staRdom: Versatile Software for Analyzing Spectroscopic Data of Dissolved Organic Matter in R
Journal article, 2019

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.

Author

Mathias Pucher

University of Natural Resources and Life Sciences

WasserClusterLunz-Biologische Station GmbH

Urban Wuensch

Chalmers, Architecture and Civil Engineering, Water Environment Technology

Gabriele Weigelhofer

University of Natural Resources and Life Sciences

WasserClusterLunz-Biologische Station GmbH

Kathleen Murphy

Chalmers, Architecture and Civil Engineering, Water Environment Technology

Thomas Hein

WasserClusterLunz-Biologische Station GmbH

University of Natural Resources and Life Sciences

D. Graeber

WasserClusterLunz-Biologische Station GmbH

University of Natural Resources and Life Sciences

Water

20734441 (eISSN)

Vol. 11 11 2366-

Improved specificity for drinking water treatment monitoring

Formas (2017-00743), 2018-01-01 -- 2020-12-31.

Subject Categories

Earth and Related Environmental Sciences

DOI

10.3390/w11112366

More information

Latest update

10/5/2022