Principal Component Analysis Hotelling

The ideas behind principal component analysis were first described by Pearson [36] more than 90 years ago. The field of application at that time was in psychology. The method was further developed by Hotelling [37] in the thirties and is basically... 

The mathematics of what is described above is equivalent to principal component analysis. The ideas of principal component analysis (PCA) go back to Beltrami [1873] and Pearson [1901], They tried to describe the structural part of data sets by lines and planes of best fit in multivariate space. The PCA method was introduced in a rudimentary version by Fisher and Mackenzie [1923], The name principal component analysis was introduced by Hotelling [1933], An early calculation algorithm is given by Muntz [1913], More details can be found in the literature [Jackson 1991, Jolliffe 1986, Stewart 1993],... 

Modern NIR technology relies heavily on the computer (and the microprocessor in particular), not only for its ability to control and acquire data from the instrument, but to facilitate calibration and data analysis. The foundations of data analysis were laid down in the 1930s. Work on the diffuse scattering of light in both transmission and reflection, by Kubelka and Munk [14] in 1931, opened the door to NIR measurements on solids. In 1933, Hotelling [15] wrote a classic paper on principal components analysis (PCA), and Mahalanobis formulated a mathematical approach for representing data clustering and separation in multidimensional space. 

H. Hotelling, Analysis of a complex of statistical variables into principal components. J. Educ. 

Hotelling, H., Analysis of a Complex of Statistical Variables into Principal Components /. Educ. 

Horchner, U. and Kalivas, J. H. (1995) Further investigation on a comparative-study of simulated annealing and genetic algorithm for wavelength selection. Analytica Chimica Acta, 311,1-13. Hotelling, H. (1933) Analysis of a complex of statistical variables into principal components. 

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