AI in Food Safety: Potential, Applications and Challenges

Food plays an important role in healthcare. Along with malnutrition and unhealthy junk food, foodborne diseases like food poisoning are major concerns for healthcare organizations and the health-conscious population. While there are several food safety guidelines and standards, the practical enforcement is a challenge. A keen observation of these guidelines reveals that many of them are in desperate need of modernization. AI can play a vital role here due to its predictability, computational speed, and in-depth analytics along with human-like competency, adaptability, and integrity.

AI can help in facilitating the transformation of food safety standards and help in minimizing the cases of food borne diseases due to adulterated food or other deliberate anomalies on part of food businesses. In this blog we will have an overview of the use of AI in food inspection with major challenges and potential:

AI for food safety: A quick overview

AI is a broad term but in simple words, it studies massive data to find patterns, pinpoint anomalies, and look for logical correlations between different elements that help it to learn and grow on its own.

In the context of food safety the AI learns and grows on its own by studying complicated big data patterns for predicting outcome with remarkably higher precision rate and lower processing time as compared to humans. Some of the techniques it utilizes in the process include random forests, deep learning, and neural networks.

The need to widen the scope of testing parameters

One thing to keep in mind here is that AI can help in speeding up the present conventional food inspection process with better accuracy. However, it is equally if not more important to widen the field of testing process thought automating and improving the risk factors like constantly changing safety profile depending upon weather, etc.

Overcoming uncertainty in food inspection consistency

One of the major challenges in the field of food safety is uncertainty in terms of inspection consistency and lack of reported data for foodborne diseases. For instance, on an average, less than 3% of total cases of nontyphoidal Salmonella spp. or Campylobacter spp get recorded in the lab monitoring process. Likewise, other types of food-borne diseases are not properly captured in lab monitoring systems and it causes huge information gaps and inconsistency.

The analytical and logical inference capabilities of ML can accurately fill the crucial gaps of such unconventional data thus turning it into a reliable information resource for supporting the food safety ecosystem.

Data related issues

Another issue is huge swathes of untapped data due to unstructured format and randomness. Sources of such data include platforms where people generally share product reviews (including food reviews) like Facebook, Twitter, Yelp, etc. With the help of AI, this unorganized but highly useful big data can easily be converted into a usable format and utilized to overcome the challenges of traditional surveillance models

We need to have precise objectives

Deploying an AI application without precise goals and quantifiable outcome criteria is, to be frank, a worthless “cosmetic exercise”. It is even truer when we talk about unorganized data sources like Facebook, Twitter, etc. Additionally, the credibility of information on social media is always a gray area- so we need to have reliable validating parameters and techniques before extracting and perusing such data.

Combining artificial intelligence and conventional wisdom

Some of the reliable review sites like Yelp do have strict guidelines to ensure unbiased and genuine reviews while at the same time providing tangible commercial rewards in terms of customer trust and better digital reputation. Such information plays an important role in training AI systems but the tricky question is that should it work mutually with conventional food inspection methods or replace them.

We need to design a wholesome strategy to appropriately set priorities as well. What’s more important- channeling attention to smaller (and so digitally uncovered) food joints or inspecting bigger F&B brands with negative reviews on Facebook. Policymakers as well as AI companies need to find out appropriate answers for such questions before going ahead.

Real life outcomes of AI in food safety programs

In one of the instances the Yelp reviews in Seattle and San Francisco were employed to calculate violations of health and food standards. Such studies concentrate on specific relevant terms like food poisoning, ill, sick, etc. on related sites like Twitter, Amazon, and Facebook to detect more serious food-related risks and violations.

Just like GFT, the Google researchers employ search/location logs for calculating health code breaches. Employing a more streamlined approach a specific website was created to encourage people to share the information related to food poisoning or related diseases. The website named iwaspoisoned.com encourages people to share their food poisoning and related instances. The initial studies are “so far so good” but we still need some time to come up with conclusive data.

AI for contemporary analysis and enforcement of relevant rules

One of the major things to consider here is that when talking about food inspection regulations- mindless strictness is as harmful as over leniency. With so many enforcement standards in place, it would be next to impossible to start or run a food business. A wise inspector is the one who can impartially and logically determine the specific standards that need to be enforced based on factors at the time of inspection like weather, foodborne pandemic (if any) or magnitude of the crowd at a specific food joint.

Once incorporated the AI can help inspectors overcome computational or logical challenges while weighing these variables without compromising with the fundamental objectives of food safety.

AI for determining underrepresented pockets- Going above headcounts

A beneficial way to use AI technology in food inspection methodology is to determine the underrepresented pockets- the areas that fewer than required food inspectors. In other areas, the corruption, negligence, or leniency of inspectors causes major accuracy gaps. The matters are more complicated than it seems from the surface. We don’t have to rely just on headcount.

An area with low but very strict and sharp inspectors may not need more of them. On the other hand, an area with a good number of inspectors- most of whom are influenced, corrupt, or negligent- needs more attention. For humans, it is an uphill- almost impossible task to simultaneously calculate these factors to reach a wholesome conclusion. AI’s 360-degree multi-analytical capabilities can be put to good use here for organizing, processing, and simultaneously studying big data like the magnitude of reported violations, several inspectors, the ratio of food inspectors, and F&B establishments, historical foodborne incidents of major significance, etc. AI can establish logical correlations among disparate data points to provide more accurate recommendations.