Bolus Formation and Disintegration during Digestion of Food Carbohydrates Gail M. Bornhorst and R. Paul Singh
Abstract: The first step in the digestion process is mastication, or chewing, when food is broken down, lubricated with saliva, and formed into a cohesive mass known as the food bolus. Upon swallowing, the bolus moves to the stomach and undergoes further breakdown during gastric digestion. The subject of this review is the formation of the food bolus and its subsequent breakdown in the stomach. Bolus formation has been widely studied, especially in terms of food particle size and lubrication. However, information about bolus disintegration is limited, and this review focuses on the breakdown of bread and starch-based foods. Bolus formation and disintegration are key steps in the overall digestion process, as they control the rate at which ingested food components and nutrients are absorbed and released into the body. Information on the rate kinetics of bolus disintegration is necessary in developing a quantitative understanding of the food digestion process.
Introduction The first step in the digestion process is oral digestion, which begins with mastication, or chewing. During mastication, the simultaneous processes of food comminution and lubrication occur, the end product being the food bolus. After formation, the bolus will be swallowed for further digestion in the stomach. Although mastication seems like a simple process, there are many factors involved. Physiological characteristics of the individual performing the chewing action, such as facial anatomy, gender, age, personality type, time of day, dentition status, as well as properties of the food being chewed, such as hardness, moisture content, fat content, food portion size, and food structure (Yurkstas 1965; Gonzalez and others 2004; Rey and others 2007) all have an effect on the formation of the food bolus. After formation, the bolus will be swallowed, transported through the esophagus, and move into the stomach. As boluses enter the stomach, they will “stack up” in the curvature of the stomach according to the time they were ingested (Schulze 2006). Layers will begin to form according to density and solids content. During the gastric digestion process, the boluses are physically reduced in size while being chemically broken down due to the acidic and enzymatic conditions of the gastric secretions. The rate at which foods disintegrate will control the rate at which they are emptied from the stomach and move to the intestines where nutrients are absorbed. The first topic of this review focuses on the importance of food mechanical properties during bolus formation. Food properties such as hardness, moisture content, and fat content will influence MS 20110656 Submitted 5/25/2011, Accepted 10/26/2011. Author Bornhorstis is with Dept. of Biological and Agricultural Engineering, Univ. of California, Davis, 1 Shields Ave., Davis, CA 95618, U.S.A. Author Singh is with Dept. of Biological and Agricultural Engineering, Univ. of California, Davis, 1 Shields Ave., Davis, CA 95618, U.S.A., and Riddet Inst., Massey Univ., Palmerston North, New Zealand. Direct inquiries to author Singh (E-mail:
[email protected]). c 2012 Institute of Food Technologists® doi: 10.1111/j.1541-4337.2011.00172.x
bolus formation as a result of variations in saliva secretion and differences in chewing forces and duration. Especially, in the case of starch-based foods, the amount of saliva incorporated in the bolus may play a significant role in the breakdown of starches, due to the α-amylase content of saliva. The second topic of this review focuses on the breakdown of bread and other starchy foods during gastric digestion. This breakdown has been studied in vivo in terms of gastric emptying rate and glucose response after consumption of a meal. Similarly to bolus formation, gastric emptying and glucose response of a meal have been correlated to both food macro- and microstructure in bread and starchy foods. Since many in vivo studies have been performed on humans using noninvasive methods to measure gastric emptying or glucose response after a meal, it has been difficult to quantify the precise relationship between food structure and its bolus breakdown during gastric digestion. However, in vitro systems can be used for this purpose and a brief description of a variety of in vitro gastric digestion systems is presented.
Bolus Formation Stages of oral processing After a food product is ingested, it is processed in the oral cavity. The oral process is comprised of 4 key steps: Stage I, processing, Stage II, and Stage III (Hiiemae and Palmer 1999). During Stage I, the ingested food is moved from the front of the mouth to the teeth so that it can be broken down. This stage occurs quite fast and takes about the same duration for most food types, around 280 ms. The processing phase occurs when the food particles are broken down via crushing/grinding with the teeth; this step takes longer for harder products. Stage II (transport) occurs gradually during the processing phase; as particles are broken down to the appropriate size, and they are transported to the back of the oral cavity to form a bolus, leaving larger particles to be further broken down (Hiiemae and Palmer 1999; Smith 2004). A bolus is formed
Vol. 11, 2012 r Comprehensive Reviews in Food Science and Food Safety 101
Bolus formation and disintegration . . . by folding and manipulating food particles with the tongue (Prinz and Heath 2000). Stage III occurs after a bolus is formed, which is the preswallowing stage; the bolus is moved to the back of the tongue in preparation for swallowing (Hiiemae and Palmer 1999; Smith 2004).
