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Next generation X-ray phasecontrast imaging for food quality and process engineering.

Many properties of food, plants or seeds that are relevant to process engineering or quality are related to microstructure. Insight in food microstructure is therefore essential to control the quality of food. In the food factory of the future, flexible and efficient processes require dedicated sensor technology and automated analysis methods. In this context, X-ray computed tomography (XCT) is gaining traction as a non-destructive method to produce extremely detailed images of both internal and external features. Current XCT based analysis of food has a number of limitations however:i) Many microstructural features of food remain invisible due to poor image contrast in soft matter. ii) Visibility and quantification of structure from absorption XCT images strongly depends on image resolution, while relevant sub-resolution size features often remain undetectable. iii) Quality control requires reliable detection and classification methods that should be compatible with process line speeds and dedicated instrumentation that is currently out of reach to the food industry. With phase contrast XCT, images can be acquired with unprecedented contrast far surpassing conventional XCT contrast. This technique was only available at large-scale synchrotron facilities, but recent developments now allow for low brilliance, polychromatic X‐ray sources in lab XCT systems. The applicability to food analysis is however to a large extent unexplored and the 3D inline application is hindered by the long acquisition time. The aim of this project is to overcome these limitations by developing novel (inline) XCT phase contrast acquisition, reconstruction and inspection algorithms specific for the food industry. This will enable us to address issues such as limited visibility of microstructural features, non-detection of sub-resolution size features and incompatibility of reliable detection and classification methods with process line speeds.
Date:1 Oct 2020  →  Today
Disciplines:Applied and interdisciplinary physics, Signal processing, Image and language processing
Project type:Collaboration project