Source: UNIVERSITY OF CALIFORNIA, DAVIS submitted to NRP
ENHANCING FUNCTIONALITY OF PLANT PROTEINS: ENGINEERING NOVEL STRUCTURES AND DEVELOPING A PREDICTIVE FRAMEWORK FOR FOOD PRODUCT DEVELOPMENT
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
Reporting Frequency
Annual
Accession No.
1030287
Grant No.
2023-67017-39832
Cumulative Award Amt.
$594,000.00
Proposal No.
2022-09212
Multistate No.
(N/A)
Project Start Date
Jun 1, 2023
Project End Date
May 31, 2027
Grant Year
2023
Program Code
[A1364]- Novel Foods and Innovative Manufacturing Technologies
Recipient Organization
UNIVERSITY OF CALIFORNIA, DAVIS
410 MRAK HALL
DAVIS,CA 95616-8671
Performing Department
Biological and Agricultural Engineering, Food Science & Tech
Non Technical Summary
Plant-based proteins have emerged as a promising and sustainable alternative source of dietary protein for diverse and novel food products, and are viewed favorably by many consumers. However, despite significant progress in developing liquid and semi-solid foods with plant-proteins (e.g. nut milk or yogurt alternative products), these foods suffer from limited dispersibility of proteins after pasteurization, resulting in sedimentation and an unpleasant mouthfeel when consumed (such as a gritty or chalky texture). In addition, although many consumers view these plant-based milk and yogurt alternatives as healthier, it is likely that their protein digestibility is reduced, which could instead negatively impact consumer health. As a result of these serious limitations in development of dairy alternatives with plant proteins, this project aims to characterize the functionality of plant proteins, and to develop novel protein microstructures that may help to ultimately increase protein functionality. Protein functionality will be assessed in model liquid (a milk alternative or protein shake) and model semi-solid (a yogurt alternative or dip) foods. Functionality will be considered as a combination of stability (how well the proteins remain dispersed in the product after heating), texture (the flow properties and mouthfeel of the product), and digestibility (how much of the protein may ultimately be absorbed by the consumer to benefit health). To allow for translation of the data collected in this project to the US food manufacturing industry, a machine learning model will be developed that will allow for food producers to input the protein concentration and properties into the model and it will predict the level of stability, texture, or digestibility for a given protein and food system. This tool will streamline product development of new foods with plant proteins and allow the food manufacturing industry to deliver higher-quality products to consumers.
Animal Health Component
65%
Research Effort Categories
Basic
35%
Applied
65%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
5022410101020%
5022410202050%
5022410200030%
Goals / Objectives
The proposed research brings together an interdisciplinary and comprehensive approach that addresses the broader goals of: (a) extracting, fractionating, and characterizing protein fractions from diverse plant sources (pea, almond, oat, and algae); (b) formulation engineering of sub-micron protein structures (e.g., microgels and nanoparticles) to improve the functionality of the protein fractions, such as dispersibility and stability with thermal processing; (c) translation of these protein fractions to develop model liquid beverages and semi-solid foods and characterization of their stability, texture, and digestibility; and (d) development of a machine learning predictive framework to reduce empirical testing for future food product development using plant-based proteins.Ultimately,the overall goal of this project is to provide this information to the US food manufacturing industry to streamline development of novel foods using plant proteins that will have improved quality. This overall goal will be accomplished through the followingspecific objectives of the research plan: 1. Evaluate the role of physico-chemical properties of plant protein fractions (concentrates, isolates, soluble proteins) in their stability in aqueous solutions and as ingredients to develop sub-micron scale assemblies based on physical structuring mechanisms to enhance stability in aqueous solutions.2. Characterize the functionality (stability, texture, and digestibility) of protein fractions and novel protein structures in liquid and semi-solid model food systems.3. Develop a machine learning predictive framework to aid in the development and manufacturing of novel plant-based foods with optimal functionality.
