Influence of Surface Area and Digestion Time on Buffering Capacity of Whey Protein Gels
Author: Charles Ding
Peer Reviewer: Himanshi Verma
Yamile Mennah Govela
Buffering capacity is the characteristic of foods to resist changes in pH during gastric digestion, and has been observed to affect the physicochemical breakdown of food by influencing the rate of gastric acid secretion, thus affecting the intragastric pH. A more thorough understanding of the relationship between different food properties and buffering capacity is needed to create standardized models that may better estimate how buffering capacity affects the rate of gastric acid secretion.
Proteins in food have been shown to strongly influence the intragastric pH and have very high buffering capacities during digestion. Many protein products are currently marketed in varying vehicles, from powder drinks to compact bars, with varying physical properties and behaviors during digestion. Therefore, the objective of this project was to observe the influence of surface area and digestion time during digestion on the buffering capacity of 20% whey-protein gels varying the gastric juice pH. The buffering capacity of the gels after digestion were measured via acid titration and quantified using a recently proposed method.
Results showed that for gastric juice of pH 0.8, higher surface areas resulted in higher buffering capacities, and for gastric juice of pH 1.8, lower surface areas yielded higher buffering capacities, but there was no significant relationship between digestion time and buffering capacity.
During gastric digestion, the chemical breakdown of food takes place by peristaltic contractions and mixture of gastric acid and enzymes. Several factors have been shown to affect the rate of gastric secretions, such as food particle size (Luo, Q. 2018), food pH and buffering capacity (Salaun et al., 2005), but the intragastric pH has been specifically demonstrated to be a key parameter that modulates gastric secretions (Fordtran and Walsh, 1973). The intragastric pH and gastric secretions impact enzymatic hydrolysis from both alpha amylase, found in saliva, and pepsin, found in gastric juice. Alpha amylase functions at its maximal activity at pH 6.8 and is inactivated at pH 2, whereas pepsin has its maximal activity at pH 2 and is inactivated at pH > 5.5 (Fordtran and Walsh, 1973). A study by Walsh et. al has shown that the rate of gastric acid secretion is greatly affected by the initial pH of a meal, with higher pH levels resulting in higher rates of gastric acid secretion (Walsh et al., 1975). Therefore, the drop in the intragastric pH during digestion, which is crucial to the function of enzymes and chemical breakdown of food as well as the rate of gastric acid secretion, may be influenced by the food’s buffering capacity. However, the extent of the influence of buffering capacity on gastric acid secretion has not been well defined, and the direct relationship between food properties and buffering capacity is not yet clear (Taylor et al., 1978).
Buffering capacity refers to the resistance of a solution to a change in pH with the addition of a base or acid, primarily by the presence of proteins, acid/base groups, and organic acids. Proteins behave as strong buffers due to their amino and carboxylic groups (acid/base groups), and long chains of amino acids, both of which attach to the H+ ions found within gastric juice and resist changes to the intragastric pH (Salaun et al., 2005). Therefore, studies have been conducted examining how multiple food properties influenced the buffering capacity of proteins in order to better understand their relationships (Taylor et al., 1978).
A study has demonstrated that the protein concentration and initial particle size of foods directly affect the buffering capacity (Mennah-Govela et al., 2019). They showed than egg and whey protein concentrations as well as smaller food particle size increased buffering capacity. It was suggested that higher protein concentrations increased the amount of acid/base groups and amino acid chains found within proteins that act as buffers to pH change; in addition, smaller particle size was suggested to increase the proteins’ surface area, thus creating more sites for pepsin activity, leading to the breakdown of proteins into smaller, and more, amino acid chains. From this study, it was inferred that surface area played an important role in the buffering capacity of proteins, though the relationship between the two is still unknown.
Medical texts have also shown that the digestion for protein-rich foods is significantly longer than other foods (Luo, Q. 2018), prompting further examination into the potential role of buffering capacity in the increased digestion time, and how the buffering capacity of proteins may change over time during digestion.
The objective of this study is to observe the effects of surface area and time of digestion on the buffering capacity and better understand their relationship, as well as to incorporate more factors into the regression model developed by Mennah-Govela, Singh, and Bornhorst to predict buffering capacity (Mennah-Govela et al., 2019). It is hypothesized that higher surface area and longer digestion times would yield higher buffering capacities, building upon Mennah-Govela’s findings that higher concentrations of protein yielded higher buffering capacities (Mennah-Govela et al., 2019) and Luo’s examination of the proteins’ amino acid interactions with the stomach’s gastric acid (Luo, Q. 2018).
