Theory of error in factor analysis

Abstract

Hg-Ni OTTLE at this pH, showing that the glyoxylic acid wave is obscured by hydrogen evolution at this electrode. As a result of the extended negative range, glyoxylic acid could be analyzed by thin-layer coulometry and by thin-layer differential pulse voltammetry in the Hg-Au OTTLE (26, 27).Reduction of Metals. The utility of the Hg-Au minigrid for the reduction of metal ions to the elemental form is limited by the tendency of the gold which is dissolved in the mercury film to form intermetallic compounds with other metals (Cd, Mn, Sn, Zn) deposited in the mercury (28). For example, lead, which does not form an intermetallic compound with gold, gave a well-behaved, reversible cyclic voltammogram at the Hg-Au minigrid. By contrast, cadmium gave a good reduction wave, but the oxidation wave for Cd°was absent due to the formation of a Cd-Au intermetallic compound.

FTIR/PCA study of propanol in argon matrix: The initial stage of clustering and conformational transitions

“…These treatments are based e.g. on 2D correlation analysis (2DCOR) [22,23], mowing windows 2D concept (MW2D) [24,25], factor (FA) and principal component analysis (PCA) [26][27][28][29], etc.…”

“…3). The number of the statistically significant PCs was evaluated using the factor indicator function, which reaches a minimum when the correct number of factors (components) is employed [26,32]. It was found that statistically significant can be first five PCs.…”

Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image

“…eigenvalues of the covariance or correlation matrix. Methods in this group are GAP [32], [33], RAE (ratio of adjacent eigenvalues) [34], SORTE (second order statistic of the eigenvalues) [33], [32], RAESORTE [34], and EIF (empirical indicator function) [35], [36]. Former methods estimate mode-n rank from the unfolded tensor ( ) n X by searching for dominant eigenvalues of the Gramm matrix ( ) ( ) T n n X X according to some criterion, e.g.…”

Chemometrics in f-element speciation: a metrological challenge?

“…The first two singular vectors interpret almost 98% of the variance leaving a marginal difference between a possible third factor and the noise in the data. Not surprisingly, a larger number of methods have been developed to extract the likely number of factors from experimental data [e.g., 3,[25][26][27]. Given the narrow margin left for decision, it is always recommended to check whether the system can be reasonably interpreted with more or less species that suggested by whatever test criterion.…”