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A quantitative measure of nonlinearity

K Emancipator¹, M H Kroll

¹Department of Pathology, New York Hospital-Cornell Medical Center, NY 10021.

Clinical Chemistry

|May 1, 1993

Summary

New quantitative measures define analytical method nonlinearity, offering objective criteria for linearity. These calculations use curve-fitting, such as polynomial regression, to assess response curve deviations.

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Area of Science:

Analytical Chemistry
Method Validation

Background:

Assessing the linearity of analytical methods is crucial for reliable quantitative analysis.
Existing linearity criteria can be subjective, necessitating objective measures.

Purpose of the Study:

To define quantitative measures for the nonlinearity of analytical methods.
To establish objective criteria for evaluating method linearity.

Main Methods:

Defined "dimensional nonlinearity" as the deviation of a response curve from a best-fit straight line.
Defined "relative nonlinearity" by normalizing dimensional nonlinearity with the assay range.
Utilized polynomial regression for demonstrating nonlinearity calculations.

Main Results:

Introduced precise mathematical definitions for dimensional and relative nonlinearity.
Demonstrated the calculation of these nonlinearity measures using polynomial regression.
Showcased how these definitions enable the development of objective linearity criteria.

Conclusions:

The proposed quantitative measures provide objective assessments of analytical method nonlinearity.
These definitions facilitate the development of practical and objective criteria for linearity in analytical methods.
The framework accommodates various curve-fitting techniques beyond polynomial regression.

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