Type and Amount of Lipids Influence the Molecular and Textural

Article pubs.acs.org/JAFC

Type and Amount of Lipids Influence the Molecular and Textural Properties of a Soy Soft Pretzel Amber L. Simmons,†,‡ Ian R. Kleckner,§ and Yael Vodovotz*,†,∥ †

Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, 223 Animal Sciences Building, 2029 Fyffe Road, Columbus, Ohio 43210, United States § Department of Psychology, Northeastern University, 125 Nightingale Hall, 360 Huntington Avenue, Boston, Massachusetts 02115, United States ∥ Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Court, Columbus, Ohio 43210, United States S Supporting Information *

ABSTRACT: Altering baked goods by the addition of nutrient-rich ingredients, such as soy and ground almonds, affects the water and lipid distribution of the product and, subsequently, its final quality. Here, we studied how three lipid sources, shortening, canola oil, and ground almonds, affected texture and water distribution in a baked soy pretzel and the molecular mobility in the dough. Pretzel crumb from all formulations exhibited 40−43% moisture with a little more than half present as “freezable” water. Firmness and chewiness decreased with increased shortening and canola oil, whereas firmness and chewiness increased with additional almonds. In contrast, neither springiness nor cohesiveness was affected by the lipid quantity or source. Finally, magnetic resonance imaging of the soy pretzel dough revealed two or three populations of dough components that have distinct molecular mobilities. With increased lipid content, the mobility of each population increased in magnitude and heterogeneity. Interestingly, almonds had the smallest effect on the molecular mobility of the dough but had the largest effect on textural properties. These results provide quantitative insight into the mechanisms by which the lipid source can influence molecular properties that have textural implications for bakery products. KEYWORDS: soy, lipid, almond, physical properties, bread, magnetic resonance, pretzel

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INTRODUCTION In order to deliver bioactive compounds in soy bread and related bakery products, 1−3 we must understand the interactions between the macromolecular constituents. Herein, we focus on the key plasticizing components in a soy soft pretzel,4 water and lipids, and their influence on the texture of dough and the final product. Lipids serve an essential structural role in the production of bakery products, such as soft pretzels. The amphipathic properties of lipids lubricate the gluten strands and adsorb to the gas−lipid interface of gas cells, allowing for the bubbles to develop and be evenly distributed in the crumb.5 Solid fat (traditionally lard, now more commonly shortening) promotes a lighter, more homogeneous crumb than liquid oil (for a review, see the study by Pareyt et al.6). Oil does not adhere to the surface of gas cells as well as solid fat, and therefore, oil promotes a less consistent distribution of gas cells and gluten in the dough. An unrefined lipid source, for example, ground nuts, is unique in that it is composed of mostly unsaturated oils but is present in a solid matrix. It was hypothesized that oil from ground nuts can participate in molecular interactions that promote the growth of gas cells and serve the essential role of lipids in the developing crumb. As a representative unrefined lipid source for use in these studies, ground almonds were selected. Almonds were chosen because of their natural β-glucosidase activity driving the conversion of soy isoflavones to aglycones and, thereby, © 2014 American Chemical Society

potentially promoting the absorption of these phytochemicals.4,8 Accordingly, the soy soft pretzel formulation used in these experiments contained about 25 mg of soy isoflavone aglycone equivalents per 59 g of pretzel, and a soy pretzel made with 5.5% ground, raw almonds doubled the amount of aglycones in the pretzel (more than 25% aglycones).4 It was hypothesized that ground almonds could be substituted for shortening in a soy pretzel while maintaining favorable crumb texture. As a comparison, in addition to using shortening and almond lipid sources, canola oil was selected as a commonly used, refined, pure lipid source with a similar triglyceride profile as almonds.7 Several prior studies have examined the effects of the type and amount of lipid on the crumb texture of bread. A reciprocal relationship has been established between consumer acceptability and crumb firmness, since increased crumb firmness is an indicator of bread staling.9 Because of the role of shortening in facilitated gas formation and structural support of the gas cells in bakery products,6 it was hypothesized that its incorporation would result in the softest crumb compared to pretzel rolls made with canola oil or ground almonds. Describing the state of the water and its interaction within the matrix will help Received: Revised: Accepted: Published: 717

August 8, 2013 December 19, 2013 January 7, 2014 January 7, 2014 dx.doi.org/10.1021/jf403531f | J. Agric. Food Chem. 2014, 62, 717−724

