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1H Nuclear Magnetic Resonance (NMR) and Differential Scanning Calorimetry (DSC) Studies of Water Mobility in Dough Systems Containing Barley Flour

Zhanhui Lu1 and Koushik Seetharaman1,2

ABSTRACT Cereal Chem. 90(2):120–126

This study used 1H nuclear magnetic resonance (NMR) spin-spin re-laxation time (T2) and differential scanning calorimetric (DSC) meas-urements of unfreezable water content (UFW), to assess water behavior in freshly prepared (25°C), refrigerator-stored (4°C, one day), or freezer-stored (–35°C, one day) doughs containing 5, 10, or 30% whole grain, air-classified β-glucan-diminished, and air-classified β-glucan-enriched (BGB-E) barley flours. Three populations of water were detected by NMR, depending on moisture content of dough, namely, tightly (T21, 2–5 msec), less tightly (T22, 20–50 msec), and weakly (T23, 100–200 msec) bound water. T22 peak was always detectable, and T22 peak time

linearly correlated to moisture content of dough in a range of 0.7–2.0 g/g db (r = 0.99, P < 0.05). Freezer storage showed less effect on water mobility in dough compared with refrigerator storage, whereas cooking and cool storage of cooked dough significantly decreased the water mobility (P < 0.05). Adding barley flour steadily decreased the water mobility in dough, and the reduction was more significant with adding BGB-E (P < 0.05). Immobile water content was calculated by extrapo-lating T22 peak time versus total moisture content in dough and signifi-cantly correlated to the UFW content measured by DSC (r = 0.72, P < 0.05).

Water in food systems has a dominant influence on processing

parameters, ingredient functionality, sensory and textural attrib-utes, and storage stability of products (Given 1991; Skendi et al 2010b). The dynamic properties of water and its distribution within dough influence the rheological behavior and the machina-bility of dough (Assifaoui et al 2006), as well as gluten (Cherian and Chinachoti 1996), and thus govern the dough properties (Chinachoti and Schmidt 1991). However, water is not evenly distributed among the flour constituents in dough (Raun et al 1999). Depending on moisture content and characteristics of flour components, water is either bound to components or is free in dough. Bound water equals the amount of water necessary to fully hydrate and plasticize the wheat components and contributes di-rectly to the supramolecular organization of dough structure. Free water is partly responsible for the flow and mobility properties of dough (Roman-Gutierrez et al 2002). Thus, the balance of bound water and free water could directly impact on the elasticity and extensibility of dough and so control the texture of final products. Because different degrees of water “boundness” exist, the func-tion of water in dough becomes ambiguous if only the water amount is considered (Lin et al 2001).

Water redistribution from gluten to other components in dough during cold storage increased water mobility (Esselink et al 2003). This phenomenon could also occur during dough resting or condi-tioning, because dough wetting and softening are often observed during resting or short-term storage. This resting is an essential unit operation during production of various wheat-based products such as Asian noodles or steamed bread. However, few studies have considered the impact of resting and short-term storage on water mobility in dough. Water redistribution in dough also hap-pens in flour blends. Wheat flour fortification with barley β-glu-can is a common practice to enhance health attributes of products. When barley β-glucan was added to wheat flour, an increase in water absorption (Skendi et al 2010a) and even two peaks of wa-ter absorption on the farinograph curve were observed (Knuckles et al 1997). The same behavior has been observed for β-glucan-enriched flour fractions from different sources (Knuckles et al 1997; Cavallero et al 2002). Although β-glucan in barley con-tributes to improved health attributes including lowering serum

cholesterol levels (Bourdon et al 1999) and moderating postpran-dial insulin responses and blood glucose levels (Wood et al 1991; Cavallero et al 2002), it also results in negative effects because of the dilution or weakening of the wheat gluten protein network (Pomeranz et al 1977), resulting in reduced loaf volume (Gill et al 2002). Nevertheless, the latter issue can be easily resolved by supplementing vital wheat gluten, which is already a common practice in the noodle industry. The remaining concern is that barley β-glucan competes for water with gluten and thus impacts the development of the gluten network. To our knowledge, no systematic studies on water mobility in dough made from wheat and barley flour blends have yet been reported.