Project Methods
Aim 1: Four plant protein sources will be utilized in this project: pulse (pea), almond, oat and microalgae (Chlorella vulgaris). Pea, oat, and almond flours and Chlorella vulgaris will be obtained from commercial suppliers. Microalgae samples will be freeze-dried, milled, and sieved. Aqueous extraction of plant and algae flours will be carried out using previously developed and optimized extraction methods to yield three protein fractions from each protein source: a protein concentrate; a protein isolate; and a soluble protein fraction. The isolated plant protein fractions will be analyzed for amino acid composition, proximate analysis, and molecular weight distribution of protein fractions using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE).Protein stability of each protein fraction will be assessed in an aqueous dispersion as a function of protein concentration (6 levels between 0.5 - 10%) and pH (5 levels between 3 - 7). The stability will be assessed through a combination of measurements: protein dispersibility index, heat coagulation time, and particle size and surface charge.Based on the protein stability measurements, those protein fractions with the highest stability will be utilized to develop microgels, while those protein fractions with lower stability will be utilized to develop nanoparticle dispersions. After the plant protein microgels and nanoparticle dispersions are developed, the stability of these sub-micron scale assemblies will be determined as a function of protein concentration and pH using the same methods as for the protein fractions.Aim 2: Liquid foods will be developed that will serve as a model for a milk alternative or a protein-enhanced beverage/protein shake, depending on the protein content. Those products with 1-4% protein will target a milk alternative product, and those products with 5-15% protein will target a protein shake, as these ranges are similar to currently available products. All liquid model foods will have 2% (w:v) glucose and will be produced at a constant pH (between 6.5 - 8.5). The protein content (3 levels each for milk alternatives and protein shakes between 1 - 15%), and fat (0, 2, or 4%) will be varied to assess the functionality of systems with similar properties as the currently available plant-based milk alternatives, but with higher protein content. The protein fractions or the concentrated protein microgels or nanoparticles will be mechanically dispersed in water at the appropriate concentration for production of the model food systems. The fat content will be added as soybean oil at either 0, 2, or 4%.Semi-solid foods will be developed that will serve as a model for a yogurt alternative or similar product (e.g. dip, sour cream, etc.) produced with the protein fractions and sub-micron assemblies from the plant proteins in Aim 1. All semi-solid model foods will contain 2% (w:v) glucose and 1% pectin (w:v), suspended in deionized water. There will be a minimum total solid content of 10% with a pH of 3.9 - 4.5. The protein (3 levels between 3 - 15%) and fat content (0, 2, or 4%) will be varied to assess the maximum protein content that is functional in the semi-solid model food, and to assess if the addition of the novel protein structures may act as a fat-replacer. To avoid additional variability in model food systems due to fermentation, and to increase the feasibility of testing a large number of experimental conditions, the yogurt alternative products will not be produced using active cultures, but the pH (3.9 - 4.5) will be adjusted with L-(+)-lactic acid or another organic acid to the relevant range to assess the protein functionality. The fat content will be tested at 0, 2, or 4% and will be added as soybean oil.The model liquid and semi-solid food products and control liquid and semi-solid food products developed with whey protein isolate (as a gold standard for a high functionality protein) will be characterized before and after thermal pasteurization for stability, using the methods described for the protein fractions dispersed in water in Aim 1. Product texture will be assessed through a combination of four approaches to determine textural aspects related to the product quality and mouthfeel: 1) measurement of rheological properties (flow and oscillatory behavior); 2) tribology; 3) particle size distribution; and 4) microstructural analysis using confocal scanning laser microscopy. Product digestibility will be assessed through measurement of protein digestion during and in vitro biochemical gastrointestinal digestion. Digestibility will be measured as the degree of protein hydrolysis (determined from free amino group measurement), soluble amino acid release at the