Bite size and chewing time To determine the time of the entire chewing sequence (from Stage I to Stage III) for various food products, Hiimae and others (1996) tested apple, banana, and biscuit (hard cookie) as 3 different foods with vastly different texture on 11 human subjects. They measured both the “natural” (self-selected) bite sizes as well as the chewing sequence period and number of swallows during chewing. They found that the size of the bite varied inversely with product hardness. For a soft food, such as banana, the mean bite size was 12.45 ± 3.45 g, although for a hard food, such as a biscuit, the mean bite size was 2.48 ± 0.88 g, and for an intermediate food, such as an apple without the peel, the mean bite size was 7.19 ± 2.28 g. The total chewing duration varied slightly for each of the foods; this variation was attributed to the type of food and the varying amount of food ingested. Although the total chewing duration was not affected by food texture, the number of chews required increased with food hardness. Soft foods (banana) required a lower number of chews, while harder foods (biscuit) required a larger number of chews. This study demonstrates the importance of food hardness in the overall chewing process, with both bite size and number of chews being influenced by food hardness.
Saliva Saliva plays an important role in the digestive process, especially in digestion that occurs in the upper digestive tract (mouth, pharynx, esophagus, stomach, and duodenum). There are many functions of saliva in the oral cavity, including, but not limited to, lubrication of the surfaces of the oral cavity, facilitation of speech and mastication, formation of the food bolus, cleaning food in the oral cavity, neutralizing oral acids, cooling hot food, acting as an antimicrobial agent, and beginning the digestion of starches and lipids by means of salivary α-amylase and lipase, respectively (Pedersen and others 2002).
Saliva production About 90% of whole saliva is released by three major salivary glands in the oral cavity: the parotid, the submandibular, and the sublingual glands. The parotid releases a low-viscosity, amylaserich saliva, while the submandibular and sublingual glands release a higher viscosity, mucin-rich saliva. The remaining 10% of whole saliva is released by minor glands, distributed throughout the oral cavity, that release the majority of salivary proteins. In normal healthy individuals, between 0.5 and 1.5 L of saliva is produced daily (Pedersen and others 2002). The specific production of saliva depends on many factors and shows high interindividual variation, with up to 40% average variation for daily saliva secretion over a period of 3 days (Richardson and Feldman 1986). Salivary secretion also varies with circadian rhythm throughout the day, in both flow rate and composition (Dawes 1972). Stimulated salivary secretion can be measured either by expectoration of saliva after a stimulus (subjects are given a piece of candy or gum and they are asked to spit out any saliva in the oral cavity), or the saliva can be removed using a catheter and a suction pump. Both methods have shown to give similar results and are highly correlated with one another (Richardson and Feldman 1986). Unstimulated saliva
can be measured by a draining method, where saliva is allowed to drain through a funnel in the mouth; a spitting method, where subjects spit out any accumulated saliva at certain intervals; a suction method, where plastic tubing attached to a vacuum pump is inserted under the tongue to collect salivary secretion; or the swab method, where dental cotton rolls are weighed, placed into the mouth to absorb any secreted saliva, then removed and reweighed to determine the amount of saliva absorbed. All of these methods result in similar mean saliva flow rates; however, their variability and reproducibility differ. The swab method produces the highest variability and lowest reliability, making it the least preferred method. The suction method may actually stimulate salivary secretion, leading to erroneously increased secretion values. The spitting or draining methods are the preferred methods for collection of unstimulated saliva (Navazesh and Christensen 1982).
