Journal of Cereal Science 33 (2001) 59–69 doi:10.1006/jcrs.2000.0343, available online http://www.idealibrary.com on
NMR-baking and Multivariate Prediction of Instrumental Texture Parameters in Bread S. B. Engelsen∗, M. K. Jensen, H. T. Pedersen, L. Nørgaard and L. Munck The Royal Veterinary and Agricultural University, Food Technology, Department of Dairy and Food Science, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark Received 4 January 2000
ABSTRACT To study the kinetics of the bread baking process, transverse relaxation (T2) of protons was measured during a baking process performed inside the magnet of a pulsed low field 1H nuclear magnetic resonance (NMR) instrument. Experimental NMR relaxation data were analysed both by chemometric data analysis and by multi-exponential curve-fitting. Throughout the entire baking process from dough to bread three T2-components were determined. During the NMR-baking process significant shifts were observed in the characteristic time constants at c. 55 °C (gelatinisation of starch) and at c. 85 °C. In a second experiment staling of white bread crumb aged 0–8 days was investigated by texture analysis and NMR relaxation. High correlations (r>0·9) between texture parameters and NMR relaxation data of bread crumb were found by partial least squares regression (PLSR) models. Firmness and elasticity as measured by a Texture Analyser were predicted with an estimated error (RMSECV) of 150 (range 200–2200) and 0·032 (range 0·4–0·7), respectively. Future texture of the bread samples was also predictable by use of NMR relaxation data from the early storage period (day 0 to day 3). 2001 Academic Press
Keywords: NMR, water mobility, dough, bread, baking, texture, chemometrics.
INTRODUCTION Low-field 1H nuclear magnetic resonance (NMR) has recently been introduced in investigations of bread and dough (see below). The method has the advantage that it enables the detection of changes : NMR=low-field 1H nuclear magnetic resonance; MRI=magnetic resonance imaging; CPMG=Carr Purcell Meiboom Gill; FID=free induction decay; T2=transverse relaxation time; T2∗= composite transverse relaxation time; T1=longitudinal relaxation time; MLR=multiple linear regression; PCA=principal component analysis; PC=principal component; PLSR=partial least squares regression; RMSECV=root mean square error of cross-validation; r=correlation coefficient; DS=dry solid; RH=relative humidity. ∗ Corresponding author. E-mail:
[email protected] 0733–5210/01/010059+11 $35.00/0
in the distribution and mobility of mainly water protons crucial to the process of making dough to bread and to staling. Characteristic relaxation time constants (T1 and T2) represent the relaxation rates of the different types of protons (e.g. fat, structural water, bulk water) in the sample. In general, transverse T2 relaxation of complex food systems such as bread and dough, exhibit multi-component behaviour (usually 2–4 components) in which the individual components can be interpreted as representing different water regions and diffusive exchange between separate regions in the system.1 The investigation of water mobility in dough and bread using NMR has to date yielded ambiguous results, as the number of components and related time constants vary.2–7 Based on water absorption values of various components, 46% of the total water in dough is estimated to be associated with starch, 31% with proteins, and 23% with pen 2001 Academic Press
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tosans. In bread, 77% of the total water is estimated to be associated with starch, none with proteins, and 23% with pentosans.8 The major storage quality problem with bread is staling, which results in a firm and inelastic breadcrumb. Staling is generally associated with starch retrogradation, but changes in other components and their interactions with starch may also play important roles in the staling process. However, whatever the mechanism, the protons in the system should become increasingly less mobile during staling. Decreasing time constants during staling, indicating an overall decrease in water mobility, has been reported in several NMR experiments.3,6,9–11 Strong significant correlations between NMR relaxation data and firmness, measured by texture analysis, have also been reported.6,11 These correlations were based on the NMR solid phase ratio signal (R11–R70) and relaxation time constants of protons in bread, respectively. Moreover, changes in T2 values, found by magnetic resonance imaging (MRI), are also highly correlated to firmness measured by texture analysis (r=0·91).4 The present work aims at investigating a connection between texture parameters and NMR data and investigating the diverging literature results regarding water distribution in dough and bread. The work is divided into two parts. The objective of Part 1 is to study the water distribution in dough and to investigate the kinetics of the baking process