end of in vitro gastrointestinal digestion, as well as through band intensity measurement after SDS-PAGE.Aim 3: Machine learning models will be developed for classification of functionality of both plant protein fractions and sub-micron scale protein structures. The inputs of the machine learning model will be compositional and physico-chemical properties of the plant protein fractions and of the dispersion/model food system that will be characterized in this project. The model output for each protein fraction or sub-micron structure:food system combination will consist of three values. These values will represent the classification of Poor, Low, Medium, or High in each of the three categories of plant protein functionality: stability, texture, and digestibility. Multiple machine learning algorithms will be evaluated to discover the optimal model for predicting protein functionality based on the input data. The selected algorithms will include logistic regression, decision trees, random forest, support vector machine, gradient boosting, and neural networks. In addition, ensemble learning approaches will be explored, in which each prediction is produced by a collection of multiple trained algorithms.Feature selection techniques will be utilized to improve the model learning. Established feature selection methods such as genetic algorithms and greedy forward selection will be evaluated for selecting features from the properties measured in Aims 1 and 2. The role of individual selected features in explaining the model will be assessed based on increase in error after permuting a feature while the model remains untouched.To evaluate the machine learning models, we will randomly select 20% of the data generated in Aims 1 and 2 to be held out for model validation, and the remaining 80% of the data will be used for model training and testing. Model training and testing will be done through 5-fold cross-validation, where the data will be split into 5 folds. For each fold, the model will be trained on training datasets and evaluated on test datasets. The default loss function (multi log loss) will be used to construct the objective function. Predictions will be evaluated based on the receiver operating characteristic curve and confusion matrix and will be aggregated for all 5 folds to give the final values. The model will be validated separately on the 20% of the un-seen data that was held out from the model training and testing.

Progress 06/01/24 to 05/31/25

Outputs
Target Audience:The target audience in this reporting period are graduate students and other academic researchers in the scientific community. Changes/Problems:In the first year of the project, we had significant challenges in recruiting personnel to join the study team. However, we were able to recruit a visiting scholar (who is now a Postdoctoral scholar), who started in August 2024, and a new PhD student joined our project team in September 2024. These recruitments enabled us to conduct a significant amount of work in the past year. What opportunities for training and professional development has the project provided?This project has provided training and professional development for 1 PhD student in Food Science and 1 visiting scholar (who is now a postdoctoral scholar in the project) in the Biological and Agricultural Engineering department. Two undergraduate students (Food Science and Technology) have also had opportunities for training in laboratory and data analysis in the past year. How have the results been disseminated to communities of interest?Significant data collection was conducted in the past year, and the data analysis is ongoing. The project team has one publication that is in progress based on the results from the project on pea protein behavior, and several additional publications are planned for the future based on the experiments with other types of proteins and the pea protein microgels. The project team also plans to submit abstracts to national or international scientific conferences (e.g. Food Structures, Digestion, and Health International Conference or the Society of Food Engineering Annual Meeting) to further disseminate project results to communities of interest. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, a publication will be prepared and submitted on the work that has been completed with pea protein fractions and their functionality. Additional experiments will be conducted on development and characterization of pea protein microgels, and on the functionality of the other protein systems planned in this study (oat, spirulina, almond). Trials will also begin on development and characterization of the model food systems with the protein dispersions or microgels.