Journal of Agricultural and Food Chemistry

Article

textural analysis). Stickier formulations were dusted lightly with wheat flour to facilitate roll formation. Balls were submerged in 1.0% sodium hydroxide solution at 60−65 °C for 60 s and placed in the center of a lightly greased mini loaf pan. Rolls were proofed for 25 min (43 °C and 100% humidity) and then baked at 149 °C for 16 min in a convection oven (Jet-Air oven, JA14, Doyon, Linière, Québec, Canada). Rolls were cooled on wire racks, stored in plastic bags overnight, and analyzed the following day. At least four pretzel rolls were prepared and analyzed per batch. Analysis of all of the parameters of pretzel rolls made from 2.9% shortening from two different batches prepared on 2 different days showed that the variability between samples of different batches was similar to the variability between samples of the same batch. Therefore, one batch was prepared for each of the other variables. Quantification of Total Water, FW, and UFW. The moisture content and water desorption patterns were assessed using TGA (Thermogravimetric Analyzer Q5000, TA Instruments, New Castle, DE). Samples of pretzel crumb (15−20 mg) were subjected to a linear heat ramp from room temperature to 200 °C at 10 °C/min. Under the assumption that weight loss resulted solely from water evaporation,15 the weight of the sample at 150 °C was subtracted from the initial weight to yield the moisture content. The derivative of the thermogravimetric analysis curve (dTG) was calculated by Advantage for the Q Series, version 2.8.0.394 (TA Instruments−Waters LLC, New Castle, DE). The amount of FW was quantified using DSC. Pretzel crumb (10− 15 mg) was placed in a hermetically sealed stainless-steel pan with an O-ring (PerkinElmer, Waltham, MA). The temperature was lowered to −50 °C, held isothermally for 2 min, and then increased linearly at 5 °C/min to 150 °C. The peak near 0 °C was integrated to yield the change of enthalpy (ΔH) associated with the phase transition from ice to water. The latent heat of fusion of water (333 J/g) was used to quantify the amount of FW in the dough sample.16,17 Subsequently, the amount of FW was subtracted from the amount of total water to yield UFW. Textural Profile Analysis (TPA). Pretzel rolls were sliced into two adjacent 2.5 cm cubes that included pretzel crumb free of crust (electric carving knife, Toastmaster, St. Louis, MO). Each cube of pretzel crumb was subjected to a double compression test to 40% compression at a crosshead speed of 100 mm/min (Instron Universal Texture Analyzer 5542, Instron, Norwood, MA). Firmness, springiness, chewiness, and cohesiveness of the pretzel crumb were calculated using Bluehill 2 software, version 2.17 (Instron, Norwood, MA).18 Firmness was reported as the maximum force at the first compression. Cohesiveness was calculated as the ratio of the energy to maximum load during the second compression to that at the first compression. At least five samples were analyzed per treatment. Dough Preparation for MRI. Dough was prepared by combining 189.6 g of wheat flour, 57.6 g of soy flour, 19.2 g of soy milk powder, 4.6 g of wheat gluten, 0.6 g of dough conditioner, 5.0 g of salt, 20.0 g of sugar, and 188.5 g of water (yeast was omitted to avoid changes in the sample during image acquisition). The dough was then divided into 6 × 75 g portions to ensure that the only difference between dough samples was the added lipid. Subsequently, lipid was added to produce six formulations: 1.6 g for 2.9% shortening or canola oil, 5.1 g for 10.0% shortening or canola oil, 3.3 g for 2.9% ground almonds, or 10.4 g for 10.0% ground almonds. Lipid was incorporated by kneading by hand, and then dough was placed in 6 g glass vials. Vials were capped and then sealed with Parafilm M (Bemis Co., Neenah, WI) to prevent moisture equilibration. Proton Intensity and T2-Weighed Images. MRI scans were performed the same day that the dough was prepared. The experiments were performed at ambient temperature on a 4.7 T/40 cm magnet controlled by a Bruker Avance Console (Bruker Biospin, Billerica, MA). The instrument was equipped with a 260 mm inner diameter gradient coil and a 200 mm inner diameter proton volume radio-frequency coil. For an intensity reference, a phantom was prepared by combining water and deuterated water at a proportion that resulted in an intensity similar to that of an average dough sample. Both the dough sample and the phantom were placed inside the coil.

