Application of two-dimensional cross-correlation spectroscopy to analyse infrared (MIR and NIR) spectra recorded during bread dough mixing

Journal of Cereal Science 48 (2008) 678–685

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Application of two-dimensional cross-correlation spectroscopy to analyse infrared (MIR and NIR) spectra recorded during bread dough mixing A. Aı¨t Kaddour, M. Mondet, B. Cuq* UMR 1208 Inge´nierie des Agropolyme`res et Technologies Emergentes, CIRAD, INRA, Montpellier SupAgro, 2 place Viala, Universite´ Montpellier 2, F-34000 Montpellier, France

a r t i c l e i n f o

a b s t r a c t

Article history: Received 5 December 2007 Received in revised form 6 March 2008 Accepted 11 March 2008

During dough mixing chemical, biochemical and physical transformations occur that allow dough formation to be characterized by common chemical and biochemical methods. Recently, spectrometric methods were used to characterize the dough mixing. The Mid-infrared (MIR) and the Near-infrared (NIR) spectroscopy allow information concerning chemical content and composition of food products to be obtained. The aim of this study is to apply FT-NIR and FT-MIR spectroscopy to monitor dough chemical changes, and to correlate those signals by the 2D Cross-Correlation (2D CORR) method. The 2D CORR was used to emphasize chemical assignment of the NIR band modifications (particularly for protein) during dough mixing. The 2D CORR analysis of the raw NIR and MIR spectra demonstrated that five NIR regions are highly correlated to protein vibrations. The 2D CORR analysis of the NIR and MIR spectra after second derivative demonstrated that the amide bands present high R2 for the NIR bands at (1189–1216), (1351–1474) and (1873) nm. A low R2 is obtained between the amide I and amide II bands and the (2026–2123) and (2280–2325) nm regions. The amide III band presents a slightly higher R2 for those NIR regions. The 2D CORR analysis of NIR and MIR spectra allow more specific NIR regions associated to chemical modifications of protein structure to be identified. The 2D CORR analysis of the second derivative spectra is more precise for the identification of the NIR regions implied in dough mixing compared to the 2D CORR analysis of raw NIR and MIR spectra. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Near-infrared spectroscopy Mid-infrared spectroscopy Gluten Amide III Starch Water

1. Introduction Mixing is the process in which flour and water form a dough by both blending and distributing the dough ingredients and developing the gluten structure. The energy input contributes to a uniform distribution of the different ingredients (flour, water, yeast, etc.), to the hydration of flour particles (starch, proteins, nonstarch polysaccharides, etc.) and to the formation of a continuous gluten matrix surrounding the starch granules (Hoseney and Finney, 1974; Peighambardoust et al., 2006). The dough must be mixed for a specific time, known as optimum dough development, to ensure optimal loaf volume and bread texture. Stopping before the optimal point results in an under-mixed dough that gives bread of inferior volume and crumb quality. Mixing beyond the optimal development point induces a dough stickiness, decreases dough consistency and negatively affects bread quality. During dough mixing complex chemical, biochemical and physical transformations occur that can be characterized by using microscopic, physical and biochemical methods (Amend and

* Corresponding author. Tel.: þ33 (0) 4 99 61 28 60; fax: þ33 (0) 4 99 61 30 76. E-mail address: [email protected] (B. Cuq). 0733-5210/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcs.2008.03.001

Belitz, 1990; Graveland et al., 1985; Kilborn and Preston, 1981; Newberry, 1995; Weegels et al., 1996). Recently, spectrometric methods have been used to characterize dough formation during bread dough mixing. Near-infrared (NIR) spectroscopy was largely considered to monitor bread dough mixing in-line (Aı¨t Kaddour et al., 2007a,b; Alava et al., 2001; Olewnick et al., 2004; Wesley et al., 1997, 1998, 2002). These studies showed the potential of the NIR spectroscopy technique for providing information on physical and chemical modifications that occur during dough development (Aı¨t Kaddour et al., 2007a,b; Alava et al., 2001; Wesley et al., 1998). Nonetheless, the overtones and combination vibrations at the origin of NIR absorption spectra make difficult the direct assignment of one NIR band to one specific chemical bond of dough components. Mid-infrared (MIR) spectroscopy is the principal region of molecular vibration. MIR spectra give precise and directly accessible information concerning X–H chemical bonds (X: C, H, O, and N) and has thus been used to determine chemical structure of food components like water, proteins, lipids, etc. (Bertrand and Dufour, 2000, 2006). Pertinent information about protein structural changes is accessible through MIR spectroscopy (Anderle and Mendelsohn, 1987; Cai and Singh, 2004; Robertson et al., 2006; Seabourn et al., 2004; van Velzen et al., 2003; Wellner et al., 1996). Aı¨t Kaddour et al. (in press) used Fourier Transform

