Hyperspectral Imaging Techniques for Lyophilization: Advances in Data-Driven Modeling Strategies and Applications

Area of Science:

• Biopharmaceutical Engineering
• Food Engineering
• Analytical Chemistry
• Process Analytical Technology (PAT)

Background:

• Lyophilization (freeze drying) is critical for biotherapeutic product manufacturing.
• Optimizing lyophilization cycles and formulations is time-consuming.
• On-line analytics are needed to accelerate process development.

Purpose of the Study:

• To review the application of hyperspectral imaging (HSI) in lyophilization.
• To discuss data modeling strategies for HSI datasets in lyophilization.
• To address challenges and future prospects of HSI for lyophilization.

Main Methods:

• Literature review of HSI applications in lyophilization.
• Discussion of data preprocessing techniques.
• Exploration of spectral unmixing, classification, regression, and data fusion methods for HSI data analysis.

Main Results:

• HSI is a rapid, non-invasive, and automated technique with significant potential for lyophilization analysis.
• Effective modeling strategies are crucial for interpreting large HSI datasets.
• Various data-driven approaches can be applied to HSI data for lyophilization optimization.

Conclusions:

• HSI offers a promising avenue for real-time monitoring and optimization of lyophilization processes.
• Advanced data modeling is essential to unlock the full potential of HSI in biopharmaceutical manufacturing.
• This review provides guidance for researchers and engineers on leveraging HSI for lyophilization challenges.