Impacts
What was accomplished under these goals? Significant progress was made on Objective 1. To evaluate different protein fractions, a pea protein extraction protocol was optimized to obtain both pea protein concentrate and pea protein isolate. Raw peas were sourced from a local market. Peas were ground, and the resulting pea flour was sieved (0.5 mm mesh), followed by solvent defatting using n-hexane (1:3 w/v) under constant agitation for 3 hrs. The defatted flour was subjected to alkaline extraction (pH 9.0, 0.1 N NaOH, 1:6 w/v) under continuous stirring for 1 hr. The supernatant was separated by centrifugation (4200 rpm, 40 min, 20°C), and the extraction was repeated 2-3 timesto maximize the recovery of alkali-soluble proteins. The combined supernatants were acidified to pH 4.5 using 0.1 N HCl to induce isoelectric precipitation. The resulting precipitate was collected by centrifugation, neutralized to pH 7.0, and designated as the pea protein isolate. The intermediate supernatant, which remained non-precipitated, wasadjusted to pH 7.0 to yield the protein concentrate. Both fractions were frozen at -80°C, freeze-dried for 48-72 hrs, ground, and sieved to 130 mesh (isolate) and 200 mesh (concentrate). To allow for comparison of the different protein fractions produced on a lab-scale, commercial protein powders from pea (Pisum sativum), almond (Prunus dulcis), oat (Avena sativa), and spirulina (Arthrospira platensis) were sourced from various suppliers. Whey protein concentrate and isolate werepurchased and analyzed as gold standards for comparison with the plant protein samples. All samples, both lab-produced and commercial, underwent compositional analysis for moisture (gravimetric), protein (combustion), lipids (Soxhlet extraction), and ash (gravimetric), following AOAC official methods. All the pea protein (laboratory-produced and commercially-sourced) isolates and concentrates underwent functional characterization at pH 3, 4, 5, 6, and 7, and at protein concentrations of 0.5, 1, 3, 6, 9, and 12% (w/v). Solubility was assessed by determining the protein content in the supernatant after centrifugation (10,000×g, 10 min, 20°C), using the bicinchoninic acid (BCA) assay with bovine serum albumin as the standard. Protein dispersibility index was measured by blending 100mL protein dispersion with a high-speed blender (500 rpm, 10 min), allowing the suspension to settle for 10 mins, and quantifying the protein content of the supernatant using the BCA method. Heat coagulation time was determined by heating 2.5mL protein dispersion at 130°C in sealed glass tubes, with the onset of coagulation recorded visually. Particle size was measured using laser diffraction, and zeta potential was assessed with a zeta analyzer. All measurements were conducted on triplicate batches of protein dispersion. Solubility, dispersibility, and thermal stability varied, depending on protein fraction, extraction method, and pH. At pH 3 and 3% protein, solubility ranged from 0.3±0.1 g for commercial pea protein isolate (CPPI) to 1.1 ±0.1 g for lab-extracted pea protein concentrate (PPC); lab-extracted pea isolate (PPI) and commercial pea protein concentrate (CPPC) had an average of 0.6±0.1 g soluble protein in 100mL. At 9% protein, solubility increased across all samples, reaching 2.2±0.3 g (PPC), 1.2±0.2 g (PPI), 0.7±0.1 g (CPPC), and 0.7±0.1 g (CPPI). Dispersibility followed the same trend in the pea protein isolate samples. Heat coagulation time was higher at pH 3 in the pea protein concentrate, with PPC at 8.3±1.0 min and CPPC at 7.2±1.0 min (3%), while PPI and CPPI had lower HCT values at pH 3 (3.6±1.0 and 3.5±0.8 min, respectively). At 9% protein (pH 3), HCT values remained relatively consistent across samples, except for CPPC, which showed a slight reduction to 4.0±0.4 min. At pH 4, the HCT was around 2.5 minfor all samples. At pH 6, the HCT values were the lowest of all pH conditions, ranging from 1.7±0.3 to 2.0±0.3 min. These findings reinforce the importance of aligning protein selection and processing strategy with the intended food application. To further enhance these properties, especially under conditions of limited solubility, tests of additional processing, including ultrasound and high-pressure homogenization are ongoing. In addition to the significant work completed on characterization of pea protein fractions, preliminary experiments were conducted utilizing pea protein concentrate to form microgels with different gelation and processing methods. The aim of these experiments was to fabricate microgels that were stable at various pH levels and had a uniform particle size distribution. To fabricate microgels, commercial pea protein concentrate (52.59% protein) was dispersed in deionized water for 2 hrs to obtain 15% protein dispersions. Cross-linking was induced by heating at 90ºC for 1 hr. Homogeneous microgel dispersions (2.4% protein) were prepared by dispersing the gel in deionized water. Size reduction was achieved by high-speed shearing (10,000; 