Currently, the most successful techniques used to probe the wa-ter mobility in food systems are nuclear magnetic resonance (NMR) spectroscopy and differential scanning calorimetry (DSC). NMR spin-spin or transverse relaxation time constant T2 has been used to indicate the state of water in dough because of the ease of T2 measurement. Water mobility is usually presented as degree of boundness estimated by T2 relaxation time, rather than a specific value, because of its dependence on total moisture content in dough. Delineation of water fractions based on molecular mobil-ity is possible with this technique (Zimmerman and Lasater 1958; Leung et al 1979). Some researchers have calculated the percent-age of water populations for quantifying the water mobility based on integration of peak areas of T2 distribution profile (Tananuwong and Reid 2004). However, there are limitations to this procedure because 1) it is dependent on total moisture content; 2) accurate integration of peak area becomes impossible because of overlap-ping T2 peaks in the dough system; and 3) it is incorrect because the calculation is based on total proton numbers and includes proton signals from other dough components such as protein or starch. Therefore, more reliable methods to exploit the NMR sig-nals remain a question for quantifying water mobility in foods.

Another measure of water mobility in dough is the unfreezable water content (UFW) measured by DSC (Roman-Gutierrez et al 2002). Bushuk and Mehrotra (1977) indicated that the UFW of wheat flour seemed not to depend on flour strength, protein con-tent, amount of damaged starch, dough mixing time, added chemi-cals (salt or ascorbic acid), or conditioners including pentosans. This statement could be questioned by the sensitivity of the method, because in practice added water amount is a critical fac-tor controlling processing parameters of dough. DSC easily pro-vides an evaluation of UFW content in foods (Ollivon 1991), which is indirectly deduced by the difference between total and frozen water contents (Roman-Gutierrez et al 2002). One pre-ferred way to arrive at an accurate value for UFW is to calculate

1 Department of Food Science, University of Guelph, Guelph, ON, Canada. 2 Corresponding author. Phone: (519) 824-4120. Fax: (519) 824-6631. E-mail:

kseethar@uoguelph.ca

http://dx.doi.org/10.1094 / CCHEM-09-12-0116-R © 2013 AACC International, Inc.

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the x-axis intercept of a plot of enthalpy versus total moisture content (Schenz et al 1991). The obtained UFW content is an independent indicator of water-binding capability of flour, regard-less of moisture content of the dough. However, the shortcoming of this technique is that during the heating of dough, some physi-cal and chemical changes such as protein denaturation and starch gelatinization may occur. Thus, the results obtained may not re-flect the true water-binding characteristics of the system (Leung et al 1979). Cherian and Chinachoti (1996) reported that a good agreement exists in the results of water mobility occurring around a glassy-rubbery transition of gluten obtained from DSC, dynamic mechanical analysis, 2H NMR, 17O NMR, and sorption isotherm, even though the experimental techniques and time scales inher-ently varied. In the present study, the principle of calculating UFW content was applied to NMR data, and a rapid method to evaluate the immobile water content in dough through NMR was anticipated.

The objectives of this study were to characterize the influence of barley flour addition, storage condition, and storage time on the water mobility in fresh, cooked, and cooked and cool-stored dough, and the data were also used to develop a rapid and reliable method for estimating the immobile water content in dough sys-tems.

MATERIALS AND METHODS

Ontario soft wheat was kindly provided by Dow AgroScience (Nairn, ON, Canada). Refined wheat flour (moisture content, 14.0%; ash content, 0.34%; protein content, 7.6%) was obtained with a Quadrumat Jr. flour mill (Brabender, Duisburg, Germany) equipped with a 60 mesh (U.S.) reel sifter. Whole grain (WGB), air-classified β-glucan-diminished (ACB-D), and air-classified β-glucan-enriched (BGB-E) barley flour was provided by Par-heim Mills (Winnipeg, MB, Canada). All flour samples were stored at –20°C until analysis.

