BACKGROUND

Food safety can ultimately depend on how pathogens are transmitted through the air or are passed along through consecutive surface touches. Surface transmission is further complicated by thin biofilms on food processing surfaces. By developing digital twin and machine learning algorithms, effective mitigation and treatments can be more accurately applied.

GOALS

• Create and validate a digital twin model simulating the spread of viral pathogens through droplets under simulated food facility conditions.
• Create and validate a digital twin model simulating the spread of Listeria in a food processing facility based on interactions among people, equipment, and food products.

• Apply machine learning models in the detection of biofilms on food contact surfaces using imaging/spectroscopic approaches.
• Create and apply digital twin models of light propagation and biofilm absorbance to deliver adequate dosimetry for the inactivation of viral particles.

IMPACT

• Worker safety can be better protected at food processing facilities through modeling and management of aerosols.
• Consumer safety can be increased through more targeted decontamination of biofilms.