12,000; or 15,000 rpm for 5 min) using a T10 homogenizer. The dispersed microgels either underwent ultrasound processing (50% amplitude, 5 min), based on the success of using ultrasound for particle size reduction in our previous work, or were stirred for 1 hr as a control. The pea protein microgels were adjusted to pH 7 and particle size was measured. Preliminary results showed that the microgel dispersion prepared using magnetic stirring exhibited a particle size distribution ranging from 10 to 1000 µm. Increasing the speed of homogenization (from 10,000 - 15,000 rpm) reduced the microgel particle size, and ultrasound processing improved the size uniformity of the microgels. The combination of 5-minhomogenization at 15,000 rpm and 5-minultrasound processing yielded pea protein microgels with a median particle size of ~15 µm. In addition, multiple gelation methods (heat-induced and acid-induced) to prepare microgels were evaluated. For heat-induced gelation, the pea protein concentrate dispersion was incubated at 90ºC for 1 hr. For acid-induced gelation, the dispersion was pre-heated at 90ºC for 15 min, and gelation was induced by adding 1% glucono-delta-lactone. The cross-linked gels from both methods were stored at 4ºC overnight before dispersing in water. Based on the results from the processing study, the dispersed microgels underwent 5-minhomogenization at 15,000 rpm and 5-minultrasound processing with 50% amplitude. After processing, the pea protein microgels were adjusted to pH 3, 4, 5, 6, 7, or 9 using 1 M HCl or 1 M NaOH. The particle size, surface charge, dispersibility, and heat coagulation time were evaluated in the same way as the pea protein dispersions. Microstructural analyses were conducted using a laser scanning confocal microscope. The pea protein microgels obtained through both cross-linking methods were affected by pH. Pea protein microgels prepared by heating were more sensitive to pH changes; they showed variations in particle size distribution at the different pH levels. At pH 7 or 9, both types of pea protein microgels were more dispersible, with over 60% of protein dispersed after a 4-hr settling period. At pH 4 or 5, pea protein microgels were less thermally stable and had the lowest dispersibility (<20% dispersible protein after a 30-min settling period). The heat coagulation time of the pea protein microgels varied depending on the gelation method, especially at pH 7 and 9. For example, pea protein microgels formed by heat-induced gelation had a heat coagulation time of approximately 20 mins at pH 7 compared to a heat coagulation time of <2 min for the acid-induced microgels at pH 7. These results will be used to inform the microgel formation and processing methods to be used in subsequent trials with the different protein fractions for optimal functionality.

Publications


    Progress 06/01/23 to 05/31/24

    Outputs
    Target Audience:The target audience in this reporting period are graduate students and other academic researchers in the scientific community. Changes/Problems:In 2023, we attempted to recruit a PhD student, but our offer was not accepted. Subsequently, we hired a postdoctoral scholar to begin work on the project. This postdoc started in September 2023, but unfortunately, was only able to work for several weeks prior to resigning (for personal reasons). As a result, we are in the process of hiring a new postdoctoral scholar to begin work on the project in the end of August 2024 and will have a PhD student starting in Fall 2024. Additionally, we had significant delays in preparing the protein fractions due to malfunction of the spray dryer planned for use in this project. While utilizing the spray dryer as part of a related project, there was a serious issue with the atomizer, and we are currently waiting for it to be replaced prior to preparation of the protein fractions for subsequent experiments in the coming year. Despite this major problem, significant progress was made on extraction of plant protein fractions from beans, lentils, chickpeas, and almonds. What opportunities for training and professional development has the project provided?This project has provided training for multiple PhD students in the Biological Systems Engineering and the Food Science and Technology Departments. How have the results been disseminated to communities of interest?As data is still being processed and additional experiments are being conducted, the results have not yet been disseminated to communities of interest. However, with additional data gathered in the next year, it is anticipated that results will be presented to the scientific community at a professional meeting in the coming year and publications will be prepared to disseminate results. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period significant experimental work is anticipated to extract plant protein fractions, and to characterize their functionality and to utilize these plant protein fractions to develop novel plant protein structures.

