Profile
Agriculture / Food Smart Lab
Associate Professor TAKAYAMA Yuki
International Center for Synchrotron Radiation Innovation Smart
Concurrent : Graduate School of Agricultural Science
- Themes
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・Development of coherent X-ray diffraction imaging techniques and its application to life, agricultural and food sciences
・Development of spectral image analysis techniques incorporating machine learning
・Promotion of the usage of synchrotron X-rays in life, agricultural and food sciences - Keywords
- Biophysics, Food and agricultural science, X-ray imaging, X-ray optics, Machine learning
- Research Activities
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Investigating the relationships between micro- to nano-scale hierarchical structures and biological functions / food properties via synchrotron X-ray imaging
Visualization of spatially hierarchical structures from micro- to nanometer scales provides key insights for understanding the structural basis of biological functions in life and agricultural sciences. Properties of foods including texture, taste, and cooking/processing properties also arise from micro- to nanoscale structures. To investigate those structure-function relationships, we are developing imaging techniques using synchrotron X-rays and promoting their applications in life, agricultural and food sciences, and industries. I have particularly been developing the lensless nanoimaging techniques using coherent X-rays, called coherent diffraction imaging (CDI) and ptychography, under atmospheric and cryogenic conditions (Fig.1). In addition, I have developed machine-learning-based analysis technique for spectral images including X-ray absorption spectroscopic or small-angle X-ray scattering (SAXS) imaging to visualize heterogeneous distribution of chemical states or material phase transitions involving biological phenomena or food processing (Fig.2). We are also promoting the application of those techniques to various fields including soft materials, energy devices, etc.
CDI / ptychographic observation of the unicellular red algae, raw rice bran, and ABS resin.
Analysis of the SAXS image via machine learning approach.
