A new criterion to assess distributional homogeneity in hyperspectral images of solid pharmaceutical dosage forms

Abstract

During galenic formulation development, homogeneity of distribution is a critical parameter to check since it may influence activity and safety of the drug. Raman hyperspectral imaging is a technique of choice for assessing the distributional homogeneity of compounds of interest. Indeed, the combination of both spectroscopic and spatial information provides a detailed knowledge of chemical composition and component distribution.Actually, most authors assess homogeneity using parameters of the histogram of intensities (e.g. mean, skewness and kurtosis). However, this approach does not take into account spatial information and loses the main advantage of imaging. To overcome this limitation, we propose a new criterion: Distributional Homogeneity Index (DHI). DHI has been tested on

Thorough characterization of a Self-Emulsifying Drug Delivery System with Raman hyperspectral imaging: A case study

“…Both have a correct API predicted content (98.5%, 100.0% and 104.2% for sample A, B and C respectively) but they exhibit different distribution pattern. In order to obtain an objective value regarding this difference of distribution, the Distributional Homogeneity Index (DHI) approach was used (Sacré et al, 2014b). The smaller and close to 1 the DHI value, the more homogeneous the sample.…”

Quantitative Evaluation of Drug Distribution in Tablets of Various Structures via Raman Mapping

“…The method proceeds until the total map size is reached. Linear relationship has been observed between DHI values and content uniformity values [15]. For certain samples these methods proved to be very suitable for the purposes, however for other samples such approaches are not discriminative enough.…”

“…The DHI is a very suitable measure when samples come from very similar distribution as it has been used for controlling content uniformities of certain batches in an earlier paper [15]. Nevertheless the DHI is a relative value due to the calculation method in which the real distribution is compared with an artificial randomized image.…”

“…Further objective measures have been developed in recent years [14][15][16]. Rosas et al [14] considered Poole-index as the most adaptable index for the hyperspectral imaging.…”

“…They binarized at first the studied map using arbitrary threshold and worked with non-overlapping macropixels. Sacre et al [15] developed another criterion called Distributional Homogeneity Index (DHI). This method is based on the approach of the Continuous-Level Moving Block (CLMB) [13].…”