Noise effects on band boundaries in multivariate curve resolution of three-component systems

Olivieri, Alejandro C.; Sawall, Mathias; Neymeyr, Klaus; Tauler, Romà

doi:10.1016/j.chemolab.2022.104636

Cited by 2 publications

(5 citation statements)

References 24 publications

(43 reference statements)

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“…It can also be seen that the range of feasible noisy profiles is larger than those in the absence of noise, when comparing analogous panels from Figures 4 and 3. These results are in line with previous observations on the effect of noise on the range of feasible profiles 11,12 …”

Section: Resultssupporting

confidence: 93%

“…Although it can be applied to any number of components, the MCR-BANDS calculated extreme profiles do not enclose, in general, the full set of bilinear solutions. 21 In comparison with other methods, the N-BANDS and SW-N-BANDS models to be described below show the following interesting characteristics: (1) they handle the presence of instrumental noise in a more efficient manner, as was shown by extensive Monte Carlo noise addition validation studies, 11,12 and (2) they can in principle be applied to any number of components. Specifically, N-BANDS computer time requirements for multiple components are not serious.…”

Section: Rotational Ambiguitymentioning

confidence: 99%

“…These results are in line with previous observations on the effect of noise on the range of feasible profiles. 11,12 A compact way of visualizing the above results is by plotting the projections of all the individual SW-N-BANDS profiles onto the space spanned by the principal components of the data matrix. For three components, three scores are estimated, but scaling the second and third score after division by the first one leads to two variables (t 12 and t 13 ) allowing for a simplified presentation in two dimensions.…”

Section: Simulationsmentioning

confidence: 99%

“…It is important to notice that both methods succeed in considering the presence of instrumental noise, which is unavoidably present in all experimental measurements. 11,12 The present report is organized as follows. The two new methods will be explained in detail, highlighting their specific applications to analytical chemistry.…”

Section: Introductionmentioning

confidence: 99%

“…The former permits the estimation of the uncertainty in analyte concentration produced by rotational ambiguity, and the latter provides qualitative information for the correct interpretation of the component profiles retrieved by bilinear decomposition. It is important to notice that both methods succeed in considering the presence of instrumental noise, which is unavoidably present in all experimental measurements 11,12 …”

Section: Introductionmentioning

confidence: 99%

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Two new methods for the estimation and interpretation of the range of feasible profiles in multivariate curve resolution and their implications to analytical chemistry

Olivieri

2024

Journal of Chemometrics

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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.

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Section: Resultssupporting

confidence: 93%