Simultaneous quantitative UV spectrophotometric determination of multicomponents of amino acids using linear neural network

Basketball sports neural network model based on nonlinear classification

With the continuous innovation of science and technology, the mathematical modeling and analysis of bodily injury in the process of exercise have always been a hot and difficult point in the research field of scholars. Although there are many research results on the nonlinear classification of the basketball sports neural network model, usually only one model is used, which has certain defects. The combination forecasting model based on the ARIMA model and neural network based on LSTM can make up for this defect. In the process of the experiment, the most important is the construction of the combination model and the acquisition of volunteer data in the process of the ball game. In this experiment, the ARIMA model is used as the linear part of the data, and LSTM neural network model is used to get the sequence of body injury. The results of the empirical study show that: it is reasonable to divide the injury of thigh and calf in the process of basketball sports, which is very consistent with the force point of the human body in the process of sports. The results of the two models predicting the average degree of bodily injury for many times are about 0.32 and 0.38 respectively, which are far less than 1. The execution time of the program for simultaneous prediction on the computer is about 1 minute, which is extremely effective.

Application of independent component analysis, principal component regression and partial least squares methodologies for the simultaneous potentiometric titration of some amino acids

Potentiometric titration and chemometric methods were applied to the simultaneous determination of the four amino acids, alanine (ALA), phenylalanine (PHE), leucine (LEU) and lysine (LYS).

QSPR study on the octanol/air partition coefficient of polybrominated diphenyl ethers by using molecular distance-edge vector index

BACKGROUND

The quantitative structure property relationship (QSPR) for octanol/air partition coefficient (K OA) of polybrominated diphenyl ethers (PBDEs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PBDEs. The quantitative relationship between the MDEV index and the lgK OA of PBDEs was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation was carried out to assess the predictive ability of the developed models. The investigated 22 PBDEs were randomly split into two groups: Group I, which comprises 16 PBDEs, and Group II, which comprises 6 PBDEs.

RESULTS

The MLR model and the ANN model for predicting the K OA of PBDEs were established. For the MLR model, the prediction root mean square relative error (RMSRE) of leave one out cross validation and external validation is 2.82 and 2.95, respectively. For the L-ANN model, the prediction RMSRE of leave one out cross validation and external validation is 2.55 and 2.69, respectively.

CONCLUSION

The developed MLR and ANN model are practicable and easy-to-use for predicting the K OA of PBDEs. The MDEV index of PBDEs is shown to be quantitatively related to the K OA of PBDEs. MLR and ANN are both practicable for modeling the quantitative relationship between the MDEV index and the K OA of PBDEs. The prediction accuracy of the ANN model is slightly higher than that of the MLR model. The obtained ANN model shoud be a more promising model for studying the octanol/air partition behavior of PBDEs.

Simultaneous determination of thiocyanate and salicylate by a combined UV-spectrophotometric detection principal component artificial neural network

A modified principle component artificial neural network (PC-ANN) model is developed for simultaneous determination of thiocyanate and salycilate concentration after passing through the bulk of a liquid membrane by tri-phenyl benzyl phosphonium chloride. All calibration, and test samples data were obtained using UV-Vis spectrophotometer. In this way, a modified PC-ANN consisting of three layers of nodes was trained by combination of Bayesian-Levenberg-Marquardt as training rule. Sigmoid and liner transfer functions were used in the hidden and output layers respectively to facilitate nonlinear calibration. The model could accurately estimate the concentration of components with acceptable precision and accuracy, for mixtures. The PC-ANN model exhibits a good ability for the simultaneous determination of the thiocyanate and salycilate in concentration range 0.5 x 10(-4) mol.l(-1) up to 5.0 x 10(-4) mol.l(-1) with Root Mean square error (2.22% and 2.20%, for thiocyanate and salycilate, respectively) and high correlation coefficients (R2= 0.998 or greater). Results obtained with modified trained PC-ANN were compared with stepwise linear regression (SMLR) model. Validation of the two models shows a better ability in estimation of the modified PC-ANN as compared with the SMLR model (MSRE given are 3.12%, 6.31%.).

Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets

Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB? Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study.

Simultaneous Determination of Aniline and Cyclohexylamine by Principal Component Artificial Neural Networks

A specterophotometric method for simultaneous determination of aniline and cyclohexylamine using principal component artificial neural networks is proposed. This method is based on the reactions involving aniline and/or cyclohexylamine, with bis(acetylacetoneethylendiamine)tributylphosphine cobalt(III) perchlorate as a complexing reagent. A nonionic surfactant, Triton X-100, was used for dissolving the complexes and intensifying the signals. The absorption data were based on the spectra registered in the range of 350 - 550 nm. An artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. The predictive ability of artificial neural networks was examined for the determination of aniline and cyclohexylamine in synthetic mixtures.