Producing food out of agricultural products involves a combination of exact science and precise engineering. Food processing businesses produce enormous amounts of data along the way. Data is necessary for the effective operation of logistical systems, processing equipment, and food safety.

Daily data can be analyzed using statistical methods, the results of which can be summarized for different uses. This data can be divided into three types:

1. Structured data such as numerical information, times and dates, serial numbers, etc.
2. Unstructured data such as text, email correspondence, machine data, survey replies, and transcripts
3. Semi-structured data such as tables, graphs, PowerPoint presentations, transcripts, audio and video files, XML documents, etc.

All three categories of data together have some traits in common, such as:

• Volume: Every food processing plant and every food-related corporation has enormous amounts of data stored in their databases.
• Value: The resident data contains a lot of useful information, in order to be useful to operations and decision-makers, these insights must be found, extracted, and processed.
• Variety: A wide range of big data types can be analyzed separately or collectively to look for any more significant patterns, trends, or correlations
• Velocity: Large data can be examined historically, but depending on the situation, it can also be examined in real-time. For example: Sensors are being installed in food processing plants to collect data at very high speeds. For quick answers, traditional analytical methods are frequently insufficient.

Insider threats pose a growing threat to businesses as they have the potential to negatively impact physical security, food defense, brand quality, and food product safety. For example, real-time or almost real-time data could be used to spot system anomalies and possibly even show that a disgruntled worker was trying to tamper with a continuing process step.

Using artificial intelligence (AI) to support individuals tasked with data analysis is one way to handle the data.

Using AI to Process Data for Food Safety

• Machine learning: It allows the systems to learn from experience and get better over time. It helps in finding the trends, abnormalities, and patterns in big datasets that are related to food safety. Therefore it can be used to forecast possible outbreaks of foodborne illness based on environmental factors and historical data.
• Natural language processing: It makes it possible for computers to comprehend, interpret, and produce human language. This helps in identifying potential problems with food safety and customer sentiments by analyzing text data from sources such as social media, customer reviews, and regulatory documents.
• Computer vision technology: It helps in interpreting and comprehending visual data, including images and videos. It can also be used for checking food contaminants, and spoilage.
• Deep learning: It can be applied to enhance predictive models for food safety incidents and to improve image recognition during food quality inspections.

Here are some instances of how various AI can be applied to big data analytics for food safety utilizing currently accessible technologies

1. Analytical prediction
2. Quality assurance and verification
3. Transparent supply chain
4. Social media monitoring and sentiment analysis
5. Adherence to regulations
6. Tailored food safety guidelines

AI Biosurveillance:

In the future, artificial intelligence will continue to have a bigger influence on food safety.

Threats from biological sources can arise accidentally or on purpose. Thus, biosurveillance serves the interests of public safety (avoidance of intentional food adulteration, bioterrorism, etc.) as well as public health (i.e., prevention of foodborne illness).

Through the logistical chains, AI-assisted biosurveillance systems could be integrated into every phase of the food chain, from pre-harvest to post-harvest.

Every sensor or analyzer that comes into contact with an agricultural product intended for human consumption will produce a unique set of data. Then, this data could be combined and subjected to artificial intelligence (AI) analysis. It could be programmed to look for particular patterns, correlations, and discoveries of contaminated product molecules, and also new anomalies in processes or outputs that might not have been noticed before.

Subject matter experts (SMEs) and system operators could then receive the real-time AI findings and review them. It is crucial that human involvement is necessary to determine the analysis by logic, experience, and judgment. Structured, unstructured, and semi-structured data could all be combined within the AI-powered analytical system to support the analysis.

In conclusion, it is true that AI-assisted analytics is a quickly developing field given the right care, can be used to safeguard public health, reduce the likelihood of foodborne illness, and preserve brand quality and profitability. Adopting this technology requires caution, but once implemented and adjusted appropriately, AI can help users learn more about their own products and systems, offering the chance to find hidden issues as well as opportunities to find new efficiencies. 

A Future View of AI-Enhanced Biosurveillance and Comprehensive Food Safety Programs- Food Safety Magazine