Classification of Fungal Pigments by Simulating Their Optical Properties Using Evolutionary Optimization

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Modern developments in data analysis techniques and evolutionary optimization algorithms have made it possible to analyze large amounts of unstructured digital data sets. Based on the differential evolution algorithm and semiclassical quantum simulations, we have recently proposed a method for classifying and analyzing the optical properties of organic pigments. In this paper, we present the results of modeling the absorption spectra of five carotenoids synthesized during the vital activity of the ascomycetous fungi: neurosporaxanthin, neurosporene, torulene, γ-carotene, and ζ-carotene. We calculated the absorption spectra for each pigment using the multimode Brownian oscillator theory, which allows us to evaluate the influence of molecular vibrations on the electronic transitions in the pigment. We applied a generalized spectral density function method to our modeling, taking into account the contributions of 13 vibrational modes with frequencies varying between 100 cm−1 and 3000 cm−1. This approach allowed us to gain a deeper understanding of how molecular vibrations affect the absorption spectra of these organic compounds. Thus, each absorption spectrum was associated with a unique set of Huang–Rhys factors (which represent the effective electron–phonon interaction). This set can be considered as a kind of “fingerprint” that characterizes the optical response of the pigment in the solvent.