Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations

Coïc, Laureen; Sacré, Pierre-Yves; Dispas, Amandine; Bleye, Charlotte De; Fillet, Marianne; Ruckebusch, Cyril; Hubert, Philippe; Ziémons, Eric

doi:10.1016/j.aca.2021.338361

Cited by 23 publications

(23 citation statements)

References 28 publications

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“…We believe this to be the formal explanation of the effect observed in the practical studies reported in [3,4].…”

Section: A Possible Way Out: Information Selectionmentioning

confidence: 56%

“…ALS solution could already be achieved (see Figure 2b). 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].…”

Section: A Possible Way Out: Information Selectionmentioning

confidence: 99%

“…leverage, least squares, regression, curve resolution compounds observable only in correspondence of few (pure) pixels of a hyperspectral image [3,4]. In order to fully grasp the main reasons behind this particular issue, imagine a specimen composed by three different compounds (A, B and C) is actually to be characterised through a hyperspectral imaging experiment (see, for example, Figures 1a and 1b).…”

Section: Introductionmentioning

confidence: 99%

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On the 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: 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.

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“…We believe this to be the formal explanation of the effect observed in the practical studies reported in [3,4].…”

Section: A Possible Way Out: Information Selectionmentioning

confidence: 56%

Section: A Possible Way Out: Information Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Vitale

Ruckebusch

2022

Preprint

Self Cite

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“…Compared with conventional bidimensional images obtained by light microscopy, the hyperspectral imaging system provides superior resolution and sensitivity as well as the capability to simultaneously process multidimensional images [ 30 , 31 ]. HSI was initially applied to fields such as remote sensing [ 32 ], archeology [ 33 , 34 ], and drug identification [ 35 , 36 ]. In recent years, the extension of HSI to the biomedical field has started to achieve promising results.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

Wei

Yang

et al. 2021

BMC Nephrol

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Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

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“…In analytical chemistry domain, spatially distributed spectral properties of complex analytical systems are widely explored with spectral imaging. For example, detection of microplastics [1], analysis of fish [2,3] and meats [4,5], pharmaceutical formulations [6,7], and many more [8][9][10][11]. Spectral imaging captures information in two modes, i.e., imaging as well as spectroscopy, where the spatial information captured by imaging carries the context information of the imaged scene, while the spectral information captures the physicochemical properties of the samples [8,12].…”

Section: Introductionmentioning

confidence: 99%

GAN meets chemometrics: Segmenting spectral images with pixel2pixel image translation with conditional generative adversarial networks

Mishra

Herrmann

2021

Chemometrics and Intelligent Laboratory Systems

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In analytical chemistry, spectral imaging of complex analytical systems is commonly performed. A major task in spectral imaging analysis is to extract signals related to important analytes present in the imaged scene. Hence, the first task of spectral image analysis is to perform an image segmentation to extract the relevant signals to analyze. However, in the chemometric domain, the traditional image segmentation methods are limited either to threshold-based or pixel-wise classification, therefore, no approache uses the contextual information present in the imaging scene. This study presents for the first time a pixel2pixel (p2p) image translation using conditional generative adversarial networks (cGAN) for the segmentation of spectral images. The p2p cGAN trains two neural models simultaneously where one model (generator) learns to segment and the other model learns to detect if the segmentation performed by the generator model is correct. During the process of generation and detection, the model automatically learns to segment the spectral images accurately. As an application of the p2p cGAN, a case of segmenting visible and near-infrared spectral images of plants was presented. Furthermore, as a comparison, threshold-based and pixel-wise image classification based on partial least-square discriminant analysis were also presented. The results showed that the p2p cGAN based image translation performed the best segmentation task with an intersection over union score of 0.95 AE 0.04. The advanced new DL based image processing approaches can complement spectral image processing.

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Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations

Cited by 23 publications

References 28 publications

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

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

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

GAN meets chemometrics: Segmenting spectral images with pixel2pixel image translation with conditional generative adversarial networks

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