This chapter explores the evolution, classification, and health implications of food processing, while emphasizing the transformative role of machine learning, artificial intelligence (AI), and data science in advancing food informatics. It begins with a historical overview and a critical review of traditional classification frameworks such as NOVA, Nutri-Score, and SIGA, highlighting their strengths and limitations, particularly the subjectivity and reproducibility challenges that hinder epidemiological research and public policy. To address these issues, the chapter presents novel computational approaches, including FoodProX, a random forest model trained on nutrient composition data to infer processing levels and generate a continuous FPro score. It also explores how large language models like BERT and BioBERT can semantically embed food descriptions and ingredient lists for predictive tasks, even in the presence of missing data. A key contribution of the chapter is a novel case study using the Open Food Facts database, showcasing how multimodal AI models can integrate structured and unstructured data to classify foods at scale, offering a new paradigm for food processing assessment in public health and research.

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a class-imbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world \textit{TWEET-FID} data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

In the introduction of the first part, everyone has a relatively comprehensive understanding of the types of artificial neural network in smart food processing. Today I bring you the introduction of the second part of the review entitled "Smart Food Processing: A Journey from Artificial Neural Network to Deep Learning": Smart Food Processing Based on Artificial Neural Network (ANN). Artificial neural network have been used in many fields. In the past few years, research work based on artificial neural network has seen an astonishing growth in application and development. In these food-based applications, artificial neural network play a vital role in the processing of fruits, vegetables, juices, wine, olive oil, meat, fish, various grains, and soft drinks.

Food recalls could be a thing of the past if artificial intelligence (AI) is utilized in food production, according to a recent study from UBC and the University of Guelph. The average cost of a food recall due to bacterial or microbial contamination, like E. coli, is US$10 million according to study co-author Dr. Rickey Yada, a professor and the dean of the UBC faculty of land and food systems. We spoke with Dr. Yada about how AI can help optimize the current systems used in the food processing industry, and how it can help make our food supply safer. What are some of the current limitations when it comes to food processing? The current challenge is that food safety problems tend to show up after the fact once the products have been shipped, sold, or in some cases already consumed.

Food recalls could be a thing of the past if artificial intelligence (AI) is utilized in food production, according to a recent study from UBC and the University of Guelph. The average cost of a food recall due to bacterial or microbial contamination, like E. coli, is US$10 million according to study co-author Dr. Rickey Yada (he/him), a professor and the dean of the UBC faculty of land and food systems. We spoke with Dr. Yada about how AI can help optimize the current systems used in the food processing industry, and how it can help make our food supply safer. The current challenge is that food safety problems tend to show up after the fact once the products have been shipped, sold, or in some cases already consumed. This then leads to recalls that are damaging both economically and reputationally.

On July 14, Key Technology debuted its new FM Alert software driven by artificial intelligence (AI). The new AI alert system can help processors control foreign materials entering product streams, as well as improving documentation and overall food safety. It will be a part of the company's exhibit at Pack Expo in October at booth S-3547. The AI system captures and saves images of foreign materials (FMs) that a sorter detects and rejects from its stream, with data available immediately to alert operators. "Thanks to the application of advanced artificial intelligence, our new FM Alert software achieves uniquely accurate results -- identifying, recording and acting on true FM findings on the line," said Marco Azzaretti, director of marketing at Key. "The food processing industry continues to focus more and more on elevating food safety. By making product safer, this effective FM-fighting tool helps customers protect their brand's reputation and avoid costly recalls. Every food processor wants to prevent contamination, making FM Alert universally beneficial across all applications."

Key Technology introduces AI-driven FM alert software for its digital sorting systems. This powerful tool captures and saves digital images of critical foreign material (FM) contaminants that the sorter detects and rejects from the product stream. Data outputs from the software can be utilized to immediately alert operators and/or signal a downstream device. AI-enhanced FM Alert helps processors better control FM and improve documentation to protect food safety. "Thanks to the application of advanced artificial intelligence, our new FM Alert software achieves uniquely accurate results – identifying, recording, and acting on true FM findings on the line," said Marco Azzaretti, director of marketing at Key. "The food processing industry continues to focus more and more on elevating food safety. By making product safer, this effective FM-fighting tool helps customers protect their brand's reputation and avoid costly recalls. Every food processor wants to prevent contamination, making FM Alert universally beneficial across all applications."

The Middleby Corporation announced the acquisition of Proxaut, a leading manufacturer of Auto Guided Vehicles (AGVs) for the food industry and industrial processing companies. The company is based in Italy near Bologna with approximately $15 million USD in annual sales. "We are leading the trend for Industry 4.0 in food processing. Our recent strategic investments in automation are coming to fruition, as we see order demands for this technology" Proxaut AGV technology is used by industry leading manufacturers in a variety of capacities, primarily to move materials and products safely and operate alongside people. Proxaut automation decreases repetitive movements from traditional labor and ergonomically improves workflows.

Many of these challenges are caused by inefficiencies in the food processing and distribution supply chain, which is a vital value-added step in our food system. The pieces of the puzzle are all there: ubiquitous sensors and devices that generate data with unprecedented volume, velocity and veracity; mature computational methods to make use of them; connected markets that can take advantage of these innovations at a global scale; and a need to transform antiquated, obsolete components of the current system, whether because of consumer demand for personalization and empowerment, or the need for global food safety and sustainability. Millions are spent every year in both the private and public sector to bring forth innovative solutions in capturing market preference, food safety, food security, provenance and traceability, all the while creating superior products that taste good, are good for your health and don't break the bank.

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