8 Artificial intelligence in microbial food safety
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Dominic Panaligan
Abstract
Over 500 million people get sick annually due to the consumption of foods contaminated with microbial hazards like bacteria, viruses, and parasites. Conventionally, these are addressed through integrated food safetyfood safety systems implemented along the food supply chain. However, these have become increasingly globalized, with foods often crossing multiple international borders as they move from “farm to fork.” This increasing globalization, combined with the resource-intensive nature of conventional systems, causes disparities and lapses in food safety compliance. AIAI has the potential to bridge these gaps by offering more efficient and cost-effective tools for the detection, monitoring, and control of microbial foodborne diseasesfoodborne diseasesFBDs (FBDs). Furthermore, AI can process existing data; including search results, social media posts, and various databases; and correlate them to FBDs. The global state-of-the-art use of AI for microbial food safety applications as well as future potential applications will be discussed in this chapter.
Abstract
Over 500 million people get sick annually due to the consumption of foods contaminated with microbial hazards like bacteria, viruses, and parasites. Conventionally, these are addressed through integrated food safetyfood safety systems implemented along the food supply chain. However, these have become increasingly globalized, with foods often crossing multiple international borders as they move from “farm to fork.” This increasing globalization, combined with the resource-intensive nature of conventional systems, causes disparities and lapses in food safety compliance. AIAI has the potential to bridge these gaps by offering more efficient and cost-effective tools for the detection, monitoring, and control of microbial foodborne diseasesfoodborne diseasesFBDs (FBDs). Furthermore, AI can process existing data; including search results, social media posts, and various databases; and correlate them to FBDs. The global state-of-the-art use of AI for microbial food safety applications as well as future potential applications will be discussed in this chapter.
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