from dough to bread as performed and monitored inside the NMR magnet evaluated by both multi-exponential fitting and chemometrics. The objective of Part 2 is to study the staling process by correlating instrumental texture (firmness and elasticity) of breadcrumb to NMR relaxation data using multivariate chemometric techniques and to investigate water distribution in breadcrumb. To ensure a large variation in the texture properties the doughs were treated with a number of different anti-staling enzymes (mainly alpha-amylases). EXPERIMENTAL Samples and sample preparation Samples were made according to the following recipe. The ingredients are expressed as a percentage of the flour by weight: 4% yeast, 1·5% salt, 1·5% sugar, 100% flour, ascorbic acid and water (amount adjusted to the quality of the flour)
as well as various anti-staling enzymes. The doughs were allowed to rest for 20+15 min at room temperature and subsequently fermented for 55 min at 32 °C and 82% relative humidity (RH). The moisture content of the dough was approximately 45·3% (w/w). Part 1 Two different types of dough were made: a standard dough and an alpha-amylase treated dough. In this experiment with abnormal small-sized bread the resting and fermentation process was shortened to 1 h at 32 °C and with no control of the humidity. The dough (3·00 g) was placed in an 18 mm NMR tube and equilibrated at 28 °C before the NMR-baking. During the NMR baking process, NMR measurements were performed with regular intervals while increasing the baking temperature in three steps from 35 °C to 120 °C (over 25 min) in order to simulate the centre temperature in a normal bread baking process. Three samples (triplicates) from each dough were baked and measured in the NMR instrument. Part 2 A total of 25 different white bread doughs were prepared using the same basic recipe but with different enzyme systems added (mainly alphaamylases, but including one glucoamylase). Loaves of 350 g were baked at 230 °C for 25 min in lidded pans (h: 9·5 cm, w: 8·0 cm, l: 18·5 cm), cooled for 2 h, packed in plastic bags and stored at room temperature (22±2 °C). During a storage period of 8 days samples for NMR and texture analysis were taken at least four times according to the following plan. For each measurement two loaves were cut into 20 mm thick slices of which only the three centre slices were used. Texture analysis was performed on the centre of the six whole bread slices. Subsequently, from the nearby centre region of the same slices, two cylinders of bread (approximately 0·8 g) were stamped out with a cork borer, transferred into a glass tube (15 mm diameter), sealed and equilibrated to 22 °C for a few minutes and then inserted into an 18 mm tube for NMR analysis. This experimental design resulted in six texture and six NMR measurements per sample which were averaged prior to data analysis—a total of 122 averaged samples when taking into account the storage time.
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Texture measurement A TA-XT2 Texture Analyser, Stable Micro System (Surrey GU7 1JG, U.K.) was used to perform puncture tests on bread slices. A cylindrical acrylic probe of 40 mm with blunted edges was attached to the load cell. The test mode was set to measure compression force. Pretest, test and posttest speeds were set to 10, 1·7 and 10 mm/s respectively, and the trigger force was 7 g. The force (measured in g) needed to press the probe 6·29 mm into the centre of a bread slice (i.e. 31·5% compression) was taken to represent the firmness of the sample. The elasticity of the samples was expressed as a ratio between the force required to keeping the probe 10·00 mm into the sample for 30 s and the force needed to press the probe 10·00 mm into the sample.
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Part 2 In this experiment the NMR resonance frequency was adjusted by a vegetable oil sample prior to each measurement. Free induction decay (FID) and Carr-Purcel-Meiboom-Gill (CPMG) measurements were performed at 22 °C. The settings were: FID: RD=1 s; NS=32; DW=0·5 s; points=16 384 and CPMG: RD=1 s; PECH=1; NS=32; echoes=4096; tau=15 s. All relaxation data were divided by sample weights prior to chemometric analysis. FID relaxation curves were without loss of information reduced to 3276 points for practical reasons by taking averages of five consecutive points. For the acquired CPMG data, only even numbered echoes are used in the data analysis, resulting in 2048 data acquisition points per sample for subsequent data analysis. Temperature and moisture measurement
NMR measurement A Maran Benchtop pulsed NMR Analyser (Resonance Instruments, Witney, U.K.) operating at 23·2 MHz and equipped with an 18 mm variable temperature probe head was used in these experiments. The sample temperature was controlled by a continuous flow (c. 500 L/h) of dried air with an operational range from −50 to 150 °C. The dead time of the instrument was 10 s.
