Comparison of hyperspectral imaging techniques for the elucidation of falsified medicines composition

Recent advancement of chemical imaging in pharmaceutical quality control: From final product testing to industrial utilization

“…Multivariate curve resolution (MCR) is based on the fundamental assumption of Beer-Lambert law in a multivariate scale. 2 The spectral intensity is correlated with pure component concentration and the total spectral information in each pixel is a weighted result of each pure component. 34 MCR resorts to a bilinear decomposition of original data (D 0 ) into concentration pro¯les (C) and pure component spectra (S).…”

“…They are near-infrared (NIR) imaging, Raman imaging, terahertz pulsed imaging (TPI), ultraviolet (UV) imaging and Fourier transformed infrared (FT-IR) imaging. [2][3][4][5][6] NIR imaging and Raman seem to be the most used ones among them because of the deep spectral understanding and relatively low prices but each imaging technique has their own special expertise. Data fusion of various spectroscopy sources is therefore put forward such as the combination between NIR and Raman, or between visible and NIR (435-1042 nm) and short-wave infrared (898-1751 nm).…”

Raman mapping coupled to self‐modelling MCR‐ALS analysis to estimate active cosmetic ingredient penetration profile in skin

“…Therefore, advanced data mining protocols are usually necessary to extract and interpret the spectral information inside the large data sets generated by hyperspectral mapping, and preprocessing can also play a key role in correcting or diminishing spectral variability originating from several optical and instrumental effects [10]. Recently, the development of multivariate analysis (ie, chemometrics) has greatly contributed to increasing the potential of spectroscopy mapping for tissue, cell and surface analysis of material but also in pharmaceutical and cosmetic fields [3,7,[11][12][13][14][15][16][17][18][19][20]. Principal components analysis (PCA) and K-means clustering methods are examples of descriptive unsupervised methods, widely used to better understand the hyperspectral data [21].…”

“…In contrast, multivariate curve resolution alternating least squares (MCR-ALS) regression analysis is an unmixing method which can provide an accurate molecular decomposition of the spectroscopic information contained in the data set, while also dealing with such band shifts [27]. MCR-ALS has been applied to detect modification in ingredients and their dosing in falsified medicine [16,[18][19][20]28]. The method has also been applied on FT-IR and Raman data to study effects of pathology, anatomy, environmental or genetic factors [27] and on Raman data collected from plant cell walls to study the mechanical stability of cells by changing their form, their thickness or their composition [29].…”

Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data

“…The analysis and processing of spectral data by an appropriate manifold learning algorithm can objectively and reliably reflect the overall information of the sample tested, which is characterized in a fast, nondestructive, and accurate manner. It has been widely used for testing the quality of seeds, (9,10) drugs, (11,12) the interior and exterior of fruit and vegetables, (13,14) and meat. (15,16) The hyperspectral imaging used in apple quality testing mainly focuses on species classification, (17,18) damage detection, (19,20) and internal component detection.…”