1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      pyMCR: A Python Library for Multivariate Curve Resolution Analysis with Alternating Regression (MCR-AR)

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          pyMCR is a new open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating measurement signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of error functions for minimization, constraints, and regressors. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.

          Related collections

          Most cited references7

          • Record: found
          • Abstract: not found
          • Article: not found

          A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Vibrational spectroscopic image analysis of biological material using multivariate curve resolution-alternating least squares (MCR-ALS).

            Raman and Fourier transform IR (FTIR) microspectroscopic images of biological material (tissue sections) contain detailed information about their chemical composition. The challenge lies in identifying changes in chemical composition, as well as locating and assigning these changes to different conditions (pathology, anatomy, environmental or genetic factors). Multivariate data analysis techniques are ideal for decrypting such information from the data. This protocol provides a user-friendly pipeline and graphical user interface (GUI) for data pre-processing and unmixing of pixel spectra into their contributing pure components by multivariate curve resolution-alternating least squares (MCR-ALS) analysis. The analysis considers the full spectral profile in order to identify the chemical compounds and to visualize their distribution across the sample to categorize chemically distinct areas. Results are rapidly achieved (usually <30-60 min per image), and they are easy to interpret and evaluate both in terms of chemistry and biology, making the method generally more powerful than principal component analysis (PCA) or heat maps of single-band intensities. In addition, chemical and biological evaluation of the results by means of reference matching and segmentation maps (based on k-means clustering) is possible.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Multivariate curve resolution applied to liquid chromatography—diode array detection

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Research of the National Institute of Standards and Technology
                J. RES. NATL. INST. STAN.
                National Institute of Standards and Technology (NIST)
                2165-7254
                2019
                June 24 2019
                : 124
                Affiliations
                [1 ]National Institute of Standards and Technology, Material Measurement Laboratory, Gaithersburg, MD 20899, USA
                Article
                10.6028/jres.124.018
                33b0289a-a9dc-4fd3-aadb-f4222c676ce5
                © 2019

                Comments

                Comment on this article