Materials and Methods
The experiment was designed with multiple sets of trials of in-vitro digestion, utilizing a shaking water bath and a combination of gastric juice and saliva to mimic human digestion. The word “digestion” will refer to this in-vitro digestion method. The multiple sets were conducted using gastric juice of either pH 0.8 or 1.8. Protein gel were prepared as cubes or spheres, and were digested for different amounts of time to simultaneously measure the effects of both surface area and digestion time on buffering capacity by digesting the protein gels for different amount of times. Buffering capacity was measured by both area under the curve, (pH × (?mol H+/g sample)), which measures takes into account the initial mass and millimoles of H+ added as an area, and the total buffering capacity (mmol H+/g*?pH), which measures the buffering capacity in relation to the total change in pH (Mennah-Govela et al., 2019).
Preparation of Gastric Acid and Saliva
Gastric acid and saliva were prepared following the procedure as shown in Table 1.
Two different pH levels of gastric acid were used (0.8 and 1.8) for the two sets of experiments covering both spherical and cubic whey protein gels and all times of digestion (Table 2). Three replicates of trials were carried out for each time of digestion. Due to time constraints, however, only two trial replicates were completed for cubes, and no undigested protein gels (0 minutes) were titrated when gastric juice with pH 1.8 was used.
The differences in pH for gastric juice were used to examine how buffering capacity of proteins would respond to different intragastric pH levels; gastric juice with a pH of 1.8 was closer to the pH of maximal activity for pepsin, while gastric juice with a pH 0.8 would mimic a more acidic intragastric environment, thus breaking down the proteins more rapidly. The saliva solution was prepared with a constant pH of 7.0, the most typical pH found in individuals with a healthy dental and periodontal condition (Baliga et al., 2013), for both sets of trials.
After gastric acid and saliva concentrations were prepared, milli-Q water was used to dilute concentration and raise the volumes of the solutions to 1 L. Both solutions were refrigerated after use to preserve freshness, and are stable for up to one month (Mennah-Govela et al., 2019).
Development of 20% Whey Protein Gels
20% whey protein gels were prepared following the procedure developed by Mennah Govela and Bornhorst (Mennah-Govela et al., 2019). 100 mL of whey protein dispersion were created by dissolving 22.47 grams of whey protein isolate (Hilmar Whey Protein Isolate 9000, purchased from Hilmar Ingredients) in 100 mL of milli-Q water, stirred at 350 rpm for one hour. The pH of the dispersion was then adjusted to 7.5.
To observe how differences in surface area affect the buffering capacity of the protein gels, cubes and spheres with similar volumes (132.651 mm3) but different surface areas were created. Spheres would have approximately surface areas of 125.762 mm2, while cubes would have surface areas of approximately 156.060 mm2. Surface areas were measured using a digital caliper and were calculated as follows: for cubes, the average of the measured sides were squared and multiplied by six; for spheres, the average of the measured radii was multiplied by 4?.
After 20% whey protein dispersion was created, the dispersion was wrapped in tinfoil and heated in a water bath (Fisher Scientific, Isotemp) at 90º C for one hour. Immediately after, the solidified dispersion was cooled in ice for fifteen minutes. The gel was then separated into long pieces by using a grid cookie cutter with sides of 13 mm, and then cut into cubes with sides of around 5.10 mm each, to achieve a volume of approximately 132.651 mm3.
20% whey protein spheres were created using Dippin’ Dots Frozen Dot Machine by pouring the dispersion into Dippin’ Dots trays, which, after heating for 20 minutes in a water bath (Fisher Scientific, Isotemp) at 80º C, created spheres of a volume of approximately 132.651 mm^3. Trays were very lightly coated with canola oil (Trader Joe’s Canola Oil Spray), in order to expedite the cleaning process, and were attached to each other using electrical tape to prevent sphere deformation.
After heating, the spheres were cooled at room temperature, and were then separated from the trays manually (Fig. 2).
An in-vitro digestion model was used for this study by placing the protein gels in a shaking water bath (Thermo Scientific Precision SWB 27) preheated to 37 ºC and shaking at 100 rpm. Approximately ten grams of 20% protein gel were used for each time trial of digestion (0, 60, 120, 180 minutes).
To better mimic the process of digestion, saliva and gastric juice were added to ten grams of whey protein gels for each digestion trial. Saliva and gastric juice were first warmed in the water bath (Thermo Scientific Precision SWB 27) preheated at 37 ºC, and the pH of both were then adjusted to 1.8 or 0.8 and 7.0 (respectively). 1.18 g/L of alpha-amylase and 1.82 g/L of pepsin were then added to the saliva and gastric juice (respectively) and mixed at 350 rpm.
2 mL of saliva were added to the protein gels, mixed for 30 seconds, and immediately combined with 14 mL of gastric juice. The protein gels were then placed in the shaking water bath at 37 ºC, stored in 50 mL centrifuge tubes, for the designated digestion time (0, 60, 120, and 180 min).
