Phytopigments Profiling of Lactuca Sativa Leaf Chloroplast Photosystems via Vision-based Planar Chromatography

Abstract

Phytopigments are essential indicators of plant growth. However, current methodologies use expensive laboratory devices. In this study, a low-cost approach of lettuce leaf phytopigments profiling is employed using a consumer-grade camera and integrated computational intelligence via paper chromatography. Hybrid neighborhood component analysis and ReliefF selected the blue reflectance extracted from chromatography to have the most significant impact with other leaf biophysical signatures. Chl $b$ exhibits more complex reflectance spectrum than other pigments and considered as strong indicator of energy absorbance variations. Xanthophyll and carotenoid have the strongest and weakest retardation factor and retention time, respectively. Chl a-b has weak affinity to acetone and their average blue reflectance is confirmed to absorb the highest number of photons in white light cultivation. Leaf absorbance varies by $\pm 1307.04\ \mu \mathrm{mol\ m}^{-2}\mathrm{s}^{-1}$ PPFD per ±0.1 of blue reflectance. Among other machine learning models, Gaussian processing regression bested out multigene symbolic genetic programming and recurrent neural network in predicting the average chloroplast photosystems I and II blue reflectance with R2 of 0.9974. This developed approach extends the application of paper chromatography from segmenting to phytopigment profiling.