MULTIVARIATE MULTIPLE MATRIX ANALYSIS OF ANALYTICAL AND SENSORY DATA

• US 20090055140A1
• Filed: 08/22/2007
• Published: 02/26/2009
• Est. Priority Date: 08/22/2007
• Status: Abandoned Application

• Current Assignee: MKS Instruments Incorporated
• Original Assignee: MKS Instruments Incorporated
• Inventors: Kettaneh, Nouna, Wold, Svante Bjarne
• Application Number:
  US11/843,435
• Publication Number:
  US 20090055140A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A system and method is provided for predicting consumer behavior for selected products. The method includes providing a first matrix associated with N products evaluated by a plurality of consumers, providing a second matrix associated with the N products characterized by at least one of an analytical profile or an evaluation by a plurality of experts and correlating the first matrix to the second or/and the third matrix to produce a relationship model.

Claims

1. A method of predicting consumer behavior in selected products, comprising:

providing a first matrix associated with N products evaluated by a plurality of consumers;

providing a second matrix associated with the N products characterized by at least one of an analytical profile or an evaluation by a plurality of experts; and

correlating the first matrix to the second matrix to produce a relationship model.

2. The method of claim 1, further comprising compressing the first and second matrix to the same dimensionality.

3. The method of claim 1, further comprising displaying a score plot of the relationship model.

4. The method of claim 3, wherein the score plot includes a strength of association and correlation between the first matrix and the second matrix.

5. The method of claim 1, further comprising predicting consumer responses for new products using the relationship model.

6. The method of claim 5, further comprising displaying the predicted response values with levels of confidence.

7. The method of claim 5, further comprising displaying a measure of reliability of the new products data as characterized by the second matrix.

8. The method of claim 1, further comprising:

provide a third matrix associated with the N products characterized by either an analytical profile or an evaluated by an expert sensory panel not chosen in the building the second matrix; and

correlating the first matrix to the third matrix to produce a relationship model.

9. The method of claim 8, further comprising correlating any two matrices to each other.

10. The method of claim 1, wherein each matrix is preprocessed by at least one preprocessing element to transform the data into a suitable form for analysis.

11. The method of claim 10, wherein preprocessing elements include scaling of data, mean-centering, transformation and expansion, advanced scaling, and data correction and compression.

12. The method of claim 10, wherein building the first matrix includes:

analyzing the preprocessed data using cross-validation to determine a number of significant components;

inspecting the resulting model for outliers; and

removing the outliers from the data before recomputing the model.

13. The method of claim 12, further including displaying the data to show indications of groups, trends, and outliers.

14. The method of claim 10, wherein building the first matrix includes:

analyzing the preprocessed data to determine a liking/non-liking model;

including cross-validating the liking/non-liking model to determine a number of significant components; and

dividing liking/non-liking model into liker data and non-liker data based on the number of significant components.

15. The method of claim 14, further includes computing an average value for each liker data and non-liker product and consumer response.

16. A system for predicting consumer behavior in selected products, comprising:

a first matrix module for providing a first matrix associated with N products evaluated by a plurality of consumers;

a second matrix module for providing a second matrix associated with the N products characterized by at least one of an analytical profile or an evaluation by a plurality of experts; and

a correlation module for correlating the first matrix to the second matrix to produce a relationship model.

17. The system of claim 16, further comprising a display module for displaying a score plot of the relationship model.

18. The system of claim 17, wherein the score plot includes a strength of association and correlation between the first matrix and the second matrix.

19. The system of claim 16, further comprising a prediction module for predicting consumer responses for new products using the relationship model.

20. The system of claim 19, further comprising a display module for displaying the predicted responses with a level of confidence.

21. The system of claim 19, further comprising a display module for displaying a measure of reliability of the new products as characterized by the second matrix.

22. The system of claim 16, further comprising:

a third matrix module for building a third matrix associated with the N products characterized by either an analytical profile or an evaluated by an expert sensory panel not chosen in the building the second matrix, wherein the correlation module correlates the first matrix to the third matrix to produce a relationship model.

23. The system of claim 22, further comprising correlating any two matrices to each other.

24. The system of claim 16, further comprising a preprocessing module, wherein each matrix is preprocessed by at least one preprocessing element to transform the data into a suitable form for analysis.

25. The system of claim 24, wherein preprocessing elements include scaling of data, mean-centering, transformation and expansion, advanced scaling, and data correction and compression.

26. The system of claim 24, wherein building the first matrix includes:

an analysis module for analyzing the preprocessed data using cross-validation to determine a number of significant components;

an inspection module for inspecting the number of significant components for outliers; and

an outlier module for removing the outliers from the number of significant components.

27. The system of claim 26, further including a display module for displaying the data to show indications of groups, trends, and outliers.

28. The system of claim 24, wherein building the first matrix includes:

a liking module for analyzing the preprocessed data to determine a liking/non-liking model;

a cross-validation module for cross-validating the liking/non-liking model to determine a number of significant components; and

a dividing module for dividing liking/non-liking model into liker data and non-liker data based on the number of significant components.

29. The system of claim 28, wherein the dividing module further includes computing an average value for each liker data and non-liker data.

30. The system of claim 16, further comprising compressing the first matrix to a dimensionality comparable to the dimensionality of the second matrix.

31. A method of predicting consumer behavior in selected products, comprising:

means for providing a first matrix associated with N products evaluated by a plurality of consumers;

means for providing a second matrix associated with the N products characterized by at least one of an analytical profile or an evaluation by a plurality of experts; and

means for correlating the first matrix to the second matrix to produce a relationship model.

32. A computer readable medium having prediction software stored thereon that when executed on a computing device correlates matrix data to produce a predicted relationship model, comprising:

correlating a first matrix to a second matrix to produce a relationship model; and

displaying a score plot of the relationship model.