Nutrition & Food Science
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Item DEVELOPMENT OF CHITIN NANOCRYSTALS AND THEIR APPLICATIONS IN FOOD AND AGRICULTURAL AREAS(2024) Jia, Xiaoxue; Wang, Qin; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Seafood industry generates millions of tons of waste annually, with crustacean shells being a significant component. Discarding these shells not only exacerbates environmental pollution but also represents a missed opportunity for resource recovery. This dissertation research aims to address these environmental challenges by repurposing crustacean shell waste into high-value nanomaterials, specifically chitin nanocrystals (ChNCs), and exploring their applications in the food, agricultural, and environmental sectors. The primary objective of this study is to investigate the fabrication, functionalization, and potential industrial applications of ChNCs, thereby offering a sustainable alternative to conventional synthetic materials.Traditional chitin nanocrystals ChNCs obtaining methods rely on strong acids, posing environmental risks. This research introduces a more sustainable phosphoric acid (PA) hydrolysis method, which uses significantly lower acid quantities, reduces environmental impact, and avoids corrosive waste. Moreover, the novel PA hydrolysis occurs in the solid state and can be handled by hand, simplifying operation. This method efficiently yields uniform ChNCs with positive surface charges (~+27 mV), suitable for scalable industrial applications. Additionally, 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO) oxidation was employed to produce oxidized chitin nanocrystals (O-ChNCs) with negative surface charges (~ −56 mV). ChNCs and O-ChNCs were investigated as stabilizers in Pickering emulsions. Both were able to significantly enhance the stability of oil-in-water (O/W) emulsions, particularly when pH > 9. O-ChNCs further demonstrated encapsulation efficiencies of up to 80% for bioactive compounds like quercetin, highlighting their potential in food and nutraceutical delivery systems. Additionally, ChNCs and O-ChNCs were incorporated into a colorimetric sensor array (CSA) to monitor beef freshness. The negative charged O-ChNC-based sensor exhibited sensitivity to spoilage gases, achieving 99.3% accuracy in beef freshness detection with the aid of deep learning algorithms. This innovation provides a non-invasive cost-effective method to food quality and safety monitoring. Furthermore, ChNCs were deacetylated to form chitosan nanocrystals (ChsNCs), and subsequently modified with zinc to create a ChsNCs@Zn composite for the removal of per- and polyfluoroalkyl substances (PFASs) from water. The composite achieved 50% PFAS removal within 5 minutes and ultimately achieved 68% removal, showcasing strong adsorption capabilities and offering a potential sustainable solution for PFAS remediation in contaminated water sources. In summary, this research is driven by the need to solve the environmental problem of seafood waste, while also tackling challenges in food stability and safety, as well as water purification. The findings contribute to advancing sustainable materials and practices in response to pressing environmental challenges.Item MACHINE LEARNING AND GENOMICS FOR IMPROVED FOOD SAFETY AND RISK ASSESSMENT OF SALMONELLA ENTERICA IN CHICKEN(2024) Benefo, Edmund Ofosu; Pradhan, Abani K; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Salmonella enterica is a leading cause of foodborne illnesses worldwide and is commonly associated with poultry. Salmonella has many closely related serovars, yet these serovars exhibit significant variability in many characteristics including host range, virulence, growth behavior, stress response, and antimicrobial resistance. In the past, this intricate and dynamic population heterogeneity of Salmonella severely hampered control efforts, but, today, this has improved through the sequencing of Salmonella genomes. Whole genome sequencing (WGS) provides a better understanding of the evolutionary and ecological adaptations that underlie the survival of Salmonella against antimicrobials, oxidative agents, non-optimal temperatures, and other stressors in the environment and their hosts. Coupling machine learning with WGS expands on these advantages by enabling the identification of genetic patterns that may not be immediately apparent. The overall goal of this research was to explore how machine learning and genomics can be integrated to improve food safety. First, a machine learning model was developed to identify stress response genes in Salmonella isolated from different poultry processing stages. It was found that beyond genes encoding for cold and heat shock proteins, other genes involved in lipopolysaccharide biosynthesis, DNA repair and replication, and biofilm formation are involved in Salmonella’s overall stress response mechanism. Additionally, a machine learning model was developed to predict antimicrobial resistance (AMR) phenotypes in Salmonella isolates using WGS data. The model predictions were comparable to existing bioinformatic methods for AMR prediction and identified AMR genes that are typically not the resistance determinants public health agencies focus on. Expanding this approach for AMR surveillance could lead to the discovery of novel AMR genes. Lastly, a quantitative microbial risk assessment for Salmonella in chicken that incorporated Salmonella heterogeneity in growth and virulence was developed. The findings revealed that variations in virulence have a greater impact on the risk of salmonellosis than variations in growth rate. Overall, this research contributes to efforts to enhance food safety measures and reduce chicken-associated Salmonella illnesses.Item Investigation into the Impact of Food Matrix on Bacterial Survival during Gastric Digestion(2024) Gao, Zhujun; Tikekar, Rohan V.; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Over the years, food