elucidate the mechanisms by which the type, amount, and source of lipid modulate physical properties of the crumb. Using thermogravimetric analysis (TGA), the total quantity of water in the crumb as well as the relative binding strength of the water to the macromolecules in the matrix can help determine the locational and functional relationship between the lipid and the matrix, as well as how the lipid ingredients modulate the interactions between the water and macromolecule.10 Additionally, differential scanning calorimetry (DSC) reveals the compartmentalization of water into “freezable” water (FW) and “unfreezable” water (UFW), which is useful in determining the susceptibility of the products to staling and frozen storage shelf life.11,12 In a prior study using magnetic resonance imaging (MRI), Lodi et al. did not observe large differences in the water states between soy bread and soy bread containing almonds,13 but it is unknown how the different lipid components affect the water properties in the dough or the physical attributes of the baked product. Only two studies, to our knowledge, have investigated the use of nuts as the lipid source in bread. Oliete et al.29 and Gómez et al.33 produced bread with almond, hazelnut, peanut, or walnut pastes at 5, 10, and 15% in the absence of other added lipid. They demonstrated that different types and amounts of nut pastes discretely affect dough rheology,33 as well as the firmness, chewiness, cohesiveness, and resilience of the bread.29 However, it is unknown how ground almonds would affect texture in a soy bread matrix or the mechanisms by which the lipids participate in intermolecular interactions with water. To assess the physiochemical effects of lipid constituents on a soy soft pretzel, pretzel rolls were prepared with 0, 2.9, or 6.0% lipid from shortening, canola oil, or ground almonds. To study the dough, pretzel dough was prepared with 2.9 or 10% of shortening, canola oil, or ground almonds. The crumb was subsequently assessed for texture and water distribution, and dough was assessed for water mobility. We hypothesized that the addition of ground almonds would attenuate the negative effects of the crumb texture that are associated with oil because of the differences in the distributions of water and lipid in the pretzel roll.

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MATERIALS AND METHODS

Pretzel Preparation. Soft pretzel rolls with 16.2% soy ingredients (dry weight) were produced on the basis of the protocol by Simmons et al.1,4 and soy bread patent14 with the ingredients in Table S1 of the Supporting Information. Formulations included 0, 2.9, or 6.0% lipid from shortening, ground almonds, or canola oil; none of the formulations included more than one of these lipid sources. Almonds contain approximately 50% fat by weight,7 and therefore, to achieve the traditional lipid amount, 2.9% lipid, a soy bakery product required 5.5% ground almonds (w/w). The pretzel inherently contained 1.9 ± 0.2% endogenous lipid from the other ingredients.4 To make the sponge, activated yeast, 43% of the total wheat flour, the wheat gluten, and the dough conditioner were combined with 45% of the total water in a standing mixer (Kitchen Aid, St. Joseph, MI) on a low setting with the beater attachment. The sponge was proofed at about 43 °C at 100% humidity (CM2000 Combination Module, InterMetro Industries Corp, Wilkes-Barre, PA). The remaining ingredients, except lipid, were added, and the dough was stirred with the dough hook attachment, first on low until ingredients were moistened and then on medium−high. After about 5 min, the lipid source was added. The dough was then stirred until it sheeted (about 5 min more). Pretzel rolls were formed by rolling 71.0 g of dough into slightly oblong balls (pretzel rolls were formed instead of the traditional twisted shape to produce congruent samples of crumb for 718

