Reducing Food Recalls With AI-Dri

A study found that a food recall caused by bacterial or microbiological contamination typically costs US$10 million. The present issue is that problems with food safety frequently surface after the products have already been shipped, marketed, or in some cases ingested. 

Food recalls that follow have a negative impact on both the economy and reputation. Currently, data generated by food processing equipment must occasionally be interpreted by people. To decrease the incidence of foodborne illness in its operations, the food sector needs a data-driven approach.

As anticipating food quality is essential for upholding high standards for food safety, AI-based systems have been widely used in food safety applications.

A system for forecasting changes in food conditions has been developed using artificial intelligence and deep learning. It makes use of a wireless frequency-powered detecting mode. 

In this system, the radio frequency power at 915 MHz is applied to detect the gas and moisture conditions. Gathered data on gas composition and moisture is used in the deep learning network to predict meal quality.

It was found that variations in the quality of fish and pork could be predicted with 90.6 and 97.9 percent accuracy, respectively. Precision will unavoidably surpass 99 percent as associated technology develops.