Granted Patent B2

US 8,725,469 · App. 13/039,818 · Granted May 13, 2014

Abstract

Claims (142)

1. A computerized method comprising:

receiving, by a computing device, a data set representing a plurality of samples, the plurality of samples comprises a plurality of solid samples, a plurality of liquid samples, a plurality of gas samples, or any combination thereof;

processing, by the computing device, the data set using a data processing algorithm that includes one or more processing stages, each stage using a first respective set of data processing parameters to generate processed data;

generating for the data processing algorithm, by the computing device, a design of experiment model based on the processed data and a set of response values; and

calculating for each stage of the data processing algorithm, by the computing device, a second respective set of data processing parameters based on at least the design of experiment model;

calculating a reliability index for the second set of data processing parameters; and

adjusting one or more of the data processing parameters in the first set of data processing parameters based on the respective reliability index associated with each of the data processing parameters to calculate the second set of data processing parameters.

2. The method of claim 1 , wherein calculating the reliability index comprises:

calculating a concentration vector based on a pooled sample obtained from the plurality of samples;

calculating a squared correlation between one or more peak vectors in the design of experiment model and the concentration vector; and

calculating a ratio of reliable peaks and non-reliable peaks.

3. The method of claim 2 wherein:

a reliable peak comprises a peak associated with a squared correlation value above a first predetermined threshold, and

a non-reliable peak comprises a peak associated with a squared correlation value below a second predetermined threshold.

4. The method of claim 1 , further comprising,

processing the received data set using the data processing algorithm with the second set of data processing parameters to generate a second design of experiment model.

5. The method of claim 4 further comprising:

calculating an updated reliability index for the second set of data processing parameters; and

calculating a third set of data processing parameters comprising adjusting one or more of the data processing parameters in the second set of data processing parameters based on the updated reliability index if the updated reliability indexes satisfy a criterion.

6. The method of claim 1 wherein:

(a) generating the design of experiment model comprises generating an initial optimization design model for the first set of data processing parameters associated with the respective stage based on the processed data; and

wherein calculating comprises:

(b) analyzing the initial optimization design model to determine whether the first set of data processing parameters satisfies a criterion; and

(c) if the first set of data processing parameters does not satisfy the criterion:

calculating the second set of data processing parameters comprising adjusting one or more of the data processing parameters; and

repeating steps (a) through (c) with the third set of data processing parameters.

7. The method of claim 6 wherein generating the initial optimization design model comprises:

calculating a first set of response values based on the first set of data processing parameters for the respective stage; and

generating a multivariate model based on the first set of data processing parameters and the first set of response values.

8. The method of claim 7 wherein calculating the first set of response values comprises:

calculating a second set of response values based on the one or more data processing parameters for the respective stage;

generating the first set of response values based on the second set of response values by, for each response value in the second set of response values:

calculating a comparison metric between the response value and the concentration vector; and

if the comparison metric does not satisfy a second criterion, filtering the response value from the second set of response values.

9. The method of claim 6 wherein analyzing the initial optimization model comprises:

selecting a predetermined setting of the design of experiment model;

determining whether each data processing parameter associated with a first stage is within range for the data processing parameter;

if each data processing parameter is within the range, storing a value indicative of the predetermined setting not to be adjusted; and

if one or more of the data processing parameters are not within the range, storing a value indicative of the predetermined setting to be adjusted.

10. The method of claim 6 wherein adjusting comprises:

if a data processing parameter is within a predetermined distance of a predetermined parameter value of the data processing parameter, using the non-adjusted data processing parameter; and

if a data processing parameter is not within the predetermined distance of the predetermined parameter value, adjusting the data processing parameter to be less than the predetermined distance.

11. The method of claim 6 wherein calculating the second set of data processing parameters comprises:

determining a data processing parameter is a qualitative data processing parameter; and

not adjusting the data processing parameter.

12. The method of claim 1 wherein the set of data processing parameters comprises at least one of a qualitative data processing parameter, a quantitative data processing parameter, or both.

