SMMART is a patented Spectral Multimodal Microscope for the Automated Recognition of Traces (SMMART) high throughput screening microscope system. It is a perfect blend of digital imaging, spectroscopy, robotics, machine learning, data structuring and analytics.
It offers automated (unsupervised) identification, enumeration and documentation of an enormous number of lifted traces. It can undertake the routine work of forensics labs, which is a subjective, fatigue prone, time consuming and labor-intensive process.
The SMMART system speeds up the trace examination/analysis processes by a factor of 200, while at the same time ensuring objectivity and standardization.
SMMART innovation makes large-scale trace analysis realistic, transferring routine work to machines and improving workflow
Modes of operation:
Hyperspectral Imaging: scanning range 340-1100nm, 20 nm tunning step
Multi-wavelength excitation fluorescence imaging
Stokes Polarimetry imaging
Retardance and Birefringence imaging
Sensors’ spatial resolution : 8 megapixels / per image
Illumination modes:
Transmission
Reflection
Fluorescence: three sources 365nm, 405nm, 450nm
Polarized
Acquisition data volume and speed:
40 images (Multimodal image set)
Acquisition time for the 40 images per objective’s: 5s
Objective:
2X, 5X, 15X (additional options possible)
Field of view (FOV): 5mm2 (5X objective), magnification 265X
Autofocus electromechanics
Spectroscopy:
Number of spectra per spectral cube: 8 million
Spectra display/comparison on mouse hovering
Polarimetry:
Four polarization angles in three wavelengths
Degree of linear polarization (DoLP) imaging
Retardation/Birefringence imaging calibrated with variable retarder
Measured retardation range: 0-2500nm
Sample stage size: the platform can accommodate in a single scan sample occupying a total 640X420 cm stage surface.
Sample scanning modes:
Fully automatic/robotic/unsupervised multimodal scanning
Manual via joystick
Scanning speed: 5h for 3600 FOVs, 40 images/frameNumber of spectra per spectral cube: 8 million.
Software:
Acquisition control
Autocalibration
Supervised and unsupervised classifiers
Morphometry-based classifiers
Machine learning/AI-based trace identification mapping and enumeration tables
User configurable system’s training
Database
