Comparative analysis of HPLC methods for measuring phytoplankton pigments in the Western Mediterranean Sea: A contribution to the satellite Cal/Val activities

High-performance liquid chromatography (HPLC) is the gold standard for calibrating and validating satellite-derived Chlorophyll a (TChl a) concentration. Other phytoplankton pigments quantified by HPLC can provide taxonomic and functional insights into the composition and abundance of phytoplankton communities. This study assesses the uncertainties associated with HPLC measurements by comparing results from two analytical laboratories, one from the Joint Research Centre (JRC) and the other from the Italian National Agency for New Technologies, Energy, and Sustainable Economic Development (ENEA). These laboratories employed different analytical methods to examine natural water samples from the oligotrophic waters of the Western Mediterranean Sea, collected during the Sentinel 2017 campaign. Chlorophyll a concentrations in these samples ranged from 0.048 to 0.653 mg m−3 in the JRC dataset. The present study evaluated phytoplankton community composition using multiple techniques, including chemotaxonomic methods based on the analysis of biomarker pigments and CHEMTAX method (Mackey et al., 1996), alongside unsupervised machine learning approaches such as Hierarchical Clustering Analysis (HCA), Principal Components Analysis (PCA), and Network-Community Analysis (NCA). Significant differences in pigment quantification were observed between the two laboratories, particularly for chlorophylls c (85.5 % difference) and peridinin (56.6 % difference). However, differences in total TChl a quantification were within 6.1 %, indicating that both laboratories are capable of supporting satellite data validation and algorithm development (Hooker et al., 2000). The results highlighted both limitations and advantages of this comparative approach, related to different methods for estimating uncertainties, providing insights into the consistency and reliability of HPLC measurements in a challenging low concentration matrix.

CANUTI Elisabetta;  ARTUSO Florinda;  DI CICCO Annalisa; 

2025-06-16

ELSEVIER

JRC139191

1872-7581 (online),   

https://www.sciencedirect.com/science/article/pii/S0304420325000313,    https://publications.jrc.ec.europa.eu/repository/handle/JRC139191,   

10.1016/j.marchem.2025.104516 (online),