13. The method of claim 1 wherein the data set comprises mass spectroscopy data, liquid chromatography data, or both.

14. A computer program product, tangibly embodied in a non-transitory computer readable medium, the computer program product including instructions being configured to cause a data processing apparatus to:

receive a data set comprising a plurality of samples, the plurality of samples comprise a plurality of solid samples, a plurality of liquid samples, a plurality of gas samples, or any combination thereof;

process the data set using a data processing algorithm that includes one or more processing stages, each stage using a first respective set of data processing parameters to generate processed data;

generate for the data processing algorithm a design of experiment model based on the processed data and a set of response values;

calculate for each stage of the data processing algorithm a second respective set of data processing parameters based on at least the design of experiment model;

calculate a reliability index for the second set of data processing parameters; and

adjust one or more of the data processing parameters in the first set of data processing parameters based on the respective reliability index associated with each of the data processing parameters to calculate the second set of data processing parameters.

15. The computer program product of claim 14 , wherein instructions to calculate the reliability index cause a data processing apparatus to:

calculate a concentration vector based on a pooled sample obtained from the plurality of samples;

calculate a squared correlation between one or more peak vectors in the design of experiment model and the concentration vector; and

calculate a ratio of reliable peaks and non-reliable peaks.

16. The computer program product of claim 15 wherein:

a reliable peak comprises a peak associated with a squared correlation value above a first predetermined threshold, and

a non-reliable peak comprises a peak associated with a squared correlation value below a second predetermined threshold.

17. The computer program product of claim 14 , further comprising instructions being configured to cause a data processing apparatus to:

process the received data set using the data processing algorithm with the second set of data processing parameters to generate a second design of experiment model.

18. The computer program product of claim 17 , further comprising instructions being configured to cause a data processing apparatus to:

calculate an updated reliability index for the second set of data processing parameters; and

calculate a third set of data processing parameters comprising adjusting one or more of the data processing parameters in the second set of data processing parameters based on the updated reliability index if the updated reliability indexes satisfy a criterion.

19. The computer program product of claim 14 , wherein:

(a) instructions for generating the design of experiment model cause a data processing apparatus to generate an initial optimization design model for the first set of data processing parameters associated with the respective stage based on the processed data; and

wherein instructions for calculating cause a data processing apparatus to:

(b) analyze the initial optimization design model to determine whether the first set of data processing parameters satisfies a criterion; and

(c) if the first set of data processing parameters does not satisfy the criterion:

calculate the second set of data processing parameters comprising adjusting one or more of the data processing parameters; and

repeat steps (a) through (c) with the third set of data processing parameters.

20. The computer program product of claim 19 , wherein instructions for generating the initial optimization design model cause a data processing apparatus to:

calculate a first set of response values based on the first set of data processing parameters for the respective stage; and

generate a multivariate model based on the first set of data processing parameters and the first set of response values.

21. The computer program product of claim 20 , wherein instructions for calculating the first set of response values cause a data processing apparatus to:

calculate a second set of response values based on the one or more data processing parameters for the respective stage;

generate the first set of response values based on the second set of response values by, for each response value in the second set of response values:

calculate a comparison metric between the response value and the concentration vector; and

if the comparison metric does not satisfy a second criterion, filter the response value from the second set of response values.

22. The computer program product of claim 19 , wherein instructions for analyzing the initial optimization model cause a data processing apparatus to:

select a predetermined setting of the design of experiment model;

determine whether each data processing parameter associated with a first stage is within a range for the data processing parameter;

if each data processing parameter is within the range, store a value indicative of the predetermined setting not to be adjusted; and

if one or more of the data processing parameters are not within the range, store a value indicative of the predetermined setting to be adjusted.

23. The computer program product of claim 19 , wherein instructions for adjusting cause a data processing apparatus to:

if a data processing parameter is within a predetermined distance of a predetermined parameter value of the data processing parameter, use the non-adjusted data processing parameter; and

if a data processing parameter is not within the predetermined distance of the predetermined parameter value, adjust the data processing parameter to be less than the predetermined distance.

24. The computer program product of claim 18 , wherein instructions for calculating the second set of data processing parameters cause a data processing apparatus to:

determine a data processing parameter is a qualitative data processing parameter; and

not adjust the data processing parameter.

25. The computer program product of claim 14 , wherein the set of data processing parameters comprises at least one of a qualitative data processing parameter, a quantitative data processing parameter, or both.

26. The computer program product of claim 14 , wherein the data set comprises mass spectroscopy data, liquid chromatography data, or both.

