Self-modeling curve resolution of multi-component vibrational spectroscopic data using automatic band-target entropy minimization (AutoBTEM)

Tan, Suat-Teng; Zhu, Haohao; Chew, Wee

doi:10.1016/j.aca.2009.02.054

Cited by 18 publications

(10 citation statements)

References 46 publications

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“…The right‐singular vectors v , or loadings, are then searched for interesting spectral features, whose relevant spectral channels are named band targets (BTs) in this context. Band targets may be identified by visual inspection, by SIMPLISMA (which finds the purest spectral variables), or by an algorithm that selects peaks that exceed a threshold based on the singular value and the estimated noise level . Next, a number of “significant” loading vectors v is determined, based on either their singular values, visual inspection, or by the loading in which the BT is identified; this number can even be different for each spectral reconstruction .…”

Section: Methodsmentioning

confidence: 99%

“…Band targets may be identified by visual inspection, by SIMPLISMA (which finds the purest spectral variables), or by an algorithm that selects peaks that exceed a threshold based on the singular value and the estimated noise level. 46 Next, a number of "significant" loading vectors v is determined, based on either their singular values, visual inspection, or by the loading in which the BT is identified; this number can even be different for each spectral reconstruction. 47 For all simulated data sets, 3 v vectors were utilized, a number equal to the components in all systems and also to the loadings used in the PCA-filtering step in MCR-ALS.…”

Section: Band-target Entropy Minimizationmentioning

confidence: 99%

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Systematic comparison and potential combination between multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM)

Bertinetto

Juan

2018

Journal of Chemometrics

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This work does a systematic comparative evaluation of 2 methods originating from different fields, both dedicated to the problem of curve resolution/unmixing: multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM). The MCR‐ALS factorizes the data matrix into spectral and concentration profiles that satisfy constraints expressing physicochemical knowledge on the analyzed system. The BTEM reconstructs the pure components' spectral profiles as linear combinations of singular vectors that minimize the spectral entropy and contain specific peaks. Both methods were applied to 40 simulated and one real data set. The simulated data were generated from real spectral and concentration profiles that include different types of spectroscopy (mass spectrometry, Raman, and UV‐visible), data structures (random mixtures, images, and reaction system), and noise levels; the real data set was a Raman image of kidney calculus. For most data sets, both methods yielded accurate solutions, with a correlation between reference and resolved profiles >0.99. However, MCR‐ALS (here used with nonnegativity constraint only) was affected by rotational ambiguity in the recovery of spectral profiles coming from systems with high correlation or overlap in the concentration direction, whereas BTEM tended to distort UV‐visible spectra, a kind of measurement far in nature from low entropy conditions. MCR‐ALS solutions were more stable than BTEM to the increase in noise level. This work also explores the possibility of combining the 2 methods by performing them in sequence. The results show that this combination can significantly improve the outcome as compared to either method applied alone.

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Section: Methodsmentioning

confidence: 99%

Section: Band-target Entropy Minimizationmentioning

confidence: 99%

Systematic comparison and potential combination between multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM)

Bertinetto

Juan

2018

Journal of Chemometrics

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“…These additional constraints helped to overcome the problem of rotational ambiguity, providing numerically unique solutions in the limit of high‐SNR data. Since then, several improvements and modifications have been implemented to automate the decomposition of mixtures composed of an unknown number of component species in addition to generating multiple spectral estimates without the need for band‐specific targets …”

Section: Introductionmentioning

confidence: 99%

“…Typically, the number chosen is system dependent, but usually one of two general strategies is applied for their determination. One is to retain a very large number of basis vectors, even arbitrarily large, the second is to select a relatively small fixed number for a given system type . To date, there has only been limited discussion of the rationale for selecting the precise number of factors, and no routine implementation of suitable statistical tests to aid appropriate factor compression exists—some authors have chosen the number visually .…”

Section: Introductionmentioning

confidence: 99%

“…Typical numerical weights of each of the nonentropy terms can be found in several papers . Extensions of the basic BTEM approach suggested in an early paper involve the use of the sum of absolute spectral derivatives (first, second, or fourth order) and the numerical integrated intensity of the spectral estimate in addition to the basic Shannon information entropy term or sum of higher order derivatives

\min F = \sum_{ν} (||, {italicds}_{ν}) + \sum_{ν} (||, {\overset{a}{true}}_{ν}) + P ((), {\overset{truê}{a}}_{1 \times v}, {\overset{truê}{C}}_{k \times 1}, {\overset{truê}{a}}_{1 \times v}^{\max}),

where

d s_{ν} = \frac{d^{n} {\overset{truê}{a}}_{ν}}{d ν^{n}}

and n is the order of differentiation.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive band target entropy minimization: Optimization for the decomposition of spatially offset Raman spectra of bone

Churchwell

Sowoidnich

Chan³

et al. 2019

J Raman Spectroscopy

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We report a novel variant of band target entropy minimization (BTEM), adaptive band target entropy minimization (A-BTEM), which offers an improved ability to accurately decompose mixed spectra obtained from complex multicomponent systems. Several key challenges have existed in the application of the basic BTEM approach to decompose spatially offset Raman spectra of bone underneath soft tissues and other multilayer systems demonstrating high collinearity between the spectra of individual components; these have included instabilities, signal mixing, and spectral artefacts that have precluded the reliable use of BTEM in such situations. By using automatic factor selection, an adaptive penalty function in addition to metaheuristic optimization strategies, we demonstrate that high-quality spectral reconstructions of underlying pure component spectra can be obtained with typical correlation coefficients >0.996. Furthermore, we ascertain the behaviour of A-BTEM with different input datasets, both synthetic and real, displaying varying signal-to-noise ratio, signal composition, and numbers of spectra. We thereby identify the multifarious parameter space in the application of A-BTEM and the quality of data required for the most accurate spectral estimates of pure component spectra. KEYWORDSband target entropy minimization, bone disease, multivariate curve resolution, self-modelling curve resolution, spatially offset Raman spectroscopy

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Surface and Bulk Approach to Time‐resolved Characterization of Heterogeneous Catalysts

et al. 2012

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The evolution of time‐resolved experiments, covering a multipurpose surface and bulk characterization of catalytic solids, during their preparation process and under reaction conditions, is presented. Particular emphasis is given to recent studies that attempt to unveil contemporary unresolved questions concerning the control of physicochemical properties of nanosolids, as well as the evolution of such solid materials under reactive atmospheres. To reach this goal, representative in situ and in operando works, using either modern (mostly synchrotron radiation based) approaches or multi‐technique methods (mainly, although not exclusively, X‐ray–vibrational coupling), and considering experiments in liquid and gas phase, as well as with amorphous, nanocrystalline and surface‐deposited materials, are reviewed.

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Self-modeling curve resolution of multi-component vibrational spectroscopic data using automatic band-target entropy minimization (AutoBTEM)

Cited by 18 publications

References 46 publications

Systematic comparison and potential combination between multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM)

Systematic comparison and potential combination between multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM)

Adaptive band target entropy minimization: Optimization for the decomposition of spatially offset Raman spectra of bone

Surface and Bulk Approach to Time‐resolved Characterization of Heterogeneous Catalysts

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