On problematic practice of using normalization in Self-modeling/Multivariate Curve Resolution (S/MCR)

TL;DR

This paper critically examines the misuse of normalization in Self-modeling/Multivariate Curve Resolution (S/MCR), emphasizing proper practices, clarifying concepts, and providing tools for better implementation.

Contribution

It introduces the concepts of external and internal normalization, clarifies the position of signal extremities, and offers an executable Matlab Notebook for improved understanding.

Findings

Highlighting common normalization misuses in S/MCR

Clarification of external and internal normalization concepts

Provision of a Matlab Live Editor Notebook for practical guidance

Abstract

The paper is briefly dealing with greater or lesser misused normalization in self-modeling/multivariate curve resolution (S/MCR) practice. The importance of the correct use of the ode solvers and apt kinetic illustrations are elucidated. The new terms, external and internal normalizations are defined and interpreted. The problem of reducibility of a matrix is touched. Improper generalization/development of normalization-based methods are cited as examples. The position of the extreme values of the signal contribution function is clarified. An Executable Notebook with Matlab Live Editor was created for algorithmic explanations and depictions.