Factors affecting saliva secretion Saliva is secreted both in the presence and absence of external stimuli. Saliva secreted without any external stimuli is referred to as unstimulated saliva (Navazesh and Christensen 1982). Unstimulated saliva flow rates are in the range of 0.3–0.5 mL/min (Pedersen and others 2002; Gavi˜ao and others 2004). Saliva secretion can be stimulated at chewing forces of 5% of the normal chewing force (Gavi˜ao and others 2004). The amount of saliva secreted is influenced by gustatory stimulation, when certain tastes are incited such as sour, salt, and sweet (Dawes and Watanabe 1987), as well as material properties of the food being chewed, such as texture and water and fat contents (Gavi˜ao and others 2004; Gavi˜ao and van der Bilt 2004). Gavi˜ao and others (2004) examined the salivary secretion in response to chewing toast, toast with margarine, various sizes of cake, and cheese. Although the results of the study showed no difference in saliva secretion in mL/min, the total amount of saliva secreted for the various products was different in terms of mL/g food, due to differences in the chewing time (longer chewing time results in more saliva/g food incorporated). Samples of unbuttered toast required the highest levels of saliva (mL/g) and the longest chewing time, but had the lowest moisture and fat contents. This substantiates previous studies that suggest dry, lower fat products that need more saliva in order to adequately moisten them and prepare a cohesive bolus to be swallowed. Chewing time increased and saliva secretion (mL/g) slightly decreased with an increasing sample size when small, medium, and large portions of cake were chewed. The chewing time for portions of cake ranged from 17.4 (small) to 30.7 s (large), with the salivary incorporation ranging from 0.40 mL/g (small) to 0.33 mL/g (medium). The chewing time was significantly different according to portion size for all 3 portions; the salivary incorporation was significantly different between the small cake portion and the medium and large cake portions. In a similar study by Engelen and others (2005), chewing time and saliva flow rates were measured on 7 natural foods: cake, melba toast, bread, toast, carrot, peanut, and cheese. They found that chewing time for an equal volume of product varied with product moisture and hardness in the following order: cake and bread < toast < cheese < peanut < melba toast < carrot. This demonstrates the relationship between product hardness and number of chewing cycles until swallowing, with harder foods requiring a greater number of chewing cycles. This study also showed that when the cake, melba toast, and toast were chewed with butter, they required a lower number of chewing cycles, exhibiting an inverse relationship with fat content and chewing cycles.
102 Comprehensive Reviews in Food Science and Food Safety r Vol. 11, 2012
c 2012 Institute of Food Technologists®
Bolus formation and disintegration . . . Assuming that the salivary secretion (mL/min) is constant, as shown by (Gavi˜ao and others 2004), this signifies that for a longer chewing time, more saliva will be incorporated into the food product. Similarly, a study by Brudevold and others (1990) tested chewing times and saliva secretion for cookies with varying sucrose and fat contents. They found chewing times varied from 28 to 43 s. The addition of sucrose and fat both shortened the chewing time and the chewing times were significantly different according to type of cookie chewed. The percentage of water in the bolus ranged from 39.1%–49.3%; however, the amount of sucrose did not influence the final water content of the bolus. The cookies with added fat had a lower water percentage than those with no fat added. The saliva flow rates varied from 6.3 to 8.3 mL/min with increasing levels of sucrose, and from 6.9 to 7.0 mL/min with increasing levels of fat.
Salivary enzymes The main enzyme found in saliva is α-amylase, which breaks down carbohydrates into maltose, maltotriose, and α-limit dextrins by cleaving the α-1–4 glycosidic bond in the complex carbohydrates (Robyt and French 1970). These products are then further hydrolyzed by brush-border enzymes to produce the glucose that can be absorbed in the small intestine (Rosenblum and others 1988). Saliva, by some, is not considered to have a major impact on carbohydrate digestion, as its optimum pH level is 6.8 and becomes inactivated by the low pH of the gastric acid in the stomach (Pedersen and others 2002). However, although salivary amylase does become inactive by encountering very low concentrations of hydrochloric acid, it may still be present for up to 15–30 min into gastric digestion; and it accounts for an average digestion of 76% of the starch in mashed potatoes and 59% of the starch in bread (Bergeim 1926). Depending on the specific pH conditions of the stomach, salivary α-amylase may actually reach the small intestine without becoming inactive. Fried and others (1987) showed that salivary α-amylase became inactive in a pH range of 3.3–3.8 during in vitro testing, and accounted for about 14% of the total (active) amylase found in the small intestine (the remaining 86% being comprised pancreatic amylase), indicating that there is still significant starch digestion that may occur in the stomach due to the presence of salivary α-amylase. The inactivation of α-amylase may also be influenced by specific meal composition. Rosenblum and others (1988) showed that the presence of 0.1% and 1% starch will significantly decrease the α-amylase inactivation at pH 3.0. They demonstrated this same effect with increasing concentration of maltose and maltotriose (up to 5%), showing that α-amylase will be inactivated at a slower rate, even at pH 3.0, in the presence of polysaccharides and oligosaccharides. They hypothesized that this effect may be due to an interaction of the starch at the active site of the α-amylase that influences the amylase inactivation. Another enzyme found in saliva is lingual lipase, which breaks down lipids. However, lingual lipase only breaks down a small fraction of the ingested lipids, as most of triglyceride digestion is caused by pancreatic lipase (Pedersen and others 2002). Other effects of saliva on oral digestion Other properties of saliva can also have an effect on oral digestion, such as the surface tension and viscosity of the saliva. The surface tension of saliva is partially responsible for the adhesive effects of saliva, both to the oral cavity and also for the food particles in the bolus. Saliva surface tension was measured to be 53.1 dynes/cm, averaged from 24 individuals (Glantz 1970). The c 2012 Institute of Food Technologists®
viscosity of saliva can be affected by many factors, some of which may even be related to diet. It has been shown that dietary tannins (the astringent compounds found in foods such as coffee, tea, and red wine) significantly decrease the viscosity and increase the friction in saliva. These changes will affect the lubricating properties of saliva, and ultimately, the bolus formation (Prinz and Lucas 2000).
