these complexes generate might result in apoptosis and cell death, they are cytotoxic agents.In this study, 28 compounds derived from phosphorus-substituted quinoline are subjected to a quantitative structure–activity relationship (QSAR) using partial least squares, principal component regression, and multiple linear regression. The Gaussian 09 software and the Molecular Operating Environment program were used to calculate molecular descriptors. The anti-proliferative activity was correlated with a variety of electronic and structural characteristics of the molecules, such as EHOMO (energy of the highest occupied molecular orbital) and ELUMO (energy of the lowest unoccupied molecular orbital), which provided evidence for the modeling. The B3LYP/6-31G (d, p) level of theory's Density Functional Theory (DFT) approach was used to compute these electronic properties, and Principal Component Analysis (PCA) was used to test for collinearity between the descriptors. In fact, three alternative prediction models were created using various 2D and 3D descriptor counts, and they were each assessed using the statistical metrics of coefficient of determination (R2) and root mean squared error (RMSE). A MLR model had the best predictive performance of all the constructed models, as indicated by R2 and RMSE of 0.865 and 0.316, respectively.Three proteins (6G77, 2NS2, and 5K47) for lung, ovarian, and kidney malignancies showed strong binding affinities via hydrophobic interactions and H-bonds with the pertinent chemicals by crystal structure modeling. Compounds C11, C19 and C26, respectively, showed the highest binding energy for ovarian, kidney and lung cancer. The outcomes of the molecular dynamic MD simulation diagram were produced to support the molecular docking findings from earlier research, which demonstrated that inhibitors were stable in the active sites of the selected proteins for 10 ns. This raises the possibility that these chemicals could serve as a valuable model for the development and synthesis of more effective anticancer prospects

In Silico Approaches for Some Sulfa Drugs as Eco-Friendly Corrosion Inhibitors of Iron in Aqueous Medium

This paper addresses the prediction of the adsorption behavior as well as the inhibition capacity of non-toxic sulfonamide-based molecules, also called sulfa drugs, on the surface of mild steel. The study of the electronic structure was investigated through quantum chemical calculations using the density functional theory method (DFT) and the direct interaction of inhibitors with the iron (Fe) metal surface was predicted using the multiple probability Monte Carlo simulations (MC). Then, the examination of the solubility and the environmental toxicity was confirmed using a chemical database modeling environment website. It was shown that the presence of substituents containing heteroatoms able to release electrons consequently increased the electron density in the lowest unoccupied and highest occupied molecular orbitals (LUMO and HOMO), which allowed a good interaction between the inhibitors and the steel surface. The high values of EHOMO imply an ability to donate electrons while the low values of ELUMO are related to the ability to accept electrons thus allowing good adsorption of the inhibitor molecules on the steel surface. Molecular dynamics simulations revealed that all sulfonamide molecules adsorb flat on the metal surface conforming to the highly protective Fe (1 1 0) surface. The results obtained from the quantum chemistry and molecular dynamics studies are consistent and reveal that the order of effectiveness of the sulfonamide compounds is P7 > P5 > P6 > P1 > P2 > P3 > P4