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(FT) MIR spectroscopy to describe chemical changes occurring in bread dough (at 47.9 g of water/100 g of dough) during mixing. Two other studies evaluated the potential of MIR spectroscopy to monitor chemical changes occurring during mixing of dough at low water content (36.8 g of water/100 g of dough) (van Velzen et al., 2003) or at high water content (54.7 g of water/100 g of dough) (Robertson et al., 2006). van Velzen et al. (2003) used the MIR signal to demonstrate that hydrated gluten might be one of the major contributors to dough stickiness, and that dough mixing and stretching results in a decrease of the amount of a-helix accompanied by an increase of the content of the extended bsheet conformations. Robertson et al. (2006) demonstrated that FT-MIR spectroscopy can be used to monitor relative changes in protein secondary structures during batter mixing. The maximum ratio of protein non-sheet structures to protein sheet structure was observed to reach its maximum value at the time where separation of the gluten and starch fractions is the most effective. Nonetheless, the analysis of the batter mixing by MIR spectroscopy did not allow the time at maximum batter consistency to be identified. It would be interesting to correlate the NIR spectral absorption bands to the MIR spectral absorption bands, to give a better understanding of the NIR signal modifications recorded during bread dough mixing. The correlation between NIR spectral and MIR spectral data can be evaluated by using two-dimensional (2D) spectroscopic methods. The basic concept of perturbation-based 2D spectroscopy applicable to the infrared spectral region was proposed by Noda (1986). The initial 2D spectroscopy evolved to a more suitable 2D method named Generalised two-dimensional correlation. This became a very versatile and broadly applicable technique (Noda, 1993, 2000), which gained considerable popularity among scientists. Parallel to the development of Generalised two-dimensional correlation spectroscopy by Noda, some other variants of 2D correlation methods have been proposed. Barton et al. (1992) developed the statistical cross-correlation coefficient mapping (2D CORR) that displays correlation coefficients between two series of spectra. This method was first applied to correlate spectroscopy across the NIR and MIR regions. Its primary use has been to interpret and explain the NIR spectra and to provide additional confidence in the analytical models developed with empirical data (Barton et al., 1992, 1996; Barton and Himmelsbach, 1993). The aim of the present study is to evaluate the correlation FTMIR and FT-NIR spectroscopy to give a better understanding of the spectral modifications recorded during bread dough mixing. The correlation of the NIR and MIR signals was investigated in an attempt to precisely identify the NIR bands associated with flour component (starch and water) changes and more particularly to protein changes during bread dough mixing. 2. Materials and methods 2.1. Raw materials Experiments were undertaken using one industrial common wheat flour for bread applications, obtained from Moulins Soufflet SA (Nogent/Seine, France). The flour was stored at 10  C until experiments. Wheat flour moisture content, ash content, protein content, and alveograph properties were determined according to the French standards NF V03-707, NF V03-720, NF V03-750, and NF ISO 5530.4, respectively. The characteristics of the selected flour were 0.5 g ash, 9.4 g protein, and 13.3 g moisture content per 100 g of flour. The alveograph properties were 65 mm for dough elasticity, 117 mm for dough extensibility, 0.55 for alveograph ratio, and 0.0226 J for deformation energy. Tap water was incorporated in the bread formulation.