Modeling of Particle Breakdown During Mastication The biological objective of chewing, when foods are ingested and broken down in the mouth, is to increase the surface area of the particles in order to release flavor compounds in the mouth and to facilitate further breakdown and enzymatic digestion later in the gastrointestinal tract. This increase in surface area of particles is caused by fracturing and deforming the ingested food by means of shear stresses applied by the teeth. Both the elastic behavior of food materials and their toughness are essential parameters to determine food fracturability. The elastic behavior of a food material, as long as it has an approximately linear stress–strain relationship, can be quantified as the stress–strain gradient, or Young’s modulus (Lucas and others 2004). The toughness can be measured with a variety of methods, such as recording the force required to shear a certain material (Friedman and others 1963). Since the key food properties that affect food breakdown can be measured, these properties can be used in a variety of models to better understand the effects of food material properties on their breakdown during chewing. Models have been established for the selection and breakdown of particles during the mastication process, the effect of material properties on food breakdown, and the particle size distribution after chewing.
General breakdown theory In researching the degradation of coal and other hard materials, Epstein (1947) introduced an asymptotic logarithmic-normal particle size distribution based on the probability of the material to fracture as well as the degree of fragmentation of the particles. Although this model was not originally used for food materials, it has been applied to the mastication process because essentially, the same steps are taking place: a solid material (food or coal) is being broken apart by the crushing and grinding of an outside tool (the teeth or coal crushing apparatus). The fundamental concept to this model is that any fracturing process can be broken down into various discrete steps, each step being a separate “breakage event” or one step in the degradation process. This degradation process can then be described after any finite number of steps. Selection and breakage functions Lucas and Luke (1983a,b) used the fundamental breakdown theory of Epstein (1947) to create a more complex model simulating the breakdown of solids during human mastication. They separate the mastication process into 2 distinct processes. The first process is the arrangement of the particles in the mouth so that they can be broken apart (selection). The second process concerns the actual fracture of the particles and their subsequent size distribution (breakage). The first process, or selection function (S), is the proportion (by either weight or volume) of particles of a finite size range, x to δx, which is broken during one chew. The average selection function can be described as P2 ¯ = 1 − C2 − C1 S(x) , (1) P1
Vol. 11, 2012 r Comprehensive Reviews in Food Science and Food Safety 103
Bolus formation and disintegration . . . where P 1 is the percentage of particles of average size of x¯ after C 1 number of chews and P 2 is the percentage of these particles remaining after C 2 number of chews. If the percentage of particles remaining after a certain number of chews is assumed to be a geometric series with 1 − S(x¯ ) as the common ratio of the series, P 2 can then be described as ¯ C2 −C1 . P2 = P1 (1 − S(x))
(2)
From these expressions, the selection function can be derived as defined in Equation 1. This equation assumes that S(x¯ ) does not vary with the values of C 1 , C 2 , and C 2 −C 1 (Lucas and Luke 1983a,b). The second step of the size reduction process, or the breakage function (B), is the proportion of broken particles that have a size below a given size y (where y > x). By definition, B = 1, when y/x = 1. The distribution of the breakage function can be described by these two equations: y r 1− B =s 1− , (3) x where s and r are constants and y/x is the ratio of the particle size of interest over the initial particle size, and y a , (4) B=b x where a and b are constants and y/x is the same as in Equation 3. These equations to describe the breakage function were