dx.doi.org/10.1021/jf403531f | J. Agric. Food Chem. 2014, 62, 717−724


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For all images acquired, the voxel size was 234.375 × 234.375 μm with a 5 mm slice thickness (i.e., 0.275 mm3 per voxel). Each of five slices used 256 × 128 voxels for a 60.0 × 30.0 mm field of view. The proton intensity images were T1-weighed, using a multi-slice FLASH pulse sequence with two averages (Bruker, Billerica, MA); there was a 0.5 s repetition time (TR), a 2.6 ms echo time (TE), and a 32° flip angle. The T2 images were acquired using a multi-slice, multi-echo (MSME) Carr−Purcell−Meiboom−Gill (CPMG) pulse sequence (Bruker, Billerica, MA) with a 90° flip angle and a 5 s TR, with TE of 10, 20, 35, 55, 75, 95, 115, 135, 150, and 160 ms. The set of 10 images in the echo series were used to estimate T2 values for each voxel by fitting intensity(TE) = amp × exp(−TE/T2) + offset, where intensity(TE) is the observed intensity of the voxel in the image with echo time TE, amp is the amplitude of the T2 decay curve, T2 is the T2 of the decay curve, and offset is the intensity at infinite TE (the magnitude of the noise). These fits yielded T2 maps for each formulation and were the focus of additional analyses. The program ImageJ (version 1.46r; http://rsbweb.nih.gov/ij) was used to prepare T2 histograms using a region of interest analysis of the T2 spatial maps. For each formulation, a circular region of interest was drawn within the edges of the dough image in each slice to avoid edge effects. Each T2 histogram contained 200 bins between 0 and 50 ms (0.25 ms per bin) with 3228 voxels per five slices, for a total of 16 140 voxels per formulation. T2 histograms were analyzed using a deconvolution analysis programmed in MATLAB (The Mathworks, Natick, MA). In this analysis, the T2 histogram of each formulation was fit to a sum of one, two, or three Gaussian curves to quantify distinct populations of T2 values in the sample. Each Gaussian curve was modeled as counts(T2) = amp × exp(−((T2 − μ)2)/(2σ2))/(σ√(2π)), where counts(T2) is the number of counts in the T2 histogram, amp is the amplitude of the Gaussian curve, μ is the mean T2 value for the population, and σ is the standard deviation in T2 value for the population. The fitted parameters were amp, μ, and σ. For the two-population deconvolution analysis, the observed data, countsobs(T2), were fit to the simulated curve countstot(T2) = countsA(T2, ampA, μA, σA) + countsB(T2, ampB, μB, σB). For the three-population deconvolution analysis, observed data were fit to countstot(T2) = countsA(T2, ampA, μA, σA) + countsB(T2, ampB, μB, σB) + countsC(T2, ampC, μC, σC). Population fractions were calculated using numerical integrals of fitted counts(T2) curve of each population compared to the numerical integral of the observed data curve countsobs(T2). Confidence intervals for each fitted parameter (amp, μ, and σ for each population) and for the population fractions were estimated using 1000 iterations of Monte Carlo bootstrapping from residuals.19 Statistical Analysis. For quantity of total moisture, FW, and UFW as well as texture properties, statistical analysis was performed to fit the model Y = lipid type + lipid amount + (lipid type × lipid amount), where Y is the parameter at hand, lipid type is shortening, almonds, or canola oil, lipid amount is 2.9 or 6.0%, and lipid type × lipid amount is the interaction between the two variables. Analysis of variance and least-squares means for the (lipid type × lipid amount) interaction were calculated using SAS 9.1 (SAS Institute, Cary, NC). Statistical significance was evaluated with an α of 0.05; the Bonferroni correction was used where appropriate. Average ± standard deviation is reported throughout the text.

suggests that there were little to no differences in water migration, distribution between the crust and crumb, or evaporation from the crust during baking. Water desorption as determined by TGA occurred in a biphasic manner for the shortening and almond samples and a monophasic manner in canola oil samples and samples without added lipid (Figure 1 and Table 1). The absence of the peak at

Figure 1. Example water desorption patterns from samples with 6.0% added lipid measured using TGA. Black lines represent samples made with shortening; dark gray lines represent samples made with ground almonds; and light gray lines represent samples made with canola oil. Dashed lines show the mass of the sample as a function of the temperature, whereas solid lines show the derivative of the weight loss curve. SHO, shortening; CAN, canola oil; and ALM, ground almonds.

Table 1. Peak Rate of Water Loss during Heating of Soy Pretzel Crumb type of lipid

amount added (%)

no added lipid shortening shortening almond almond canola oil canola oil

0.0 2.9 6.0 2.9 6.0 2.9 6.0

dTG1 (°C)

dTG2 (°C)

