Perspective on essential information in multivariate curve resolution

Ruckebusch, Cyril; Vitale, Raffaele; Ghaffari, Mahdiyeh; Hugelier, Siewert; Omidikia, Nematollah

doi:10.1016/j.trac.2020.116044

Cited by 36 publications

(37 citation statements)

References 47 publications

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“…For a tentative exploration of the thermodynamic phenomena mentioned in Section 4.4 , the pathlength-corrected absorbance spectra, obtained by reconstructing and averaging the 42 motion-compensated hyperspectral video frames after EMSC and OTFP processing (see Figure 10A ), were decomposed by standard PCA and graphed in the scores plot in Figure 10B . This plot clearly highlights the occurrence of a two-phase transition process during wood drying affecting mainly the water bands of such NIR spectra (see the loadings in Figures 10C,D ) and characterised by 10 archetypal time instants (see the grey dots in Figure 10B )— Ruckebusch et al (2020) . Figures 11 , 12 provide an illustration of the distribution of the EMSC coefficients and the OTFP scores over the surface of the wood sample at three of these time instants.…”

Section: Resultsmentioning

confidence: 81%

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding

et al. 2022

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Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral videos usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed. Clearly, standard chemometric techniques do need to be somehow adapted or expanded to be capable of dealing with such massive amounts of information. In addition, hyperspectral video data are often affected by many different sources of variations in sample chemistry (for example, light absorption effects) and sample physics (light scattering effects) as well as by systematic errors (associated, e.g., to fluctuations in the behaviour of the light source and/or of the camera). Therefore, identifying, disentangling and interpreting all these distinct sources of information represents undoubtedly a challenging task. In view of all these aspects, the present work describes a multivariate hybrid modelling framework for the analysis of hyperspectral videos, which involves spatial, spectral and temporal parametrisations of both known and unknown chemical and physical phenomena underlying complex real-world systems. Such a framework encompasses three different computational steps: 1) motions ongoing within the inspected scene are estimated by optical flow analysis and compensated through IDLE modelling; 2) chemical variations are quantified and separated from physical variations by means of Extended Multiplicative Signal Correction (EMSC); 3) the resulting light scattering and light absorption data are subjected to the On-The-Fly Processing and summarised spectrally, spatially and over time. The developed methodology was here tested on a near-infrared hyperspectral video of a piece of wood undergoing drying. It led to a significant reduction of the size of the original measurements recorded and, at the same time, provided valuable information about systematic variations generated by the phenomena behind the monitored process.

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Section: Resultsmentioning

confidence: 81%

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding

et al. 2022

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“…This featured communication was conceived in the attempt of clarifying an aspect (that, at a first glance, might seem counterintuitive) related to the effect that the number of analysed data points can have on the quality and the reliability of the solutions that least squares-based unmixing approaches may provide: in MCR scenarios, enhancing the importance of extreme measurement observations (increasing directly or indirectly their respective leverage values) can aid such approaches in achieving more accurate outcomes. Here, such an enhancement was accomplished by data pruning and information selection, but alternative strategies like object weighting based on measures of essentiality for the sake of curve resolution are currently being explored [17,19,20]. The implications of these strategies on the uncertainty and stability of the final results are, of course, of interest and will be investigated in future research.…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, in most cases, random pixel selection might be a suboptimal approach when MCR-ALS is to be run for hyperspectral image analysis [3]. A more adequate strategy to identify the most relevant spectral pixels for MCR relies on the estimation of the convex hull of the aforementioned normalised projection scores cloud [16,17,18]. When convex hull-based pruning is performed before the application of MCR-ALS to the data of Figure 2a, strikingly, the considerable reduction of the number of data points (see Figure 2g) leads to a correct and reliable unmixing of A, B and C (see Figures 2h and 2i).…”

Section: A Possible Way Out: Information Selectionmentioning

confidence: 99%

On a black hole effect in bilinear curve resolution based on least squares

Vitale

Ruckebusch

2022

Preprint

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Least squares-based estimations lay behind most chemometric methodologies. Their properties, though, have been extensively studied mainly in the domain of regression, in relation to which the effect of well-known deleterious factors (like object leverage or data distributions deviating from ideal conditions) on the accuracy of the prediction of an external response variable have been thoroughly assessed. Conversely, much less attention has been paid to what these factors might yield in alternative scenarios, where least squares approaches are still utilised, yet the objectives of data modelling may be very different. As an example, one can think of multivariate curve resolution (MCR) problems which are usually addressed by means of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). In this respect, this article wants to offer a perspective on the basic principles of MCR-ALS from the regression point of view. In particular, the following critical aspects will be highlighted: i) in the presence of minor components, if the number of analysed data points is too large, the leverage of those that may be essential for a MCR-ALS resolution might become too low for guaranteeing its correctness and ii) in order to overcome this black hole effect and improve the accuracy of the MCR-ALS output, data pruning can be exploited. More in detail, this communication will provide a practical illustration of such aspects in the field of hyperspectral imaging where even single experimental runs may lead to the generation of massive amounts of spectral recordings.

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“…Nonetheless, in most cases, random pixel selection might be a suboptimal approach when MCR-ALS is to be run for hyperspectral image analysis [3]. A more adequate strategy to identify the most relevant spectral pixels for MCR relies on the estimation of the convex hull of the aforementioned normalised projection scores cloud [15,16,17]. When convex hull-based pruning is performed before the application of MCR-ALS to the data of Figure 1e, strikingly, the considerable reduction of the number of data points leads to a correct and reliable unmixing of A, B and C.…”

Section: A Possible Way Out: Information Selectionmentioning

confidence: 99%

On the black hole effect in bilinear curve resolution based on least squares

Vitale

Ruckebusch

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Least squares-based estimations lay behind most chemometric methodologies. Their properties, though, have been extensively studied mainly in the domain of regression, in relation to which the effect of well-known deleterious factors (like object leverage or data distributions deviating from ideal conditions) on the accuracy of the prediction of an external response variable have been thoroughly assessed. Conversely, much less attention has been paid to what these factors might yield in alternative scenarios, where least squares approaches are still utilised, yet the objectives of data modelling may be very different. As an example, one can think of multivariate curve resolution (MCR) problems which are usually addressed by means of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). In this respect, this article wants to offer a perspective on the basic principles of MCR-ALS from the regression point of view. In particular, the following critical aspects will be highlighted: in certain situations, i) if the number of analysed data points is too large, the leverage of those that may be essential for a MCR-ALS resolution might become too low for guaranteeing its correctness and ii) in order to overcome this black hole effect and improve the accuracy of the MCR-ALS output, data pruning - i.e., the reduction of the amount of observations of the investigated datasets - can be exploited. More in detail, this communication will provide a practical illustration of such aspects in the field of hyperspectral imaging where even single experimental runs may lead to the generation of massive amounts of spectral recordings.

show abstract

Perspective on essential information in multivariate curve resolution

Cited by 36 publications

References 47 publications

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding

On a black hole effect in bilinear curve resolution based on least squares

On the black hole effect in bilinear curve resolution based on least squares

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