MicroNIR™ PAT-W for Blend Endpoint Analysis in a High Dosage Product

Near Infrared Spectroscopy (NIRS) has been utilized in different forms in the past to examine the endpoint of powder blending procedures in different types of mixers used in the food, pharmaceutical, and consumer product sectors.

MicroNIR PAT-W is the best pick of spectral sensing system for monitoring the endpoint of powder blending processes in the different forms of tumble blender available for the following reasons:

  1. It does not have any moving parts, making it study for process conditions.
  2. It can be easily interfaced with tumble blenders through use of its built-in Wi-Fi connectivity and an internal battery.
  3. It measures high quality spectra quickly, permitting these spectra to be collected during each rotation of the blender.
  4. It has an easy-to-operate user interface and OPC connectivity that makes it possible for results to be sent to a control system, for real time feedback and process control.
  5. The high signal-to-noise ratio of the spectrum measured by MicroNIR PAT-W is fit for most of the applications regularly encountered in blend monitoring.

These features alone permit traditional methods, such as thief sampling to be abandoned, as they necessitate the process to be physically opened to the environment and sampled in a non-representative way. They also require samples to be sent to a QC laboratory, reducing the Overall Equipment Efficiency (OEE) and keeping the product in a state of Work in Progress (WIP) until a product for uniformity is achieved.

NIRS can be utilized in a Quality by Design (QbD) manner where the blending process is carried out until the preferred state of the product has been accomplished. This preferred state of blend uniformity lies within the design space established for the process. In this way, a 21st Century method for pharmaceutical quality can be applied, permitting 100% quality assurance for each and every batch, in-line, which also allows an organization to shift into a constant verification quality paradigm.

The following white paper explains how MicroNIR PAT-W was incorporated into a commercial manufacturing environment for the purposes of monitoring the endpoint of a granulated product in real time.

Equipment and Process Description

For the purposes of this illustration, a 500 Kg double cone blender was used. The rotation speed was put to 15 rpm, resulting in 1 rotation every 4 seconds. MicroNIR PAT-W was interfaced to the material addition port end of the blender by the use of a Viavi Solutions standard PAT lid mounting kit. The mounting kit has a sapphire window inserted so that it is flush to the lid of the blender and makes no projection into the process itself. The complete blender/instrument interface is shown in figure 1 below.

Interface of the MicroNIR PAT-W into the lid of a double cone blender.

Figure 1. Interface of the MicroNIR PAT-W into the lid of a double cone blender.

The MicroNIR PAT-W instrument was auto-integrated, adjacent to a white spectralon reference material, and the scanning conditions in Table 1 were utilized to gather data on the process.

Table 1. Optimized scanning conditions for NIRS blend endpoint monitoring.

Aquisition Mode Diffuse Reflectance
Integration Time (ms) 8.9
Scan Count 100
Scan Mode Autonomous (Gravity)
Delay Time (ms) 333

The typical validated blending time for the chosen process was 15 minutes. The product consisted of an active material (40%) and 4 other excipients. After the validated endpoint time was achieved, magnesium stearate (0.5 w/w%) was added in a staged process with a final blending time of 1 minute before the blend was sampled using traditional methods.

The MicroNIR PAT-W was configured to scan both the main blending and lubricant addition, and the raw spectra of the entire set for one batch is shown in figure 2.

Raw NIR spectra obtained for a blend uniformity analysis.

Figure 2. Raw NIR spectra obtained for a blend uniformity analysis.

A second derivative pre-treatment was applied to the data using 5 smoothing points, and the method of Moving Block Standard Deviation (MBSD) was employed to evaluate the endpoint. A block size of 30 was selected for the analysis, based on previous knowledge acquired from the uniformity of test blends and the MBSD chart for the data, shown in figure 3.

Moving Block Standard Deviation (MBSD) Chart for blend uniformity analysis.

Figure 3. Moving Block Standard Deviation (MBSD) Chart for blend uniformity analysis.

Assessment of figure 3 illustrates that the true endpoint of the process occurs around the 150 rotation point, compared to the traditional approach which rotates the blend 430 times. The NIRS method has the benefit of reducing blend time, but more significantly, not exposing the blend to over processing, which can lead to the production of fines and cause issues during compression.

Validation Against a Second Blend

The MicroNIR Pro software environment permits the application of developed models to new data in both routine analysis methods and in a test environment. Figure 4 illustrates the application of the process to a new batch of material.

Figure 4 demonstrates that a similar endpoint was also attained for the second batch of material. The process would carry on for every batch produced in a constant verification environment. It would typically take 5-10 batches to fully purify the endpoint criteria, and with the new features in MicroNIR Pro software, superior control scripts can be developed based on multiple criteria.

Application of the Moving Block Standard Deviation (MBSD) model to a validation batch.

Figure 4. Application of the Moving Block Standard Deviation (MBSD) model to a validation batch.

Assessing Lubricant Mixing

Lubricants such as magnesium stearate are only added to blends in tiny proportions (0.5-2% w/w% is typical). Although the lubricant only makes up a little component of the mix, its effect on the blend structure and NIR spectra are apparent. Figure 5 demonstrates the MBSD chart for the blending of magnesium stearate into the final blend using a block size of 5.

Monitoring lubricant blending using MicroNIR PAT-W.

Figure 5. Monitoring lubricant blending using MicroNIR PAT-W.

Figure 5 illustrates that after the 1 minute of blending time, the blending curve reaches a minimum point. Figure 6 illustrated the application of this model to the second batch of data, where a similar trend to a minimum point was observed after 1 minute of blending.

Application of lubrication blend model to a validation batch.

Figure 6. Application of lubrication blend model to a validation batch.

Summary

This application demonstrated that for the particular product and blender combination, MicroNIR PAT-W and the MicroNIR Pro software suite were able to create models that detected the endpoints of the main blending process and the lubrication step. MicroNIR Pro software offers a variety of tools for the development of endpoint models, and this example highlights one of many potential uses.

This information has been sourced, reviewed and adapted from materials provided by Viavi Solutions.

For more information on this source, please visit Viavi Solutions.

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