A perspective on modeling evolution

Title: A perspective on modeling evolution
Creator: de Juan, Anna; Mas, Sílvia; Maeder, Marcel; Tauler, Romà
Relation: Journal of Chemometrics Vol. 34, Issue 7, no. e3205
Publisher Link: http://dx.doi.org/10.1002/cem.3205
Publisher: John Wiley & Sons
Resource Type: journal article
Date: 2020
Description: Data modeling is a wide concept that exists since long and encompasses all possible ways to interpret the information associated with a process, analytical measurement or set of related parameters that presents a systematic variation. Data modeling can follow the path of knowledge and be based on first principles or can focus on measurements and empirical models. These different approaches are known as hard- and soft-modeling, respectively. It seemed to us very appropriate to dedicate this article to Paul Gemperline, a person who has significantly contributed to the worlds of hard- and soft-modeling, presumably acknowledging the value of looking at data from all possible perspectives. The following pages do not intend to be an extensive review, but a personal perspective on values, milestones and progress of hard- and soft-modeling and on the necessary existence and valuable combination of both ways to interpret chemical information.
Subject: hard-modeling; hybrid hard-and soft-modelling; soft-modeling
Identifier: http://hdl.handle.net/1959.13/1425872
Identifier: uon:38324
Identifier: ISSN:0886-9383
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format