N/A 23.7 ± 1.5 23.8 ± 2.1 23.1 ± 0.2 25.8 ± 4.9 N/A N/A

64.2 64.1 63.5 63.3 65.1 55.7 53.8

a a a a

± ± ± ± ± ± ±

7.9 2.5 4.8 2.5 2.7 3.6 2.5

b b b b b c c

the lower temperature range (23−40 °C) in the canola oil samples and samples without added lipid suggests that (1) water loosely bound to the matrix exhibited a vaporization temperature lower than room temperature and quickly evaporated during sample preparation and/or (2) water did not exist in the fraction that was more easily removed. Also, in the water desorption thermograms, there was a peak near 60 °C that represented the majority of the water evaporation for all formulations (Figure 1 and Table 1). The temperature of this peak was similar for the shortening samples, almond samples, and samples without added lipid (approximately 60−68 °C) but was lower for the canola oil samples (51−59 °C; p < 0.05 when comparing canola oil samples to other samples; differences in peak temperatures between 2.9% canola oil and 6.0% canola oil samples were not statistically significant). Because the water evaporated at different temperatures, this suggests that canola oil caused different interactions between the water and macromolecular structures, such as starch, lipids, and proteins, as observed historically (Pareyt et al.,6 and references within). Both the physical state of the lipid and the chemical composition of the lipids likely contributed to the disparity in water desorption patterns observed in the samples with canola

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RESULTS AND DISCUSSION Moisture Content, FW, and UFW. Soy soft pretzel rolls were produced with lipid from shortening, canola oil, and ground almonds at two different levels and assessed for total moisture content, percent FW, and percent UFW. All pretzels contained 41.9 ± 4.3% total moisture, while FW comprised 58.7 ± 2.6% of the total water. There were no statistical differences in total moisture content or percent FW or UFW between the formulations (p > 0.09). Because the same amount of water was added to all of the soy pretzel formulations and almonds have only about 5% moisture,7 this observation 719



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oil. Specifically, the melting points of the individual triglycerides within each lipid ingredient can dictate both lipid−lipid interactions (such as the size and distribution of fat crystals20) and interactions between the lipid and other macromolecules. Shortening is about 19% saturated fat and forms solid lipid crystals that reduce surface tension at the oil− water interface and promote air incorporation and gas cell stabilization during baking.6 On the other hand, canola oil is less than 10% saturated fat and liquid at room temperature, thereby contributing a much more uniform triglyceride profile and very little emulsification properties; it is expected that water would be associated with fewer microenvironments within the matrix. Almond as a lipid source differs drastically from shortening and canola oil in that part of the lipid provided by ground almonds is bound in the nut matrix and not involved in macromolecular interactions with the other ingredients. For example, more mastication of almonds led to a greater in vivo bioaccessibility of lipid and less lipid in the feces;21 therefore, it is expected that the lipid bound in the almonds within the food matrix is also inaccessible to its surrounding environment. It is interesting to note that ground almonds also promoted the diphasic water desorption pattern, despite the low percentage of saturated fat (about 7% compared to shortening at about 19%7). Almonds contain polysaccharide-rich cell wall material22 that likely promoted water−lipid−protein interaction during proofing and baking, although the extent of the endogenous emulsifying properties of almond has yet to be explored. This is the first study, to our knowledge, showing that different sources and amounts of lipid did not lead to differences in the partitioning of water into FW and UFW compartments in the pretzel formulations studied. Consistent with these results, Matuda et al. did not observe a change in UFW in dough with and without shortening when investigating the effects of vegetable shortening and different dough conditioners in French bread dough.23 These results are also consistent with findings by Lodi and Vodovotz that showed that the addition of almond to soy bread (both formulations contained shortening) led to only small temperature differences in the peak rates of water loss in a similar TGA experiment.24 Together, these results demonstrate that the soy matrix can accommodate ground almonds with or without shortening but not canola oil without drastically altering water content, water state, or water desorption patterns. Texture Analysis. This soy pretzel formulation contained 16.2% soy ingredients (dry weight; see Table S1 of the Supporting Information) and, consequently, less gluten, more protein, more fiber, and more water (for optimal workability2) compared to a wheat pretzel without soy. The changes in the macromolecular composition and microscale ingredient interactions led to changes in texture when comparing bakery products made with or without soy ingredients, including firmness and chewiness.25,26 However, the role of the lipid component in modulating textural properties of a soy-based bakery product had not been explored. In Figure 2, the firmness, chewiness, and cohesiveness of the pretzel rolls are illustrated relative to the properties of the 2.9% shortening product. The values for the 2.9% shortening product were firmness, 9.37 ± 1.63 N; chewiness, 57.8 ± 9.6 N; and cohesiveness, 0.62 ± 0.03 (ratio). The springiness values were 7.83 ± 0.66 mm, with no difference between formulations (p > 0.05). Although it was expected that the canola oil products would be firmer and chewier than the shortening products

