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Copyright © LightForm Inc, 2017
LightForm Inc: Pioneering Analytical Hyperspectral Microscopy Since 1996

Hyperspectral Software: PARISS® Reduces or Eliminates the Pain

Associated with Typical Linear Spectral Un-Mixing Algorithms

PARISS software uses linearity independent algorithms : Utilizes proprietary algorithms developed in house:   based on Spectral Waveform Cross Correlation Analysis (SWCCA) Linearity independent All SWCCA algorithms assumes that non-linear spectral mixing is far more  likely than linear mixing in biological samples.  SWCCA enables tolerance driven spectral segmentation of up to 15 spectra simultaneously. Build libraries:  Create spectral libraries that truly represent your samples. Enables accurate spectral segmentation.  Spectra acquired with PARISS can be standardized, are publication ready, and can be compared with spectra acquired on any other analytical instrument. Powerful topographical mapping: Spectra from the FOV  that “correlate” with library spectra can be pseudo-colored and “painted” onto a gray-scale image with pixel perfect accuracy.
* For literature references go here
Literature Refs Literature Refs
Includes: Logical operators: “equals” and “not equals” at a user defined threshold.  Controls risk of false positives or negatives User created “real life” libraries:  By the time a fluorophore or chromophore is bound in an organic environment it is unlikely that its spectrum will be the same as that “in the book” PARISS captures change natural variations in spectral profile that indicate change such as: pH, ion-concentration, charge, conformation, Measure change over time: PARISS can acquire spectra automatically over a user defined period.  Then play the results back as a movie, observe raw spectra or export to a third party math program
Continued Continued
About linear un-mixing algorithms
LightForm_Logo
Copyright © LightForm Inc, 2017
LightForm Inc: Pioneering Analytical Hyperspectral Microscopy Since 1996
Hyperspectral Software: PARISS Reduces or Eliminates the Pain Associated with Typical Linear Spectral Un-Mixing Algorithms
PARISS software uses linearity independent algorithms : Utilizes proprietary algorithms developed in house:   based on Spectral Waveform Cross Correlation Analysis (SWCCA) Linearity independent All SWCCA algorithms assumes that non-linear spectral mixing is far more  likely than linear mixing in biological samples.  SWCCA enables tolerance driven spectral segmentation of up to 15 spectra simultaneously. Build libraries:  Create spectral libraries that truly represent your samples. Enables accurate spectral segmentation.  Spectra acquired with PARISS can be standardized, are publication ready, and can be compared with spectra acquired on any other analytical instrument. Powerful topographical mapping: Spectra from the FOV  that “correlate” with library spectra can be pseudo-colored and “painted” onto a gray-scale image with pixel perfect accuracy.
* For literature references go here
Literature Refs Literature Refs
Includes: Logical operators: “equals” and “not equals” at a user defined threshold.  Controls risk of false positives or negatives User created “real life” libraries:  By the time a fluorophore or chromophore is bound in an organic environment it is unlikely that its spectrum will be the same as that “in the book” PARISS captures change natural variations in spectral profile that indicate change such as: pH, ion-concentration, charge, conformation, Measure change over time: PARISS can acquire spectra automatically over a user defined period.  Then play the results back as a movie, observe raw spectra or export to a third party math program
Continued Continued
About linear un-mixing algorithms