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2.2. Dough mixer A 6 kg Mahot mixer (VMI, labo 25 Montaigu, France) equipped with a rotating bowl and an oblique shaft was used for dough mixing (Aı¨t Kaddour et al., 2007a). Experiments were done at a constant mixing rate (80 rpm) and during a long mixing time (20 min) to reach over-mixing conditions. A standard formulation of 3 kg flour and water was used. The proportion of added water was adjusted based to a 12% flour moisture basis to reach bread dough conditions (47.9 g water/100 g of dough). The initial water, flour and mixer temperatures were fixed at 20  C (1  C). All the mixing experiments were realised in an air-conditioned room allowing to keep the room temperature at 20  C. 2.3. NIR spectrometer NIR spectra during dough mixing were recorded using a NicoletÒ AntarisÒ FT-NIR Analyser (Thermo Electron Corporation, France). The NIR instrument was connected to a fiber optic probe that was positioned inside the mixer directly at the contact of the dough (Aı¨t Kaddour et al., 2007a,b). The NIR spectra were recorded between 1000 and 2500 nm at 2 nm intervals. Each spectrum resulted from 30 scans (2 scans/s) which were averaged to give absorbance values [log (1/R)] in function of wavelength (nm). All the mixing experiments were performed at least three times. The NIR spectra recorded during dough mixing were analysed using MATLAB 7.0.4 software (The MathWorks, Inc.). 2.4. MIR spectrometer MIR spectra during dough mixing were recorded using a NicoletÒ AntarisÒ 6700 FT-IR spectrometer (Thermo Electron Corporation, France) equipped with a MCT-Aþþ detector (cooled by liquid nitrogen before measurements) and a horizontal ATR accessory (Model Smart Durasamplir, Diamond crystal with a 35 angle of incidence, Dicomp). The MIR spectra were recorded between 4000 and 800 cm1 at 4 cm1 intervals. A background spectrum of the empty measurement cell was collected before each sample. The MIR spectrum of dough was collected by rapidly (60 s) transferring freshly mixed dough to the measurement cell using a spatula. The dough samples where taken from the same position in the mixer to prevent variation due to the sampling effect. The dough samples were pressed firmly onto the crystal by using a press (4000-801 Press Tip-concave, Smiths detection) to achieve perfect contact. Based on previous studies investigated by Aı¨t Kaddour et al. (in press) for the same bread dough and by Robertson et al. (2006) for a batter we can assume that the mixing action on dough is higher than the small changes (probably due to dough relaxation) observed during the time necessary to record the MIR spectra. It was clearly demonstrated that the MIR signal variations recorded during mixing are principally associated to dough chemical modifications and not to the dough manipulations before and during MIR measurements. Each spectrum resulted from 64 scans (40 s/ spectrum) which were averaged to give absorbance values as a function of wavenumber (cm1). After each measurement, the dough sample was removed and the measurement cell was cleaned by using water and then ethanol to prevent any contamination between samples. All the mixing experiments were performed at least three times to obtain triplicate spectra. 2.5. Statistical 2D Cross-Correlation The Barton II statistical 2D Cross-Correlation (2D CORR) analysis was used to correlate NIR and MIR spectra (Barton et al., 1992). The NIR and MIR spectra were collected at the same mixing times. The 2D CORR analysis was applied on NIR and MIR spectra after

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standard normal variate (SNV) correction and after second derivative treatment. The second derivative spectra were obtained by using the Savitsky–Golay derivative. The smoothing and derivative parameters, filter width, polynomial order and derivative order were respectively 31, 2, and 2. The spectra pre-treatments were calculated by using the MATLAB 7.0.4 software (The MathWorks, Inc.). The 2D CORR analysis was investigated between the whole NIR (1000–2325 nm) and MIR (4000–800 cm1) spectral ranges. The 2D CORR analysis was carried out with the MATLAB 7.0.4 software (The MathWorks, Inc.). 3. Results and discussion 3.1. Description of raw infrared spectra The raw MIR spectra and the raw NIR spectra after SNV obtained during bread dough mixing are presented in Fig. 1A and C respectively. Strong and distinctive absorption bands of the main constituents of the dough can be identified on the raw MIR and raw NIR spectra. The identification and chemical assignment of the different NIR bands for bread dough mixing have already been proposed (Aı¨t Kaddour et al., 2007a,b; Alava et al., 2001; Olewnik et al., 2004; Psotka et al., 1999). A broad band centred at 1460 nm and an intense feature centred at 1940 nm are assigned to water. Two small absorption bands at 1200 and 1783 nm have been assigned to C–H vibrations. It is difficult to extract useful information directly from the raw NIR spectra because of low signal-to-noise ratio, large baseline changes from one spectrum to another one, and broad absorption peaks.

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The MIR and NIR spectra after the second derivative obtained during bread dough mixing are presented in Fig. 1B and D respectively. The use of the second derivative treatment removed the non-horizontal baseline spectra. Overlapping absorbances are

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The identification and chemical assignment of the different MIR bands have already been proposed (Aı¨t Kaddour et al., in press; Robertson et al., 2006; van Velzen et al., 2003). The MIR bands at 3300 cm1 (O–H stretching) and 1460 cm1 (O–H bending) are most often assigned to water molecules. The MIR bands at 1020, 1080, and 1150 cm1 are associated with the coupled C–O and C–C stretching vibrations that can be principally assigned to starch molecules. Absorption bands identified at 1650, 1546, and 1245 cm1 can be associated, respectively, to amide I (80% C]O stretch, 10% C–N stretch, and 10% N–H bend), amide II (60% N–H bend and 40% C–N stretch), and amide III (predominantly due to N–H bend and C–H stretch). The amide I band is strongly overlapped with the O–H deformation band due principally to water O–H bond. The amide I and amide II have been largely considered for the study of secondary protein structure modifications (Pe´zolet et al., 1992; Robert et al., 2002; Wellner et al., 1996, 2005; Yu, 2005). However, they are overlapped with the water (O–H) vibrations that thus need to be subtracted. In contrast, the amide III spectral region, usually neglected in MIR studies due to its relative weakness, is very useful because neither liquid water nor water vapour vibrations overlap with this band (Anderle and Mendelsohn, 1987; Cai and Singh, 2004).

