PARADISe is a software program for resolving untargeted Gas Chromatography Masss Spectromety (GC-MS) data. This page contains a description of the software and the official versions available for downloading, starting from PARADISe version 6.
With PARADISe you can import .CDF data files which most instruments can export to. Alternatively, you can import MATLAB formatted data from so-called mat-files if relevant. A template for how to structure the mat-file is bundled as part of the installation.
Once data have been imported, you define retention time intervals which contain chemical peaks and PARADISe will then resolve and separate signals, such that the spectrum and peak area (relative concentration) are purified from baseline and overlapping/coeluting compounds. PARADISe will output a peak table, in excel format, containing the peak area and the purified spectrum for each resolved compound. A more detailed guideline is provided in the program under the welcome tab.
If you have NIST MSSEARCH, PARADISe also includes the best matches based on your NIST MSSEARCH settings.
The official version is an executable (*.exe) for Windows 10/11, but you can also download the software as a MATLAB app for use in MATLAB version 2022a.
Get the latest stable version 6.0.1 here:
- PARADISe for Windows version (download without MATLAB runtime or with MATLAB runtime)
- PARADISe MATLAB app only for MATLAB version 2022a or later (download)
If you are using PARADISe for scientific publications, we encourage you to reference this page and specify the version used, as well as the paper
Beatriz Quintanilla-Casas, Rasmus Bro, Jesper Løve Hinrich et al. Tutorial on PARADISe: PARAFAC2-based Deconvolution and Identification System for processing GC–MS data, 19 January 2023, PROTOCOL (Version 1) available at Protocol Exchange [https://doi.org/10.21203/rs.3.pex-2143/v1]
Resources and help
You can find the PARADISe discussion board here where you can post comments and questions.
We have a tutorial on how to use the software. Tip: go to Supplementary for a better typeset version
The history of PARADISe as seen through papers
Marie Roald, Carla Schenker, Vince D. Calhoun, Tülay Adali, Rasmus Bro, Jeremy E. Cohen, and Evrim Acar, et al. (2022). “An AO-ADMM Approach to Constraining PARAFAC2 on All Modes.” SIAM Journal on Mathematics of Data Science 4(3): 1191-1222
Huiwen Y, Bro R, Gallagher N, PARASIAS: A new method for analyzing higher-order tensors with shifting profiles, Analytica Chimica Acta, 2022
G Baccolo, B Quintanilla-Casas, S Vichi, D Augustijn, R Bro (2021). “From untargeted chemical profiling to peak tables – A fully automated AI driven approach to untargeted GC-MS.” TrAC Trends in Analytical Chemistry 145: 116451
Yu H, Bro R, PARAFAC2 and local mínima, Chemometrics and Intelligent Laboratory Systems 219 (2021) 104446
Yu, H., Augustijn, D., Bro, R., Accelerating PARAFAC2 algorithms for non-negative complex tensor decomposition, Chemometrics and Intelligent Laboratory Systems, 2021, 214, 104312.
Risum A. B., Bro R., Using deep learning to evaluate peaks in chromatographic data, Talanta, (2019), 204, 255-260
K. Tiana, L. Wu, S. Min, R. Bro, Geometric search: A new approach for fitting PARAFAC2 models on GC-MS data, Talanta 185 (2018) 378–386
Cohen J.E., Bro R. (2018) Nonnegative PARAFAC2: A Flexible Coupling Approach. In: Deville Y., Gannot S., Mason R., Plumbley M., Ward D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science, vol 10891. Springer, Cham.
Petersen, M. A. and R. Bro. PARADISe – a ground-breaking tool to treat complex GC-MS datasets. Flavour Science. Proceedings of the XV Weurman Flavour Research Symposium. B. Siegmund and E. Leitner: (2017) 421-426
L. G. Johnsen, P. B. Skou, B. Khakimov, and R. Bro. Gas chromatography – mass spectrometry data processing made easy. Journal of Chromatography, A 1503:57-64, 2017.
L. G. Johnsen, J. M. Amigo, T. Skov, and Rasmus Bro. Automated resolution of overlapping peaks in chromatographic data. J.Chemom. 28:71-82, 2014.
M. Kamstrup-Nielsen, L. G. Johnsen, and Rasmus Bro. Core consistency diagnostic in PARAFAC2. J.Chemom. 27:99-105, 2013.
R. Bro, R. Leardi, and L. G. Johnsen. Solving the sign indeterminacy for multiway models. J.Chemom. 27:70-75, 2013
J. M. Amigo, T. Skov, and R. Bro. ChroMATHography: Solving Chromatographic Issues with Mathematical Models and Intuitive Graphics. Chemical Reviews 110:4582-4605, 2010.
J. M. Amigo, T. Skov, J. Coello, S. Maspoch, and R. Bro. Solving GC-MS problems with PARAFAC2. Trends in Analytical Chemistry 27 (8):714-725, 2008.
Thomas Skov and R. Bro. Solving fundamental problems in chromatographic analysis. Analytical and Bioanalytical Chemistry 390:281-285, 2008.
Thomas Skov, F. van den Berg, G. Tomasi, and R. Bro. Automated alignment of chromatographic data. J.Chemom. 20:484-497, 2007.
H. A. L. Kiers, J. M. F. ten Berge, R. Bro, PARAFAC2 – Part I. A direct fitting algorithm for the PARAFAC2 model, J. Chemom., 13, 275-294, 1999
R. Bro, H. A. L. Kiers, C. A. Andersson, PARAFAC2 – Part II. Modeling chromatographic data with retention time shifts, J. Chemom., 13, 295-309, 1999
R. Bro, N. Sidiropoulos, Least squares algorithms under unimodality and non-negativity constraints, J. Chemom., 1998, 12, 223-247
R. Bro, S. de Jong, A fast non-negativity constrained least squares algorithm, J. Chemom., 1997, 11, 393-401