Non negative matrix factorisation
K-means factorisation
Fuzzy c-means factorisation
Discriminant Analysis (DA)
Combinations of the foregoing analysis approaches can also be used, such as PCA-LDA, PCA-MMC, PLS-LDA, etc.
Analysing the sample spectra can comprise unsupervised analysis for dimensionality reduction followed by supervised analysis for classification.
By way of example, a number of different analysis techniques will now be described in more detail.
Multivariate Analysis—Developing a Model for Classification
By way of example, a method of building a classification model using multivariate analysis of plural reference sample spectra will now be described.
FIG. 15 shows a method 1500 of building a classification model using multivariate analysis. In this example, the method comprises a step 1502 of obtaining plural sets of intensity values for reference sample spectra. The method then comprises a step 1504 of unsupervised principal component analysis (PCA) followed by a step 1506 of supervised linear discriminant analysis (LDA). This approach may be referred to herein as PCA-LDA. Other multivariate analysis approaches may be used, such as PCA-MMC. The PCA-LDA model is then output, for example to storage, in step 1508.
