@article{felten_vibrational_2015,
	title = {Vibrational spectroscopic image analysis of biological material using multivariate curve resolution-alternating least squares ({MCR}-{ALS})},
	volume = {10},
	issn = {1750-2799 (Electronic) 1750-2799 (Linking)},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/25569330},
	doi = {10.1038/nprot.2015.008},
	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 (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 {\textless}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.},
	language = {en},
	number = {2},
	urldate = {2021-06-07},
	journal = {Nat Protoc},
	author = {Felten, J. and Hall, H. and Jaumot, J. and Tauler, R. and de Juan, A. and Gorzsas, A.},
	month = feb,
	year = {2015},
	note = {Edition: 2015/01/09},
	keywords = {*Multivariate Analysis, *Spectroscopy, Fourier Transform Infrared, *Spectrum Analysis, Raman, Animals, Image Processing, Computer-Assisted/*methods, Islets of Langerhans/chemistry, Least-Squares Analysis, Mice, Inbred C57BL, Populus/chemistry, User-Computer Interface, Xylem/chemistry},
	pages = {217--40},
}

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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 \\textless30-60 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.","language":"en","number":"2","urldate":"2021-06-07","journal":"Nat Protoc","author":[{"propositions":[],"lastnames":["Felten"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Hall"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Jaumot"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Tauler"],"firstnames":["R."],"suffixes":[]},{"propositions":["de"],"lastnames":["Juan"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Gorzsas"],"firstnames":["A."],"suffixes":[]}],"month":"February","year":"2015","note":"Edition: 2015/01/09","keywords":"*Multivariate Analysis, *Spectroscopy, Fourier Transform Infrared, *Spectrum Analysis, Raman, Animals, Image Processing, Computer-Assisted/*methods, Islets of Langerhans/chemistry, Least-Squares Analysis, Mice, Inbred C57BL, Populus/chemistry, User-Computer Interface, Xylem/chemistry","pages":"217–40","bibtex":"@article{felten_vibrational_2015,\n\ttitle = {Vibrational spectroscopic image analysis of biological material using multivariate curve resolution-alternating least squares ({MCR}-{ALS})},\n\tvolume = {10},\n\tissn = {1750-2799 (Electronic) 1750-2799 (Linking)},\n\turl = {https://www.ncbi.nlm.nih.gov/pubmed/25569330},\n\tdoi = {10.1038/nprot.2015.008},\n\tabstract = {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 {\\textless}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. 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