Hyperspectral imaging has shown a high potential to analyze falsifications of solid pharmaceutical products since the last decade. Thanks to the non-destructive, ecological and non-invasive properties, it is a preferred technique for these kinds of applications. Moreover, thanks to the spectroscopic properties, it is possible to detect as well organic compounds as inorganic compounds in a single analysis. Therefore, we recommend using it as second-line laboratory analysis technique. Raman microscopy and Fourier Transform Infrared (FT-IR) microscopy are two interesting techniques that are complementary. In this study, the potential of the two hyperspectral imaging techniques is evaluated to elucidate the composition of falsified antimalarial tablets. Hyperspectral data are analyzed by Multivariate Curve Resolution-Alternating Least Square (MCR-ALS). The results obtained from this study show that Raman hyperspectral imaging seems to be more suited to detect low dosed compounds possibly due to a smallest sampling volume. It has been also possible to link formulations of falsified samples of two different brands.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.talanta.2019.02.032 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Feasibility study of hyperspectral colposcopy as a novel tool for detecting precancerous cervical lesions.
Sci Rep
January 2025
Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain.
Cervical cancer remains a major global health concern, with a specially alarming incidence in younger women. Traditional detection techniques such as the Pap smear and colposcopy often lack sensitivity and specificity and are highly dependent on the experience of the gynaecologist. In response, this study proposes the use of Hyperspectral Imaging, a pioneering technology that combines traditional imaging with spectroscopy to provide detailed spatial and spectral information.
View Article and Find Full Text PDFPoint spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data.
Sci Rep
January 2025
Hannover Centre for Optical Technologies (HOT), Leibniz University Hannover, Hannover, Germany.
Hyperspectral imaging (HSI) systems acquire images with spectral information over a wide range of wavelengths but are often affected by chromatic and other optical aberrations that degrade image quality. Deconvolution algorithms can improve the spatial resolution of HSI systems, yet retrieving the point spread function (PSF) is a crucial and challenging step. To address this challenge, we have developed a method for PSF estimation in HSI systems based on computed wavefronts.
View Article and Find Full Text PDFNanoscale characterization of space weathering in lunar samples.
Sci Rep
January 2025
School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Nanoscale Fourier transform infrared (Nano-FTIR) imaging and spectroscopy correlated with photoluminescence measurements of lunar Apollo samples with different surface radiation exposure histories reveal distinct physical and chemical differences associated with space weathering effects. Analysis of two sample fragments: an ilmenite basalt (12016) and an impact melt breccia (15445) show evidence of intrinsic or delivered Nd and an amorphous silica glass component on exterior surfaces, whereas intrinsic Cr and/or trapped electron states are limited to interior surfaces. Spatially localized 1050 cm/935 cm band ratios in Nano-FTIR hyperspectral maps may further reflect impact-induced shock nanostructures, while shifts in silicate band positions indicate accumulated radiation damage at the nanoscale from prolonged space weathering due to micrometeorites, solar wind, energetic x-rays and cosmic ray bombardment.
View Article and Find Full Text PDFOn the analysis of adapting deep learning methods to hyperspectral imaging. Use case for WEEE recycling and dataset.
Spectrochim Acta A Mol Biomol Spectrosc
December 2024
TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160, Derio - Bizkaia, Spain; University of the Basque Country, Plaza Torres Quevedo, 48013 Bilbao, Spain.
Article Synopsis
- Hyperspectral imaging is increasingly using deep learning techniques instead of traditional methods for processing, but many researchers don't adequately analyze how spectral and spatial information interact.
- The paper assesses how different combinations of spatial (texture) and spectral (light wavelength) features affect deep learning models for image segmentation, focusing on performance, energy usage, and speed.
- Results indicate that integrating spatial data with spectral information enhances segmentation accuracy, but not all spectral wavelengths are needed for optimal efficiency, and adapting existing RGB image models for hyperspectral tasks needs further innovation.
Evaluation of Band Selection for Spectrum-Aided Visual Enhancer (SAVE) for Esophageal Cancer Detection.
J Cancer
January 2025
Department of Mechanical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan.
Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. In medical imaging, narrow-band imaging (NBI) is an imaging technique for endoscopic diagnostic medical tests, where light of specific blue and green wavelengths is used to enhance the detail of certain aspects of the surface of the mucosa.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!