Dough Preparation Dough samples with a series of moisture contents (0.7, 0.9, 1.1,

1.3, 1.5, 1.75, 2.0, 2.5, or 3.0 g/g db) were prepared for this study. The flour (10 g) and a prescribed amount of distilled water were mixed at 63 rpm for 10 min in a 10 g mixer attached to a farino-graph (Brabender). The obtained dough was sealed in a zippered plastic bag, equilibrated at room temperature for 30 min, and then manually mixed with a spatula until a homogeneous mixture was obtained. A sample (0.8 g) of the dough was packed in an NMR glass vial with a screw stopper, and NMR and DSC analyses were conducted within 30 min.

For preparation of dough samples containing different types of barley flour (WGB, ACB-D, or BGB-E), wheat flour was re-placed by barley flour at levels of 5, 10, and 30%. The flour blend was premixed in the 10 g mixer of the farinograph for 10 min before adding water. The samples were handled as before for NMR and DSC analyses.

Dough Storage, Cooking, and Cool Storage After Cooking To check the effect of storage and processing on water mobility

in dough, the freshly prepared dough samples from the mixing bowl of the farinograph were sealed in NMR vials and stored at 4 or –35°C for one day each. The samples were then equilibrated or thawed at room temperature for 1 hr before NMR analysis. For cooking, the freshly prepared dough was packed in the NMR glass vial with a screw stopper and heated directly in boiling wa-ter for 15 min. The vial was cooled by rinsing it in running tap water for 2 min and then allowed to stand for 1 hr at room tem-perature before NMR analysis. After NMR measurements, the NMR tube with the sample inside was stored at 4°C for seven days and then equilibrated at room temperature for 30 min before NMR analysis again.

T2 Relaxation Time Measured by Pulsed NMR Measurement The proton spin-spin relaxation studies were carried out at 20

MHz with a Bruker Minispec PS/20 NMR spectrometer (Bruker Optics, Milton, ON, Canada). The transverse relaxation time (T2) was determined at 30°C following the Carr–Purcell–Meiboom–Gill technique. The 90–180° pulse separation (τ) was 0.25. The number of data points for fitting was 2,000, and the number of echoes not fitted was 1. Four scans were accumulated to increase the signal-to-noise ratio. The recycle delay was 5 sec. The data were analyzed with the continuous distribution model (CONTIN). Duplicate samples were prepared for each measurement. Dough samples prepared with a series of moisture contents were analyzed. Immobile water content was estimated from the x-axis intercept of a plot of T2 peak relaxation time versus total moisture content.

UFW Content Measured by DSC UFW content of the dough samples was measured with a DSC

(2920 Modulated DSC, TA Instruments, New Castle, DE, U.S.A.) equipped with a refrigerated cooling system. The purge gas was nitrogen. The instrument was calibrated with indium and sapphire, and an empty pan was used as the reference. Briefly, the dough sample (12 mg) was weighed into an aluminum DSC pan. The pan was hermetically sealed and scanned at a cooling or heating rate of 10°C/min. The temperature profile was as follows: 1) equi-libration at 30°C for 5 min, 2) cooling to –40°C, 3) equilibration at –40°C for 5 min, 4) heating to 140°C, 5) cooling to –40°C, and 6) reheating to 40°C. The enthalpy (ΔH) of melting peak was determined with Universal Analysis software (TA Instruments). Dough samples prepared with a series of moisture contents were analyzed. Freezable water (FW) content was calculated directly from the measured enthalpy divided by the enthalpy of pure wa-ter. UFW was calculated from the x-axis intercept of a plot of FW content versus total moisture content.

Maximal Viscosity Measured by Farinograph in Response to Moisture Content

A Brabender Farinograph-E was used to measure the maximal viscosity of dough during mixing in response to moisture content (0.7–1.5 g/g db), following AACC International Approved Method 54-21.02 but using a 10 g mixing bowl and adding the prescribed amount of water.

