Imaging the Blending Process

Hyperspectral imaging can be used to optimize blending, by monitoring the distribution of excipients and APIs in formulation.

By Gabor Kemeny and Gina Stuessy, Middleton Research

1 of 2 < 1 | 2 View on one page

Blending, one of the most basic of pharmaceutical unit operations, can also be one of the most challenging to control. Solid formulations contain multiple ingredients beyond the active pharmaceutical ingredients: fillers, tabletting agents, disintegrants, and absorption enhancers or agents that slow down and control absorption. Ingredients from different vendors may behave differently due to their particle size and shape and other factors, and their tendency to form aggregates. 

Materials must be chosen to ensure the desired flow characteristics, potency, proper dissolution profile, and absorption of specific formulations. Proper particle size grades of the ingredients must be selected to produce an optimum blend for capsule filling.

There are strong economic drivers for optimizing blending. Reinforcing these is the pharmaceutical quality by design (QbD) framework advanced by FDA, which requires a deeper understanding of pharmaceutical manufacturing processes, how ingredients blend and how blending progresses through different stages.

Traditionally, formulation scientists and technologists have used destructive analytical methods, such as dissolution followed by HPLC or UV, to optimize blending. This requires running the process, pulling and analyzing samples, which can lengthen the time required for development.

Recently, hyperspectral imaging (HSI) has been applied to study the behavior of solid particles in various unit processing steps as well as during multistep continuous processes.  This nondestructive method generates thousands of spectra per second, providing more compositional information than conventional methods.

This article summarizes the results of blend monitoring studies using a new HSI device, in situ without stopping the blending and pulling samples. In this case, a batch-type blender was equipped with a computer controlled drive mechanism capable of imaging blending through a window mounted on the blender. The push-broom HSI camera is timed synchronously with the rotation of the blender. The hypercubes of spectral information acquired from the blend at each rotation are used to assess whether the nominal composition is achieved, to reveal uniformity of the blend to establish that the blending is properly done, and to avoid incomplete blending, large aggregates or re-aggregation.

About HSI
Hyperspectral imaging (HSI), or chemical imaging (CI), is the combination of spectroscopy and digital imaging. A hyperspectral image contains many spectra, one for each individual point on the sample’s surface. The image contains information about the spatial distribution of the materials within the sample.

A hyperspectral camera (Figure 1) integrates an imaging spectrograph with a matrix array sensor. In these studies, we applied near infrared (NIR) spectroscopy, using NIR hyperspectral imaging to analyze the average composition, and the distribution of ingredients. Hyperspectral camera literature refers to the full 1000-2500 nm range as short-wave infrared (SWIR).


A special lens images the sample onto a slit of a transmission spectrograph. The spectrograph produces a spectrum imaged on a focal plane array detector, preserving the location of respective points on the slit and thus the points of the line on the sample.

In push-broom HSI, successive lines on the sample measured over time form a complete HS dataset. This data from a HS camera is called a “hypercube,” containing information in two spatial dimensions and one spectral dimension. The hypercube is typically ratioed with similar hypercube measurements of a highly reflective white reference material and with the residual background signal, the latter of which is measured when no light is falling on the focal plane array.

The resultant corrected spectra are produced in transmittance, reflectance, or absorbance similar to traditional spectroscopic measurements. The results can be further processed, scaled, smoothed, and eventually compressed to produce the information sought from the measurements, such as composition maps.

Push-broom Imaging
One of the significant differences between HSI and conventional single-point spectroscopy is the very large amount of data generated. Processing software and hardware are necessary to keep up with the data stream and provide compressed and processed data, thus producing composition maps and other technologically meaningful information.

Push-broom HS cameras gather a complete spectrum of each point on one spatial line at a time [1]. The area of the object is scanned, one line at a time in rapid succession. To image the whole sample, either the sample or the camera must move. The hypercube is collected by compiling the optical data from each spatial line. Since push-broom imaging detects one line at a time, the spectral data in the hypercubes correlate with the same sample point, thus push-broom HSI cameras are used with the samples moving, which is the case in many pharmaceutical manufacturing lines, schematically shown in Figure 2.

hypercube data collection

In pharmaceutical production, there are many points where the increased amount of spectral information and the spatial information could provide additional insights, such as during transdermal manufacture, tablets, capsule filling, and blending [2-6]. For different magnifications, required by the different pharmaceutical applications, the same type of push-broom camera can be used with different optics. In this research project, for instance, a larger magnification was used to resolve the aggregates of the various pharmaceutical ingredients found in solid formulations.

1 of 2 < 1 | 2 View on one page
Show Comments
Hide Comments

Join the discussion

We welcome your thoughtful comments.
All comments will display your user name.

Want to participate in the discussion?

Register for free

Log in for complete access.


No one has commented on this page yet.

RSS feed for comments on this page | RSS feed for all comments