Part 1 In this experiment the dough/bread remained in the NMR instrument in an open NMR tube throughout the entire baking process and the NMR resonance frequency was adjusted on the dough sample prior to each measurement. A FID-CPMG pulse sequence with the following settings was used: relaxation delay, pause between scans (RD)=1 s, points per echo (PECH)=1, number of scans (NS)=4, dwell time (DW)=0·5 s, FID: data points collected (points)=128, CPMG: tau= 100 s, echoes=2048. Only even numbered echoes acquired in the CPMG data are used in the data analysis to avoid influence of imperfect pulse settings. The NMR-baking temperature program was set to a three-step procedure: (1) 35 °C for 5 min (10 measurements), (2) 75 °C for 3 min (10 measurements) and (3) 120 °C for 17 min (71 measurements).
To monitor the temperature profile at the centre of the dough samples during baking, a temperature stick (Ama-digit, ad 14th) was inserted into the centre of a sample during a run of the temperature program. To determine moisture loss, the weight of the samples was noted before and after the baking process. Exponential curve fitting In order to establish the number of components in the system the NMR relaxation curves were fit to a sum of (a few) decaying exponentials, each with different characteristic time constants and corresponding amplitudes as given by the equation: M (t)=
M i
2i
·e
−t +B T2i
where i=1, 2 or 3. M2i is the amplitude of the ith exponential and T2i is the characteristic transverse relaxation time constant for the ith exponential. M relates to the amount of the different components (quantity) and the characteristic time constant, T2, gives information about their origin (quality). The least squares residuals from each curve fitting are summed into a residual value called F. The size of the F-value determines the goodness of the fit and the decreasing trend as a function of number
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The temperature profile in the centre of bread during NMR-baking. Minutes of baking vs temperature.
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Four CPMG relaxation curves obtained at different centre temperatures of the dough sample during NMR-baking.
of fitted components provides an indication of the number of components in the system. Since this approach is rather subjective, we introduced jackknife validation in order to make an objective
choice of the number of exponentials. This procedure was performed using segmented cross-validation in which each relaxation curve was divided into 10 consecutive segments, and exponential fits
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Figure 3 PCA score plot (PC1 vs PC2) of six NMR-baking experiments each represented by 91 NMR measurements over a period of 25 min of NMR-baking: (a) FID relaxation data and (b) CPMG relaxation data. (o) is enzyme treated dough and (x) is standard dough. The approximate centre temperatures during the baking process are indicated by arrows.
were performed leaving out one segment at a time. Validation of the exponential model (calculation of F) was performed with the left out segment.12 The curve-fitting process was carried out using a
Simplex algorithm for the non-linear characteristic relaxation time constant T2i combined with a simple least squares fit of the linear amplitude parameters M2i . 13
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Figure 4 F-values from (F1) mono-, (F2) bi-, and (F3) triexponential curve fitting of 91 CPMG relaxation curves obtained during 25 min of NMR-baking. The curves represent averages of the six NMR-baking experiments.
Solid phase signal—univariate approach The difference in the signals at 11 s and 70 s of the FID relaxation curves were used as an estimate of the amount of protons in the solid phase: S= R11−R70. R11 is assumed to be proportional to the total number of protons in the sample, while R70 is assumed to be proportional to the amount of protons in the liquid phase. Chemometrics Chemometrics is the science of relating measurements made on a chemical system or process to the complex state of the system via multivariate
Table I
statistical methods.14 One of the main advantages of chemometric methods is that it is possible to explore complex co-linear multivariate information in a graphic display. NMR relaxation data is extremely co-linear, as 99·5% of neighbouring time points correlate with a coefficient above 0·9.13 Principal component analysis (PCA) is the fundamental chemometric method based on vector algebra.14,15 The main purpose of the method is to reduce dimensions of complex multivariate data and to simplify data interpretation by finding new orthogonal variables, principal components (PCs), describing the variance in data. Only a limited number of PCs, equal to the chemical rank of data, are relevant in describing the systematic information in data. In this approach the original data matrix (X), here the NMR relaxation data, is ¯ +TPT+E decomposed into new matrices: X=X ¯ where X is the average relaxation profile. The score matrix (T) contains information about the samples, the loading matrix (P) contains information about the variables and the residuals are gathered in a matrix (E). The loadings can be interpreted as hidden profiles that are identical for all samples. It is the amount (scores) of the hidden profiles (loadings) that differentiate the samples.16 Partial least squares regression (PLSR) is an extension of PCA applied to relate two sets of variables (e.g. relaxation decays and texture data) from identical samples by a regression model.14,17 The main purpose is to make a linear model which enables prediction of a specific characteristic (yvariable) from a measured spectrum (x-variable). In this study PLSR models are validated using
Literature comparison between 1H transverse relaxation constants determined on breadcrumb and dough
Material
Reference
Freq (MHz)
Tau (ms)
Moisture (g H2O/g DS)
Temp (°C)
Crumb
This study This study Leung et al.3a Chen et al.6 Roudaut et al.7 This study Leung et al.2 Leung et al.3a D’Avignon et al.5
23·2 23·2 13·8 20 20 23·2 90 13·8 300
0·015 0·1 ? 0·015 1·6 0·1 2 ? 0·05
0·76 ? 0·57 0·65 0·10 0·83 0·64–0·93 0·5–0·9 0·54, 0·82
22 100 30 ? 22 28 30 30 30
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a b
D2O instead of H2O. T2∗ (composite relaxation time constant, measured by FID).
