PARAllel FACtor analysis (PARAFAC) is increasingly used to decompose fluorescence excitation emission matrices (EEMs) into their underlying chemical components. In the ideal case where fluorescence conforms to Beers Law, this process can lead to the mathematical identification and quantification of independently varying fluorophores. However, many practical and analytical hurdles stand between EEM datasets and their chemical interpretation. This article provides a tutorial in the practical application of PARAFAC to fluorescence datasets, demonstrated using a dissolved organic matter (DOM) fluorescence dataset. A new toolbox for MATLAB is presented to support improved visualisation and sensitivity analyses of PARAFAC models in fluorescence spectroscopy.

The roles of dissolved organic matter (DOM) in microbial processes and nutrient cycles depend on its composition, which requires detailed measurements and analyses. We introduce a package for R, called staRdom (“spectroscopic analysis of DOM in R”), to analyze DOM spectroscopic data (absorbance and fluorescence), which is key to deliver fast insight into DOM composition of many samples. staRdom provides functions that standardize data preparation and analysis of spectroscopic data and are inspired by practical work. The user can perform blank subtraction, dilution correction, Raman normalization, scatter removal and interpolation, and fluorescence normalization. The software performs parallel factor analysis (PARAFAC) of excitation–emission matrices (EEMs), including peak picking of EEMs, and calculates fluorescence indices, absorbance indices, and absorbance slope indices from EEMs and absorbance spectra. A comparison between PARAFAC solutions by staRdom in R compared with drEEM in MATLAB showed nearly identical solutions for most datasets, although different convergence criteria are needed to obtain similar results and interpolation of missing data is important when working with staRdom. In conclusion, staRdom offers the opportunity for standardized multivariate decomposition of spectroscopic data without requiring software licensing fees and presuming only basic R knowledge.

Despite considerable research on the linkages between dissolved organic matter (DOM) and bacteria, it is not yet clear how the abundance of the main aquatic clades relates to DOM composition in natural aquatic systems. We evaluated this relation using PARAFAC modeling of excitation–emission fluorescence spectroscopy and spectroscopic indexes to characterize DOM composition, and fluorescence in situ hybridization, to quantify the major bacterial groups in a subtropical lagoon. The DOM exhibited marked temporal variations in concentration, molecular weight, aromaticity, color, degree of humification, and freshness, and proportion of the three different fluorescent components identified. All major bacterial clades (Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Cytophaga‐Flavobacteria) were significantly linked to DOM concentration and/or composition, being those crucial factors for modeling their abundance in situ. The combination and significance of the factors was specific for each bacterial group, strongly indicating that they behave as coherent and distinctive units. Cytophaga‐Flavobacteria and Betaproteobacteria were the groups which correlated with more DOM properties. Alphaproteobacteria and Gammaproteobacteria abundances were significantly explained by low or high dissolved organic carbon concentrations, respectively. The significant relationships between DOM properties and the main bacterial groups delineated a profile of each group regarding DOM preferences/dislikes, in agreement with evidence derived from genome analysis to single‐cell substrate uptake. These results highlight the specificities of the main bacterial clades, providing support for a functional classification of the bacterioplankton regarding DOM processing at the level of bacterial classes.