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separated and the peak resolution is improved, making analysis easier. The identification and chemical assignment of the different NIR bands have already been proposed (Aı¨t Kaddour et al., 2007a; Alava et al., 2001). The direct description of the second derivative NIR absorption spectra demonstrated absorption features at specific wavelengths (1385, 1401–1480, 1900, 2173, and 2239 nm). The peaks were assigned to chemical changes of the dough components, and more particularly to starch vibrations (1900 and 1385 nm), to proteins vibrations (2173 and 2239 nm), and to C–H2, CONH2, H2O, starch and CONHR vibrations (1401–1480 nm). Analysis of the MIR spectra demonstrated absorption bands that can be mostly assigned to starch (1153, 1088, and 1011 cm1), water (3383, 3269, 3209, and 3068 cm1), proteins (1647, 1545, 1246 cm1),

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and lipids (2968, 2927, 2864, 1454, 1408, and 1327 cm1). The MIR spectra after second derivative permits to identify MIR bands that can be mostly assigned to lipid vibrations. After the second derivative transformation, the O–H vibration of the water band previously identified at 3300 cm1 dissociates in four small MIR bands that can be tentatively assigned to different water bond energies in the dough. The analysis of the MIR spectra after second derivative gives more precise chemical assignment of absorption bands compared to the analysis of the raw MIR spectra. 3.3. Analysis by 2D Cross-Correlation spectroscopy The statistical 2D CORR analysis method has been used to identify correlations between the MIR and NIR spectral regions. The 0.9

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2D contour map allows the NIR bands highly correlated to the MIR bands to be identified. The 2D CORR analyses were conducted directly on MIR spectra without subtracting the water spectrum to avoid a substantial decrease in the signal-to-noise ratio and modification of the spectral shape (Robert et al., 2002). 3.4. 2D CORR applied to raw MIR and NIR spectra Fig. 2A presents a correlation contour map of raw MIR spectra between 800 and 4000 cm1 (y-axis) versus raw NIR spectra between 1000 and 2500 nm (x-axis). High coefficients of determination (R2 > 0.6) are observed between the NIR bands centred at 1100, 1250, 1515, 1655, 1820, 1940 nm and the MIR bands centred at 3300 (O–H), 1650 (amide I), 1546 (amide II), 1245 (amide III), and 980 cm1 (C–H). This suggest that the NIR bands at 1100, 1250, 1515, 1655, 1820, and 1940 nm can be assigned to O–H, protein, and C–H vibrations. The 1020, 1080, and 1150 cm1 MIR bands usually assigned to polysaccharides (principally starch) are not highly correlated (R2 < 0.5) to the NIR bands at 1100, 1250, 1515, 1655, 1820, and 1940 nm. This suggests that starch vibration does not hugely contribute to the variation of the NIR bands at 1100, 1250, 1515, 1655, 1820, and 1940 nm during bread dough mixing. These results are consistent with the NIR description of food protein structure. Delwiche (1998) associated the 1100 nm absorption band to wheat protein vibration (1106 nm). Czarnik-Matusewicz et al. (1999) assigned the region around 1250 nm to the combination of amide II with the 1st overtone of amide A (1255 nm) in milk. The band region around 1515 nm was also associated with protein

