Device and method for detecting and identifying extracellular vesicles in a liquid dispersion sample

A non-transitory storage media including program instructions for implementing a method for detecting nanoparticles in a liquid dispersion sample, the program instructions including instructions executable by an electronic data processor to carry out a process, wherein the process includes:

• emitting a laser modulated by a modulation frequency onto each specimen;
• capturing a temporal signal from laser light backscattered by each specimen for a plurality of temporal periods of a predetermined duration for each specimen;
• calculating specimen DCT or Wavelet transform coefficients from the captured signal for each of the temporal periods;
• using the calculated coefficients to pre-train the machine learning classifier;
• using a laser emitter having a focusing optical system coupled to the emitter to emit a laser modulated by a modulation frequency onto the sample;
• using a light receiver to capture a signal from laser light backscattered by the sample for a plurality of temporal periods of a predetermined duration;
• calculating sample DCT or Wavelet transform coefficients from the captured signal for each of the temporal periods;
• and
• using the pre-trained machine learning classifier to classify the calculated sample coefficients as having, or not having, nanoparticles present,
• wherein the nanoparticles have a particle size, in any particle direction, below 1 μm,
• and further wherein the focusing optical system is a focusing optical system suitable to provide a field gradient pattern.

A method for detecting nanoparticles while dispersed in a liquid dispersion sample, said method being performed on a device comprising a laser emitter;

• a focusing optical system coupled to the emitter;
• an infrared light receiver;
• and an electronic data processor arranged to classify the sample as having, or not having, nanoparticles present using a machine learning classifier which has been pre-trained using a plurality of nanoparticles liquid dispersion specimens, while dispersed, comprising the steps of:
• emitting a laser modulated by a modulation frequency onto each specimen;
• capturing a temporal signal from laser light backscattered by each specimen for a plurality of temporal periods of a predetermined duration for each specimen;
• calculating specimen DCT or Wavelet transform coefficients from the captured signal for each of the temporal periods;
• and
• using the calculated coefficients to pre-train the machine learning classifier;
• utilizing the pre-trained machine learning classifier to classify sample coefficients of the liquid dispersion sample as having, or not having, nanoparticles present, by:
• using the laser emitter to emit a laser modulated by a modulation frequency onto the sample;
• using the light receiver to capture a signal from laser light backscattered by the sample for a plurality of temporal periods of a predetermined duration;
• and
• calculating sample DCT or Wavelet transform coefficients from the captured signal for each of the temporal periods;
• wherein the nanoparticles have a particle size, in any particle direction, below 1 μm;
• wherein the focusing optical system is a focusing optical system suitable to provide a field gradient pattern.