Introducing PAT, Using NIR Analysis, to a Pharmaceutical Blending Process

Using NIR to measure absorbance at significant wavelengths for the granule and lubricant as the blend proceeded in real time, the authors found a relationship between blend uniformity and the standard deviation of the absorbance.

By Krishel Naicker and Gareth Kilian, Nelson Mandela Metropolitan University, and Johan Olivier, Aspen Pharmacare

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This study investigates the use of a laboratory-scale blender fitted with a near-infrared (NIR) probe to monitor lubricant uniformity in a granule blend. A software method was developed to monitor the change in absorbance at significant wavelengths for the granule and lubricant (magnesium stearate) as the blend proceeded in real time. The standard deviation of the absorbance was plotted as a function of time to monitor the change in the blend. With near-infrared spectra, when a process is complete, the spectra will not change; therefore the standard deviation will be small6.


Process analytical technologies are systems for the analysis and control of manufacturing processes to assure acceptable end-product quality11. This is achieved by timely measurements of critical parameters and performance attributes of raw material and in-process material and processes11. The desired goal of process analytical technology (PAT) is to design and develop processes that can consistently ensure a predefined quality at the end of the manufacturing process1. To “build quality into” a product requires the manufacturing process to be monitored and controlled as opposed to only testing the product at the end of the manufacturing process to assure quality1.

The blending of solids is a critical step in the production of many pharmaceutical products4, 7. Traditional methods for blend uniformity determination involve sampling of the blend using a sample thief and laboratory analysis of the samples using chemical methods2. In addition, blend uniformity in the “traditional sense” focuses on the distribution of the active pharmaceutical ingredient in the blend as opposed to distribution of the excipients in the blend, which can influence the desired performance of the pharmaceutical product2.

Magnesium stearate is a commonly used tablet lubricant that forms a film of low-shear strength around the granule, thereby reducing the friction at the die wall during tablet ejection8. Blending for longer durations than is necessary could result in the incorporation of magnesium stearate intra-granularly, which can influence the bioavailability by decreasing the dissolution rate of the product, due to the hydrophobic nature of magnesium stearate9. In addition, overmixing could lead to the physical destruction of the granule leading to poor compression profiles9.

The common problems associated with poor lubrication are: binding where tablets have vertically scratched edges, lack smoothness or gloss and are often fractured at the top edges; sticking where tablet faces appear dull; filming, which is the early stage of sticking; picking, which represents the advanced stages of sticking and capping; and lamination, which is normally associated with poor bonding and can also be a result of a system that is over-lubricated5.

Due to the competitive nature of the pharmaceutical industry and the continuous emphasis on quality from the regulatory authorities, pharmaceutical manufacturers require a system to monitor all materials in a blend with little or no sample preparation and the ability to “build” quality into the product and predict end-points in real time. PAT offers these advantages and near-infrared (NIR) spectroscopy is one such tool that is commonly used3.

The majority of active pharmaceutical ingredients and excipients absorb NIR radiation; therefore, NIR has the ability to provide information of all the components in the blend and is non-invasive, speedy and requires no sample preparation2. In this study, NIR spectroscopy was used for the on-line monitoring of magnesium stearate in a granule blend.


Magnesium stearate (EP 5.02) from approved suppliers to the pharmaceutical manufacturer was used as the lubricant. A proprietary granule was chosen to mix with the magnesium stearate.

The study utilized a laboratory-scale blender fitted with an NIR probe, operated by equipment specific software programs. During the intermediate bulk container (IBC) inversion, the NIR probe measured the NIR spectrum of the material in the IBC. Therefore, each and every rotation captured an NIR spectrum.

Obtaining fingerprint spectrum
The pure spectrum was obtained by placing approximately 50 g of each material on the Corona head and measuring the NIR spectrum. The NIR spectra of four random samples of magnesium stearate and six random samples of the granule were measured and the mean spectrum of each material was calculated to obtain a fingerprint spectrum. The fingerprint spectrum of magnesium stearate and the granule were superimposed.

The significant wavelengths for blend monitoring for the materials were obtained by examining the spectra in the second derivative. The software was used to highlight the peaks on the spectra (as seen in Figure 1). Preliminary blends were conducted using the wavelengths obtained from the software. The criterion used to choose the most suitable wavelengths was that the standard deviation of the absorbance at the wavelength representing the magnesium stearate and the granule should increase when the magnesium stearate was added to the blend, and level off after a period of time. Following the preliminary blends using the wavelengths isolated by the software, 1213 nm was chosen to represent magnesium stearate and 1591 nm was chosen to represent the granule, as highlighted in Figure 1.

Figure 1

Next, a method was created on the software to interpret the spectral data in terms of the rate of change in the blend.

The second derivative was used for spectral pre-processing and the results were evaluated at the wavelengths of significance. The software was set to evaluate the standard deviation of the absorbance at the wavelengths of significance in a “moving block of eight” as outlined below.

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