Data Analysis All samples were tested at least in duplicate. Statistical analysis

(t test and one-way ANOVA) was conducted with SAS (version 9.1 for Windows, SAS Institute, Cary, NC, U.S.A.). When appro-priate, the difference among means was determined with Tukey’s multiple comparisons. Pearson correlation coefficients were de-termined to evaluate relationships between variables. Statistical significance was set at the 5% level of probability.

RESULTS

T2 Distribution of Fresh Dough at Different Levels of Moisture T2 relaxation time distributions of fresh dough at different mois-

ture levels are presented in Figure 1. The different binding modes and dynamics of water in these materials were reflected in their relaxation time distributions. As shown in Figure 1, in most cases, three relaxation peaks with shifted peak positions (T21, T22, and T23) were detected, especially in the moisture range of 1.1–2.0 g/g db. Below the moisture level of 1.1 g/g db, the T23 peak disap-peared, whereas the T21 and T22 peaks seemed to merge into a broad single peak with further decrease of moisture level (<0.9 g/g db). When the moisture was higher than 2.0 g/g db, a new peak was separated from T22, whereas T23 merged into T22 and formed a broader T22 peak (Fig. 1).

With increasing moisture content, both T21 and T23 displayed no clear trend in response to moisture content, and the magnitude

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was relatively weak. However, T22 showed a strong intensity at any given moisture level and significantly correlated to the mois-ture content in dough (r = 0.99, P < 0.05).

Effect of Storage and Processing on T2 Relaxation Time The T2 distribution of fresh dough at a moisture content of 0.9

g/g db under different processing (cooked or not) and storage (fresh, cool-stored, or frozen) conditions is shown in Figure 2. Fresh dough exhibited two overlapped water populations. This merged peak differentiated into two distinct peaks following stor-age at 4°C for 24 hr, and the second peak (T22) significantly shifted to a higher relaxation time (P < 0.05) (Fig. 2). However, the T2 distribution showed almost no change when the freshly prepared dough was stored at –35°C in a freezer for 24 hr (P > 0.05).

Cooking the freshly prepared dough sample differentiated the two T2 peaks, and they significantly shifted to shorter T2 time compared with those of fresh sample (P < 0.05). The shifting was more significant for the T21 peak after storage at 4°C for seven days (P < 0.05). Cold storage also induced the appearance of the third peak (T23) at 727 msec (Fig. 2), which might relate to water of syneresis resulting from starch retrogradation.

T2 Distribution of Dough Containing Different Levels of WGB T2 relaxation time of fresh dough containing 0, 5, 10, and 30%

WGB at 1.1 g/g db moisture content is shown in Figure 3. All

three groups of water in different dynamic statuses appeared more immobile (shorter T2 relaxation time) with an increasing ratio of WGB. T22 peak time significantly negatively correlated to the blend ratio of WGB (r = –0.99, P < 0.05). Three distinct peaks were observed in control dough. However, these three peaks tended to merge with an increasing ratio of barley flour (Fig. 3).

T2 Distribution in Fresh, Cooked, and Cool-Stored Dough Containing Different Barley Flours

T2 distribution of fresh, cooked, and cooked and stored dough containing 10% of WGB, ACB-D, and BGB-E flours at 1.5 g/g db moisture content is shown in Figure 4. A similar T2 distribution was observed among freshly prepared doughs with the three types of barley flour, except that the less tightly bound water (T22 peak) in BGB-E dough was more immobile (shorter T2 relaxation time) than those in the other two (P < 0.05). After cooking and storage at 4°C for seven days, all T2 peaks shifted to a lower position, as previously mentioned (Fig. 2), and the difference was significant among the doughs with different types of barley flour added (P < 0.05). The T22 peak time, from low to high, was in the following order: BGB-E < WGB < ACB-D (Fig. 4).

