Systems and methods for detecting radiation, biotoxin, chemical, and biological warfare agents using a multiple angle light scattering (MALS) instrument

• US 20070195324A1
• Filed: 11/29/2006
• Published: 08/23/2007
• Est. Priority Date: 09/19/2005
• Status: Active Grant

• Current Assignee: JMAR LLC
• Original Assignee: JMAR Technologies, Inc.
• Inventors: Adams, John, McCarty, David, Teters, Cherish, Crousore, Kristina, Ricardi, John, Tutrow, Michael
• Granted Patent: US 7,518,723 B2
• Time in Patent Office: Days
• US Class Current: 356/338
• CPC Class Codes: G01N 15/147 the analysis being performed on a sample stream
• CPC Class Codes: G01N 2015/019 Biological contaminants; Fouling
• CPC Class Codes: G01N 2015/1477 Multiparameters
• CPC Class Codes: G01N 2015/1486 Counting the particles
• CPC Class Codes: G01N 2015/1497 Particle shape
• CPC Class Codes: G01N 21/53 within a flowing fluid, e.g. smoke
• CPC Class Codes: G01N 33/1893 using flow cells

Abstract

A particle detection system to identify and classify particles is programmed to capture digitized images of the particle generated by directing a light source through a fluid that includes the particle. The particle scatters the light and the scattered light is detected using a detector. The detector creates a digital signal corresponding to the particle, which is used by the system to generate a Bio-Optical Signature. This Bio-Optical Signature can then be used to classify the event, or particle. A count rate of the classified particles is monitored to detect a change that is representative of a toxin attack.

Claims

1. A system for detecting an addition of a toxin to liquid, the system comprising:

an optical axis;

a target zone, the optical axis intersecting the target zone;

at least one light source configured to generate a light beam and direct the light beam through the target zone;

an optic lens system configured to collect light scattered by a particle associated with the toxin in the liquid in the target zone and direct the scattered light to at least one detector, the detector configured to detect the light and generate a digital signal; and

a computer system coupled with the detector, the computer system configured to execute a set of programmed instructions to identify and classify the particle associated with the toxin, compare counts per unit time to a threshold, and generate an alarm, the instructions causing the computer system to;

extract events from the digital signal;

calculate the similarity between the extracted event and a known event;

classify the event based on the similarity;

calculate a new count rate for the classification type associated with the toxin;

compare the new count rate to a threshold; and

generate an alarm if the count rate falls below the threshold.

2. The system of claim 1, wherein the toxin is a chemical toxin.

3. The system of claim 1, wherein the toxin is a biotoxin.

4. The system of claim 1, wherein the toxin is a radiation generating toxin.

5. The system of claim 1, wherein similarity is determined by calculation of the coefficient of correlation between the extracted event and a known event.

6. The system of claim 1, wherein similarity is determined by calculation of pdist between the extracted event and a known event.

7. The system of claim 1, wherein similarity is determined by calculation of the ratios of outer pixels to inner pixels and comparing between the extracted event and a known event

8. The system of claim 1, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to generate a intensity versus time vector (L-events vector) for the extracted event.

9. The system of claim 8, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to tuned differential vector from the L-events vector.

10. The system of claim 9, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to compare the tuned differential vector to a threshold.

11. The system of claim 10, wherein comparing the tuned differential vector to a threshold comprises comparing the tuned differential vector to a low threshold and then a high threshold.

12. The system of claim 10, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to begin counting subsequent frames received from the detector based on the comparison, and to determine an interval for the event based in the number of frames counted.

13. The system of claim 12, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to determine an event duration based in the number of frames counted and to compare the duration to a threshold.

14. The system of claim 13, wherein comparing the event duration to a threshold comprises comparing the event duration to a minimum and a maximum threshold.

15. The system of claim 13, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to locate a peak position frame for the event.

16. The system of claim 15, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to remove noise from the digital signal by summing on a pixel by pixel basis the peak position frame and the previous frame and subtracting (0.5 multiplied by the sum of corresponding pixels from the three frames before the peak event and 0.5 multiplied by the sum of corresponding pixels from the three frames after the event).

17. The system of claim 16, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to normalize the frame that results from the summing and subtracting.

18. The system of claim 17, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to correct the gain for the normalized frame.

19. The system of claim 18, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to correct dead pixels in the normalized frame.

20. The system of claim 1, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to perform a mean amplitude calculation on the normalized frame.

21. The system of claim 20, wherein the instructions are further configured to cause the computer system to extract events by causing the computer system to back rotate the normalized frame to the standard orientation of a major axis using a rotation angle.

22. The system of claim 1, wherein the instructions are further configured to cause the computer system to precondition the extracted events.

23. The system of claim 22, wherein the instructions are further configured to cause the computer system to precondition the extracted events by causing the computer system to generate a normalized array of events, each event of the array of events comprising a standard orientation, and to adjust the mean value of each event in the array of events.

24. The system of claim 23, wherein adjusting the mean value comprises adjusting the mean value to a predetermined value.

25. The system of claim 23, wherein the instructions are further configured to cause the computer system to precondition the extracted events by causing the computer system to perform log normal processing for each event of the array of events.

26. The system of claim 25, wherein the instructions are further configured to cause the computer system to precondition the extracted events by causing the computer system to readjust the mean for each event of the array of events after the log normal processing.

