Validation of analytical methods using a regression procedure

H W Zwanziger1, C Sârbu

  • 1Fachbereich Chemie [Formula: see text] und Umweltingenieurwesen, Fachhochschule Merseburg, Germany, and Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, RO-3400 Cluj-Napoca, Romania.

Related Concept Videos

Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Development of Analytical Methods01:21

Development of Analytical Methods

An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...