Principal component analysis regularity

Principal component analysis (PCA) is a statistical technique which, over the last decade, has become a regular tool for analysing chemical data [3-6]. If there is a relationship among any samples in a data set, the PCA will separate the samples into groups. 

In this study, tablets were stored in a hydrator for up to 168 h with tablets withdrawn at regular intervals. After removal from the hydrator, the tablets were weighed and NIR spectra collected prior to the HPLC analysis. Spectra of the intact tablets were collected on an InfraAlyzer 500 in the 1100 to 2500 nm region, using the double-reflecting sample apparatus described by Lodder and Hieftje [82]. The spectra were processed by principal component analysis, and the scores analyzed by the quantile-BEAST algorithm. 

The brown mussel Pemapema may be also found in tropical and sub-tropical regions of the Atlantic Ocean, as a native species. Thus, it is regularly used for biomonitoring studies in Brazil and other South American countries. Transplanted P pema was exposed to four sites of suspected PAHs, metal and municipal waste pollution (Pereira et al. 2010). At the end of every exposure period a number of enzyme activities were quantified. Principal Component Analysis (PCA) attributed some of the variations found to individual pollutants as far as PAHs were concerned, suppression of CAT, GPx and GR activities were linked with PAHs burden. It was apparent that other pollutants, probably of pharmaceutical origin, also contributed to the results. 

Friedman and Frank [75] have shown that SIMCA is similar in form to quadratic discriminant analysis. The maximum-likelihood estimate of the inverse of the covariance matrix, which conveys information about the size, shape, and orientation of the data cloud for each class, is replaced by a principal component estimate. Because of the success of SIMCA, statisticians have recently investigated methods other than maximum likelihood to estimate the inverse of the covariance matrix, e.g., regularized discriminant analysis [76], For this reason, SIMCA is often viewed as the first successful attempt by scientists to develop robust procedures for carrying out statistical discriminant analysis on data sets where maximum-likelihood estimates fail because there are more features than samples in the data set. 

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