METHOD OF CONTAMINANT PREDICTION

• US 20120226653A1
• Filed: 09/24/2010
• Published: 09/06/2012
• Est. Priority Date: 09/24/2009
• Status: Active Grant

• Current Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Original Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Inventors: McLaughlin, Michael John, Forrester, Sean Thomas, Janik, Leslie Joseph
• Granted Patent: US 8,914,312 B2
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G01N 21/3563 for analysing solids; Preparation of samples therefor
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/129 Using chemometrical methods
• CPC Class Codes: G01N 33/241 for hydrocarbon content

Abstract

Disclosed is a method of selectively predicting hydrocarbon concentration in a sample of unknown hydrocarbon concentration (the unknown sample). The method comprises the steps of: (i) separately subjecting two or more samples of known hydrocarbon concentration to infrared (IR) radiation; (ii) separately detecting an IR signal from the samples of known hydrocarbon concentration; (iii) analysing the IR signals using a multivariate chemometric technique to produce a training data set; (iv) generating a predictive model for hydrocarbon concentration based on the training data set; (v) subjecting the unknown sample to infrared (IR) radiation; (vi) detecting an IR signal from the unknown sample; (vii) applying the predictive model to the IR signal from the unknown sample; and thereafter (viii) selectively predicting hydrocarbon concentration in the unknown sample. Also disclosed is a method for generating a model to selectively predict hydrocarbon concentration in a sample of unknown hydrocarbon concentration and a software program for selectively predicting hydrocarbon concentration from inputted IR signal data of an unknown sample.

Claims

1-17. -17. (canceled)

18. A method of selectively predicting hydrocarbon concentration in a sample of unknown hydrocarbon concentration (the unknown sample), said method comprising the steps of:

(i) separately subjecting two or more samples of known hydrocarbon concentration to infrared (IR) radiation, at least two of the samples having different hydrocarbon concentrations;

(ii) separately detecting an IR signal from the samples of known hydrocarbon concentration;

(iii) analysing the IR signals using a multivariate chemometric technique to produce a training data set;

(iv) generating a predictive model for hydrocarbon concentration based on the training data set;

(v) subjecting the unknown sample to infrared (IR) radiation;

(vi) detecting an IR signal from the unknown sample;

(vii) applying the predictive model to the IR signal from the unknown sample; and

thereafter

(viii) selectively predicting hydrocarbon concentration in the unknown sample.

19. A method according to claim 18, wherein step (i) and step (v) comprise the crushing, grinding, sieving and/or drying of the samples prior to subjecting the samples to IR radiation.

20. A method according to claim 18, wherein step (i) and step (v) comprise subjecting the sample to IR radiation spanning at least 450 cm⁻

1 to 7800 cm<sup>−

1</sup>.

21. A method according to claim 18, wherein step (i) comprises subjecting three or more samples of known hydrocarbon concentration to infrared (IR) radiation, wherein each sample of known hydrocarbon concentration has a different concentration.

22. A method according to claim 18, wherein step (viii) comprises selectively predicting petroleum hydrocarbon concentration in the unknown sample.

23. A method according to claim 18, wherein step (i) and step (v) comprise subjecting the sample to IR radiation spanning a region consisting of one or more of:

about 1380 cm<sup>−

1</sup>, about 2690 cm<sup>−

1</sup>, about 2730 cm<sup>−

1</sup>, about 2830 cm<sup>−

1</sup>, about 2850 cm<sup>−

1</sup>, about 2870 cm<sup>−

1</sup>, about 2930 cm<sup>−

1</sup>, about 2950 cm<sup>−

1</sup>, about 4160 cm<sup>−

1</sup>, about 4164 cm<sup>−

1</sup>, about 4250 cm<sup>−

1</sup>, about 4256 cm<sup>−

1</sup>, about 4300 cm<sup>−

1</sup>, about 4330 cm<sup>−

1</sup>, about 4350 cm<sup>−

1</sup>, about 4385 cm<sup>−

1</sup>, about 5690 cm<sup>−

1</sup>, about 5800 cm<sup>−

1</sup>, about 5870 cm<sup>−

1</sup> and/or about 5890 cm<sup>−

1</sup>.