derived empirically, using in vivo data from masticated carrot pieces, and were found to be a good representation for the data (Lucas and Luke 1983b). While determining these relationships, it was also noted that the selection did not vary with the number of chews for particle sizes above 4.8 mm, showing that the selection function has little dependence on the particle size. The breakage function took about 10 chews for the distribution to establish itself, as all of the particles were initially one size and needed to start the breakdown process. The above-mentioned equations were developed using in vivo chewing data to describe the selection and breakage functions (Equations 1, 3, and 4), and then were used to theoretically examine particle breakage over a selected number of chews. To determine the total percentage of particles in each size range, the percentage of particles below size y that is produced during each chew can be calculated as P (y) =
N−x max
(Equation 1) to further relate it to particle size. Baragar and others (1996) took a slightly different approach, deriving an analytical expression for the measures of the central tendencies of the particle size distribution and degree of particle fracture for both small and large volumes at the beginning of the chewing process. This model was shown to have a good correlation to in vivo chewing data (van der Bilt and others 1987). Prediction of the particle size distribution after mastication may not be as straightforward as the simple selection and breakage functions (and modifications of these functions). As the particles begin to adhere to each other during bolus formation, their selection function will be changed, resulting in different selection functions for foods with higher or lower cohesive forces. However, these complex interactions have not been extensively modeled at this point. Also, none of the above models have taken into account any of the food properties, such as hardness or fracturability when determining the particle breakdown. In addition, these models have not taken into account the effect of ingesting a mixed solid–liquid meal and how the fluid dynamics of the stomach may later influence the particle breakdown kinetics.
Food Property Influence on Breakage In an investigation on the influence of food material properties on food breakdown during chewing, Agrawal and others (1997; 1998) proposed the use of several mechanical property indices to relate food mechanical properties to their rate of breakdown. The response of a solid food particle to fracture will depend on 2 key mechanical properties: the fracture stress and Young’s modulus E. The fracture stress will be represented by R, the toughness, or the energy required to form a crack in the material. By testing various food products, including cheeses, raw vegetables, and nuts, it was found that if stresses are limiting in a food, then (ER)1/2 is the fragmentation criterion, otherwise (R/E)1/2 is the appropriate index for the fragmentation criterion. These fragmentation criteria were correlated to changes in specific surface area of food products and also to surface electrical activity of the jaw muscles during chewing, showing that they can give an accurate representation of at least part of the role played by material properties in particle breakdown during chewing. As the fragmentation criterion demonstrates, there is a relationship between properties of food materials and their breakdown during chewing. Such relationship may be a useful addition to a model predicting particle breakdown, which has yet to be established.
Bolus Particle Size δ P (x) · S(x) · B(y,x),
(5)
x=y
where xmax is the largest particle size present, y is the particle size of interest, δP(x) is the percentage of the total volume of particles that fall within a size range of x to x + δx, S(x) is the selection function (Equation 1), and B(y,x) is the breakage function (Equations 3 and 4), assuming that all particles have an equal probability of being broken, regardless of size or the number of chews. The use of these models had a good correlation with particle size distributions from in vivo chewing data, showing the possibility of using a simple twostep model to effectively describe particle breakdown during the mastication process.
