The need to control and improve quality in clinical laboratories has grown hand in hand with the growth in technological developments leading to an impressive reduction of analytical errors over time.
Many automated routine methods for SCr measurement exceed the desirable imprecision criterion of ≤ 2.2%; therefore, reduction of analytical bias ≤ 3.4% in creatinine assays by standardization of calibration is needed [ 1].
Three main modern approaches in HPLC methods enable the reduction of analytical time without compromising resolution and separation efficiency: the use of monolith columns, liquid chromatography conducted at high temperatures, and liquid chromatography at ultra-high pressures using columns packed with sub-2-micron particles [ 26, 27].
Our critical view over the mainstream of this specialized literature refuses both the analytical reduction from a wide view of technology to a simple absorption of technical innovations, and also the reduction from a extensive view of economy to a simple matter of cost containment.
Using the entropy concept analytical reduction to a pressure velocity coupling is found.
The Kantorovich method together with the strategy of convolution was used to reach dimensional reduction when deriving analytical formulas.
The Kantorovich method together with strategy of convolution was used to reach dimensional reduction when deriving analytical formulas.
The work illustrates that large-scale scientific computation coupled together with analytical reduction, mathematical analysis, and experimental data, can provide significant understanding and intuition about the possible mechanisms of cortical response.
Because of N ≫ n, this results in a very significant reduction of the analytical work.
Use of this method resulted in the reduction of the analytical burden and time.
The design allows the simultaneous analysis of the samples at various exposure times, i.e. under repeatability conditions, thus greatly improving the sensitivity of the study to detect degradation and increasing the significance of the result because of the reduction of the analytical variation.
