1. Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
- Author
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Judith Felten, András Gorzsás, Romà Tauler, Anna de Juan, Hardy C. Hall, Joaquim Jaumot, Universitat de Barcelona, and European Commission
- Subjects
Multivariate statistics ,principal component analysis ,Image processing ,Spectrum Analysis, Raman ,Least squares ,General Biochemistry, Genetics and Molecular Biology ,Islets of Langerhans ,User-Computer Interface ,symbols.namesake ,Xylem ,Spectroscopy, Fourier Transform Infrared ,Image Processing, Computer-Assisted ,Anàlisi multivariable ,Animals ,Espectroscòpia d'infraroigs per transformada de Fourier ,Least-Squares Analysis ,Cluster analysis ,Pixel ,Chemistry ,business.industry ,Fourier transform infrared spectroscopy ,Pattern recognition ,Processament d'imatges ,multivariate curve resolution alternating least square analysis ,Sample (graphics) ,Espectroscòpia Raman ,Mice, Inbred C57BL ,Populus ,Fourier transform ,bioassay ,Multivariate analysis ,Multivariate Analysis ,Principal component analysis ,Raman spectroscopy ,symbols ,Artificial intelligence ,business - Abstract
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 (MCRCR-ALSALSALS) 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, The authors thank the Vibrational Spectroscopy Core Facility of the Chemical Biological Centre at Umeå University for full Raman and FTIR microspectroscopy instrumentation access and resources dedicated to method development. We thank Prof. Björn Sundberg at the Umeå Plant Science Centre for initializing the project using hybrid aspen and continued support. Prof. Ulf Ahlgren and Christoffer Nord at the Umeå Centre for Molecular Medicine are acknowledged for providing the mouse pancreas sample. Anna de Juan and Romà Tauler acknowledge financial support of the European Union project CHEMAGEB and Joaquim Jaumot from the Spanish government (grant CTQ2012-11572).