Figure 2. Textural properties of the pretzel rolls, including firmness, springiness, chewiness, and cohesiveness. The actual values were normalized to the 2.9% shortening pretzel roll (the traditional formulation; i.e., values obtained for the 2.9% shortening formulation were set to 1.0). Formulations with no added lipid (NAL) are black bars; formulations with 2.9% added lipid are solid white or gray bars; and formulations with 6.0% shortening have black dots. Shortening (SHO) samples are white; almond (ALM) samples are light gray; and canola oil (CAN) samples are dark gray. Different letters above the bars indicate statistical significance for each property. Error bars are standard deviations across five independent measurements.

because of their inability to promote gas cell development,6 less than a 25% increase in firmness was observed when ground almonds or canola oil was used in place of 2.9% shortening (Figure 2). The presence of polar lipids (e.g., soy lecithin) in the soy ingredients perhaps attenuated changes in gas cell formation27 compared to crumb of a wheat product without soy. An increase in the lipid content to 6.0% lipid caused a 40% decrease in firmness for both the shortening and, unexpectedly, the canola oil products. Baldwin et al. proposed that shortening melts during proofing and baking, thereby providing a constant source of lubrication for bubble formation and strengthening for air retention; perhaps the high amount of lipid in the 6.0% canola pretzel roll provided ample lipid for coating growing gas cells during proofing, while the soy ingredients aided in gas cell stabilization.28 Additionally, large, inhomogeneous gas cells would also exhibit low degrees of firmness. The gas cell size was not quantified here but may be important in future studies. The higher amount of almond did not soften the crumb, corroborating TGA and water mobility data and suggesting that the lipid in the almond is bound within the nut matrix. Similarly, although the lipid is more accessible in an almond paste compared to ground almonds, an increase in firmness and chewiness was also observed with the addition of different kinds of nut pastes to wheat bread likely because of other components of the nut.29 The springiness and cohesiveness of the crumb were similar between all formulations (Figure 2). Similarly, no practical changes were observed in springiness when comparing wheat and soy bread,25 suggesting that springiness is very resilient to such changes in bread formulation. The cohesiveness values were similar to what has been reported previously in soy, and these values were greater than wheat bread.25 Similar to the trends observed with firmness, chewiness was increased slightly in formulations with ground almonds and canola oil at 2.9% (Figure 2). Moreover, at 6.0% added lipid, pretzels made with 720



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Figure 3. T2 histograms from each formulation reveal that each formulation has a different magnitude and heterogeneity of T2 values, reflecting different molecular level mobilities of the dough. (A) Each formulation is shown overlaid. (B) Deconvolution analysis quantifies the population fraction, mean T2, and heterogeneity in T2 for each of 2−3 subpopulations in each formulation. The results for the 10.0% canola oil formulation are shown here. Results for all formulations are listed in Table 2. Figures for the other formulations are in Figures S4−S6 of the Supporting Information.

This is likely due to the inaccessibility of the almond lipid to the dough matrix environment, as was observed in TGA. Mechanistically, one possible explanation for the observation that adding more lipid increased the molecular mobility of the dough is that water droplets became larger (i.e., more like mobile bulk water than less mobile bound water) because of the immiscibility between the water and lipid. Moreover, the T2 histogram from each formulation suggests that there were several populations of T2 values, which were quantified using a deconvolution analysis (Figure 3B and Table 2). From this analysis, the shortening and almond formulations each revealed two populations of T2 values, whereas the canola oil formulations both revealed three populations of T2 values. Each population is likely important because it made up a significant fraction of the observed protons (i.e., the smallest population was 25%). Unfortunately, assigning the molecular identity of these populations (e.g., water, lipid, or carbohydrate) could not be accomplished with these data alone. However, on the basis of the changes of the proton relaxometry during baking of bread dough, Engelsen et al. labeled the two proton populations with the longest relaxation times as starch-bound water and protons that are diffusing between starch, protein, and water.31 In contrast, Lodi et al. observed a peak from not just water protons but also lipid protons in MRI images of baked soy bread.13 Careful assignment of these populations may be important in future studies. In any event, the range of T2 values observed here was more broad than those in soy and soy−almond bread,24 indicating that molecular mobilities of this dough were much less homogeneous than the molecular mobilities of baked bread. Taken together, these MRI data support that, with increased lipid content, the average proton mobility both increased and became more heterogeneous across voxels. This is consistent with increasing size of the components (e.g., water and fat) within each 0.275 mm3 voxel, such that with greater lipid content, some voxels were more water or more lipid than when there was a smaller percentage of fat. These MRI experiments have several limitations. First, the T2 values presented here result from fitting a mono-exponential function to the T2 decay curve at each voxel. However, each voxel likely contained a mixture of molecules with different T2 values, and thus, the T2 decay curve is best described by a multiexponential function. Unfortunately, these multiple T2 values cannot be determined using these data because the temporal resolution was too low to trust the fitted parameters from a multi-exponential function. Thus, the T2 values reported here are influenced by all of the molecules within each voxel, and the

