Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl
Preferences help
Polish version English version Language
enabled [disable] Abstract
Number of results

Results found: 2

Number of results on page
first rewind previous Page / 1 next fast forward last

Search results

help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Content available remote

Comparison of IFCC and NGSP methods for determination of glycated haemoglobin using advanced regression techniques

100%
Mrázová V., Mocák J., Bednárová A., Balla J.
Open Chemistry
|
2010
|
vol. 8
|
issue 6
1216-1222
EN
The objective of this work was a correct statistical comparison of two assay methods for determination of glycated haemoglobin HbA1c. The immunoturbidimetric determination of HbA1c was performed in two ways: using an automatic analyser Hitachi 912, calibrated according to the IFCC reference system (International Federation of Clinical Chemistry and Laboratory Medicine) and using an analyser Advia 1200 and the NGSP reference system (National Glycohaemoglobin Standardization Program). For statistical comparison of these two analytical methods several advanced regression methods were used, which respect random errors of both compared methods. Specifically, Deming regression with and without weights, orthogonal regression, and Passing-Bablok regression were employed. The results demonstrate that the investigated analytical assay methods do not correspond to each other. The summarized results indicate usefulness of better harmonisation of two existing reference systems.
2
Content available remote

Characterization of Slovenian coal and estimation of coal heating value based on proximate analysis using regression and artificial neural networks

76%
Kavšek D., Bednárová A., Biro M., Kranvogl R., Vončina D., Beinrohr E.
Open Chemistry
|
2013
|
vol. 11
|
issue 9
1481-1491
EN
Abstract Chemical composition of Slovenian coal has been characterised in terms of proximate and ultimate analyses and the relations among the chemical descriptors and the higher heating value (HHV) examined using correlation analysis and multivariate data analysis methods. The proximate analysis descriptors were used to predict HHV using multiple linear regression (MLR) and artificial neural network (ANN) methods. An attempt has been made to select the model with the optimal number of predictor variables. According to the adjusted multiple coefficient of determination in the MLR model, and alternatively, according to sensitivity analysis in ANN developing, two descriptors were evaluated by both methods as optimal predictors: fixed carbon and volatile matter. The performances of MLR and ANN when modelling HHV were comparable; the mean relative difference between the actual and calculated HHV values in the training data was 1.11% for MLR and 0.91% for ANN. The predictive ability of the models was evaluated by an external validation data set; the mean relative difference between the actual and predicted HHV values was 1.39% in MLR and 1.47% in ANN. Thus, the developed models could be appropriately used to calculate HHV. Graphical abstract [...]
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.