SWiVIA – Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging

Oliveira, Rodrigo Rocha de; Juan, Anna de

doi:10.1016/j.aca.2021.338852

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…In a noteworthy endeavor, Huang et al introduced a hyperspectral imaging technique coupled with a one-dimensional convolutional neural network (1D-CNN) model, achieving near-perfect precision and sensitivity [5]. Furthermore, Rodrige et al presented an innovative sliding window variographic image analysis technique [6], underlining the imperative to conceive novel information extraction paradigms tailored to the unique properties of hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

A Nondestructive Methodology for Determining Chemical Composition of Salvia miltiorrhiza via Hyperspectral Imaging Analysis and Squeeze-and-Excitation Residual Networks

Zhu,

Bao,

Tao

2023

Sensors

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The quality assurance of bulk medicinal materials, crucial for botanical drug production, necessitates advanced analytical methods. Conventional techniques, including high-performance liquid chromatography, require extensive pre-processing and rely on extensive solvent use, presenting both environmental and safety concerns. Accordingly, a non-destructive, expedited approach for assessing both the chemical and physical attributes of these materials is imperative for streamlined manufacturing. We introduce an innovative method, designated as Squeeze-and-Excitation Residual Network Combined Hyperspectral Image Analysis (SE-ReHIA), for the swift and non-invasive assessment of the chemical makeup of bulk medicinal substances. In a demonstrative application, hyperspectral imaging in the 389–1020 nm range was employed in 187 batches of Salvia miltiorrhiza. Notable constituents such as salvianolic acid B, dihydrotanshinone I, cryptotanshinone, tanshinone IIA, and moisture were quantified. The SE-ReHIA model, incorporating convolutional layers, maxpooling layers, squeeze-and-excitation residual blocks, and fully connected layers, exhibited Rc2 values of 0.981, 0.980, 0.975, 0.972, and 0.970 for the aforementioned compounds and moisture. Furthermore, Rp2 values were ascertained to be 0.975, 0.943, 0.962, 0.957, and 0.930, respectively, signifying the model’s commendable predictive competence. This study marks the inaugural application of SE-ReHIA for Salvia miltiorrhiza’s chemical profiling, offering a method that is rapid, eco-friendly, and non-invasive. Such advancements can fortify consistency across botanical drug batches, underpinning product reliability. The broader applicability of the SE-ReHIA technique in the quality assurance of bulk medicinal entities is anticipated with optimism.

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Section: Introductionmentioning

confidence: 99%

A Nondestructive Methodology for Determining Chemical Composition of Salvia miltiorrhiza via Hyperspectral Imaging Analysis and Squeeze-and-Excitation Residual Networks

Zhu,

Bao,

Tao

2023

Sensors

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“…Several methods were developed to analyze the chemical images, such as the distributional homogeneity index (DHI, an index based in macropixels and continuous-level moving block) 10 , Poole-index (where the algorithm binarized the maps and works with non-overlapping macropixels) 11 and variographic analysis (where the variance values are estimated by comparing pairs of observations at different lags) 12 , which have brought progress in describing sample homogeneity. 13,14 For instance, Ma et al 15 have successfully used near infrared (NIR) images and DHI to evaluate the homogeneity of commercial chlorpheniramine maleate tablets. Mitsutake et al 16 used Raman imaging and DHI to compare the homogeneity of natural and synthetic lipid in mixtures used to do nanostructured lipid carriers (NLC).…”

Section: Introductionmentioning

confidence: 99%

Neither too little nor too much: finding the ideal proportion of excipients

Mitsutake

Silva

Breitkreitz

et al. 2022

Preprint

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Excipient’s homogeneity is of paramount importance during the development of pharmaceutical formulations due to its relation to stability, safety and efficacy. A direct and unique approach to evaluate such property is 3D Raman imaging. This technique characterizes the surface and inner part of preformulation samples, allowing to determine phase separation in the early stages of pharmaceutical development. Aiming to promote controlled release of the local anesthetic butamben (BTB), confocal 3D Raman microscopy was used to obtain its optimal proportion in Apifil®, Capryol® 90 and Transcutol®. Even if the microscopic images of some samples displayed very homogeneous surfaces, analysis of 3D maps showed that chemical distribution throughout the material was different. To investigate how concentration affects the homogeneity, mixture experimental design (DoE) was employed. From this analysis, it was revealed that correct amount of Capryol® 90 enhances both miscibility and solubility. Furthermore, suitable miscibility was observed in two ratio proportions of excipients: Solution 1: Apifil® 30.00%, Capryol 20.00% and Transcutol 10.00% (w/w), with a desirability of 0.783; and Solution 2: Apifil® 25.00%, Capryol 25.00% and Transcutol 10.00% (w/w), with 0.742 desirability. These results unequivocally demonstrated that confocal Raman microscopy combined to DoE can bring pharmaceutical development to a higher level.

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Deep generative neural networks for spectral image processing

Mishra

2022

Analytica Chimica Acta

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SWiVIA – Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging

Cited by 7 publications

References 31 publications

A Nondestructive Methodology for Determining Chemical Composition of Salvia miltiorrhiza via Hyperspectral Imaging Analysis and Squeeze-and-Excitation Residual Networks

A Nondestructive Methodology for Determining Chemical Composition of Salvia miltiorrhiza via Hyperspectral Imaging Analysis and Squeeze-and-Excitation Residual Networks

Neither too little nor too much: finding the ideal proportion of excipients

Deep generative neural networks for spectral image processing

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