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Big data is more than a buzzword. It is an indispensable part of all industries, including the food safety industry. Indeed, big data is about to change the ways that consumers and producers make decisions regarding food purchases and production practices.


Today’s consumers want to know what’s in their food and where it’s coming from. Big data can help companies provide greater transparency. Let’s take a closer look at the ways in which the food industry is already leveraging big data to meet consumer demand.


A Quick Primer: Artificial Intelligence and Machine Learning


When we are talking about big data, we are referring to massive amounts of data, both structured and unstructured. During those discussions, terms like artificial intelligence (AI) and machine learning are bound to pop up because they offer us ways to gather insights from the world of big data.


While some insiders use the terms AI and machine learning interchangeably, there are differences. AI describes the way that a machine interacts with the world around it. By using software and hardware, AI machines and devices can mimic the way humans behave and think.


Machine learning is a subset of or approach to artificial intelligence. A machine uses algorithms to analyze a massive amount of data, recognize patterns among the data set, and make predictions. The machine learns over time.


Now back to the ways in which big data is changing the food industry.


Big Data Can Help Crop Production


Precision agriculture is set to become a $43.4-billion-dollar business by 2025, and that’s, in large part, thanks to big data. One key area of precision agriculture is phytobiome and soil microbiome analysis, which can identify microbial communities that are vital to plant health.


By gathering a vast amount of phytobiome and soil microbiome data, machine learning tools can be used to identify those microbial communities that may play a role in crop disease resistance, yield increase, and drought tolerance. Companies like Indigo and AgBiome have invested heavily in this technology.


Big Data Can Help Prevent Fraud Detection


Food fraud has been around for millenia. Some of the earliest cases involved olive oil, tea, wine, and spices. Though many fraud incidences do not pose a public health threat, they undermine consumers’ trust in a brand. The Center for Food Integration conducted a study in 2015 that revealed that consumers not only want transparent food product labeling and ingredients; they want companies to be transparent about their business practices.


Big data can help brands monitor similar products across brands and suppliers to create composite trends and detect any fraudulent products at any point throughout the supply chain. For example, you can identify ingredient substitution, GMO percentage, the presence and quantities of allergens, toxins, and toxigenic organisms. At Clear Labs, we’ve done several studies on hot dogs, burgers, and seafood.


Big Data Can Help Predict Shelf Life


How accurate are your shelf life estimates? Traditionally, they are based on rudimentary statistical models, and due to their limitations, food scientists have begun looking for alternatives. One way is through analysis of the microbial composition of the food as measured by the microbiome.


Storage conditions, packaging, pH, temperature, and water activity can influence food quality and shelf life. By using next-generation sequencing (NGS) technologies, scientists can gain a more complete picture of the microbial composition of foods and how those microbial communities are influenced by intrinsic and extrinsic factors.


Let’s face it. It’s impractical to analyze the microbiome of every food product, but by analyzing a large selection of samples, we can develop more accurate predictive models, which use the observed (or expected) microbiome profile of the fresh product and estimate its remaining shelf life.


Big Data Can Help with Predictive Risk Assessment


Typically, we ask, “Is this pathogen or microbe or toxin present in this sample?” By using big data, we can ask a much more complicated and compelling question: “What does the microbial makeup indicate about the safety of this product?”


When a product contains a pathogen, the microbial composition will be different from the composition of a product without the pathogen. If we build a microbiome database, we can track those differences and use machine learn tools to predict the contamination of a product. By examining the entire microbiome instead of looking for specific pathogens, we can assess the food safety risk of a product with more confidence, ultimately leading to a reduction in the number of recalls, foodborne illnesses, and deaths.


Clear Labs: Safening the Global Food Supply through Big Data


The use of big data in the food industry isn’t a distant future. It’s happening now, and Clear Labs is at the forefront. To learn more about how we can help, contact us. We’d love to talk.

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