vibrations in milk and wheat grain (Cocchi et al., 2006; CzarnikMatusewicz et al., 1999; Sˇasˇic´ and Ozaki, 2000). The NIR band located at 2231 nm seems to be highly correlated with the MIR band at 3300 cm1 and to a lesser extent to the amide band. This implies that the NIR band at 2231 nm can be mostly assigned to O–H vibration. The NIR region centred at 2231 nm was previously identified as a band sensitive to the protein secondary structure in wheat grain or purified food proteins (Brunn, 2006; Downey and Byrne, 1983; Robert et al., 1999) and to water vibrations in wheat grains (Downey and Byrne, 1983; Osborne, 1984). To improve the description of the NIR bands associated with protein vibrations, we decided to construct the determination coefficient (R2) spectra. These spectra are useful to present R2 values between one MIR wavenumber and all the NIR wavelengths (1000– 2325 nm), or one NIR wavelength against all the MIR wavenumbers (4000–800 cm1). The R2 spectra analysis has been limited to the amide I, amide II, and amide III MIR bands, that are mostly representative of the protein secondary structure modifications. Fig. 3 presents the R2 spectra calculated between the amide bands (1650, 1546, and 1245 cm1) and the NIR wavelength region (1000– 2325 nm). It is then possible to clearly identify the wavelength position of the NIR bands that are correlated to the MIR protein bands. The NIR bands between 1100 and 1167 nm are highly correlated (R2 z 0.87) to the MIR amide bands. This NIR region was previously assigned to proteins in wheat kernel (Delwiche, 1998) and to water molecule vibrations (Bertrand and Dufour, 2000; Maeda et al., 1995; Starck and Lucker, 1990). The NIR bands between 1235 and 1268 nm are highly correlated (R2 z 0.91) to the

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amide bands. It was previously reported that this NIR region was sensitive to the combination of amide II with the 1st overtone of amide A (1255 nm) (Czarnik-Matusewicz et al., 1999). The 1508– 1834 nm region is also highly correlated (R2 z 0.83) to the amide bands. Law and Tkachuk (1977) assigned this NIR region to the N–H 1st overtone (1550 and 1570 nm) and to the C–H 1st overtone of wheat gluten (1700, 1730, and 1760 nm). Bruun (2006) reported that the 1600–1800 nm region was identified by earlier experiments as a region highly associated with protein information since this region is not highly overlapped with water bands. Nonetheless, the 1508–1834 nm region was also assigned to starch and lipid vibrations in wheat grain and derived cereal products (Guy et al., 1996; Law and Tkachuk, 1977; Zardetto, 2005). The 1928–1952 nm NIR region presented a R2 z 0.90 with the MIR amide bands. The 1928–1952 nm region was essentially associated with water vibrations in wheat grain and starch (Delwiche et al., 1991; Law and Tkachuk, 1977) but Brunn (2006) assigned the 1927 nm band to gluten structure. The NIR bands at 2235 nm are highly correlated (R2 z 0.73) with the MIR amide bands and were previously assigned with food protein structure vibrations (2239 nm) (Brunn, 2006; Robert et al., 1999). It is assumed that the 2100–2300 nm region was also of the highest interest for protein information since these regions are not overlapped much with water bands (Brunn, 2006). The NIR bands at (1100–1167), (1235–1268), (1508–1834), (1928–1952), and (2200–2231) nm are all highly correlated (0.91  R2  0.73) to the MIR amide bands. The correlation

spectrum observed for the amide III band is almost the same as the amide I and amide II correlation spectra. The different NIR regions aforementioned (1100–1167, 1235–1268, 1508–1834, 1928–1952, 2200–2231 nm) can thus be associated with bands sensitive to modification of protein secondary structure. The 2D CORR analysis demonstrated that chemical and physicochemical analysis of the raw NIR spectra can be used to identify specific wavelength regions associated to a specific chemical bond vibration. The R2 spectra calculated between MIR and NIR bands allowed NIR wavelength regions that are correlated to the secondary protein structure variations to be identified. Nonetheless, it can be noticed that the NIR wavelength regions correlated to protein vibrations present large width. 3.5. 2D CORR applied to MIR and NIR spectra after second derivative Fig. 2B presented correlation contour map of MIR between 800 and 4000 cm1 (y-axis) versus NIR between 1000 and 2500 nm (x-axis) spectra obtained after the second derivative. The 2D contour map shows a high coefficient of determination (R2  0.6) between the MIR region at 1700–1520 cm1 (amide I and amide II) and the NIR regions around 1200, 1380–1480, and 1860 nm. The MIR region at 1700–1520 cm1 is highly associated with amide bands and with O–H (mostly water) molecule vibrations suggesting that the NIR bands around 1200, 1380–1480, and 1860 nm can be mostly assigned to vibrations of water molecule

