Apparatus and method for the detection and classification of articles using flow cytometry techniques

An apparatus for the detection of particles in a sample comprising:

• sensing means, including a sensing zone, for sensing characteristic features of particles;
• means for moving particles of an unknown class, substantially one at a time, in a fluid flow stream through said sensing zone;
• means for detecting data associated with the characteristic features of each moving particle as the particle passes through said sensing zone;
• means for storing data from a known class of particles having common characteristics in accordance with a pattern of data collected from such known class of particles;
• and
• means for recognizing that sample particles from said unknown class belong to said known class responsive to the recognition of said pattern by data detected from said sample particles.

A flow cytometry apparatus for the detection of biological particles of interest from a heterogeneous population of biological particles comprising:

• means for moving particles, substantially one at a time, in a liquid flow stream;
• means for providing an incident beam of illumination directed at the particles in said flow stream;
• means for simultaneously detecting light scattered by and fluorescence emitted from each moving particle as the particle passes through said beam of illumination;
• means for storing data representing a known class of particles having common characteristics based on scattered light and fluorescence data collected from such known class of particles;
• means for transforming light data detected from sample particles of an unknown class in order to cluster the data points so transformed;
• means for extracting features from said stored data for the discrimination of data related to said unknown particles;
• and
• means associated with said transforming and said extracting means for determining that particles from said unknown class belong to said known class as a result of matching patterns of respective light-related data.

A flow cytometry apparatus for the detection and classification of biological particles of interest from a sample of an unknown class of biological particles comprising:

• means for moving particles from a sample of unknown biological particles, substantially one at a time, in a fluid flow stream;
• means for providing an incident beam of illumination directed at the particles in said flow stream;
• means for detecting light-related data associated with each moving particle as the particle passes through said beam of illumination;
• means for storing data representing a known class of particles having common characteristics based on said light data collected from such known class of particles;
• means for separating data from said known class into a plurality of subclasses thereof, each subclass having its light-related data stored separately;
• means for applying such stored data to light data detected from sample particles of said unknown class in order to match patterns between the data of the known and unknown particles;
• and
• means for recognizing that particles from said unknown class belong to said known class as a result of matching patterns of respective light-related data and for identifying the subclasses to which the unknown particles belong.

An apparatus for the detection and classification of particles in a sample comprising:

• sensing means, including a sensing zone, for sensing characteristic features of particles;
• means for moving particles of an unknown class, substantially one at a time, in a fluid flow stream through said sensing zone;
• means for detecting data associated with the characteristic features of each moving particle as the particle passes through said sensing zone;
• means for storing data from a plurality of known subclasses of particles each having common characteristics in accordance with a pattern of data collected from such known subclasses of particles;
• and
• means for recognizing that sample particles from said unknown class belong to said known subclasses responsive to the recognition of said patterns by data detected from said sample particles.

A method for detecting biological particles of interest from a sample of an unknown class of biological particles comprising:

• moving particles from a sample of unknown biological particles, substantially one at a time, in a fluid flow stream;
• providing an incident beam of illumination directed at the particles in said flow stream;
• detecting light-related data associated with each moving particle as the particle passes through said beam of illumination;
• storing data representing a known class of particles having common characteristics based on light data collected from such known class of particles;
• applying such stored data to light data detected from sample particles of said unknown class in order to match patterns between the data of the known and unknown particles;
• and
• recognizing that particles from said unknown class belong to said known class as a result of matching patterns of respective light-related data.

A method for detecting particles in a sample comprising:

• moving particles, substantially one at a time, in a fluid flow stream through a sensing zone;
• detecting data associated with characteristic features of each moving particle as the particle passes through said sensing zone;
• storing data from a known class of particles having common characteristics in accordance with a pattern of data collected from such known class of particles;
• and
• recognizing that sample particles from an unknown class belong to said known class responsive to the recognition of said pattern by data detected from said sample particles.

A method for detecting and classifying biological particles of interest from a sample of an unknown class of biological particles comprising:

• moving particles from a sample of unknown biological particles, substantially one at a time, in a fluid flow stream;
• providing an incident beam of illumination directed at the particles in said flow stream;
• detecting light-related data associated with each moving particle as the particle passes through said beam of illumination;
• storing data representing a known class of particles having common characteristics based on said light data collected from such known class of particles;
• separating data from said known class into a plurality of subclasses thereof, each subclass having its light-related data stored separately;
• applying such stored data to light data detected from sample particles of said unknown class in order to match patterns between the data of the known and unknown particles;
• and
• recognizing that particles from said unknown class belong to said known class as a result of matching patterns of respective light-related data and identifying the subclasses to which the unknown particles belong.

A method for detecting and classifying particles in a sample comprising:

• moving particles of an unknown class, substantially one at a time, in a fluid flow stream through a sensing zone;
• detecting data associated with characteristic features of each moving particle as the particle passes through said sensing zone;
• storing data from a plurality of subclasses of particles each having known common characteristics in accordance with patterns of data associated with such known subclasses of particles;
• and
• recognizing that sample particles from said unknown class belong to said known subclasses responsive to the recognition of said patterns by data associated with said sample particles.