Making food safety smart, the neural way
11 MINS READ
How often do you see cases of foodborne illness or food recall? A February 2021 report by a U.S. Congress subcommittee stated that arsenic and toxic metals were found in baby food, including organic brands. This raises the question of how food safety issues occur, despite the presence of national and international food regulatory authorities. In the example above, we can deduce that the grain-based cereals contained arsenic as they were exposed to water containing the chemical. The World Health Organization (WHO) estimated that 600 million people fall ill every year due to the consumption of unsafe food.
Today, as the population rises, food wastage has emerged as a major cause that hampers food security. According to a US-based study of pre-harvest losses of vegetables, more than half of the crops were not harvested on time, indicating a huge source of food wastage. According to the Food Waste Index Report 2021, each year, 17% of global food production is wasted, 26% from food services, and 13% from retail. The issue is so acute that the United Nations has stated that by 2030, it intends to reduce food wastage as part of its Sustainable Development Goals. Countries and organizations are working to this end by incorporating innovative approaches and technologies such as predictive supply chain analytics or institute collaborations to develop a profitable product from food waste.
CHALLENGES IN FOOD SAFETY MANAGEMENT
The food value chain spans farm production, collection and storage, food processing, food packaging, and retail and food services. Food safety-related challenges are spread across all these touchpoints and are different at each stage, as illustrated in Figure 1.
Figure 1: Challenges across food value chain
The lack of knowledge of soil health forces producers to use inadequate agricultural inputs such as fertilizers and pesticides. In some cases, the dearth of knowledge leads to an overdose of these inputs, which leave chemical traces, creating health hazards when consumed. According to the National Academy of Agricultural Sciences, 10% of every 1,000 lakh metric tons (MT) of wheat is wasted in India due to improper storage, and losses are associated with spillage, rodent attacks, pilferage, and more. In late 2019, approximately 1,941 pounds of raw chicken produced by a Santa Clara, California-based food processor were recalled due to mislabeling by the US government’s Agriculture’s the Food Safety and Inspection Service (FSIS), which is part of the United States Department of Agriculture (USDA).
Such challenges are spread across the food value chain. While regulatory bodies across regions, such as the GLOBALG.A.P. (Europe) and the FSIS, have their standard operating procedures to keep food processing practices in check, these are not uniformly implemented due to complex reasons. For instance, while food processing companies need to follow the standard guidelines set by authorities like the GLOBALG.A.P. and the USFDA, they still may face food safety issues since the raw materials they use may not be produced keeping in mind food safety guidelines.
Regulatory bodies such as the GLOBALG.A.P. typically offer recommendations about farming practices, but do not state food processing standard operations. Currently, no regulatory body traces these challenges across the entire food processing value chain. As a result, many cases of food recall in North America have occurred due to the wrong choice of raw materials, rather than any issues with food processing operations.
NEURAL NETWORKS FOR INNOVATIVE FOOD SAFETY INTERVENTIONS
With rising complexities in the food value chain, the food manufacturing and distribution business has become more customer centric. Companies with a resilient business structure reduce variability and earnings sensitivity to external shocks, such as supply chain disruptions due to war. Having end-to-end visibility in the food network helps food companies and their stakeholders synchronize all activities and offers them a comprehensive understanding of their operations at all levels.
Technology is the lynchpin in such cases where machine learning (ML), artificial intelligence (AI), big data, internet of things (IoT), digital twin, and smart packaging lend neural traits to the entire food value chain. Neural networks provide organizations with the right tools to predict potential problems and, in some cases, offer preventive steps, as described below (also see Figure 2):
Figure 2: A neural network for the food safety ecosystem
CLOSING THOUGHTS ON FOOD SAFETY
The adoption of connected, automated, cognitive, resilient, and intelligent technologies across the food value chain supports the sustainable journey of companies. Despite the challenges of food safety, large conglomerates, governments, and food regulatory authorities have adopted digital tools to ensure food security with a focus on enhancing consumer experience. The neural approach helps companies and stakeholders in the food safety ecosystem predict potential problems, enhance decision-making, and take preventive measures to reduce food wastage and foodborne illnesses.
For fresh insights on the manufacturing industry, please visit our page on tcs.com and/or write to us at firstname.lastname@example.org.