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and dough proteins. A relatively high coefficient of determination (R2  0.6) is obtained between the MIR band at 1245 cm1 (amide III) and the NIR bands at 1200 and 1460 nm. On the other hand, a low coefficient of determination (R2 < 0.5) is obtained with the band at 1860 nm. Those observations suggested that the NIR band at 1200 and 1400 nm are more specific to protein vibrations compared to the 1860 nm bands. The 1860 nm band can be principally assigned to O–H vibrations of water molecules. The 2D contour map allows identification of other correlated bands (R2  0.5) between the MIR band at 1445 cm1 and the NIR bands centred at 1200, 1430, and 1860 nm. The 1445 cm1 has already been assigned to C–H2 and C–H3 vibrations (Bertrand and Dufour, 2000, 2006). This implies that the NIR bands at 1200, 1430 and 1860 nm can be assigned to C–H vibration and more precisely to the C–H vibration involved in hydrogen bonds between water molecules and protein backbone. The band centred at 2900 cm1 presents high R2 with the entire NIR region (1000–2325 nm). The MIR band at 2900 cm1 is associated with C–H2 and C–H3 vibrations (Stuart, 2004) and suggests that all the wavelength bands in the NIR region are sensitive to C–H vibrations (Bertrand, 2002). To highlight the NIR bands associated with protein vibrations, we decided to plot correlation spectra between NIR and MIR spectra after the second derivative. The Fig. 4 presents the R2 spectra between the different amide bands and the overall NIR wavelength region (1000–2325 nm). They allow us to identify more precisely the wavelength position of the NIR bands correlated to the MIR protein bands. The amide I, amide II, and amide III present almost similar correlation spectra with high R2 (0.70  R2  0.55) for the NIR bands at (1189–1216), (1351–1474), and (1873) nm. Those different NIR wavelength bands were previously assigned to protein vibrations in wheat grain. The 1189–1216 nm band was associated with gluten and protein vibrations (Law and Tkachuk, 1977; Maghirang and Dowell, 2003). The 1351–1474 nm was assigned to protein vibration and also to gluten vibrations (C–H combination band and to NH 1st overtone) (Dowell, 2000; Law and Tkachuk, 1977; Maghirang and Dowell, 2003). The 1873 nm was not reported in the literature as a protein band, but this band is usually assigned to water and more particularly to bound water (Rubenthaler and Pomeranz, 1987; Starck and Lucker, 1990; Williams et al., 1988). We could thus tentatively assign this band to water–protein interaction. Low R2 (0.53  R2  0.39) is obtained between the amide I and amide II bands and the (2026–2123) and (2280–2325) nm regions. The amide III band presents a slightly higher R2 (from 0.53 to 0.56) for those NIR bands. This implies that the NIR regions at (2026– 2123) and (2280–2325) nm also describe secondary protein structure modifications. The 2026–2123 and 2280–2325 nm regions have been associated with wheat protein vibrations in the literature (Brunn, 2006; Law and Tkachuk, 1977; Wesley et al., 1999; Williams, 2002). 4. Conclusion This study demonstrated that FT-NIR spectroscopy coupled to ATR FT-MIR spectroscopy by using the 2D CORR method is a useful tool to ascribe to NIR wavelength chemical changes implied in bread dough development during mixing. The results also demonstrated that MIR spectra can be applied without water subtraction to identify relevant NIR bands assigned to changes during dough development. The cross-correlation analysis of the raw NIR and raw MIR spectra and NIR and MIR spectra after the second derivative allows identification of specific NIR wavelength regions associated with chemical and physicochemical modifications of protein structure and more particularly to gluten structure. The cross-correlation analysis between MIR and NIR spectra after