shortening and canola oil exhibited a slight although not statistically significant decrease in chewiness, likely because of a more airy structure. In contrast, pretzels made with 6.0% lipid from ground almonds were chewier than all other formulations (89.2 ± 14.1 N versus 78.5 ± 10.5 N or less). Collectively, these data show that (1) both 2.9% canola oil and increasing amounts of ground almonds led a greater force requirement to deform the product and, because deformations are equally reversible with all formulations, more total force would be required to chew the product, (2) addition of more than twice the traditional amount of shortening provides a softer, less chewy crumb, and, interestingly, (3) addition of greater amounts of canola oil, for example 6.0%, reversed the increase in firmness and chewiness seen at 2.9% canola oil, perhaps because of very large air bubbles. Proton Intensity and T2-Weighted Images of Pretzel Dough. MRI allows for the spatial resolution of water content and water mobility within a sample13,30 and can provide insight into the molecular properties and interactions between ingredients. Proton density-weighed images minimize the effects of T2 relaxation and correlate with the water content of the dough at each voxel. Because the dough samples had equal amounts of added water, it was expected that these images would be similar, as was observed (see Figure S1 of the Supporting Information). In all samples, the water appeared to be evenly distributed in the dough on a centimeter scale. However, the mottled appearance indicates that the water content of each voxel is slightly different from neighboring voxels. This mirrors what was seen previously in MRI images of non-yeasted wheat and soy dough.30 T2-weighed images reported the mobilities of protons from water, lipids, carbohydrates, and proteins in the dough. T2 histograms revealed that each formulation had a different magnitude and heterogeneity of T2 values, reflecting different molecular level mobilities of the dough (Figure 3A). There are several noteworthy features of these results. First, the T2 values in all three 2.9% lipid formulations (shortening, canola oil, and ground almonds) exhibited similar T2 values, suggesting that their proton mobilities were similar. However, when more lipid was used (i.e., the 10% lipid formulations), the mean T2 increased, which implies that the protons were more mobile, the deviation in T2 increased, which implies that there were greater differences in the mobilities of the protons in the sample, and the samples appeared increasingly different from one another. Moreover, samples with canola oil and shortening exhibited much greater shifts in average and standard deviation T2 upon higher lipid addition compared to the almond samples. 721


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47.0] 60.1] 52.5] 51.4] 52.7] 59.2]

16.53 24.13 15.81 18.15 16.68 22.46

[16.42, [23.54, [15.74, [17.73, [16.38, [21.84,

17.02] 26.05] 15.91] 18.83] 17.28] 23.64]

1.01 2.13 0.86 1.06 1.00 2.30

[0.84, [1.43, [0.82, [0.76, [0.68, [1.59,

1.05] 2.33] 0.91] 1.18] 1.15] 2.67]

0 0 0 0 25.1 [16.0, 33.3] 25.8 [11.3, 39.8]

18.96 [18.28, 19.87] 26.37 [24.76, 28.96]

2.04 [1.51, 2.33] 3.64 [2.47, 4.23]

