LumaCyte’s Radiance® instrument measures dozens of parameters that reflect the intrinsic biophysical and biochemical properties of cells. This quantitative machine learning enables complex data to be turned into actionable results.
Multivariate Analysis
Turn Machine Learning Into Actionable Results
Moving to Advanced Predictive Analytics
Multivariate data can have significant benefits for qualitative and quantitative analytical modeling/calibration, and better predictive results versus simple univariate data sources. The measurements can be used to build a sophisticated model that can better understand a process, identify new patterns, and predict important variables.
Know More with Multi-Dimensional Data
As an example, to the left is a selection of data collected with Radiance® from a 3-component particle mixture including eccentricity (non-spherical aspect ratio), deformability (particle stretch while experiencing optical forces), and velocity (proportional to optical force). The video shows that the particles are partially resolved in eccentricity and deformability but have significant separation in the velocity dimension.
See the Previously Unseen
The power of a multivariate analysis tool is that even for very overlapped data, small differences can be utilized and correlations established using a vast array of machine learning tools including partial least squares regression (PLS) analysis, principal component analysis (PCA), linear discriminant analysis (LDA), and neural networks.
