[Lake algae chemotaxonomy technology based on fluorescence excitation emission matrix and parallel factor analysis]

Area of Science:

• Environmental Science
• Analytical Chemistry
• Marine Biology

Background:

• Accurate determination of algae community structure is crucial for ecological monitoring and understanding aquatic ecosystems.
• Traditional methods for algae identification can be time-consuming and labor-intensive, limiting large-scale analyses.

Purpose of the Study:

• To develop a novel in vivo three-dimensional fluorescence method for rapid and accurate algae community structure determination.
• To integrate parallel factor (PARAFAC) analysis and CHEMTAX for enhanced algae classification.

Main Methods:

• Application of PARAFAC analysis to fluorescence excitation-emission matrices (EEMs) of algae species to identify fluorescent components.
• Utilized Bayesian discriminant analysis (BDA) for classifying algae species based on identified fluorescent components.
• Developed the EEM-PARAFAC-CHEMTAX method by constructing a reference fluorescent component ratio matrix for taxonomic group differentiation.

Main Results:

• Identified 12 distinct fluorescent components using PARAFAC analysis.
• Achieved high correct discrimination ratios (CDRs) for single-species samples (average 99.1%) and mixtures (dominant species >98.5%, subdominant >90.5%).
• Demonstrated accurate classification of algae at the division level, with exceptions for Chlorophyta (97.5%).

Conclusions:

• The developed EEM-PARAFAC-CHEMTAX method provides a robust and accurate approach for algae community structure analysis.
• This fluorometric technique offers a low-cost, rapid solution for analyzing large batches of samples, aiding ecological research.