Selecting spectral elements and components for optical analysis systems

1. A method of selecting spectral elements and system components for a multivariate optical analysis system, the method comprising:

(a) providing spectral calibration data for a sample of interest;

(b) identifying a plurality of different combinations of system components selected from the group consisting of detectors, light sources, spectral elements, and optical components;

(c) obtaining modeling performance of a pilot system with one of the combinations of system components;

(d) determining a prediction accuracy of the pilot system utilized in step (c);

(e) determining a signal-to-noise ratio of the pilot system utilized in step (c);

(f) repeating steps (c), (d) and (e) for each of the plurality of combinations identified in step (b); and

(g) selecting optimal system components from among the plurality of combinations of system components based on optimal signal-to-noise ratio.

2. The method as in claim 1 , wherein the sample of interest is one of a chemical mixture, a chemical compound or a chemical liquid solution.

3. The method as in claim 1 , wherein the performance is modeled using a multivariate optical analysis.

4. The method as in claim 1 , wherein the performance is modeled by designing a multivariate optical element for the sample of interest and utilizing the multivariate optical element as a system component in step (b).

5. The method as in claim 4 , further comprising generating design data related to the multivariate optical element and combining the spectral calibration data and multivariate optical element design data.

6. The method as in claim 5 , further comprising calculating a standard error of calibration and optimizing within a limitation of the multivariate optical element to determine optimal spectral elements for the sample of interest.

7. The method as in claim 5 , further comprising calculating a standard error of prediction and optimizing within a limitation of the multivariate optical element to determine optimal spectral elements for the sample of interest.

8. The method as in claim 1 , wherein the performance is an overall system performance or a multivariate optical element performance.

9. The method as in claim 1 , wherein the system components include a plurality of detectors and wherein the optimal signal-to-noise ratio is determined by evaluating expected signal levels and using a noise equivalent power of the detectors as a noise level.

10. The method as in claim 1 , wherein the system components include a plurality of detectors and wherein the optimal signal-to-noise ratio is determined by evaluating an expected signal level and using photon-limited noise where photon noise associated with the signal level is sufficiently high to overwhelm detector noise.

11. The method as in claim 1 , further comprising ranking the system components relative to each other to select an optimal system.

12. The method as in claim 11 , further comprising manufacturing the optimal system.

13. A method of selecting spectral elements and system components for a multivariate optical analysis system, the method comprising:

(a) providing spectral calibration data for a sample of interest;

(b) identifying a plurality of different combinations of system components selected from the group consisting of detectors, light sources, spectral elements, and optical components;

(c) obtaining modeling performance of a pilot system with one of the combinations of system components;

(d) determining a prediction accuracy of the pilot system utilized in step (c);

(e) determining measurement characteristics of the pilot system utilized in step (c);

(f) repeating steps (c), (d) and (e) for each of the plurality of combinations identified in step (b); and

(g) selecting optimal system components from among the plurality of combinations of system components based on optimal measurement characteristics.

14. The method as in claim 13 , wherein determining optimal characteristics includes determining a prediction accuracy of the pilot system.

15. The method as in claim 13 , wherein determining optimal characteristics includes determining an optimal signal-to-noise ratio of the pilot system.

16. A method of selecting spectral elements and system components for a multivariate optical analysis system, the method comprising:

(a) providing spectral calibration data for a sample of interest;

(b) identifying a plurality of different combinations of system components selected from the group consisting of detectors, light source, spectral elements, and optical components;

(c) obtaining modeling performance of a pilot system with one of the combinations of system components using a multivariate optical analysis;

(d) determining a prediction accuracy of the pilot system utilized in step (c);

(e) determining measurement characteristics of the pilot system utilized in step (c);

(f) repeating steps (c), (d) and (e) for each of the plurality of combinations identified in step (b);

(g) selecting optimal system components from among the plurality of combinations of system components based on optimal measurement characteristics; and

(h) manufacturing a multivariate optical analysis system utilizing the selected optimal system components.

17. The method as in claim 16 , wherein determining optimal characteristics includes determining a prediction accuracy of the pilot system.

18. The method as in claim 16 , wherein determining optimal characteristics includes determining an optimal signal-to-noise ratio of the pilot system.

19. The method as in claim 16 , wherein the system components are selected from the group consisting of a plurality of detectors, a plurality of light sources, a plurality of spectral elements, a plurality of optical components and combinations thereof.