Non Technical Summary
Cold atmospheric pressure plasma (CAPP) is a novel food processing technology. CAPP, or Cold plasma, is the fourth state of matter. Plasma from the air, which has multiple reactive species, is used to ensure food safety, improve food quality and shelf-life, and reduce food allergens and toxins. However, determining the optimal dosage of CAPP is very challenging due to the simultaneous action of multiple reactive species and other agents in CAPP.In this project, we plan to develop enzyme- and DNA-based biosensors that will correspond to the level of microbial inactivation and percent chemical degradation at various doses of CAPP. Spectroscopy and other data responses generated from the biosensors will be used to train machine-learning models to predict the doses of CAPP. The models will then be applied and recalibrated for plasma-treated food and food contact surfaces to predict plasma dose and correlate to food quality.The complementary skills of our research team will ensure the successful execution of this project and will provide fundamental knowledge on plasma dosage. Standardizing plasma treatment will aid the advancement of this novel technology in the industry and help with regulatory approval.

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
501	1122	2020	50%
501	1830	2000	30%
501	4010	1100	20%

Knowledge Area
501 - New and Improved Food Processing Technologies;

Subject Of Investigation
1122 - Strawberry; 4010 - Bacteria; 1830 - Peanut;

Field Of Science
2020 - Engineering; 1100 - Bacteriology; 2000 - Chemistry;

Goals / Objectives
Our long-term goal is to develop biosensing elements-based, dosimetry-enabled applications of cold plasma technologies and validate them using data analysis approaches. These field-deployable biosensors, along with machine learning algorithms, can help process control by predicting plasma dosage.The supporting objectives are:To conduct cold atmospheric pressure plasma treatment of various doses with different plasma generating systems and processing parameters to study the effect on inactivation of spoilage microorganisms, their biofilms, enzymes, and changes selected chemicals, such as allergens and toxins in model systems (either in DI water or on stainless steel surface)To develop suitable field-deployable biosensors corresponding to target microorganisms and chemicals and test their responses to varying doses of cold atmospheric pressure plasma treatmentTo develop machine learning/artificial intelligence models and other modeling approaches to predict outcomes of microorganisms and chemicals using responses from biosensorsTo apply developed models in selected foods (fresh produce, powdered food) and food contact surfaces to predict atmospheric pressure plasma treatment doses and co-relate the models to their quality parameters of food

Project Methods
Varying doses of CAPP will be characterized and applied to the model system to understand changes in concentrations of spoilage microorganisms, biofilms, enzymes, allergens, and toxins. Enzyme- and DNA-based biosensors will be developed to generate responses from biosensing elements (spectroscopy and other data). The responses from biosensors will be correlated to the plasma dosage and microbial inactivation and chemical degradation. The models will then be applied and recalibrated for plasma-treated food and food contact surfaces to predict plasma dose and correlate to food quality.