Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence
The accurate quantification of glycemic index (GI) remains crucial for diabetes management, yet current methodologies are constrained by resource intensiveness and methodological limitations. In vitro digestion models face challenges in replicating the dynamic conditions of the human gastrointestina...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Food Chemistry: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590157524010204 |
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| author | Mohammed Salman C K Muskan Beura Archana Singh Anil Dahuja Vinayak B. Kamble Rajendra P. Shukla Sijo Joseph Thandapilly Veda Krishnan |
| author_facet | Mohammed Salman C K Muskan Beura Archana Singh Anil Dahuja Vinayak B. Kamble Rajendra P. Shukla Sijo Joseph Thandapilly Veda Krishnan |
| author_sort | Mohammed Salman C K |
| collection | DOAJ |
| description | The accurate quantification of glycemic index (GI) remains crucial for diabetes management, yet current methodologies are constrained by resource intensiveness and methodological limitations. In vitro digestion models face challenges in replicating the dynamic conditions of the human gastrointestinal tract, such as enzyme variability and multi-time point analysis, leading to suboptimal predictive accuracy. This review proposes an integrated technological framework combining non-enzymatic electrochemical sensing with artificial intelligence to revolutionize GI assessment. Non-enzymatic sensors offer superior stability and repeatability in complex matrices, enabling real-time glucose quantification across multiple timepoints without enzyme degradation constraints. Machine learning algorithms, both supervised and unsupervised, enhance predictive accuracy by elucidating complex relationships within digestion data. This technological convergence represents a paradigm shift in food science analytics, promising improved throughput and precision in GI assessment. Future developments should focus on system scalability and broader applications across nutritional science, advancing diabetic management and personalized nutrition strategies. |
| format | Article |
| id | doaj-art-2dcde4d81e654c7c9acf37c180758bdb |
| institution | Kabale University |
| issn | 2590-1575 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Food Chemistry: X |
| spelling | doaj-art-2dcde4d81e654c7c9acf37c180758bdb2025-02-12T05:32:08ZengElsevierFood Chemistry: X2590-15752025-01-0125102132Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligenceMohammed Salman C K0Muskan Beura1Archana Singh2Anil Dahuja3Vinayak B. Kamble4Rajendra P. Shukla5Sijo Joseph Thandapilly6Veda Krishnan7Division of Biochemistry, ICAR-Indian Agricultural Research Institute (IARI), New Delhi 110012, IndiaDivision of Biochemistry, ICAR-Indian Agricultural Research Institute (IARI), New Delhi 110012, IndiaDivision of Biochemistry, ICAR-Indian Agricultural Research Institute (IARI), New Delhi 110012, IndiaDivision of Biochemistry, ICAR-Indian Agricultural Research Institute (IARI), New Delhi 110012, IndiaSchool of Physics, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram, 695551, IndiaDepartment of Electrical & Computer Engineering, North Carolina State University, 890 Oval Dr., Raleigh, NC 27695, USAAgriculture and Agri-Food Canada, Morden Research and Development Centre, Richardson Centre for Food Technology and Research, 196 Innovation Drive, Winnipeg, MB R3T 6C5, Canada; Corresponding authors.Division of Biochemistry, ICAR-Indian Agricultural Research Institute (IARI), New Delhi 110012, India; Corresponding authors.The accurate quantification of glycemic index (GI) remains crucial for diabetes management, yet current methodologies are constrained by resource intensiveness and methodological limitations. In vitro digestion models face challenges in replicating the dynamic conditions of the human gastrointestinal tract, such as enzyme variability and multi-time point analysis, leading to suboptimal predictive accuracy. This review proposes an integrated technological framework combining non-enzymatic electrochemical sensing with artificial intelligence to revolutionize GI assessment. Non-enzymatic sensors offer superior stability and repeatability in complex matrices, enabling real-time glucose quantification across multiple timepoints without enzyme degradation constraints. Machine learning algorithms, both supervised and unsupervised, enhance predictive accuracy by elucidating complex relationships within digestion data. This technological convergence represents a paradigm shift in food science analytics, promising improved throughput and precision in GI assessment. Future developments should focus on system scalability and broader applications across nutritional science, advancing diabetic management and personalized nutrition strategies.http://www.sciencedirect.com/science/article/pii/S2590157524010204Glycemic indexIn vitro modelsStarch hydrolysisElectrochemical sensorArtificial intelligence |
| spellingShingle | Mohammed Salman C K Muskan Beura Archana Singh Anil Dahuja Vinayak B. Kamble Rajendra P. Shukla Sijo Joseph Thandapilly Veda Krishnan Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence Food Chemistry: X Glycemic index In vitro models Starch hydrolysis Electrochemical sensor Artificial intelligence |
| title | Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence |
| title_full | Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence |
| title_fullStr | Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence |
| title_full_unstemmed | Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence |
| title_short | Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence |
| title_sort | biomimic models for in vitro glycemic index scope of sensor integration and artificial intelligence |
| topic | Glycemic index In vitro models Starch hydrolysis Electrochemical sensor Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2590157524010204 |
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