Transverse time constants (ms) T21 T22 T23 0·5 5–5·5 ? 0·01b 0·2 4 – ? 0·35–0·4
9–10 40 5·5–15·7 0·28–0·35 20 20 20 13–30 5–7
21–30 165–175 – 2·2–2·8 80 185 60 – 15–21
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Figure 5 Evolution in the CPMG transverse time constants during NMR-baking. (a) T21, (b) T22, (c) T23. The curves represent averages of the six NMR-baking experiments.
segmented cross validation—the segments being the different kinds of dough, unless otherwise noted. Multiple linear regression (MLR) models are validated by full cross validation. Only validated results are reported. Programs All relaxation data were collected as ASCII data and imported to designated chemometric programs. Exponential curve fitting was performed using in-house software written in Matlab version 5.0 (The MathWorks Inc., Natick, MA, U.S.A.). Chemometric calculations were made using the Unscrambler program, version 7.01 (CAMO ASA, Trondheim, Norway).
RESULTS AND DISCUSSION Part 1 NMR-baking The baking process of the dough samples in the NMR instrument resulted in an average moisture loss of 7% (w/w) and a centre temperature profile, as illustrated in Figure 1. Noticeable changes in the relaxation decays were observed during NMRbaking (Fig. 2). Initially, the overall proton relaxation rate increases, despite a continuously increasing temperature (up to approximately 55 °C). From this point the relaxation rate starts to decrease due to the increasing temperature. When the centre temperature reaches 80–90 °C, the proton relaxation rate displays a significant shift in
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Figure 6 The evolution of the amplitude in a tri-exponential system (M21, M22, M23) during the NMR-baking. The numbers are the amount of the respective fraction in percentage of the total amount.
which the fast relaxation component(s) increase their relaxation rate while the slow relaxing component(s) continue to decrease their relaxation rate due to the increasing temperature. To study the overall behaviour of the dough during the baking process, a PCA was performed on both the FID [Fig. 3(a)] and the CPMG relaxation data [Fig.
2000 1800
r r2 RMSECV BIAS
3(b)]. The two unique exploratory, almost aesthetic, plots demonstrate the strength of the graphic interface of chemometrics, e.g. how to compress huge amounts of relaxation data [in Fig. 3(b) each point represents a relaxation decay vector with 2048 time points] to visualise 99% (96%+3%) and 100% (93%+7%), respectively, of the entire relaxation variation in a single display. The plots clearly demonstrate that there are, from a low-field transverse relaxation point of view, two major transitions that occur during NMR-baking: one at c. 55 °C (sharp in CPMG relaxation and very soft in FID relaxation) and one at 85 °C (very soft in CPMG relaxation and relatively sharp in the FID ‘saxophone’ plot). The change in the dough/bread system around the temperature 55 °C coincides with the onset of the gelatinisation process of wheat starch, and the change later in the baking process, around 80–90 °C, coincides with the end of the gelatinisation process from which stage evaporation of water from the surface dominates. No systematic difference in the behaviour of the standard dough and the enzyme treated dough was observed during NMR-baking in any of the score plots. This is in accordance with the expectations, as only a minor amount of the amylopectin is hydrolysed by the enzymes during baking,
= 0.95 = 0.90 = 147 = –9.6
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Figure 7 Firmness prediction of bread samples by a three component PLSR regression model based on CPMG relaxation data. Reference values vs predicted values. r2 is the squared correlation coefficient and BIAS is equal to (ypre−yref )/n where n is the number of samples. The middle diagonal line is the target line and the two other diagonal lines indicate the (±) RMSECV errors.
