Food safety practices in processing plants are largely reactive. Facilities pull random samples from production batches and send them to centralized microbiological laboratories for culture testing. Because results take 48-72 hours, contaminated batches are often packaged and distributed before pathogens are detected, triggering massive recalls and severe health outbreaks.
Develop an inline, non-destructive hardware inspection system utilizing hyperspectral/multispectral sensors and edge AI to detect bacterial and microbial contamination directly on moving food processing conveyor belts in real-time.
Hyperspectral image cubes generate gigabytes of raw data per minute; processing full spatial-spectral tensors at production conveyor speeds (>0.5 m/s) overwhelms standard edge hardware while distinguishing microbial biofilm from food surface textures.
A compact multispectral optical sensor rig covering critical NIR/SWIR absorption bands, controlled illumination chamber, edge AI accelerator (NVIDIA Jetson / FPGA) executing real-time spectral anomaly detection, and low-latency pneumatic sorter rejection triggers.