Novolyze has announced the release of advanced predictive data modeling and analytics enhanced by artificial intelligence (AI) and machine learning (ML) for its Sanitation Complete and environmental monitoring platform (EMP). The latest release enables Novolyze’s technology to automatically generate trend charts and digital heat maps using historical and real time data from multiple data inputs.

Enriching existing reporting capabilities, the new trend charts and heat maps provide a visually intuitive and comprehensive view for identifying high risk areas, outbreak forecasting, and prediction of where and when foodborne pathogens may present during certain process times and seasons. Digitalization of EMPs and sanitation programs are crucial for food and beverage manufacturers to reduce paper and provide real time data to maintain food safety and quality, especially for ready-to-eat (RTE) foods.

The use of predictive insight in EMP and sanitation can help food and beverage manufacturers identify potential areas of contamination before they present problems. By digitalizing the collection and analysis of data on environmental conditions, such as temperature, humidity, and sanitation practices, the solution develops models to predict where and when potential contamination events may occur. This enables manufacturers to take proactive measures to prevent contamination, rather than waiting for a problem to arise and then reacting to it.

For example, when manufacturers are alerted through the platform to specific areas in the facility presenting a higher risk of contamination, they can proactively take steps to increase sanitation measures in that area or adjust the production process reducing the risk of contamination. By doing so, they can prevent potential food safety issues and ensure that the RTE foods they produce are safe for consumption.

Additionally, the use of predictive modeling also helps improve product quality. Through real-time and predictive monitoring of data on environmental conditions, manufacturers can identify and address issues that potentially affect the quality of a product, such as changes in temperature or humidity. This helps ensure consistent product quality to meet the expectations of consumers.