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.

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