27. An apparatus comprising:

a computing device;

a receiving module in the computing device configured to receive a data set comprising a plurality of samples, the plurality of samples comprise a plurality of solid samples, a plurality of liquid samples, a plurality of gas samples, or any combination thereof;

an initial processing module in the computing device in communication with the receiving module configured to process the data set using a data processing algorithm that includes one or more processing stages, each stage using a first respective set of data processing parameters to generate processed data; and

a design of experiment module in the computing device in communication with the initial processing module configured to:

generate for the data processing algorithm a design of experiment model based on the second set of data processing parameters and a set of response values, the design of experiment model determining a third set of data processing parameters for each stage of the data processing algorithm;

calculate for each stage of the data processing algorithm a second respective set of data processing parameters based on at least the design of experiment model;

calculate a reliability index for the second set of data processing parameters; and adjust one or more of the data processing parameters in the first set of data processing parameters based on the respective reliability index associated with each of the data processing parameters to calculate the second set of data processing parameters.

28. The apparatus of claim 27 , wherein calculating the reliability index comprises:

calculating a concentration vector based on a pooled sample obtained from the plurality of samples;

calculating a squared correlation between one or more peak vectors in the design of experiment model and the concentration vector; and

calculating a ratio of reliable peaks and non-reliable peaks.

29. The apparatus of claim 28 , wherein:

a reliable peak comprises a peak associated with a squared correlation value above a first predetermined threshold, and

a non-reliable peak comprises a peak associated with a squared correlation value below a second predetermined threshold.

30. The apparatus of claim 27 , the design of experiment module further configured to:

process the received data set using the data processing algorithm with the second set of data processing parameters to generate a second design of experiment model.

31. The apparatus of claim 30 , the design of experiment module further configured to:

calculate an updated reliability index for the second set of data processing parameters; and

calculate a third set of data processing parameters comprising adjusting one or more of the data processing parameters in the second set of data processing parameters based on the updated reliability index if the updated reliability indexes satisfy a criterion.

32. The apparatus of claim 27 , wherein:

(a) generating the design of experiment model comprises generating an initial optimization design model for the first set of data processing parameters associated with the respective stage based on the processed data; and

wherein calculating comprises:

(b) analyzing the initial optimization design model to determine whether the first set of data processing parameters satisfies a criterion; and

(c) if the first set of data processing parameters does not satisfy the criterion:

calculating the second set of data processing parameters comprising adjusting one or more of the data processing parameters; and

repeating steps (a) through (c) with the third set of data processing parameters.

33. The apparatus of claim 32 , wherein generating the initial optimization design model comprises:

calculating a first set of response values based on the first set of data processing parameters for the respective stage; and

generating a multivariate model based on the first set of data processing parameters and the first set of response values.

34. The apparatus of claim 33 , wherein calculating the first set of response values comprises:

calculating a second set of response values based on the one or more data processing parameters for the respective stage;

generating the first set of response values based on the second set of response values by, for each response value in the second set of response values:

calculating a comparison metric between the response value and the concentration vector; and

if the comparison metric does not satisfy a second criterion, filtering the response value from the second set of response values.

35. The apparatus of claim 32 , wherein analyzing the initial optimization model comprises:

selecting a predetermined setting of the design of experiment model;

determining whether each data processing parameter associated with a first stage is within a range for the data processing parameter;

if each data processing parameter is within the range, storing a value indicative of the predetermined setting not to be adjusted; and

if one or more of the data processing parameters are not within the range, storing a value indicative of the predetermined setting to be adjusted.

36. The apparatus of claim 32 , wherein adjusting comprises:

if a data processing parameter is within a predetermined distance of a predetermined parameter value of the data processing parameter, using the non-adjusted data processing parameter; and

if a data processing parameter is not within the predetermined distance of the predetermined parameter value, adjusting the data processing parameter to be less than the predetermined distance.

37. The apparatus of claim 32 , wherein calculating the second set of data processing parameters comprises:

determining a data processing parameter is a qualitative data processing parameter; and

not adjusting the data processing parameter.

38. The apparatus of claim 27 , wherein the set of data processing parameters comprises at least one of a qualitative data processing parameter, a quantitative data processing parameter, or both.

39. The apparatus of claim 27 , wherein the data set comprises mass spectroscopy data, liquid chromatography data, or both.

Assignments (9)