New Approach to Plant Diversity Assessment Combining HPLC Data, Simplex Mixture Design and Discriminant Analysis

• Published in: Environmental modeling & assessment 2008-02, Vol.13 (1), p.17-33
• Main Authors: Semmar, Nabil, Jay, Maurice, Farman, Muhammad, Roux, Maurice
• Format: Article
• Language: English
• Subjects: Applications of Mathematics; Astragalus; Bayesian analysis; Bayesian discriminant analysis; Biodiversity; Calibration; calibration models; Chromatography; Conservation; Discriminant analysis; Earth and Environmental Science; Ecology; Environment; Experimental design; Fabaceae; Flavonoids; Flowers & plants; HPLC profiles; Liquid chromatography; Math. Appl. in Environmental Science; Mathematical Modeling and Industrial Mathematics; Mathematical models; Metabolites; mixture samples; Natural resources; Operations Research/Decision Theory; plant biochemistry; Plant diversity; plant diversity monitoring; Plant populations; Resource conservation; Resource management; Scheffé simplex matrix; Secondary metabolites; Simulation; Studies; Trends
• ISSN: 1420-2026; 1573-2967
• DOI: 10.1007/s10666-006-9082-2

The quantitative assessment of plant diversity and its monitoring with time represent a key environmental issue for management and conservation of natural resources. Assessment of plant diversity could be based on chemical analyses of secondary metabolites (e.g. flavonoids, terpenoids), because of the substantial quantitative and qualitative between-individual variability in such compounds. At a geographical scale, the plant populations become widely dispersed, and their monitoring from numerous routine individual analyses could become restricting. To overcome such constraint, this study develops a multivariate calibration model giving the relative frequency of a particular taxon from a simple high-performance liquid chromatography (HPLC) analysis of a plant mixture. The model was built from a complete set of mixtures combining different taxons, according to an experimental design (Scheffé's matrix). For each mixture, a reference HPLC pattern was simulated by averaging the individual HPLC profiles of the constitutive taxons. The calibration models, based on Bayesian discriminant analysis (BDA), gave statistical relationships between the contributions of each taxon in mixtures and reference HPLC patterns of these mixtures. Finally, these models were validated on new mixtures by using outside plants. This new biodiversity survey approach is illustrated on four chemical taxons (four chemotypes) of Astragalus caprinus (Fabaceae). The more differentiated the taxon, the better predicted its contributions (in mixtures) were by BDA calibration model. This new approach could be very useful for a global routine survey of plant diversity.