safety research often focused on the bacterial survival during food processing and storage, whereas physiological studies extensively explored the host-pathogen interaction in gastrointestinal tract. There is a need to understand the intermediate step on pathogen survival during gastric digestion and the potential impact from its food carrier. This study utilized water-in-oil (W-O) and oil-in-water (O-W) emulsion as well as deionized water (DI) as the fundamental model food matrices to study the potential protection by food matrix during simulated gastric digestion. Using Salmonella enterica subsp. enterica serovar Typhimurium as a sample foodborne pathogen, this study investigated the survival kinetics of bacteria using various models of simulated gastric digestion. In a simplified static pH simulated gastric digestion model, inoculated W-O and O-W emulsion matrices were challenged with simulated gastric fluid (SGF) containing HCl and pepsin with mixing using a stomacher for two hours. W-O emulsion showed significant protection of Salmonella survival compared to O-W emulsion and DI water. This protective effect appeared to be matrix dependent regardless of the inoculation location of Salmonella (in dispersed phase vs. in continuous phase). Within the same emulsion type, inoculating Salmonella in water phase or oil phase did not show significant difference in its survivability during simulated gastric digestion. The study was then extended to an improved gastric digestion model where the chyme pH dropped from 4.0 to 1.5 over three hours, and the chyme mixing was achieved by an orbital shaker. In addition, the new SGF was modified to be HCl solution with pepsin, amano lipase A, mucin and NaCl. Under this digestion condition, there was no significant difference in Salmonella survival between W-O emulsion, O-W emulsion, and DI water. Moreover, the dispersed-continuous phase ratio of emulsion composition also showed no impact on Salmonella survival. The simulated gastric digestion model setup was also further analyzed including the role of individual digestive enzyme, the pH impact, and the mechanical mixing approach. In the dynamic pH simulated gastric digestion model, partial activity from lipase accelerated the disruption of emulsion structure for both W-O and O-W emulsion matrices. Mild mixing using an orbital shaker also showed difference in Salmonella survival compared to vigorous mixing using a stomacher. Lastly, this study expanded from using Salmonella as the single bacteria strain into a tailored natural microbiome community. Natural microbiome communities from Golden Delicious (GD) and Empire (EP) apples were manually enriched using bacteria culturing broth at pH 5 and pH 7, respectively. The enriched apple microbiome was then collected and analyzed using 16S rRNA sequencing to study the microbial composition. With a significant decrease in Alpha diversity, the culturable apple microbiome was successfully enriched from less than 3 log CFU/ml to more than 8 log CFU/ml. There was no known foodborne human pathogens detected in the enrichment, and the most abundant genera appeared to be potential plant growth promoting bacteria. The collected apple microbiome was then inoculated in various food matrices to study its survivability during dynamic pH simulated gastric digestion including DI water, apple sauce (AS), chicken puree (CK), sweet potato puree (SP), and W-O emulsion. The enriched apple microbiome showed remarkably high survivability in W-O emulsion throughout the full three-hour digestion treatment. CK also exhibited moderate protective effect compared to SP at the same condition. There was no significant difference between DI and AS on bacterial survivability. In addition, the apple microbiome enriched at two pH levels (5 & 7) showed similar bacteria inactivation kinetics. In conclusion, this study revealed the potential impact from food matrix on bacterial survival during simulated gastric digestion. W-O emulsion offered significant protection of certain bacteria strains or communities in specific simulated gastric digestion models. The parameters in gastric digestion models also affected bacterial survival. Future work should focus on exploring the potential impact from other types of food matrices, expanding the microbial survival study into other bacterial strains as well as a more complex microbiome community, and further comparing the various gastric digestion models.Item Antiproliferative Activity of Soybean and Tempeh Extracts on Human Colorectal Cancer Cells(2024) Fan, Rongjie; Lee, Seong-Ho; Wei, Cheng-I; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Tempeh, an indigenous Indonesian soybean product, is produced through a fermentation process of soybean that enhances the bioavailability of its beneficial nutrients such as proteins and phytoestrogens. Recent studies suggest that the fermentation process of tempeh may enhance the biofunctionality properties of soybeans including anticancer activity. The current study is designed to present a comparative analysis to see if defatted extracts of unfermented soybeans and tempeh (fermented soybeans) possess anti-proliferative activity in human colorectal cancer (CRC) cells. The experimental methods involve the production and extraction of soybeans extract (SE) and tempeh extract (TE) at a concentration of 35 g/100 mL (w/v) with 70% ethanol, followed by rotary evaporation and freeze-drying. MTT assays indicated that both SE and TE exhibited inhibitory activity in viability of human CRC cells, with TE demonstrating a more pronounced dose-dependent inhibition of cell growth compared to SE. Cell cycle analysis led to a significant increase of G1 arrest in both SE and TE-treated cells. The induction of apoptosis was observed from the cells treated with both SE and TE. Western blot analysis revealed an increase of PARP cleavage for both treatments, demonstrating activation of apoptotic pathways in SE and TE-treated cells.