X-ray or gamma ray systems or devices – Specific application – Diffraction – reflection – or scattering analysis
Reexamination Certificate
1999-11-18
2001-12-04
Church, Craig E. (Department: 2882)
X-ray or gamma ray systems or devices
Specific application
Diffraction, reflection, or scattering analysis
C378S071000
Reexamination Certificate
active
06327334
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to rapidly screening multiple X-ray powder diffraction patterns, such as those generated through combinatorial chemistry. More particularly, the invention is directed toward measuring X-ray powder diffraction patterns of a set of samples, factoring the patterns using a suitable statistical technique into a small number of discrete components or factors, determining the scores corresponding to the factors for each X-ray powder diffraction pattern, and plotting the scores. The graphs of the scores are then inspected for clusters of similar materials, outliers, and trends representing transitions between material forms.
BACKGROUND OF THE INVENTION
Combinatorial chemistry is being increasingly used in the formation of new compounds. Numerous different compounds may be formed simultaneously, and what used to take days, or weeks, may now be accomplished in minutes or hours. Along with the rapid synthesis of new compounds, however, comes the task of identifying the large volume of newly synthesized compounds. For many years now, the X-ray powder diffraction analytical technique has been a favorite among chemists for identifying the structure of new compounds. However, the overall identification process may be time consuming, with each X-ray powder diffraction pattern being compared to a large number of known patterns in a library. Pattern recognition or “search and match” computer programs such as Jade 5.0, available from Materials Data Inc., have helped to more efficiently compare an unknown sample X-ray diffraction pattern to those in a library of known patterns, but the sheer volume of X-ray diffraction patterns being generated in a combinatorial chemistry application is likely to overwhelm the standard historical procedure.
This application focuses on more efficiently managing a large number of X-ray powder diffraction patterns through the use of the statistical tool of principal component analysis. Using principal component analysis allows for each X-ray powder diffraction pattern to be reduced to a set of scores which can be plotted on a 2- or more dimensional plot. A great deal of information is readily apparent to a chemist versed in the analysis of X-ray powder diffraction through inspection of the resulting plot. For example, X-ray powder diffraction patterns that are highly likely to correspond to the same compound or structure can be identified by the proximity of their scores in a cluster, thereby reducing the overall number of X-ray powder diffraction patterns that must be interpreted by comparison to libraries of known X-ray powder diffraction patterns using, for example, search and match-type software programs. Inspection of the scores plot may also indicate outliers corresponding to X-ray powder diffraction patterns that exhibit unusual characteristics as compared to the overall set of samples. A chemist may then focus attention on the X-ray powder diffraction patterns most likely to be a desired new compound without spending resources on samples represented by clusters of scores that are likely to be multiple samples of the same structure. The plot may thus reveal that of the multiple X-ray powder diffraction patterns, only a few should be investigated further. The time and labor savings to a chemist may be enormous.
Principal component analysis has been applied to other analytical data such as near infrared spectroscopy; see U.S. Pat. No. 5,862,060, for process control applications. Principal component analysis has also been used to determine the concentration of controlled substances such as heroin and cocaine when present in a mixture with other known compounds; see, Minami, Y.; Miyazawa, T.; Nakajima, K.; Hida, H.; X-sen
Bunseki no Shinpo
, 27 (1996) 107-115, and Mitsui, T.; Okuyama, S.; Fujimura, Y.
Analytical Sciences
, 7 (1991) 941-945. Haju, M. E.; Minkkinen, P.; Valkonen, J.;
Chemometrics and intelligent Laboratory Systems
, 23 (1994) 341-350 disclosed explaining and predicting ammonium nitrate solid phase transition paths between IV, III, and 11 on the basis of X-ray powder diffraction patterns and differential scanning calorimetry data by applying partial least squares regression and principal component analysis. The present invention, however, uses principal component analysis in conjunction with multiple X-ray powder diffraction patterns to gain a great amount of information on potentially widely varied samples. That is to say, the present invention is intended to be a; discovery method applied to a very large number of samples where any number of known and unknown materials may be present within the sample set. It therefore differs from the prior art which was limited to the case where all the materials present in the sample set were known a priori, and, moreover, the number of possible materials present was very limited.
SUMMARY OF THE INVENTION
The goal of the invention is to provide a method of rapidly screening multiple X-ray powder diffraction patterns. This is accomplished by reducing the large number, sometimes greater than one thousand, of angle-intensity data pairs present in each X-ray powder diffraction pattern down to a few, typically two to five, numbers called scores, which are representative of the pattern and which can be easily plotted and visualized for screening purposes. The invention involves first obtaining an X-ray powder diffraction pattern of each member in a set of samples. Principal Component Analysis (PCA) is then used to derive a number of factors representative of this data set. In conjunction with these factors, PCA simultaneously generates a corresponding set of scores assigned to each sample with each score corresponding to one of the derived factors, and together representing each pattern in the sample set. The scores of each factor are determined for each X-ray powder diffraction pattern of the sample set, and the scores are plotted in 2- or more dimensional space. The resulting plot may be visually inspected or statistically analyzed to identify clusters, trends, or outliers, which may represent new material, or possibly faulty data.
In a more specific embodiment of the invention, a subset of samples and the corresponding X-ray powder diffraction patterns may be selected. It is preferred that the subset of samples form a cluster in the first plot described above. Such clustering may be identified visually or by using various statistical techniques to define the clusters. Again, a number of factors are determined by principal component analysis which can be used in combination with scores of the factors to express each X-ray powder diffraction pattern in the subset of samples. The scores of each factor are again determined for each X-ray powder diffraction pattern of the subset of samples, and the scores are plotted in 2- or more dimensional space. As before, the resulting plot may be visually inspected or statistically analyzed to identify clusters, trends, or outliers. The overall method may be repeated where each iteration uses a selected number of the previous subset, thus using progressively smaller subsets, until the resulting plots show random scatter or there is another reason such as chemical knowledge of the sample set, to stop generating sub-clusters.
REFERENCES:
patent: 5481476 (1996-01-01), Windig
patent: 5862060 (1999-01-01), Murray, Jr.
Minami, Y.; Miyazawa, T.; Nakajima, K.; Hida, H.; X-sen Bunseki no Shinpo, 27 (1996) 107-115. (Abstract Only).
Mitsui, T.; Okuyama, S.; Fujimura, Y.Analytical Sciences,7 (1991) 941-945.
Harju, M. E.; Minkkinen, P.; Valkonen, J.;Chemometrics and Intelligent Laboratory Systems,23 (1994) 341-350.
Bratu Cheryl M.
Lewis Gregory J.
Murray, Jr. Richard C.
Church Craig E.
Maas Maryann
Molinaro Frank S.
Tolomei John G.
UOP LLC
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