After digestion, protein gels were titrated both with and without gastric juice to examine how the presence of gastric juice would affect the buffering capacity.
Protein gels titrated with gastric juice were poured out from the centrifuge tubes into 50 mL beakers, with the gastric juice and saliva mixture. Protein gels to be titrated without gastric juice were first strained using cheesecloth and an 8-cm fine mesh strainer, then re-massed to take into account absorbed gastric juice and saliva.
Buffering Capacity Measurement
To observe the effects of the surface area and digestion time on the buffering capacity, an acid titration method was used (Al-Dabbas et al., 2010). Acid titration was completed by adding 0.5 mL increments of 0.2 M HCl to the in-vitro digested protein gels, until the pH dropped to 1.5. If 7.0 mL of 0.2 M HCl were added and the pH did not yet reach 1.5, 1 mL of HCl increments were added. Three measurements of the pH of protein gels titrated without gastric juice were taken and averaged for each addition of HCl in order to account for the inconsistencies when measuring the pH of solids. For protein gels with gastric juice, only one pH measurement was taken while constantly stirring at 125 rpm in a stirring place.
Buffering Capacity Analysis
The buffering capacity of the whey protein gels was expressed as total buffering capacity, quantified by dividing the millimoles of H+ added over the product of the grams of protein gel and the change from initial to final pH (mmol H+/g*?pH) (Mennah-Govela et al., 2019). This way of expressing buffering capacity may be more useful in general studies as it can be applied to foods with different initial pH levels. Additionally, the area under the curve (AUC) of acid titration graphs were measured (pH × (?mol H+/g sample)). Larger results for total buffering capacity added and AUC’s were interpreted as higher buffering capacity.
Results and Significance
Buffering Capacity of Spheres and Cubes: pH 0.8
When defining buffering capacity as the area under the curve, it was observed that the buffering capacity was influenced by surface area for both titrations (with and without gastric juice). For titrations with gastric juice, longer periods of digestion yielded minor increases in buffering capacity, with a 0.005 increase for spheres and 0.002 increases for cubes from time 120 min to 180 min. For titrations without gastric juice, there was no direct relationship between time of digestion and buffering capacity. However, large standard deviations were observed for titrations in samples both with and without gastric juice (Figure 3). Large error bars may be attributed to human error during cube production
When observing the measurements of total buffering capacity, measurements were strongly influenced by surface area for titrations with gastric juice: cubes demonstrated higher buffering capacity than spheres for all times of digestion of up to +0.012 mmol H+/g ?pH (Figure 4). For titrations without gastric juice, cubes also exhibited slightly higher buffering capacity than spheres, of up to +0.002 mmol H+/g ?pH, for times 60-180 min. For time 0, where undigested proteins were titrated, cubes demonstrated a much higher buffering capacity than spheres, with an average of 0.045 ± 0.015 mmol H+/g ?pH for cubes compared to an average of 0.026±0.01 mmol H+/g ?pH for spheres.
The data suggests that there may be relationships between the surface areas and buffering capacity of protein-rich foods during intragastric digestion, where gastric acid is present, but more research is needed to understand how the surface area affects the buffering capacity when proteins are isolated from gastric acid, such as in the small intestine. However, due to large standard deviations, which may be results of human inconsistencies during protein gel creation, more research is needed to find more reliable data.
Buffering Capacity of Spheres and Cubes: pH 1.8
When gastric juice of pH 1.8 was used for digestion, a different relationship between surface area and buffering capacity of the titrations was observed. When buffering capacity was quantified as the AUC, cubes titrated with gastric juice demonstrated lower buffering capacity han spheres; for titrations without gastric juice, cubes demonstrated higher buffering capacity than spheres. (Figure 5) From this we can infer that the buffering capacity of protein gels greatly differs in and out of the presence of gastric juice.
When buffering capacity was calculated as total buffering capacity, spheres demonstrated higher buffering capacity than cubes for both titrations (Figure 6). However, the difference between the buffering capacity of the spheres and cubes titrated without gastric juice were less than the differences noticed in titrations with gastric juice. For example, in titrations without gastric juice, the largest difference observed between buffering capacity of spheres and cubes was 0.003 mmol H+/g ?pH , while in titrations with gastric juice, the largest difference was 0.012 mmol H+/g ?pH.
Significant differences were observed in the buffering capacity of the protein gels between the gastric juice of pH 0.8 and the gastric juice of pH 1.8 (Figures 7-8). When quantifying buffering capacity as the area under the curve, protein gels digested with the gastric juice at pH 1.8 had higher buffering capacity than gels digested with gastric juice of pH 0.8, of up to a 0.39 difference in cubes and 0.41 difference in spheres, and for titrations with and without gastric juice (Figure 7). However, the difference in buffering capacity was less in titration without gastric juice than titrations with gastric juice. Time 0 protein gels did not undergo digestion, and therefore were not used to compare the effects of different gastric juice pH levels.
