Portable apparatus for liquid chemical characterization

Patent metadata

Field	Value
Publication number	US-10697953-B2
Application number	US-201816165472-A
Country	US
Kind code	B2
Filing date	Oct 19, 2018
Priority date	Jun 18, 2014
Publication date	Jun 30, 2020
Grant date	Jun 30, 2020

Abstract

An apparatus or method determines a salinity or metal content a liquid sample by scanning the liquid sample using a PXRF spectrometer, receiving a PXRF spectra from the PXRF spectrometer, baseline correcting and smoothing the received PXRF spectra, extracting a Kα emission line of one or more elements from the baseline corrected and smoothed PXRF spectra using only one beam from the PXRF spectrometer, determining the salinity or the metal content of the liquid sample using the one or more processors and a predictive model that relates the Kα emission line of the one or more elements to the salinity or the metal content of the liquid sample, and providing the salinity or the metal content of the liquid sample to the one or more input/output interfaces.

First claim

What is claimed is:

1. A computerized method for determining a salinity of a liquid sample comprising:
providing a x-ray fluorescence (PXRF) spectrometer, a probe connected to the PXRF spectrometer, one or more processors communicably coupled to the PXRF spectrometer, and one or more input/output interfaces communicably coupled to the one or more processors;
scanning the liquid sample using the PXRF spectrometer;
receiving a PXRF spectra from the PXRF spectrometer;
baseline correcting and smoothing the received PXRF spectra;
extracting a Kα emission line of one or more elements from the baseline corrected and smoothed PXRF spectra using only one beam from the PXRF spectrometer;
determining the salinity of the liquid sample using the one or more processors and a predictive model that relates the Kα emission line of the one or more elements to the salinity of the liquid sample; and
providing the salinity of the liquid sample to the one or more input/output interfaces.

2. The method as recited in claim 1 , further comprising selecting the one or more elements from a list of elements detectable by the PXRF spectrometer.

3. The method as recited in claim 2 , wherein the selected elements are one or more of Cl, K and Ca.

4. The method as recited in claim 1 , wherein the predictive model uses a partial least squares regression (PLSR) multivariate algorithm or a support vector regression (SVR) multivariate algorithm.

5. The method as recited in claim 1 , wherein the predictive model relates the Kα emission line of the one or more elements to the salinity of the liquid sample by:
calculating a full width at half maximum (FWHM) and a maximum height (H max ) of each element peak using the Kα emission line for the element; and
using the calculated FWHM and H max for each element peak to predict the salinity of the liquid sample.

6. The method as recited in claim 1 , further comprising placing the probe in contact with or proximate to the liquid sample.

7. The method as recited in claim 1 , further comprising calibrating the predictive model.

8. The method as recited in claim 1 , further comprising configuring the PXRF spectrometer to detect the salinity of the liquid sample.

9. The method as recited in claim 1 , wherein the scanning, receiving, baseline correcting and smoothing, extracting, determining and providing steps are performed in situ.

10. The method as recited in claim 1 , further comprising determining a geographic location of the liquid sample using a space-based satellite navigation system.

11. The method as recited in claim 1 , further comprising determining an elevation of the liquid sample.

12. The method as recited in claim 1 , wherein the scanning, receiving, baseline correcting and smoothing, extracting, determining and providing steps are performed on site proximate to where the liquid sample was taken.

13. The method as recited in claim 1 , wherein the x-ray fluorescence (PXRF) spectrometer, the probe, the one or more processors, and the one or more input/output interfaces are integrated into a portable device.

14. An apparatus comprising:
a probe;
a x-ray fluorescence (PXRF) spectrometer connected to the probe;
one or more processors communicably coupled to the PXRF spectrometer;
one or more input/output interfaces communicably coupled to the one or more processors; and
the one or more processors scan the liquid sample using the PXRF spectrometer, receiving a PXRF spectra from the PXRF spectrometer, baseline correct and smooth the received PXRF spectra, extract a Kα emission line of one or more elements from the baseline corrected and smoothed PXRF spectra using only one beam from the PXRF spectrometer, determine the salinity of the liquid sample using a predictive model that relates the Kα emission line of the one or more elements to the salinity of the liquid sample, and provide the one or more properties of the liquid sample to the one or more input/output interfaces.

15. The apparatus as recited in claim 14 , wherein the one or more processors further select the one or more elements from a list of elements detectable by the PXRF spectrometer.

16. The apparatus as recited in claim 15 , wherein the selected elements are one or more of Cl, K and Ca.

17. The apparatus as recited in claim 14 , wherein the predictive model uses a partial least squares regression (PLSR) multivariate algorithm or a support vector regression (SVR) multivariate algorithm.

18. The apparatus as recited in claim 14 , wherein the predictive model relates the Kα emission line of the one or more elements to the salinity of the liquid sample by:
calculating a full width at half maximum (FWHM) and a maximum height (H max ) of each element peak using the Kα emission line for the element; and
using the calculated FWHM and H max for each element peak to predict the salinity of the liquid sample.

19. The apparatus as recited in claim 14 , wherein the one or more processors further calibrate the predictive model.

20. The apparatus as recited in claim 14 , wherein the one or more processors configure the PXRF spectrometer to detect the salinity of the liquid sample.

21. The apparatus as recited in claim 14 , wherein the one or more processors perform the scanning, receiving, baseline correcting and smoothing, extracting, determining and providing steps in situ.

22. The apparatus as recited in claim 14 , wherein the one or more processors further determine a geographic location of the liquid sample using a space-based satellite navigation system.

23. The apparatus as recited in claim 14 , wherein the one or more processors further determine an elevation of the liquid sample.

24. The apparatus as recited in claim 14 , wherein the one or more input/output interfaces comprise a display, a data storage, a printer or a communications interface.

25. The apparatus as recited in claim 14 , wherein the apparatus is portable.

26. The apparatus as recited in claim 14 , wherein are apparatus is used on site proximate to where the liquid sample was taken.

27. A computer program embodied on a non-transitory computer readable medium that causes one or more processors to:
scan a liquid sample using a PXRF spectrometer;
receive a PXRF spectra from the PXRF spectrometer;
baseline correct and smooth the received PXRF spectra;
extract a Kα emission line of one or more elements from the baseline corrected and smoothed PXRF spectra using only one beam from the PXRF spectrometer;
determine a salinity of the liquid sample using the one or more processors and a predictive model that relates the Kα emission line of the one or more elements to the salinity of the liquid sample; and
provide the salinity of the liquid sample to one or more input/output interfaces.

28. The computer program as recited in claim 27 , wherein the one or more elements are selected from a list of elements detectable by the PXRF spectrometer.

29. The computer program as recited in claim 27 , wherein the selected elements are one or more of Cl, K and Ca.

30. The computer program as recited in claim 27 , wherein the predictive model uses a partial least squares regression (PLSR) multivariate algorithm or a support vector regression (SVR) multivariate algorithm.

31. The computer program as recited in claim 27 , wherein the predictive model relates the

Assignees

• Univ Texas Tech System

Inventors

• Weindorf David
• Pearson Delaina
• Chakraborty Somsubhra

Classifications

• G01N33/246 Primary

  for water content · CPC title

• G01N2223/33

  scanning, i.e. relative motion for measurement of successive object-parts · CPC title

• G01N2223/304

  electric circuits, signal processing · CPC title

• G01N2223/076

  X-ray fluorescence · CPC title

• G01J3/4406

  Fluorescence spectrometry · CPC title