Automated Characterization of Macroscale Tissue 3D Spatial Material Properties via Robotically-Directed Impedimetric Sensing

Here, we report a novel method for automated characterization of bulk tissue 3D spatial properties based on reverse engineering-driven non-planar tool path planning and robotically-directed sensing. The method incorporates information on object (e.g., tissue) and inspection tool (e.g., sensor) geometry for automated inspection of tissue mechanical and dielectric properties across macroscopic nonplanar domains as large as 44 cm(2). The process avoids the need for manual sensortissue integration processes. The impact and the utility of the method were demonstrated by automated mapping of 3D spatial distributions of mechanical and dielectric properties of plant and animal tissues using multiple complementary impedimetricbased sensors of varying types and form factor, including rigid micro-electromechanical systems (MEMS) and flexible multifunctional fibers. Applications to automated characterization of food quality (e.g., type and age) are provided, including 3D spatial mapping of plant and animal tissue mechanical and dielectric property distributions. Ultimately, automated methods for 3D spatial inspection of plant and animal tissue properties are critical to agriculture, food processing, organ transplantation, and biomanufacturing industries.