A physiochemical theory on the applicability of soft mathematical models—experimentally interpreted

Abstract

a An extension of chemometric theory was experimentally explored to explain the physiochemical basis of the very high efficiency of soft modelling of data from nature. Soft modelling in self-organisation was interpreted by studying the unique chemical patterns of mutants in an isogenic barley model on endosperm development. Extremely reproducible, differential Near Infrared (NIR) spectral patterns specifically overviewed the effect on cell composition of each mutant cause. Extended Canonical Variates Analysis (ECVA) classified spectra in wild type, starch and protein mutants. The spectra were interpreted by chemometric data analysis and by pattern inspection to morphological, genetic, molecular and chemical information. Deterministic chemical reactions were defined in the glucan pathway. A drastic mutation in a gene controlling the starch/ß-glucan composition changed water activity that introduced a diffusive, stochastic effect on the catalysis of all active enzymes. 'Decision making' in self-organisation is autonomous and performed by the soft modelling of the chemical deterministic and stochastic reactions in the endosperm cell as a whole. Uncertainty in the analysis of endosperm emergence was experimentally delimited as the 'indeterminacy' in local molecular path modelling 'bottom up' and the 'irreducibility' of the phenomenological NIR spectra 'top down' . The experiment confirmed Ilya Prigogine's interpretation of self-organisation by his dynamic computer model programmed with a self-modeled non-local extension of quantum mechanics (QM). The significance of selforganisation explained by Prigogine here interpreted as physiochemical soft modelling introduces a paradigm shift in macroscopic science that forwards a major argument for soft mathematical modelling and chemometrics to obtain full scientific legitimacy.

An Overview on the Application of Chemometrics in Food Science and Technology—An Approach to Quantitative Data Analysis

“…analysis of variance). The use of ANOVA provides with valuable descriptive information; however, detailed information about relationships amongst variables and other important relationships in the entire matrix might be lost (Wold 1995;Martens and Martens 2001;Munck 2007;Munck et al 2010).…”

“…Nowadays, large datasets are collected each day in most of the laboratories around the world mainly because of the introduction of modern instrumental methods (Wold 1995;Munck et al 2010;Otto 1999;Munck 2007). In addition, the traditional methods currently in use tend to eliminate the matrix interference isolating or extracting the property (e.g.…”

“…In addition, the traditional methods currently in use tend to eliminate the matrix interference isolating or extracting the property (e.g. chemical or physical) that is measured, making the measurement apparently simple and certain (Wold 1995;Munck et al 1998Munck et al , 2010Otto 1999). However, this scientific approach overlooks the potential effects of chemical and physical interactions between the many constituents present in the sample matrix.…”

“…In recent years, several researchers have stated that this approach has prompted an Bunreal world view^where food compounds or properties are analysed independently of the entire food matrix (e.g. cereals, meat, fruit juices) (Hopke 2003;Burlingame 2004;Fardet 2014;Cozzolino 2015;Munck et al 1998Munck et al , 2010Kelly et al 2011). Hence, understanding the inherent complexity of a system such as food requires complex answers where research in this field needs to move towards a more holistic and integrative approach.…”

“…Hence, understanding the inherent complexity of a system such as food requires complex answers where research in this field needs to move towards a more holistic and integrative approach. This new approach to food of analysis determines a high degree of complexity that has not been extensively used before in food sciences (Burlingame 2004;Fardet 2014;Cozzolino 2015;Munck 2007;Munck et al 1998Munck et al , 2010Kelly et al 2011). Consequently, the modern food industry faces with several challenges derived from the use of sophisticated instrumental techniques, particularly those related to exploring, extracting and describing the data generated.…”