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Table II
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Regression models. Prediction of the future texture of breadcrumb PLSR CPMG relaxation dataa
MLR Texture datab
Day 0+1+3/4c
Day 0+1
Day 3
Day 1+3
Firmness Day 7/8c
# PC RMSECV r
2 186 0·93
4 241 0·87
150 0·93
150 0·93
Elasticity Day 7/8c
# PC RMSECV r
3 0·028 0·61
5 0·024 0·83
0·032 0·68
0·018 0·90
a
24 samples analysed by NMR and texture analysis, models are full cross-validated. Includes several new samples, only analysed by texture analysis, a total of 62 samples. c A few samples are measured on day 4 instead of day 3, or day 8 instead of day 7. b
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1600
= 0.93 = 0.86 = 186 = –5.9
1400 1200 1000 800 600 400 200 200
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Figure 8 Prediction of firmness day 7/8 by a three component PLSR model based on CPMG relaxation data from day 0+1+3/4. Reference values vs predicted values. r2 is the squared correlation coefficient and BIAS is equal to (ypre−yref )/n where n is the number of samples. The middle diagonal line is the target line and the two other diagonal lines indicate the (±) RMSECV errors.
and the anti-staling effect is first observable when the bread is allowed to cool and store. Multi-exponential analysis The CPMG data was also examined by multiexponential analysis to determine the number of components in the system. The F-values (Fig. 4) indicate that the dough system is mono-ex-
ponential at first, but becomes bi-exponential around measurement 23 (abbreviated m23). However, the cross validation test suggests that the dough system is tri-exponential throughout the baking process. Furthermore, a tri-exponential model seems most appropriate in terms of explaining the assumed processes taking place during baking as the amounts of the different phases best relate to the distribution of water, starch and
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gluten proteins in breadcrumb and dough (see above). The ranges of the characteristic time constants are listed in Table I and are not identical to any of the previously reported results; however, dough T21 and T22 seem to be in the same range as T22 and T23 reported by D’Avigon et al.5 Figure 5 illustrates the progress of the three time constants during the baking process. A rapid increase (i.e. increased mobility) is observed for the two fast relaxing components, T21 [Fig. 5(a)] and T22 [Fig. 5(b)], starting around 55 °C (m23) due to changes caused by the gelatinisation of the starch. T21 reaches a maximum around 80 °C (m36) after which it declines, whereas T22 continues to increase and stabilises when the temperature reaches 90 °C (m50). T21 represents 34% of the protons (evaluated from the amplitudes in the triexponential fit) in the dough and 22% in the bread, and T22 represents 64% and 70% in dough and bread respectively. An exchange process between the two fractions is seen in the period from 55 °C (m23) to 90 °C (m45), as the amount of T21 decreases and the amount of T22 increases correspondingly (Fig. 6). T21 can tentatively be assigned to the water associated with proteins, as the gluten proteins will lose some affinity for water during the baking process,8 and T22 could represent the water associated with the starch/pentosans, as the gelatinisation process includes absorption of water. However, the amount of the respective fractions does not correspond to the values estimated in the literature.8 The slow relaxing constant, T23, increases with temperature until 80 °C (m36), after which a decrease is observed [Fig. 5(c)]. This fraction represents a minor part of the system (Fig. 6) and is suggested to be due to a diffusive exchange between the starch/pentosan and the protein fractions. Part 2 Staling of bread The CPMG relaxation data measured on the 122 breadcrumb samples with varying enzyme systems added and varying storage times were first examined by multi-exponential analysis. In accordance with previously published results,6,11 an overall decreasing time constant (mono-exponential T2) of the bread samples was observed during the staling process. Three different components were determined in bread both by the trend in F-values as well as by the cross-validation method. The ranges of the time constants, con-
firmed by additional (non-published) experiments, are listed in Table I, which again highlights some discrepancy among the results reported in the literature. The present results appear to be in fair agreement with the results reported by Roudaut et al.7 Comparative study of texture and NMR relaxation data Before establishing PLSR models between NMR relaxation data and texture data the intercorrelations between the two texture parameters were examined and it was found that firmness and elasticity, measured on the crumb of 122 different bread samples, was negatively correlated (r= −0·71). However, if one deviating group of samples with an enzyme system that resulted in an extremely sticky crumb was removed from the calculation the simple correlation improved to −0·89. Several regression models were made to relate the NMR relaxation data to the texture data. The correlation between the tri-exponential time constants or solid phase signal (R11–R70), and the texture parameters was poor (r
