A Prototype Method for the Detection and Recognition of Pigments in the Environment Based on Optical Property Simulation
2023
Roman Y. Pishchalnikov | Denis D. Chesalin | Vasiliy A. Kurkov | Uliana A. Shkirina | Polina K. Laptinskaya | Vasiliy S. Novikov | Sergey M. Kuznetsov | Andrei P. Razjivin | Maksim N. Moskovskiy | Alexey S. Dorokhov | Andrey Yu. Izmailov | Sergey V. Gudkov
The possibility of pigment detection and recognition in different environments such as solvents or proteins is a challenging, and at the same time demanding, task. It may be needed in very different situations: from the nondestructive in situ identification of pigments in paintings to the early detection of fungal infection in major agro-industrial crops and products. So, we propose a prototype method, the key feature of which is a procedure analyzing the lineshape of a spectrum. The shape of the absorption spectrum corresponding to this transition strongly depends on the immediate environment of a pigment and can serve as a marker to detect the presence of a particular pigment molecule in a sample. Considering carotenoids as an object of study, we demonstrate that the combined operation of the differential evolution algorithm and semiclassical quantum modeling of the optical response based on a generalized spectral density (the number of vibronic modes is arbitrary) allows us to distinguish quantum models of the pigment for different solvents. Moreover, it is determined that to predict the optical properties of monomeric pigments in protein, it is necessary to create a database containing, for each pigment, in addition to the absorption spectra measured in a predefined set of solvents, the parameters of the quantum model found using differential evolution.
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