    Impacts
    What was accomplished under these goals? Objective 1: Evaluate the role of physico-chemical properties of plant protein fractions (concentrates, isolates, soluble proteins) in their stability in aqueous solutions and as ingredients to develop sub-micron scale assemblies based on physical structuring mechanisms to enhance stability in aqueous solutions: Despite the problems with the spray dryer needed to produce the large quantities of plant protein fractions needed for this prjoect, significant progress was made on extraction of plant protein fractions from beans, lentils, chickpeas, and almonds. All protein extracts were produced by aqueous (AEP) and enzyme assisted extraction processes (EAEP). AEP and EAEP were performed at pH 9.0, 50°C, 1:10 solids-to-liquid ratio for 60 min, while 0.5% of protease (neutral for almond and alkaline for bean, lentil, and chickpea) was added for the EAEP. Protein isolates were obtained by adjusting the pH of the extracts to the isoelectric point of each protein matrix, followed by centrifugation to collect the whey (soluble fraction) and curd (precipitate). In general, EAEP resulted in higher protein extractability compared with the AEP, indicating that proteolysis led to the formation of smaller and more soluble peptides that were extracted into the media. In parallel with refining the plant protein extraction conditions, the impact of pH and heat treatment (30 sec at 95°C) on a commercial pea protein extract were characterized. Protein dispersions (10 % w/w protein) were prepared with pea protein isolate (PPI). Dispersion pH was adjusted to pH 3, 5, 6 or 7 using citric acid. Dispersion stability and properties were characterized before or after heat treatment (heating to 95 °C for 30 seconds). Protein dispersion particle size, microstructure, and rheological properties were characterized. Dispersion pH and heat treatment significantly impacted all particle size parameters (p < 0.05). For example, the volume-weighted mean diameter (D[4,3]) was the lowest at pH 5 (96.41 ± 1.78 mm before heating and 101.45 ± 1.75 mm after heating) compared to the other pH values (Fig. 1). This showed that heat treatment significantly increased the particle size of the PPI dispersions, particularly at pH 3 and 7. Microstructural analysis was conducted on the unheated and heated pea protein dispersions (Fig. 2). Images confirmed the particle size results, where aggregates can be seen in the dispersions at pH 3 and 7 after heating. Future work will characterize the stability of protein dispersions in similar conditions and after formation of micro- and nano-structures. Objective 2: Characterize the functionality (stability, texture, and digestibility) of protein fractions and novel protein structures in liquid and semi-solid model food systems. Progress was made to develop plant protein gels using pea protein isolate (PPI) and soy protein isolate (SPI) and to compare their properties with polysaccharide gels (alginate and pectin). To formulate gels, protein was dissolved in a pH 3.0 buffer (15% w/w), hydrated overnight (4C), and thermally induced denaturation (95C) and ionic crosslinking with Ca+2 ions was used. The rheological properties of the gels and the stability of plant protein gels in aqueous solutions were evaluated. Both PPI and SPI formed a viscoelastic gel. The PPI gel had a higher storage modulus than the SPI gel, indicating superior elastic properties of PPI gels compared to SPI gels. The highest yield stress (152.7 Pa) was observed with pectin gels, followed by PPI gels (21.0 Pa). The gel hydration stability was investigated by incubating the gels in water for 30 min. Changes in weight of the gels due to hydration indicated the swelling index of the gels. In general, the pea and soy protein gels had a much higher swelling index (>60%) compared to the polysaccharide gels (<30%). Additional work will be completed in the following year to develop and characterize plant proteins in similar semi-solid model food systems to what was studied here.

    Publications