UFW Content and Immobile Water Content Determined by Extrapolation Method from DSC and NMR

UFW content and immobile water content determined by ex-trapolation method from DSC and NMR, respectively, for fresh, cooked, and stored cooked dough containing different types and ratios of barley flour are plotted in Figure 5. A significant Pearson correlation was observed between UFW content and immobile water content (r = 0.72, P < 0.05). In most cases, both values of UFW and immobile water content for each corresponding sample were close in magnitude (Fig. 5). Control dough showed the least

Fig. 1. T2 distribution of fresh dough at different moisture contents (g/gdb). T22 peak relaxation time significantly correlated to the moisturecontent in dough when moisture content was below 2.0 g/g db (r = 0.99, P < 0.05).

Fig. 2. Effect of storage and processing on T2 distribution of dough sam-ples (moisture content, 0.9 g/g db). Peak times with different letters abovethem differed significantly, as determined by ANOVA following Tukey’stest (P < 0.05).

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amount of UFW and immobile water among all samples, and the next least amount was seen for fresh dough containing ACB-D, whereas the dough with added BGB-E had the highest UFW and immobile water contents, especially after cooking (P < 0.05). The subsequent cool storage decreased the value somewhat but not to the extent of the fresh sample (Fig. 5).

Correlation of T22 Peak Time, FW, and Farinograph Maximal Viscosity to Total Moisture Content in Dough

Evolution of T22 peak relaxation time measured by NMR, FW content measured by DSC, and dough maximal viscosity measured by farinograph in response to total moisture content are shown in Figure 6. A linear relationship was observed for all these parame-ters in a moisture range below 1.0 g/g db. The linear regression lines from NMR and DSC signals were positively correlated to total moisture content in dough, whereas the linear regression line from farinograph measurements was negatively correlated (P < 0.05). The x intercepts of regression lines of FW content from DSC and T22 peak relaxation time from NMR converged to almost the same point, which confirmed that the corresponding moisture content can be referred to as immobile water amount (Fig. 6). Compared with NMR, the farinograph measured maximal viscos-ity in response to a quite narrow moisture range in dough.

DISCUSSION

Depending on moisture content, one to three peaks were ob-served in the T2 distribution profile in this study, and T22 was ob-

served to increase in response to increasing water content (Fig. 1). This observation is consistent with previous studies (Richardson et al 1986; Given 1991). Three T2 populations have been observed and assigned in bread dough: a peak (2–5 msec) corresponding to water tightly bound to starch; a peak (10–100 msec) correspond-ing to water rapidly sampling hydration sites in the gluten and starch surface; and a peak (100–300 msec) corresponding to cap-illary bound water (Leung et al 1979; Esselink et al 2003). In starch-water mixtures, two distinct T2 distributions at 2–5 and 10–40 msec have been observed and assigned to intra- and extragran-ular water in starch, respectively (Chatakanonda et al 2003; Choi and Kerr 2003; Tananuwong and Reid 2004; Assifaoui et al 2006). These peak positions fell into the same range in a dough sample at a moisture content of 0.9 g/g db in this study (Fig. 1). Thus, T21 population was assigned to intragranular water in starch, T22 was assigned to overlapped populations of starch extragranular water and water in the gluten matrix, and T23 was assigned to cap-illary water in this study (Fig. 1).

The insignificant change of water mobility in freezer-stored dough versus the significant change in refrigerator-stored sample implicated the progressive resting of dough during the storage period (Fig. 2). This result also confirmed the observation of dough wetting and softening during short-term storage at room

Fig. 3. T2 distribution of fresh dough containing different levels of wholegrain barley flour (moisture content, 1.1 g/g db). T22 peak time signifi-cantly negatively correlated to the blend ratio of barley flour in dough (r = –0.99, P < 0.05).

Fig. 4. T2 distribution of fresh, cooked, and cooked and cool-stored dough containing 10% of different barley flours (moisture content, 1.5 g/g db).Except between fresh doughs containing whole grain barley flour and air-classified β-glucan-diminished barley flour, significant differences were seen among types of added barley flour and among processing methods,as determined by ANOVA following Tukey’s test (P < 0.05).

