Determination of dissociation constants of 7-(carboxyphenylazo)-8-hydroxyquinoline-5-sulphonic acids by nonlinear regression of spectrophotometric data

1979 ◽  
Vol 44 (9) ◽  
pp. 2815-2827 ◽  
Author(s):  
Milan Meloun ◽  
Jaromíra Chýlková
Keyword(s):  
Linear Regression ◽  
Nonlinear Regression ◽  
Dissociation Constants ◽  
Matrix Analysis ◽  
Absorption Coefficients ◽  
Non Linear ◽  
Spectrophotometric Data

Dissociation constants of three metallochromic indicators 7-(o-, m-, p-carboxyphenylazo)-8-hydroxyquinoline-5-sulphonic acids and values of molar absorption coefficients of the forms L3-, HL2-, H2L-, H3L have been determined in 0.1M-NaClO4 medium by non-linear regression of pH-absorbance curves by the program DCLET and by regression matrix analysis of the spectra by the program FA608 + EY608, respectively. All the three derivatives have been synthesized, purified, and their purity checked by chromatography.

Study of properties of complexes of SNAZOXS and Naphthylazoxine 6S with copper(II), zinc(II) and lead(II) ions in solution by non-linear regression of spectrophotometric data

1979 ◽  
Vol 44 (7) ◽  
pp. 2032-2063 ◽  
Author(s):  
Milan Meloun ◽  
Jan Pancl
Keyword(s):  
Linear Regression ◽  
Sulphonic Acid ◽  
Matrix Analysis ◽  
Computer Assisted ◽  
Conditional Stability ◽  
Molar Ratios ◽  
Spectrophotometric Methods ◽  
Non Linear ◽  
Spectrophotometric Data ◽  
Conditional Stability Constants

Complexation equilibria of two metallochromic indicators viz. 7-(4-sulpho-1-naphthylazo)-8-hydroxyquinoline-5-sulphonic acid (SNAZOXS) and 7-(6-sulpho-2-naphthylazo)-8-hydroxyquinoline-5-sulphonic acid (Naphthylazoxine 6S) with copper(II), zinc(II), and lead(II) ions have been studied by various spectrophotometric methods in 0.1M-NaClO4 medium at 25 °C. Number of the coloured complexes in solution, their stoichiometry, and their overall and conditional stability constants have been determined by computer-assisted non-linear regression of the curves of continuous variations, the curves of molar ratios, the absorbance-pH curves, the curves of corresponding solutions, and by regression matrix analysis of the spectra. Besides the yellow complexes ML and ML2 the orange protonated forms MLH and M(LH)2 have also been determined.

scholarly journals Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions

2017 ◽  
Vol 12 (3) ◽  
pp. 155892501701200 ◽  
Author(s):  
Kenan Yıldirimm ◽  
Hamdi Ogut ◽  
Yusuf Ulcay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Nonlinear Regression ◽  
Fine Tuning ◽  
Draw Force ◽  
Non Linear ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Production Conditions

In the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the nonlinear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R2=0.97 vs. R2=0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.

Stability indicating spectrodensitometric determination of ranitidine hydrochloride using linear and non-linear regression

1999 ◽  
Vol 21 (4) ◽  
pp. 867-873 ◽  
Author(s):  
Abd El-Aziz El-Bayoumi ◽  
Abdoulah El-Shanawany ◽  
Mohamed E El-Sadek ◽  
Alaa Abd El-Sattar
Keyword(s):  
Linear Regression ◽  
Ranitidine Hydrochloride ◽  
Stability Indicating ◽  
Non Linear

scholarly journals Modeling the kinetics of essential oil hydrodistillation from juniper berries (Juniperus communis L.) using non-linear regression

2017 ◽  
Vol 71 (5) ◽  
pp. 371-382 ◽  
Author(s):  
Dragana Radosavljevic ◽  
Sinisa Ilic ◽  
Svetomir Milojevic ◽  
Zivko Bojovic ◽  
Miljana Markovic
Keyword(s):  
Linear Regression ◽  
Essential Oil ◽  
Kinetic Models ◽  
Juniperus Communis ◽  
Error Measures ◽  
Proposed Model ◽  
Non Linear ◽  
Experimental Values ◽  
Final Equation

This paper presents kinetics modeling of essential oil hydrodistillation from juniper berries (Juniperus communis L.) by using a non-linear regression methodology. The proposed model has the polynomial-logarithmic form. The initial equation of the proposed non-linear model is q = q??(a?(logt)2 + b?logt + c) and by substituting a1=q??a, b1 = q??b and c1 = q??c, the final equation is obtained as q = a1?(logt)2 + b1?logt + c1. In this equation q is the quantity of the obtained oil at time t, while a1, b1 and c1 are parameters to be determined for each sample. From the final equation it can be seen that the key parameter q?, which presents the maximal oil quantity obtained after infinite time, is already included in parameters a1, b1 and c1. In this way, experimental determination of this parameter is avoided. Using the proposed model with parameters obtained by regression, the values of oil hydrodistillation in time are calculated for each sample and compared to the experimental values. In addition, two kinetic models previously proposed in literature were applied to the same experimental results. The developed model provided better agreements with the experimental values than the two, generally accepted kinetic models of this process. The average values of error measures (RSS, RSE, AIC and MRPD) obtained for our model (0.005; 0.017; ?84.33; 1.65) were generally lower than the corresponding values of the other two models (0.025; 0.041; ?53.20; 3.89) and (0.0035; 0.015; ?86.83; 1.59). Also, parameter estimation for the proposed model was significantly simpler (maximum 2 iterations per sample) using the non-linear regression than that for the existing models (maximum 9 iterations per sample).

SAW enzyme sensor applied to the determination of enzyme kinetic constants with the aid of a non-linear regression algorithm

1997 ◽  
Vol 126 (1-2) ◽  
pp. 109-115
Author(s):  
Qingyun Cai ◽  
Ronghui Wang ◽  
Liyin Wu ◽  
Lihua Nie ◽  
Shouzhuo Yao
Keyword(s):  
Linear Regression ◽  
Kinetic Constants ◽  
Enzyme Kinetic ◽  
Enzyme Sensor ◽  
Non Linear
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