24. A method according to claim 23, wherein step (i) and step (v) comprise subjecting the sample to IR radiation of about 2730 cm⁻

1.

25. A method according to claim 23, wherein step (i) and step (v) comprise subjecting the sample to IR radiation in a combination of two or more regions.

26. A method according to claim 18, wherein the multivariate chemometric technique of step (iii) comprises a partial least squares (PLS) analysis.

27. A method according to claim 18, wherein steps (iii) and step (iv) utilise portions of the IR spectrum comprising 4000 cm⁻

1 to 4600 cm<sup>−

1</sup>, 4000 cm<sup>−

1</sup> to 4800 cm<sup>−

1</sup>, 4300 cm<sup>−

1</sup> to 4560 cm<sup>−

1</sup>, 5150 cm<sup>−

1</sup> to 5250 cm<sup>−

1</sup>, 5700 cm<sup>−

1</sup> to 5900 cm<sup>−

1</sup>, 2680 cm<sup>−

1</sup> to 2730 cm<sup>−

1</sup>, 2820 cm<sup>−

1</sup> to 2940 cm<sup>−

1</sup>, 2850 cm<sup>−

1</sup> to 2950 cm<sup>−

1</sup>, 2850 cm<sup>−

1</sup> to 2975 cm<sup>−

1</sup> and/or 2800 cm<sup>−

1</sup> to 2950 cm<sup>−

1</sup>.

28. A method according to claim 18, wherein the step of generating a predictive model for hydrocarbon concentration based on the training data set comprises:

(a) separately subjecting one or more further sample(s) of known hydrocarbon concentration to infrared (IR) radiation;

(b) separately detecting an IR signal from the further sample(s); and

(c) calibrating the predictive model using the IR signal of the further sample(s).

29. A method for generating a model to selectively predict hydrocarbon concentration in a sample of unknown hydrocarbon concentration (the unknown sample), said method comprising the steps of:

(i) separately subjecting two or more samples of known hydrocarbon concentration to infrared (IR) radiation, at least two of the samples having different hydrocarbon concentrations;

(ii) separately detecting an IR signal from the samples of known hydrocarbon concentration;

(iii) analysing the IR signals using a multivariate chemometric technique to produce a training data set;

(iv) generating a predictive model for hydrocarbon concentration based on the training data set;

(v) separately subjecting one or more further sample(s) of known hydrocarbon concentration to infrared (IR) radiation;

(vi) separately detecting an IR signal from the further sample(s) of known hydrocarbon concentration; and

thereafter

(vii) calibrating the predictive model using the IR signal of the further sample(s) of known hydrocarbon concentration;

wherein application of the predictive model to the unknown sample selectively predicts hydrocarbon concentration therein.

30. A method according to claim 29, wherein the multivariate chemometric technique of step (iii) comprises a partial least square (PLS) analysis.

31. A method according to claim 29, wherein step (iv) comprises an internal calibration step performed by cross-validation of the sample data.

32. A method according to claim 29, wherein step (vii) comprises adding a further sample of known hydrocarbon concentration to the two or more samples of known hydrocarbon concentration of step (i) and repeating steps (i) to (iv) for all of the samples of known hydrocarbon concentration to generate a calibrated predictive model for hydrocarbon concentration.

33. A software program for selectively predicting hydrocarbon concentration from inputted IR signal data of an unknown sample, wherein said program performs a calculation comprising:

(i) generating a predictive model for hydrocarbon concentration by applying a multivariate chemometric technique to inputted IR signal data from two or more samples of known hydrocarbon concentration wherein at least two of the samples have different hydrocarbon concentrations to produce a training data set, and calibrating the predictive model using inputted IR signal data of one or more further sample(s) of known hydrocarbon concentration; and

thereafter

(ii) applying the predictive model to the inputted IR signal data of the unknown sample to provide a selective prediction of hydrocarbon concentration.