Performing Department
(N/A)
Non Technical Summary
The demand for non-animal and sustainable protein sources have been increasing due to increasing population and environmental concerns. Microalgae represent a promising and sustainable source of nutrients. The market currently offers only a limited selection of products containing microalgae due to the undesirable sensory properties (e.g., the fishy off-flavor and color) and scalable production of microalgae ingredients. To broaden the applications of algae-based ingredients in the food industry, this project aims to apply genome-scale metabolic models (GSMM), an advanced computational tool, to enable enhanced nutrient content, improved flavor profiles, and efficient production in microalgae. The goal of this research is to pave the way for microalgae to become a sustainable, appealing, and nutritious component of daily diets, particularly as an alternative protein source.This project employs an innovative approach combining GSMM with practical cultivation experiments to improve nutrient and flavor profiles of microalgae. In particular, the project targets to increase protein and antioxidant pigments in the microalgae while reducing the bitterness compounds and undesirable volatiles. The optimized ingredients will be used in the development of innovative food products which will be evaluated for their consumer acceptance, product quality, shelf-life, etc. The success of the project will bring novel microalgae with desirable nutritional and sensory properties as food ingredients to the food industry and various algae-based products to the market.
Animal Health Component
30%
Research Effort Categories
Basic
40%
Applied
30%
Developmental
30%
Goals / Objectives
The proposed study aims to harness genome-scale metabolic modeling to enhance nutritional profiles, flavor characteristics, and production efficiency of microalgae. The long-term goal of the project is to expand the applications of microalgae as a versatile food ingredient and alternative protein source. The specific objectives of the proposed study include: (1) Optimization and scale-up of microalgae nutrient and pigment production using genome-scale metabolic modeling;(2) Improving flavor profile and sensory properties of microalgae ingredients; and (3) Developing microalgae-based innovative food products.
Project Methods
Scientific Methods:Detailed methodologies of the project are presented in the proposal. The general scientific methods to be used include:Genome-Scale Metabolic Modeling (GSMM): The project will use advanced computational tools to construct and simulate metabolic models for microalgae species Dunaliella salina and Chlorella vulgaris. This involves the integration of genomic, transcriptomic, and metabolomic data to predict and optimize metabolic pathways for enhanced nutrient and pigment production.Cultivation Experiments and scaling up: Experimental setups will be designed to validate model predictions under controlled laboratory conditions. Various culturing conditions will be tested to optimize growth and productivity based on the insights gained from GSMM.Sensory and Nutritional Analysis: After optimizing growth conditions, the nutritional content (proximate values, fatty acids composition, amino acid, pigments, antioxidants, etc.) and sensory properties (taste, smell) of the microalgae will be analyzed using established analytical tests, including gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), and sensory evaluations.Product development and evaluation. The selected microalgae will be used in developing innovative and sustainable food products. The ingredient functionalities (e.g., bulk density, CIELAB color, water-holding capacity, oil-holding capacity, viscosity, rheology, emulsifying capacity, emulsifying stability, foaming capacity, foaming stability, least gelation concentration, gel texture profile, etc.) and product quality (pH, water activity, shelf-life, color, texture, volatile compounds, sensory evaluation) will be analyzed.Unique Aspects:Integration of Computational and Experimental Approaches: A novel approach of this project is to integrate computational predictions with empirical data to fast-track the development of optimized culturing strategies and product formulations. The application of this approach on improving food flavor and product formulation is innovative.Data Analysis:Data Analysis: MATLAB will be used to analyze data, develop algorithms, and create models. Simulations will be performed using the Gurobi Optimizer Version 5.6.3 (Gurobi Optimization Inc.) solver in MATLAB (The MathWorks Inc.) with the COBRA Toolbox (Schellenberger et al., 2011). Agilent MassHunter softwares and MS/ID (Cerno Bioscience) will be used to analyze chromatography results. RedJade will be used to collect and analyze sensory evaluation results. Statistical tools (e.g., R and SPSS) will be used to analyze experimental data, compare it with model predictions, and refine models accordingly.Interpretation of Results: Results will be interpreted in the context of their potential to enhance the scalability and consumer acceptance of microalgae-based foods.Efforts:Development of curriculum/formal classroom instruction: Two new courses, NUTR 603 Advanced Food Analysis and NUTR 604 Sustainable Foods, will be developed and offered since 2025-2026 academic year. The courses will include evidence-based pedagogies and cover the advanced developments in the field.laboratory instruction/practicum experiences: Research and analytical skills will be educated through mentored hands-on laboratory training. The students will either receive compensation as a research assistant or receive credits in research courses (NUTR 499 and BIOL 497 for undergraduates; NUTR 798 and BIOL 797 for graduate students). Outreach: Presentations and webinars will be offered on and off campus on topics related to sustainable foods to raise awareness about the benefits of microalgae as sustainable food sources.Evaluation of Outputs/Outcomes:Product Testing Feedback: Collecting consumer feedback on sensory properties and overall acceptance of developed food products.Economic Analysis: Assessing the cost-effectiveness and economic impact of optimized cultivation techniques and products.Impact on the field: Tracking the number of publications, presentations, the number of audience reached, and citations of the work from the project.