A perspective on modeling evolution. (25th December 2019)

• Record Type: Journal Article
• Authors: de Juan, Anna; Mas, Sílvia; Maeder, Marcel; Tauler, Romà
• Is Part Of: Journal of chemometrics. Volume 34:Number 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-25
• Subjects: hard‐modeling -- hybrid hard‐ and soft‐modeling -- soft‐modeling; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.3205
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract: 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.