Alternatives to Least Squares Linear Regression Analysis for Computation of Standard Curves for Quantitation by High Performance Liquid Chromatography: Applications to Clinical Pharmacology

1994 ◽  
Vol 34 (3) ◽  
pp. 242-249 ◽  
Author(s):  
George K. Szabo ◽  
Hilary K. Browne ◽  
Alfred Ajami ◽  
Ephraim G. Josephs
Keyword(s):  
Clinical Pharmacology ◽  
High Performance Liquid Chromatography ◽  
Regression Analysis ◽  
Liquid Chromatography ◽  
Linear Regression ◽  
Least Squares ◽  
High Performance ◽  
Linear Regression Analysis

Applicability of an Enzyme-linked Immunosorbant Assay for Neopterin Detection for Screening of Blood Donations

Pteridines ◽  
1994 ◽  
Vol 5 (2) ◽  
pp. 49-54 ◽  
Author(s):  
Peter Mayersbach ◽  
Roman Augustin ◽  
Harald Schennach ◽  
Dietmar Fuchs ◽  
Ernst R. Werner ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Regression Analysis ◽  
Liquid Chromatography ◽  
Linear Regression ◽  
High Performance ◽  
Blood Donors ◽  
Linear Regression Analysis ◽  
Immunosorbant Assay ◽  
Enzyme Linked Immunosorbant Assay ◽  
Better Than

Summary We have evaluated a new commercially available enzyme-linked immunorsorbant assay for neopterin :or its suitability in the context of screening of voluntary blood donors. The assay was performed on 1040 consecutive blood donors, and compared with radioimmunoassay and. in a fraction of 142 donors . . : Iso with high performance liquid chromatography. On repetitive assays of all donations showing a concentration exceeding 8.0 nmol/L in the initial assay. three of the radioimmunoassay results were identified as gross outliers. No such gross outliers were detected for the enzyme-linked immunosorbant assay. RegarJing the reproducibility of results exceeding a cut-off limit of \0 nmol/L neopterin. the enzyme-linked ;mmunosorbant assay was better than the radioimmunoassay. Moreover. the enzyme-linked immunosorbant assay was slightly superior to radioimmunoassay when both tests were compared with high performance liquid chromatography (based on linear regression analysis. evaluation of frequencies of concentrations bant assay was slightly superior to radioimmunoassay when both tests were compared with high performance liquid chromatography (based on linear regression analysis. evaluation of frequencies of concentrations rations. Its slight superiority compared to the conventional radioimmunoassay likely results from the higher degree of automatization employed.

scholarly journals DEVELOPMENT AND VALIDATION OF REVERSE PHASE-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY ASSAY METHOD FOR ESTIMATION OF FENOFIBRATE IN TABLET DOSAGE FORM PREPARED USING CRYSTALLO-CO-AGGLOMERATES OF THE DRUG

2021 ◽  
Vol 19 (1) ◽  
pp. 19-28
Author(s):  
SANTOSH GANDHI ◽  
MANGESH BHALEKAR ◽  
RAVINA MUTHA
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Performance ◽  
Linear Regression Analysis ◽  
Hplc Method ◽  
Assay Method ◽  
Reverse Phase ◽  
Rp Hplc ◽  
Specificity And Sensitivity ◽  
Tablet Dosage

The aim of the present study was to develop a simple isocratic reverse phase-high performance liquid chromatography (RP-HPLC) method and validate for the determination of fenofibrate in tablet dosage forms. RP-HPLC method was developed using Hi Q Sil C18 (250 cm × 4.6 mm, 5 μm) and mobile phase comprising 1 mM ammonium acetate buffer: Acetonitrile (10:90 v/v) at a flow rate of 1.0 mL/min. The detection was carried out at 290 nm. The retention time was found to be 6.15 ± 0.03 min. Validation of the method was performed for precision, accuracy, linearity, robustness, specificity and sensitivity to conform to the International Conference on Harmonization (ICH) guidelines. The data of linear regression analysis indicated a good linear response in the concentration range of 5 μg/mL–30 μg/mL with correlation co-efficient (R2) of 0.997. The developed method was found to be simple, sensitive, accurate and repeatable for assay of tablets of fenofibrate prepared using crystallo-co-agglomerates of the drug.

A Fuzzy Linear Regression Analysis for Fuzzy Input-Output Data Using the Least Squares Method under Linear Constraints and Its Application to Fuzzy Rating Data

1999 ◽  
Vol 3 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Kazuhisa Takemura ◽  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Least Squares ◽  
Least Squares Method ◽  
Linear Regression Analysis ◽  
Linear Constraints ◽  
Fuzzy Linear Regression ◽  
Output Data ◽  
Rating Data ◽  
Fuzzy Input

Fuzzy linear regression analysis using the least squares method under linear constraint, where input data, output data, and coefficients are represented by triangular fuzzy numbers, was proposed and compared to possibilistic linear regression analysis proposed by Sakawa and Yano (1992) using fuzzy rating data in a psychological study. Major findings of the comparison were as follows: (1) Under the proposed analysis, the width between the maximum and minimum of the predicted model was nearer to the width of the dependent variable than that of possibilistic linear regression analysis, (2) the representative prediction by the proposed analysis was also nearer to that of the dependent variable, compared to that of possibilistic linear regression analysis.

Evaluation and comparison of a radiative energy attenuation method for determining cholesterol in serum.

1985 ◽  
Vol 31 (4) ◽  
pp. 605-608 ◽  
Author(s):  
P L Cary ◽  
C A Johnson ◽  
P D Whitter ◽  
J W Parker
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
High Performance ◽  
Linear Regression Analysis ◽  
Radiative Energy ◽  
Comparison Methods ◽  
Analytical Recovery ◽  
Wide Range

Abstract Determination of cholesterol by a radiative energy attenuation (REA) technique was evaluated and compared with results obtained by the Boehringer Mannheim High Performance Cholesterol Assay and the Du Pont aca. Within-assay and between-assay CVs for the REA method, for two sets of controls, were both less than 5%. We observed no interference with lipemic samples. Analytical recovery averaged 102.8%. We used all three methods for parallel determinations of 217 patients' samples containing a wide range of cholesterol concentrations. Linear regression analysis of the REA results vs those of the comparison methods were as follows: REA = 1.03 Boehringer - 0.072 (r = 0.993) and REA = 1.02 aca - 0.048 (r = 0.995). We also discuss bilirubin interference with the REA method for cholesterol.

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