Other models Voon and others (1986) expanded upon this simple model by adding a power law relationship to the selection function
The size of food particles in the bolus plays an important role in not only the swallowing and oral processing of the bolus, but also in the further digestion as the bolus reaches the stomach. Numerous studies have shown that different food types of varying physical properties will produce diverse particle size distributions before swallowing. In most in vivo chewing studies, the particle size of a test product is determined by allowing a subject to chew the product for either a certain number of chewing strokes or until it is felt that a swallow is about to be triggered. At this point, the test product is expectorated and the mouth is rinsed. The expectorated test product is then used for analysis. The traditional method of analyzing the particle size distribution after mastication has been sieving, where a set of sieves is used to determine the mass fraction of particles on each sieve. However, in recent years, laser diffraction and image analysis, where particle size is measured using a computer system to calculate the size and shape of particles from an image, are becoming increasingly powerful methods
104 Comprehensive Reviews in Food Science and Food Safety r Vol. 11, 2012
c 2012 Institute of Food Technologists®
Bolus formation and disintegration . . . Table 1–Values reported in the literature of median particle diameters from in vivo studies. All particle size distributions were determined by sieving methods. Food Hard-baked gram Hard-baked soya beans Peanut Ham Chicken breast Coconut Mushrooms Carrots Egg white Emmental cheese Green olives Gherkins Brazil nuts Brazil nuts Brazil nuts Brazil nuts Carrots Carrots Carrots Carrots Optocal plus Optocal plus
Median particle size (d50), mm
Number of subjects
Chewing parameters
1.445 1.597 0.82 1.28 1.6 1.68 1.88 1.9 2.29 2.4 2.68 3.04 6.6 3.45 1.7 1.2 8.7 6.55 4.5 3.4 2.19 3.35
5 7 10 10 10 10 10 10 10 10 10 10 35 35 35 35 35 35 35 35 87 87
Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered Until swallow triggered 5 chews 10 chews 20 chews 30 chews 5 chews 10 chews 20 chews 30 chews Until swallow triggered 15 chews
for particle size determination (Yurkstas and Manly 1950; Jiffry 1981; Lucas and Luke 1986; Prinz and Lucas 1995; Hoebler and others 1998; Fontijn-Tekamp and others 2004; Peyron and others 2004; Mishellany and others 2006; Jalabert-Malbos and others 2007).
Median particle size values One of the key values used to represent the particle size distribution is the median particle size (d50) of the distribution. This value represents the aperture of a theoretical sieve through which 50% of the weight of the particles could pass. The d50 is also commonly used to represent masticatory performance and swallowing threshold (Fontijn-Tekamp and others 2004). Particle size analysis and key factors affecting particle size As can be seen in Table 1, different food types produce distinct particle sizes upon swallowing. Although physiological factors (age, gender, dental status, and so on) play a role in particle size manufacture, this interindividual variability has been shown to be quite low in comparison with the variability over different food types of varying structure and texture (Peyron and others 2004). By using both laser diffraction and sieving, Peyron and others (2004) showed that although the overall distribution shape was similar between nuts (peanuts, almonds, and pistachios) and vegetables (cauliflower, radishes, and carrots), particle sizes were much larger for all types of vegetables when compared to nuts. The differences in particle size were hypothesized to be caused by variation in bolus cohesion and plasticity between the vegetables and nuts. Another study by Mishellany and others (2006) used the same test foods of nuts and vegetables, but used image analysis to quantify the particle size as well as the shape index ((perimeter2 /(4π·area), corresponding to the circularity of the particles, with a value of 1 being a perfect circle) of the particles. They also showed that nuts and vegetables resulted in distinct particle size distributions. Nuts resulted in a bolus having many particles smaller than 2 mm, and vegetables resulted in a bolus with more particles greater than 2 mm. The particle shape, as quantified by the shape index from the image analysis, was also influenced by c 2012 Institute of Food Technologists®
Reference (Jiffry 1981) (Jalabert-Malbos and others 2007)
(Lucas and Luke 1986)
(Fontijn-Tekamp and others 2004)
the food type, with the greatest differences seen between the nut group and vegetable group instead of between individual food products. These particle size results were also observed with a very low interindividual variation, suggesting that most individuals will reduce food particles to a similar size before swallowing by different means, regardless of chewing efficiency. A study by Lucas and Luke (1986) that examined the particle size distribution of carrots and Brazil nuts, with a varying number of chewing strokes, showed that Brazil nuts consistently broke down faster than carrots, although both products were swallowed after a similar number of chews (Brazil nuts had a smaller particle size upon swallowing). This study also indicated that Brazil nuts did form a cohesive bolus, while carrots did not. The differences in particle size were not fully explained, but hypothesized to be due to a combination of the overall state of the bolus (cohesive or not), the lubrication of the particles (amount of saliva and initial moisture content), and also could be due to differences in the amount of particles being swallowed in “intermediate swallows” (particles being swallowed in small quantities involuntarily). In a related study, Prinz and Lucas (1995) fed test subjects Brazil nut particles of 4 different size categories suspended in plain yogurt in various concentrations to determine the number of chewing strokes and time needed to swallow each mixture. This study showed that both the particle size and the concentration of particles affected the number of chewing strokes before swallowing and the time needed for chewing. Interestingly, these results fit with the model that a certain particle size and lubrication must be met before swallowing can take place. This was shown by quantifying the “chewing frequency” or the number of chews/total chewing time. The chewing frequency was constant at concentrations of >20% Brazil nuts, but rose sharply with concentrations of