the second derivative seems to be more precise for the identification of the NIR wavelength regions implicated in dough mixing compared to the cross-correlation analysis between raw NIR and raw MIR spectra. References Aı¨t Kaddour, A., Morel, M.-H., Barron, C., Cuq, B., 2007a. Dynamic monitoring of dough mixing using near-infrared spectroscopy: physical and chemical outcomes. Cereal Chemistry 84, 70–79. Aı¨t Kaddour, A., Barron, C., Robert, P., Cuq, B., 2007b. Physico-chemical description of bread dough mixing using two-dimensional near-infrared correlation spectroscopy and moving-window two-dimensional correlation spectroscopy. Journal of Cereal Science, doi:10.1016/j.jcs.2007.07.008. Aı¨t Kaddour, A., Mondet, M., Cuq, B. Description of bread dough mixing by FT-ATR mid-infrared spectroscopy. Cereal Chemistry, in press. Alava, J.M., Millar, S.J., Salmon, S.E., 2001. The determination of wheat breadmaking performance and bread dough mixing time by NIR spectroscopy for high speed mixers. Journal of Cereal Science 33, 71–81. Amend, T., Belitz, H.D., 1990. The formation of dough and gluten – a study by scanning electron microscopy. Zeitschrift fu¨r Lebensmittel Untersuchung und Forschung 190, 401–409. Anderle, G., Mendelsohn, R., 1987. Thermal denaturation of globular proteins. Fourier transform-infrared studies of the amide III spectral region. Biophysical Journal 52, 69–74. Barton II, F.E., Himmelsbach, D.S., Duckworth, J.H., Smith, M.J., 1992. Twodimensional vibration spectroscopy: correlation of mid- and near-infrared regions. Applied Spectroscopy 46, 420–429. Barton II, F.E., Himmelsbach, D.S., Archibald, D.D., 1996. Two-dimensional vibration spectroscopy. V: correlation of mid- and near-infrared of hard red winter wheat and spring wheat. Journal of Near Infrared Spectroscopy 4, 139–152. Barton II, F.E., Himmelsbach, D.S., 1993. Two-dimensional vibration spectroscopy II: correlation of the absorptions of lignins in the mid- and near-infrared regions. Applied Spectroscopy 47, 1920–1925. Bertrand, D., 2002. La spectroscopie proche infrarouge et ses applications dans l’industrie de l’alimentation animale. INRA Production Animales 15, 209–219. Bertrand, D., Dufour, E., 2000. La spectroscopie proche infrarouge et ses applications analytiques. In: Collection Sciences et Technologiques Agroalimentaires. Tec et Doc, Lavoisier, Paris. Bertrand, D., Dufour, E., 2006. La spectroscopie proche infrarouge et ses applications analytiques. In: Collection Sciences et Technologiques Agroalimentaires. Tec et Doc, Lavoisier, Paris. Brunn, S.W., 2006. Protein–Lipid–Carbohydrate Interactions and Importance to Food Quality. Spectroscopic Detection of Macromolecular Interactions. Biochemistry and Nutrition Group (BNG), thesis. Technical University of Denmark. Cai, S., Singh, B.R., 2004. A distinct utility of the amide III infrared band for secondary structure estimation of aqueous protein solutions using partial least square methods. Biochemistry 43, 2541–2549. Cocchi, M., Durante, C., Foca, G., Marchetti, A., Tassi, L., Ulrici, A., 2006. Durum wheat detection by NIR spectroscopy multivariate calibration. Talanta 68, 1505–1511. Czarnik-Matusewicz, B., Murayama, K., Tsenkova, R., Ozaki, Y., 1999. Analysis of near-infrared spectra of complicated biological fluids by two-dimensional correlation spectroscopy: protein and fat concentration dependent spectral changes of milk. Applied Spectroscopy 53, 1582–1594. Delwiche, S.R., Pitt, R.E., Norris, K.H., 1991. Examination of starch-water and cellulose-water interactions with near infrared (NIR) diffuse reflectance spectroscopy. Starch 43, 415–422. Delwiche, S.R., 1998. Protein content of single kernels of wheat by near-infrared reflectance spectroscopy. Journal of Cereal Science 27, 241–254. Dowell, F.E., 2000. Differentiating vitreous and non-vitreous durum wheat kernels by using near-infrared spectroscopy. Cereal Chemistry 77, 155–158. Downey, G., Byrne, S., 1983. Determination of protein and moisture in ground wheat by near infra-red reflectance spectroscopy. Irish Journal of Food Science and Technology 7, 135–146. Graveland, A., Bosveld, P., Lichtendonk, W.J., Marseilles, J.P., Moonen, J.H.E., Scheepstra, A., 1985. A model for the molecular structure of the glutenin from wheat flour. Journal of Cereal Science 3, 1–16. Guy, R.C.E., Osborne, B.G., Robert, P., 1996. The application of near infrared reflectance spectroscopy to measure the degree of processing in extrusion cooking processes. Journal of Food Engineering 27, 241–258. Hoseney, C.R., Finney, P.L., 1974. Mixing a contrary view. Bakers Digest, 24–28. Kilborn, R.H., Preston, K.R., 1981. Device senses change in dough consistency during dough mixing. Part I: with tweedy mixer. Bakers Journal, 16–19. Law, D.P., Tkachuk, R., 1977. Near infrared diffuse reflectance spectra of wheat and wheat components. Cereal Chemistry 54, 256–265. Maeda, H., Ozaki, H., Tanaka, M., Hayashi, N., Kojima, T., 1995. Near infrared spectroscopy and chemometrics studies of temperature-dependent spectral variations of water: relationship between spectral changes and hydrogen bonds. Journal of Near Infrared Spectroscopy 3, 191–201. Maghirang, E.B., Dowell, F.E., 2003. Hardness measurement of bulk wheat by singlekernel visible and near-infrared reflectance spectroscopy. Cereal Chemistry 80, 316–322. Noda, I., 1986. Two dimensional infrared spectroscopy of synthetic and biopolymers. Bulletin of the American Physical Society 31, 520–528.

