In-situ X-ray characterization of protein structural transformations during extrusion
A novel platform using SAXS/WAXS X-ray diffraction inside transparent extrusion sample environments to capture real-time protein structural data under industrial processing conditions. Enables high-throughput screening and machine learning–driven prediction of product quality attributes for food and biopolymer manufacturing.
Overview
This solution enables direct, real-time observation of protein structural changes during extrusion processing. By integrating small- and wide-angle X-ray scattering (SAXS/WAXS) into custom-built, X-ray transparent sample environments that replicate industrial extruder conditions, the platform captures dynamic structural information that is currently inaccessible with conventional off-line characterization methods.
The technology addresses a critical gap in food and biopolymer manufacturing: understanding how proteins unfold, aggregate, denature, or interact with other ingredients under defined shear, pressure, temperature, and compositional conditions. Correlating these real-time structural measurements with adjustable process parameters generates high-quality training data for machine learning models capable of predicting product key performance indicators such as texture, stability, and shelf life.
Technical specifications
Measurement capabilities:
- SAXS/WAXS diffraction for protein size, shape, size distribution, and stability
- Tracking of denaturation, depolymerization, gelation, and glass transition behavior
- Characterization of crystalline fraction, lamellar structure, fractal dimensions, and matrix component interactions
- Real-time data acquisition along the extrusion path and within the extrusion cavity
Sample environment and fabrication:
- X-ray transparent sample environments built via femtosecond laser 3D microfabrication and precision CNC machining
- Designs replicate industrial extruder conditions including shear and extensional profiles, pressure, and temperature
- Compatible with ex-situ validation techniques such as SEM/STEM, microscopy, micro-CT, and DSC
Modeling and prediction:
- High-throughput automated screening across process parameter and ingredient combinations
- Integration with computational fluid dynamics (STAR-CCM+) and machine learning tools (MATLAB/Python) to develop predictive models linking process inputs to product KPIs
Technology readiness level
The underlying SAXS/WAXS methodology is proven and widely used in food science and soft matter research, with the principal investigator's laboratory having extensive experience in dynamic and time-resolved X-ray diffraction under defined flow and processing conditions. The current project, estimated at 18 months, focuses on fabricating the specialized sample environments, conducting in-situ and ex-situ validation experiments, and building the predictive modeling framework. Resources including shared core facilities and a mechanical workshop are available to support development. The platform is positioned to advance from validated laboratory technique toward a deployable characterization and prediction tool for industrial extrusion processes.
About University at Buffalo, State University of New York
University at Buffalo is a comprehensive public research university and a leading campus in the State University of New York system. Industry engagement is anchored by co-located labs on a downtown medical campus, a research and technology park with incubators, shared core facilities, and co-op talent pipelines. Partnerships with regional hospitals enable clinical studies and translation, while proximity to the U.S.–Canada border and the binational Great Lakes corridor facilitates cross‑border collaboration. Research is supported by competitive federal funding from NIH, NSF, DOE, and DoD, alongside New York State innovation programs. A dedicated technology transfer office provides IP management, licensing, and startup support.
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