Quantification and artificial intelligence-based clustering of phenolic compounds in pearl millet genotypes

Abstract

Pearl millet, also known as Pennisetum glaucum, is a staple food crop in many parts of Africa and Asia. It is a hardy, drought-resistant crop that can grow in poor soil conditions and is well-suited to regions with low rainfall. The analysis of phenolic compounds in pearl millet can provide valuable information on the nutritional and health benefits of this crop. Ultra-high-performance liquid chromatography (UPLC) is a common method used to quantify the phenolic compounds in pearl millet. In this study six different phenolics compounds namely quercetin, epicatechin, cinnamic acid, syringic acid, gallic acid and sinapic acid were quantified in thirteen diverse pearl millet genotypes. In addition to traditional analytical methods, Artificial Intelligence (AI) techniques were used to analyse the data from UPLC and identify any correlations or patterns. This can provide insights into the variability of phenolic compounds in pearl millet depending on the environmental and agronomic conditions. The AI-based analysis presented in the study gave vital information on the phenolic properties of the target genotypes. The result of this analysis is useful in breeding and cultivation practices for pearl millet to improve its nutritional profile and health benefits for human consumption.