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temperature or in a refrigerator. Esselink et al (2003) reported that the freeze-thaw cycle mainly affected water in interaction with gluten but not starch. However, because there was less gluten than starch, the changes of water mobility (T2) in dough were rather subtle (Esselink et al 2003). This statement was consistent with results in this study. Therefore, frozen storage was better than refrigerator storage for dough. The result also implied that a sta-ble storage of dough in a refrigerator might be achieved by inclu-sion of water immobilizers.

Cooking and cold storage of dough at 4°C for seven days sig-nificantly decreased water mobility (Figs. 2 and 4) and increased UFW content (Fig. 5) (P < 0.05). This result was expected be-cause starch gelatinization during cooking was actually a hydra-tion process. Highly hydrated starch molecules had a better af-finity to water molecules, decreased their diffusion coefficient according to the obstruction scaling theory, which led to an over-all slowing down of the system dynamics, and thus cooked sam-ples had less water mobility (Wu et al 2009). The subsequent retrogradation of gelatinized starch during cold storage reduced the UFW amount to some extent (Fig. 5), even though these water components were more immobile after cooking and cold storage (Figs. 2 and 4). This result was consistent with the syneresis effect from starch retrogradation, which expels part of water molecules out of the starch gel network (Karim et al 2000). Surprisingly, the T22 peak didn’t change with cold-storage time, whereas the T21 peak shifted to a much shorter T2 relaxation time. This shift might indicate that the starch intergranular water was extensively immo-

bilized, whereas starch extragranular water did not change much during retrogradation. Gluten was denatured by cooking, so not much influence was expected. The incorporation of water into a crystalline structure during starch retrogradation has been at-tributed to the decreased water mobility of intergranular water during amylopectin recrystallization (Leung et al 1983; Lin et al 2001; Wang et al 2004), and the newly formed crystalline struc-ture could be more perfect, because it has significantly lower wa-ter mobility than that in native starch (Figs. 2 and 4).

High water-absorbing capacity of β-glucan could explain the significant decrease of water mobility and increase of UFW content with an increasing blending ratio of WGB flour in this study (Figs. 3 and 5), and it can also explain the most reduced water mobility with added BGB-E, which had the highest β-glu-can content (Figs. 4 and 5). Interestingly, T21 population was di-vided into two groups (1–2 and 3–10 msec) after cooking and following cold storage (Fig. 4). This phenomenon could be at-tributed to incompatible water-holding capability between starch and β-glucan after cooking. The population at 1–2 msec could be assigned to gelatinized starch, as shown in Figure 2. There-fore, the one at 3–10 msec could be water bound by β-glucan, which did not significantly change in mobility with cooking and cold storage (Fig. 4). Incompatibility also existed between glu-ten and β-glucan (Knuckles et al 1997). Understanding of the incompatibilities when blending barley flour with wheat flour is critical to select the optimal processing parameters in industrial production.

Fig. 5. Plotting of unfreezable and immobile water content determined by extrapolation from differential scanning calorimetry (DSC) and nuclear mag-netic resonance (NMR), respectively. Symbols: square = fresh; triangle = cooked; circle = cooked and cool stored at 4°C for seven days. The letters C, A, B, and W attached to data points represent control, air-classified β-glucan-diminished, air-classified β-glucan-enriched, and whole grain barley flours, respectively. Open, half-open, and solid symbols indicate 0, 5, and 10% of added barley flour, respectively. The horizontal and vertical lines on datapoints are the standard deviations for the respective axes. Immobile water content significantly correlated to the unfreezable water content regardless ofprocessing methods, added barley types, or concentration (r = 0.72, P < 0.05).

