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              <text>Almoselhy, Rania I. M.; Usmani, Afreen; Rajendran, Rajakumari; Thomas, Sabu</text>
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              <text>AI and Big Data Analytics for Management of Nutraceuticals</text>
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              <text>Handbook of Nutraceuticals: Science, Technology and Engineering;pp.1177-1193</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-3-031-98791-5_50" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-98791-5_50&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105032508153?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105032508153?origin=resultslist&lt;/a&gt;</text>
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              <text>Almoselhy R.I.M., Oils and Fats Research Department, Food Technology Research Institute, Agricultural Research Center, Giza, Egypt; Usmani A., Department of Pharmacology, MESCO Institute of Pharmacy, Amroha, India; Rajendran R., The Oxford College of Pharmacy, Karnataka, Bengaluru, India; Thomas S., School of Nanoscience and Nanotechnology, Mahatma Gandhi University, Kerala, Kottayam, India, Department of Physics and Electronics, Christ University, Bangalore, India, Center of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand, TrEST Research Park, Kerala, Trivandrum, India</text>
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              <text>This chapter explores the transformative impact of artificial intelligence (AI) and Big Data Analytics on nutraceutical management. As consumer demand for health-focused products such as supplements, functional foods, and herbal extracts grows, ensuring quality, safety, regulatory compliance, and personalization remains challenging. AI enhances formulation and quality assurance through predictive modeling and computer vision, while analyzing individual health data for personalized nutrition. Concurrently, Big Data leverages vast datasets from clinical trials to consumer feedback to forecast market trends, segment consumers, and guide R&amp;amp;D. The integration of AI and Big Data fosters deeper insights, driving innovation, safety, and tailored solutions. However, challenges like data privacy, standards, and ethics persist. Future developments may include AI-driven discovery of bioactives, real-time supply chain monitoring, and blockchain-based transparency. Embracing these innovations is essential for advancing safer, more effective, and personalized nutraceuticals that promote public health.  Springer Nature Switzerland AG 2026.</text>
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              <text>Artificial intelligence (AI); Big data analytics; Data mining; Nutraceuticals management; Nutritional data analysis; Personalized nutrition; Predictive analytics; Quality and safety</text>
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              <text>ISBN: 978-303198791-5; 978-303198790-8;</text>
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