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Development of Artificial Intelligence Augmented Metal-Organic Framework-Based Systems and Their Applications in Food Sectors.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Development of Artificial Intelligence Augmented Metal-Organic Framework-Based Systems and Their Applications in Food Sectors./
作者:	Ma, Peihua.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	184 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Food science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29162315
ISBN:	9798834021391

Development of Artificial Intelligence Augmented Metal-Organic Framework-Based Systems and Their Applications in Food Sectors.
Ma, Peihua.

Development of Artificial Intelligence Augmented Metal-Organic Framework-Based Systems and Their Applications in Food Sectors. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 184 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2022.

This item must not be sold to any third party vendors.

Metal-organic frameworks (MOFs), a type of cutting-edge designable porous scaffolding materials attracted attention in reticular chemistry, which satisfied fundamental demands for delivery research in the past years. In this research, UiO-66 MOF family with different modifications was applied in the food delivery system and freshness monitoring.First, zirconium (IV) chloride and benzene-1,4-dicarboxylic acid were used to make the Zr-based MOF UiO-66. Then, using a post-synthesis loading process, curcumin was encapsulated in it. The system attained a high loading capacity of 3.45 percent w/w, according to both spectroscopic and thermogravimetric measurements. X-ray diffraction (XRD), physisorption analyzer, scanning electron microscopy (SEM), and energy-dispersive X-ray spectrometer (EDS) were used to characterize the crystal structure, porosity, and morphology of the curcumin delivery system, respectively. Curcumin was shown to be released in a controlled manner in simulated intestinal fluids using an in vitro digestion test. After 180 minutes of digestion, almost 60% of the curcumin was released. Second, two types of curcumin-loaded UiO-66 (representative high biocompatibility and water-stable metal-organic framework) deliver systems, curcumin-loaded UiO-66 Pickering emulsion and curcumin loaded UiO-66 high internal-phase Pickering emulsions (HIPPE) were prepared, named curcmin UiO-66 PE and curcumin UiO-66 HIPPE, respectively. The loading capacity for the two delivery systems was reached 7.33, and 26.18% w/w respectively. All systems were characterized using X-ray diffraction (XRD), physisorption analyzer, scanning electron microscopy (SEM), and energy-dispersive X-ray spectrometer (EDS), for crystallography, morphology, physicochemical properties, with computer assistant optimization with DFT and GCMC simulation for maximum loading capacity. The result showed that these systems both exhibited extremely high surface area and porosity, as well as strong chemical and thermal stability, which demonstrated their great potential for application as a food delivery system. On this basis, the emulsion system was further optimized using the response surface method. These novel MOF nanoparticle stabilized delivery systems could be practically utilized for other bioactive components and antimicrobial agents, which would find applications in functional food, food safety, and biomedical areas in the future. Third, incorporating or positioning multi-functional MOFs into the smart package is one of the next steps toward reticular chemistry for commercial application. Here, a cheap and versatile method to incorporate MOFs into smart food packages via generic patterning was developed. Meanwhile, deep convolutional neural networks (DCNN) were combined to form a system for monitoring food freshness that provided scent fingerprint recognition. The ice-template-based UiO-66-Br/chitosan sensor array and MOF-MMM-based UiO-66-OH/PVA sensor array comprising 6 different dyes absorbed at MOF matrix formed scent fingerprints that were identifiable by DCNN. Several state-of-art DCNN models were trained for shrimp freshness monitoring by using 31584 labeled images and 13537 images for testing. The highest accuracy achieved was up to 99.94% by the Wide-Slice Residual Network 50 (WISeR50). MOF-MMM-based sensor array showed a similar result where chicken freshness estimation achieved up to 98.95%. These platforms are intuitive, fast, accurate, and non-destructive, enabling consumers to monitor food freshness.

ISBN: 9798834021391Subjects--Topical Terms:

3173303
Food science.
Subjects--Index Terms:

Colorimetrci sensor array

Development of Artificial Intelligence Augmented Metal-Organic Framework-Based Systems and Their Applications in Food Sectors.
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506 $a This item must not be sold to any third party vendors.
520 $a Metal-organic frameworks (MOFs), a type of cutting-edge designable porous scaffolding materials attracted attention in reticular chemistry, which satisfied fundamental demands for delivery research in the past years. In this research, UiO-66 MOF family with different modifications was applied in the food delivery system and freshness monitoring.First, zirconium (IV) chloride and benzene-1,4-dicarboxylic acid were used to make the Zr-based MOF UiO-66. Then, using a post-synthesis loading process, curcumin was encapsulated in it. The system attained a high loading capacity of 3.45 percent w/w, according to both spectroscopic and thermogravimetric measurements. X-ray diffraction (XRD), physisorption analyzer, scanning electron microscopy (SEM), and energy-dispersive X-ray spectrometer (EDS) were used to characterize the crystal structure, porosity, and morphology of the curcumin delivery system, respectively. Curcumin was shown to be released in a controlled manner in simulated intestinal fluids using an in vitro digestion test. After 180 minutes of digestion, almost 60% of the curcumin was released. Second, two types of curcumin-loaded UiO-66 (representative high biocompatibility and water-stable metal-organic framework) deliver systems, curcumin-loaded UiO-66 Pickering emulsion and curcumin loaded UiO-66 high internal-phase Pickering emulsions (HIPPE) were prepared, named curcmin UiO-66 PE and curcumin UiO-66 HIPPE, respectively. The loading capacity for the two delivery systems was reached 7.33, and 26.18% w/w respectively. All systems were characterized using X-ray diffraction (XRD), physisorption analyzer, scanning electron microscopy (SEM), and energy-dispersive X-ray spectrometer (EDS), for crystallography, morphology, physicochemical properties, with computer assistant optimization with DFT and GCMC simulation for maximum loading capacity. The result showed that these systems both exhibited extremely high surface area and porosity, as well as strong chemical and thermal stability, which demonstrated their great potential for application as a food delivery system. On this basis, the emulsion system was further optimized using the response surface method. These novel MOF nanoparticle stabilized delivery systems could be practically utilized for other bioactive components and antimicrobial agents, which would find applications in functional food, food safety, and biomedical areas in the future. Third, incorporating or positioning multi-functional MOFs into the smart package is one of the next steps toward reticular chemistry for commercial application. Here, a cheap and versatile method to incorporate MOFs into smart food packages via generic patterning was developed. Meanwhile, deep convolutional neural networks (DCNN) were combined to form a system for monitoring food freshness that provided scent fingerprint recognition. The ice-template-based UiO-66-Br/chitosan sensor array and MOF-MMM-based UiO-66-OH/PVA sensor array comprising 6 different dyes absorbed at MOF matrix formed scent fingerprints that were identifiable by DCNN. Several state-of-art DCNN models were trained for shrimp freshness monitoring by using 31584 labeled images and 13537 images for testing. The highest accuracy achieved was up to 99.94% by the Wide-Slice Residual Network 50 (WISeR50). MOF-MMM-based sensor array showed a similar result where chicken freshness estimation achieved up to 98.95%. These platforms are intuitive, fast, accurate, and non-destructive, enabling consumers to monitor food freshness.
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