Abstract
Watermelon, a widely commercialized fruit, is famous for its thirst-quenching property. The broad range of cultivars, which give rise to distinct color and taste, can be attributed to the differences in their chemical profile, especially that of the carotenoids and volatile compounds. In order to understand this distribution properly, water extracts of red and yellow watermelon pulps with predominantly polar metabolites were subjected to proton nuclear magnetic resonance (1H-NMR) analysis. Deuterium oxide (D2O) and deuterated chloroform (CDCl3) solvents were used to capture both polar and non-polar metabolites from the same sample. Thirty-six metabolites, of which six are carotenoids, were identified from the extracts. The clustering of the compounds was determined using unsupervised principal component analysis (PCA) and further grouping was achieved using supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The presence of lycopene, β-carotene, lutein, and prolycopene in the red watermelon plays an important role in its differentiation from the yellow cultivar. A marked difference in metabolite distribution was observed between the NMR solvents used as evidenced from the PCA model. OPLS-DA and relative quantification of the metabolites, on the other hand, helped in uncovering the discriminating metabolites of the red and yellow watermelon cultivars from the same solvent system.
Highlights
Chemometrics is a tool which utilizes mathematical and statistical models in gathering information from a chemical system
The reported carotenoids in the red watermelon are predominantly lycopene with trace amounts of phytoene, phytofluene, ζ-carotene, α-carotene, lutein, zeaxanthin, and violaxanthin, whereas those of the yellow watermelon are neoxanthin, violaxanthin, and neochrome [24,25,26]
This study proved the usefulness of 1 H-NMR in the visualization of complex chemical data of two watermelon cultivars and their relatedness to the physical property of the fruits
Summary
Chemometrics is a tool which utilizes mathematical and statistical models in gathering information from a chemical system. With the advancement of computer science, raw chemical data can be remodeled to derive patterns or new variables to address problems in the field of pharmaceutical science [1], biochemistry [2], medicine [3], natural product research [4], and agriculture [5]. This approach helps in the handling of large sets of data such as those of the omics in understanding a biological system or process. Interest in metabolome diversity of different cultivars of the same plant species is gaining traction for the wealth of information it provides, as evidenced in the study of two Oryza sativa L. [10] and 43 Camellia sinensis L. cultivars [11]
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