27. The system of claim 22, wherein the instructions are further configured to cause the computer system to precondition the extracted events by causing the computer system to generate a normalized array of events, each event of the array of events comprising a standard orientation, and to multiply each pixel time in the array of events by sin(scattering angle)ˆ

4.

28. The system of claim 27, wherein the instructions are further configured to cause the computer system to precondition the extracted events by causing the computer system to adjust the mean for each event in the array of events to a predetermined value.

29. The system of claim 1, wherein the instructions are further configured to cause the computer system to extract features for classification for the extracted event.

30. The system of claim 29, wherein the instructions are further configured to cause the computer system to extract features from the extracted events by causing the computer system to generate a normalized array of events, each event of the array of events comprising a standard orientation, and to use the entire frame corresponding to each event in the array of events for extraction.

31. The system of claim 29, wherein the instructions are further configured to cause the computer system to extract features from the extracted events by causing the computer system to generate a normalized array of events, each event of the array of events comprising a standard orientation, and to perform spatial transformation on each event in the array of events.

32. The system of claim 31, wherein the instructions are further configured to cause the computer system to extract features from the extracted events by causing the computer system to select either the RT-circular or RT-spoke data for classification.

33. The system of claim 1, wherein classifying the event comprises generating a Bio-Optical Signature.

34. A method of monitoring liquid for a toxin attack comprising;

monitoring the liquid with a multi-angle light scattering device;

classifying detected particles;

generating a count rate for the classes of particles; and

generating an alarm if the count rate falls below a threshold.

35. The method of claim 34, wherein classifying detected particles comprises:

extracting events from the digital signal;

calculating the similarity between the extracted event and a known event;

classifying the event based on the similarity.

36. The method of claim 35, wherein similarity is determined by calculation of the coefficient of correlation between the extracted event and a known event.

37. The method of claim 35, wherein similarity is determined by calculation of pdist between the extracted event and a known event.

38. The method of claim 35, wherein similarity is determined by calculation of the ratios of outer pixels to inner pixels and comparing between the extracted event and a known event

39. The method of claim 35, wherein extracting events comprises generating an intensity versus time vector (L-events vector) for the extracted event.

40. The method of claim 39, wherein extracting events comprises generating a tuned differential vector from the L-events vector.

41. The method of claim 40, wherein extracting events comprises comparing the tuned differential vector to a threshold.

42. The method of claim 41, wherein comparing the tuned differential vector to a threshold comprises comparing the tuned differential vector to a low threshold and then a high threshold.

43. The method of claim 41, wherein extracting events comprises counting subsequent frames received from a detector based on the comparison, and to determining an interval for the event based in the number of frames counted.

44. The method of claim 43, extracting events comprises determining an event duration based in the number of frames counted and to compare the duration to a threshold.

45. The method of claim 44, wherein comparing the event duration to a threshold comprises comparing the event duration to a minimum and a maximum threshold.

46. The method of claim 44, wherein extracting events comprises locating a peak position frame for the event.

47. The method of claim 46, wherein extracting events comprises removing noise from the digital signal by summing on a pixel by pixel basis the peak position frame and the previous frame and subtracting (0.5 multiplied by the sum of corresponding pixels from the three frames before the peak event and 0.5 multiplied by the sum of corresponding pixels from the three frames after the event).

48. The method of claim 47, wherein extracting events comprises normalizing the frame that results from the summing and subtracting.

49. The method of claim 48, wherein extracting events comprises correcting the gain for the normalized frame.

50. The method of claim 49, wherein extracting events comprises correcting dead pixels in the normalized frame.

51. The method of claim 35, wherein extracting events comprises performing a mean amplitude calculation on the normalized frame.

52. The method of claim 51, wherein extracting events comprises back rotating the normalized frame to the standard orientation of a major axis using a rotation angle.

53. The method of claim 35, further comprising preconditioning the extracted events.

54. The method of claim 53, wherein preconditioning the extracted events comprises generating a normalized array of events, each event of the array of events comprising a standard orientation, and adjusting the mean value of each event in the array of events.

55. The method of claim 54, wherein adjusting the mean value comprises adjusting the mean value to a predetermined value.

56. The method of claim 54, wherein preconditioning the extracted events comprises performing log normal processing for each event of the array of events.

57. The method of claim 56, wherein preconditioning the extracted events comprises readjusting the mean for each event of the array of events after the log normal processing.

58. The method of claim 53, wherein preconditioning the extracted events comprises generating a normalized array of events, each event of the array of events comprising a standard orientation, and multiplying each pixel time in the array of events by sin(scattering angle)ˆ

4.

59. The method of claim 58, wherein preconditioning the extracted events comprises adjusting the mean for each event in the array of events to a predetermined value.

60. The method of claim 35, further comprising extracting features for classification for the extracted event.

61. The method of claim 60, wherein extracting features from the extracted events comprises generating a normalized array of events, each event of the array of events comprising a standard orientation, and using the entire frame corresponding to each event in the array of events for extraction.

62. The method of claim 60, wherein extracting features from the extracted events comprises generating a normalized array of events, each event of the array of events comprising a standard orientation, and performing spatial transformation on each event in the array of events.

63. The method of claim 62, wherein extracting features from the extracted events comprises selecting either the RT-circular or RT-spoke data for classification.

64. The method of claim 35, wherein classifying the event comprises generating a Bio-Optical Signature.