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Two new models have been recently introduced for studying the remaining rotational ambiguity in the bilinear decomposition of matrix data. One of the models is N‐BANDS, which yields two extreme profiles per sample component, corresponding to maximum or minimum signal contribution function or relative component area under its concentration profile. It is highly useful for computing the relative root mean square error due to rotational ambiguity in estimated analyte concentrations (RMSERA), which numerically quantifies the impact of the phenomenon in terms of prediction uncertainty. Since N‐BANDS successfully consider the presence of instrumental noise in the data, it is extremely useful for the analysis of real data sets. The other model is SW‐N‐BANDS, which is similar to N‐BANDS, but is applied in a sensor wise manner, that is, computing the maximum and minimum intensity value at each sensor. It provides the boundaries of the full set of feasible profiles, and helps to better understand the behavior of a given component under the application of several constraints. Both models are described in light of both simulations and experimental data, illustrating their main characteristics of importance to analytical chemistry studies. The new N‐BANDS and SW‐N‐BANDS are described for estimating and interpreting rotational ambiguity effects in analytical chemistry. [ABSTRACT FROM AUTHOR]

A new characteristic term, data point importance (DPI), is introduced in order to quantify the information ranking in multivariate data. DPI defines an easily calculable value corresponding to each row or column of data matrix to reflect its impact for keeping the pattern of the data structure. Usually, a lot of data points have DPIs equal or very close to zero so that they do not carry on useful information about keeping the data pattern. DPI values for some of the data points are significant, and they have been sorted regarding to their importance values. The applicability of the proposed procedure is tested for three typical examples of simulated and experimental data including hyperspectral image data. The possible incorporation of the DPI values for more informative score plots is suggested. [ABSTRACT FROM AUTHOR]