It is believed that because pepsin functions at its maximal activity at an intragastric pH of 2.0, gastric juice with a pH level closer to that amount would better promote pepsin activity. This may result in higher buffering capacity for protein gels digested in gastric juice of pH 1.8 than gastric juice of pH 0.8 because the pepsin is more actively dismantling the protein into smaller amino acid chains, thus increasing the number of buffer sites (Walsh et al., 1975).
Because only trials were carried out for digestions using gastric juice with pH 1.8, error bars and standard deviations were not calculated.
However, when measuring buffering capacity as the total buffering capacity, the exact opposite was observed; protein gels digested with gastric juice of pH 0.8 demonstrated higher buffering capacity than those digested with gastric juice of pH 1.8 (Figure 8).
Since the measurements of total buffering capacity divide the total mmol of H+ added over the final change in pH (mmol H+/g ?pH), it is believed that the difference in initial pH between the gels digested with the two gastric juice formulations greatly influenced the measurements of total buffering capacity. The initial pH levels of both spheres and cubes were seen to be higher when the gastric juice had a pH of 1.8 compared to 0.8 (Figure 9).
It is believed that there is higher initial pH in digestions with gastric juice of pH 1.8 because the simulated intragastric pH is closer to 2.0, where pepsin is at its maximal activity (Walsh et al., 1975). As pepsin dismantles the protein into smaller fragments of amino acid chains and acid/base groups, they interact with the gastric juice to raise the pH similar to the observation discussed in the study by Walsh et. al (Walsh et al., 1975).However, more research is needed to further clarify the relationships between initial intragastric pH and buffering capacity.
Significance of Results
From the results, we can observe that the surface area of protein-rich foods greatly affects the buffering capacity of those proteins, and this knowledge can translate to how buffering capacity may influence protein breakdown rate. Since buffering capacity has been shown to influence the rate of gastric secretions, the relationships found in this study can be incorporated into the mathematical model that aims to estimate the amount of gastric juice secreted, and therefore the protein breakdown rate, developed by Mennah-Govela et al. (Mennah-Govela et al., 2019). By better understanding how specific food properties affect their buffering capacity, protein-rich food products may be manufactured to better suit individuals’ nutritional needs. For example, products designed to be easier absorbed may be manufactured with higher surface areas in order to increase buffering capacity, which in turn may increase the amount of gastric juice secreted and promote the breakdown of proteins into smaller, more easily absorbed chains of amino acids (Taylor et al., 1978).
Future work is needed to understand the relationships between more initial and intragastric food properties and buffering capacity. The implementation of known relationships may then lead to more inclusive and accurate models of how buffering capacity influences gastric juice secretion, breakdown rate, and gastric emptying.
Through this project, the influence of the surface area and digestion time of protein gels on buffering capacity were examined. It was demonstrated that when gastric juice of pH 0.8 was used, higher surface areas had higher buffering capacity, but when gastric juice of pH 1.8 was used, lower surface areas yielded higher buffering capacity. However, no clear relationships were found between digestion time and buffering capacity. It is believed that the digestion times of up to three hours were too long, because a previous study suggests that protein is digested at a maximum rate of 8-10 grams per hour (Minkus et al., 2014), therefore for each centrifuge tube containing 10 grams of 20% protein, two to three hours of digestion time be excessive, and not yield helpful data. Future experiments should use shorter digestion times and closer intervals in between titrations.
The understanding of how surface area and digestion time influence buffering capacity can be used to help create mathematical models predicting how buffering capacity affects gastric secretions by incorporating more variables (Mennah-Govela et al., 2019), and better define how the initial properties and intragastric behaviors of food matrices could influence the relationship between buffering capacity and gastric secretions.
Further studies could focus more on how the initial intragastric pH influences the buffering capacity of foods, mimicking gastric digestion after the consumption of meals with different pH levels. Additionally, future studies could further clarify the relationship between the buffering capacity of foods and both the bioavailability of nutrients, or the quantity of those nutrients that become available for absorption after gastric digestion.
The intragastric pH is an important parameter to the process of gastric digestion, influencing enzyme hydrolysis, gastric acid secretion, and gastric emptying (Williams et al., 1968). With the direct influence of buffering capacity on the intragastric pH, understanding more about how certain food properties influence buffering capacity may bring scientists closer to creating inclusive models. These models would clarify how the buffering capacity of foods affects the digestive process and how create targeted nutritional products.
Thank you to Dr. Gail Bornhorst, PhD Candidate Yamile Mennah-Govela, PhD Student Clay Swackhamer, and Postdoctoral Scholar Silvia Keppler for access to their lab and their support and encouragement.
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