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Obviously, the different water populations were developed from the same amount of water in response to water availability, be-cause the three T2 relaxation times (T21, T22, and T23) would con-verge to the same mobility with a lowering of moisture content (Fig. 1). Both T21 and T23 populations were relatively small in amplitude and only appeared at a narrow moisture range, whereas the T22 population accounted for the majority of proton signals, increased with increasing moisture content over a broad moisture range (Figs. 1 and 6), steadily responded to different storage con-ditions and the cooking process (Fig. 2), and steadily responded to different types and blending ratio of barley flours (Figs. 3 and 4). Thus, T22 relaxation time would be a reliable indicator of the quality of water mobility in dough, and it could further be possi-ble to quantify the immobile water amount in dough. Based on the highly significant Pearson correlation coefficient (r = 0.99, P < 0.05) between T22 peak relaxation time and total moisture content at dough moisture content below 2.0 g/g db (Fig. 1), and with adopting the same principle to estimate UFW content, the immobile water content was calculated by extrapolating the plot of T22 peak relaxation time versus total moisture content at T22 = 0. The calculated immobilized water content significantly corre-lated with the UFW content (r = 0.72, P < 0.05), and both values were also close in magnitude (Fig. 5). The UFW contents of a variety of doughs have been determined to be ≈0.33 g/g db (w/w) (Davies and Webb 1969). The results agreed well with NMR and DSC data of fresh dough control in this study (0.32 g/g db; Fig. 5). The authors have also stated that the value was fairly constant for doughs made from various wheat cultivars, doughs mixed to different extents, and doughs containing a wide variety of condi-tioners, including pentosans. But we detected significant differ-

ences from adding different types and ratios of barley flour as well as from different treatments (Figs. 2, 3, and 4). Cherian and Chinachoti (1996) reported that the UFW content in their gluten system was around 0.27 g/g of solid gluten obtained by both DSC and 17O NMR, in contrast to 0.32 g/g db in our dough system. The small difference (0.05 g/g db) could be a small part of UFW held by starch granules in dough. It was easier and quicker to prepare NMR dough samples, no weighing was necessary, and the meas-urement was also simpler and much faster compared with DSC measurements. Thus, NMR has a potential for online monitoring of water mobility in dough and even for rapidly analyzing mois-ture content and immobile water content based on the significant correlation between T22 peak relaxation time and global moisture content in dough (Fig. 1). Further work will continue in order to improve the precision for calculating immobile water content to minimize its standard deviation (Fig. 5).

CONCLUSIONS

NMR detected three populations of water in dough, namely, tightly bound intergranular water in starch (T21), less tightly bound extragranular water in starch and water in the gluten matrix (T22), and capillary bound water (T23). The T22 peak was always detectable in any case, and T22 peak relaxation time significantly linearly increased with moisture content of dough in a moisture range of 0.7–2.0 g/g db (P < 0.05). Freezer storage of dough showed less effect on water mobility, whereas cooking and cool storage of cooked dough significantly decreased the water mobil-ity in dough (P < 0.05). Adding barley flour steadily decreased the water mobility in dough, and the reduction was more signifi-

Fig. 6. Moisture dependence of T22 relaxation time measured by nuclear magnetic resonance (NMR), freezable water content measured by differential scanning calorimetry (DSC), and dough viscosity measured by farinograph in the flour system.

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cant when adding BGB-E (P < 0.05). A method of extrapolating the plot of T22 peak relaxation time versus total moisture content in dough at the time T22 = 0 was developed to estimate absolute immobile water content in dough systems. The obtained result significantly correlated to the amount of UFW determined by DSC (r = 0.72, P < 0.05). It was concluded that T22 relaxation time was a reliable indicator of the quality of water mobility and that it could potentially be applied online to quantify the immo-bile water amount in dough.

ACKNOWLEDGMENTS

The authors thank Fernanda Svaikauskas for her technical support for NMR and DSC measurements. The grant from the Canadian Agricultural Adaptation Program (CAAP) of Agriculture and Agri-Food Canada is gratefully acknowledged. In Ontario, the Agricultural Adaptation Council administers this grant. Additional support from Wing’s Food Products is gratefully acknowledged.

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[Received September 15, 2012. Accepted December 13, 2012.]