Multivariate Analysis of Seed Chemical Diversity among Jatropha curcas in Botswana

Abstract

Jatropha (Jatropha curcas L.) has been identified as a potential bioenergy feedstock in arid regions, but knowledge of the diversity of its chemical characteristics is limited. In this study, 61 Jatropha accessions growing in Botswana, where both severe drought and winter frosts frequently occur, were analyzed for their seed chemical properties. Histogram analyses and meta-analysis comparisons with seeds from other countries/continents showed that the median/mean dry seed weight, toxic compound phorbol esters, and C18:0 fatty acid levels in the Botswanan accessions were lower than those from other countries/continents. A clustered heat map analysis indicated five clades for the Botswanan accessions, and their physicochemical traits were also categorized into five groups. Many positive and negative correlations were observed among the chemical traits, including negative correlations between the C18:3 (linolenic acid) content and yield-related traits (lipid content and dry seed weight). Principal component analysis highlighted the existence of accessions with highly deviated seed chemical compositions, such as those enriched in C18:0/C18:1 and C16:0/C16:1/C18:3 fatty acids. Overall, the present study suggests considerable diversity in the seed chemical compositions of Botswanan Jatropha accessions. Various accessions could be useful as feedstock for specific industrial products, as well as for breeding materials for the fortification of specific chemical ingredients.

Assessment of Spatiotemporal Variations in the Water Quality of the Han River Basin, South Korea, Using Multivariate Statistical and APCS-MLR Modeling Techniques

“…Multivariate statistical analysis can appropriately interpret environmental data by reducing dimensionality and decreasing errors [17,18]. Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA), can obtain reliable environmental information by interpreting complex data matrices [19,20]. Moreover, they have been utilized to propose efficient water quality policies for water resources [11].…”