distributions shown here reflect deviations in the molecular composition of the voxels. Second, small air bubbles in the samples could have an effect on the results in a manner that depends upon the magnetic susceptibility difference between the air in the bubble and the surrounding medium, the size of the bubble, and the read-out gradient strength.33 To minimize bubble formation, yeast was not added to the samples for MRI experiments, yet it is possible that different formulations led to discernible differences in the size and distribution of air bubbles in the sample. Further experiments are required to quantify potential artifacts because of air bubbles and how these potential artifacts may differ across samples. Third, images based on magnitude data were used to calculate T2 values; fitting magnitude data to a decay curve can result in an overestimate of the decay time.34 With that in mind, these results must be analyzed relatively and not absolutely. Molecular insight into textural properties has been achieved with nuclear magnetic resonance (NMR) and MRI on baked bread, including work by Engelsen et al.,31 Seow and Teo,32 Lodi et al.,13 and Bosmans et al.35,36 However, quantitative analyses have not yet been performed to predict the texture of the bread from molecular properties of the dough. Dough made with high amounts of shortening or canola oil exhibited the longest T2 relaxation times, and pretzel rolls baked from similar formulations also exhibited the lowest degrees of firmness and chewiness. Similarly, upon increasing levels of ground almonds, both T2 relaxation times and textural properties exhibited only relatively small changes compared to changes induced by added shortening or added canola oil. Future experiments can shed light on the role of relaxometry of the protons (especially water) and the textural consequences of the baked product. Engelsen et al. developed a model to predict the degree of staling in bread from T2 relaxation behavior of protons in freshly baked bread.31 It is theoretically possible to adapt this model to dough to identify formulations that have favorable shelf stability. In conclusion, the substitution of 100% oil for shortening has historically led to a decrease in the quality of bread crumb;6 however, a denser and chewier crumb is an asset for soft pretzels. The pretzel matrix can easily accommodate a significant amount of soy while still remaining consumeracceptable,1 and this work has shown that pretzels can accommodate different types and levels of lipids, although a sensory evaluation is needed to confirm consumer acceptability (and is planned as part of a future study). The different types (shortening, canola oil, and ground almonds) and levels (2.9 and 6.0%) of added lipid did not affect the total moisture content or compartmentalization of FW and UFW. However, the water in samples with higher lipid exhibited greater proton mobility, as seen with MRI. The lipid in the ground almond appeared to be bound in the solid nut matrix, thereby attenuating added-lipid-induced changes in water mobility, firmness, and chewiness of the crumb compared the observed changes in these properties with increased shortening and canola oil. These findings emphasize that the source of the lipid can influence molecular properties and have consequential textural implications. The details of these relationships can aid functional food development in baked goods.

95% confidence intervals computed from Monte Carlo error analysis.

0.65] 1.61] 0.54] 0.78] 0.78] 1.78]

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ASSOCIATED CONTENT

S Supporting Information *

MRI intensity images of one axial slice from each sample (Figure S1), MRI T2-weighted images using a 10 ms echo time

a

[0.59, [1.32, [0.49, [0.67, [0.62, [1.40, 0.61 1.48 0.51 0.74 0.71 1.61 15.80] 22.80] 15.45] 17.17] 15.76] 20.25] [15.76, [22.47, [15.41, [17.04, [15.52, [19.66, 15.77 22.60 15.43 17.07 15.59 19.83 75.7]a 88.0] 64.2] 84.8] 56.3] 52.4] [52.5, [39.2, [47.0, [48.4, [23.8, [20.8, 2.9% shortening 10% shortening 2.9% almond 10% almond 2.9% canola 10% canola

formulation

58.8 60.8 55.7 66.9 37.5 33.2

PA (%)

mean T2 (ms)

deviation in T2 (ms)

40.7 38.3 44.0 32.5 36.7 41.2

[23.4, [10.8, [35.5, [14.5, [15.2, [16.7,

mean T2 (ms) mean T2 (ms) PB (%)

population B population A

Table 2. Populations (Denoted PA, PB, and PC) from Deconvolution Analysis of T2 Histograms


PC (%)

population C



722



Article

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of one axial slice from each sample (Figure S2), MSME method was used to acquire images, and the multi-echoes were used to calculate T2 values (Figure S3), shortening (Figure S4) and almond (Figure S5) formulations yield T2 histograms that are well-described by two populations of T2 values but not one population, canola oil formulations yield T2 histograms that are well-described by three populations of T2 values but neither one nor two populations (Figure S6), and ingredients used in pretzel rolls (Table S1). This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Telephone: 614-247-7696. Fax: 614-292-0218. E-mail: [email protected]. Present Address ‡

Amber L. Simmons: Department of Medicine, Boston University, 650 Albany Street, Boston, Massachusetts 02118, United States. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank Dr. Amir Abduljalil for help with acquiring the MRI images and data analysis. ABBREVIATIONS USED DSC, differential scanning calorimetry; dTG, derivative of the weight loss as represented in the thermogram; FW, “freezable” water; UFW, “unfreezable” water; MRI, magnetic resonance imaging; TE, echo time; TGA, thermogravimetric analysis; TR, repetition time

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REFERENCES

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Type and Amount of Lipids Influence the Molecular and Textural

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