Evaluation of Transmission Raman spectroscopy and NIR Hyperspectral Imaging for the assessment of content uniformity in solid oral dosage forms✰

Belay, Netsanet Fekadu; Busche, Stefan; Manici, Valentina; Shaukat, Manuela; Arndt, Sven-Oliver; Schmidt, Carsten

doi:10.1016/j.ejps.2021.105963

Cited by 6 publications

(1 citation statement)

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“…In the field of process analytical technology (PAT), Raman spectroscopy is used to monitor and control chemical and pharmaceutical processes [ 42 , 43 , 44 ], to predict end points of chemical synthesis reactions [ 45 ], and to track polymorphic changes in crystallisation processes [ 46 , 47 ]. Additionally, Raman spectroscopy has been reported as a powerful technique enabling quantitative analysis for a wide range of samples, such as for the determination of water in natural deep eutectic solvents [ 48 , 49 , 50 ], as a quality control tool for chemotherapeutic solutions [ 51 , 52 ], for the quantification of API in solid dosage forms [ 53 , 54 , 55 ], screening human body fluids such as serum [ 56 , 57 ] or for investigating the distribution of AI in complex cosmetic dried films to study their homogeneity [ 58 ]. While ATR-IR spectroscopy requires the withdrawal of samples, Raman spectroscopy enables in situ analysis of samples, i.e., directly through containers [ 59 , 60 ].…”

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confidence: 99%

Label-Free Quantification of Nanoencapsulated Piperonyl Esters in Cosmetic Hydrogels Using Raman Spectroscopy

et al. 2023

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Raman spectroscopy is a well-established technique for the molecular characterisation of samples and does not require extensive pre-analytical processing for complex cosmetic products. As an illustration of its potential, this study investigates the quantitative performance of Raman spectroscopy coupled with partial least squares regression (PLSR) for the analysis of Alginate nanoencapsulated Piperonyl Esters (ANC-PE) incorporated into a hydrogel. A total of 96 ANC-PE samples covering a 0.4% w/w–8.3% w/w PE concentration range have been prepared and analysed. Despite the complex formulation of the sample, the spectral features of the PE can be detected and used to quantify the concentrations. Using a leave-K-out cross-validation approach, samples were divided into a training set (n = 64) and a test set, samples that were previously unknown to the PLSR model (n = 32). The root mean square error of cross-validation (RMSECV) and prediction (RMSEP) was evaluated to be 0.142% (w/w PE) and 0.148% (w/w PE), respectively. The accuracy of the prediction model was further evaluated by the percent relative error calculated from the predicted concentration compared to the true value, yielding values of 3.58% for the training set and 3.67% for the test set. The outcome of the analysis demonstrated the analytical power of Raman to obtain label-free, non-destructive quantification of the active cosmetic ingredient, presently PE, in complex formulations, holding promise for future analytical quality control (AQC) applications in the cosmetics industry with rapid and consumable-free analysis.

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