A. Aı¨t Kaddour et al. / Journal of Cereal Science 48 (2008) 678–685 Noda, I., 1993. Generalized two dimensional correlation spectroscopy method applicable to infrared, Raman, and other types of spectroscopy. Applied Spectroscopy 47, 1329–1336. Noda, I., 2000. Determination of two-dimensional correlation spectroscopy using the Hilbert transform. Applied Spectroscopy 54, 994–999. Olewnik, M.C., Dempster, R.E., Smail, V.W., 2004. Development of a Controlled Dough Mixing System, No. 45. Conference Proceedings of Cereal and Bread Congress, Harrogate. Osborne, B.G., 1984. Investigations into the use of near infrared reflectance spectroscopy for the quality assessment of wheat with respect to its potential for bread baking. Journal of Science and Food Agriculture 35, 106–110. Peighambardoust, S.H., van der Goot, A.J., van Vliet, T., Hamer, R.J., Boom, R.M., 2006. Microstructure formation and rheological behaviour of dough under simple shear flow. Journal of Cereal Science 43, 183–197. Pe´zolet, M., Bonenfant, S., Dousseau, F., Popineau, Y., 1992. Conformation of wheat gluten proteins comparison between functional and solution state as determined by infrared spectroscopy. Federation of European Biochemical Societies 299, 247–250. Psotka, J., Chen, R., Olewnik, M., 1999. Monitoring Dough Development in Real Time Using a near Infrared Spectrometer, No. 293. 1999 AACC Annual Meeting, Seattle. Robert, P., Devaux, M.F., Mouhous, N., Dufour, E., 1999. Monitoring of secondary structure of proteins by near-infrared spectroscopy. Applied Spectroscopy 53, 226–232. Robert, P., Lavenant, L., Renard, D., 2002. Infrared two-dimensional correlation and principal component analysis applied to b-lactoglobulin aggregation in water solution. Applied Spectroscopy 56, 1180–1185. Robertson, G.H., Gregorski, K.S., Cao, T.K., 2006. Changes in secondary protein structures during mixing development of high absorption water (90%) flour and water mixtures. Cereal Chemistry 83, 136–142. Rubenthaler, G.L., Pomeranz, Y., 1987. Near infrared reflectance spectra of hard red winter wheats varying widely in protein content and breadmaking potential. Cereal Chemistry 64, 407–411. Sˇasˇic´, Sˇ., Ozaki, Y., 2000. Band assignment of near-infrared spectra of milk by use of partial least-squares regression. Applied Spectroscopy 54, 1327–1338. Seabourn, B.W., Chung, O.K., Seib, P.A., Mathewson, P.R., 2004. A Method for Monitoring the Rheology and Protein Secondary Structure of Dough During Mixing Using FT-HATR Spectroscopy, No. 320. 2004 AACC Annual Meeting, Portland. Starck, E., Lucker, K., 1990. Overview of near Infrared Spectroscopy. Course given during the Third International Conference on Near Infrared Spectroscopy, Brussels, Belgium, 25–29 June 1990. Written Paper.

685

Stuart, B., 2004. Infrared spectroscopy: fundamentals and applications. In: Analytical Techniques in the Sciences. John Wiley and Sons, New Jersey, pp. 1–242. van Velzen, E.J.J., van Duynhoven, J.P.M., Pudney, P., 2003. Factors associated with dough stickiness as sensed by attenuated total reflectance infrared spectroscopy. Cereal Chemistry 80, 378–382. Weegels, P.L., van de Pijpekamp, A.M., Graveland, A., Hamer, R.J., Schofield, J.D., 1996. Depolymerisation and re-polymerisation of wheat glutenin during dough processing. I. Relationships between glutenin macropolymer content and quality parameters. Journal of Cereal Science 23, 103–111. Wellner, N., Belton, P.S., Tatham, A.S., 1996. Fourier transform IR spectroscopic of hydration included structure changes in the solid state of u-gliadins. Journal of Biochemistry 319, 741–747. Wellner, N., Mills, E.N.C., Brownsey, G., Wilson, R.H., Brown, N., Freeman, J., Halford, N.G., Shewry, P.R., Belton, P.S., 2005. Changes in protein secondary structure during gluten deformation studied by dynamic Fourier transform infrared spectroscopy. Biomacromolecules 6, 255–261. Wesley, I.J., Larsen, N., Osborne, B.G., Skerritt, J.H., 1997. Monitoring of dough and grain properties. Australian Patent P06303. Wesley, I.J., Larsen, N., Osborne, B.G., Skerritt, J.H., 1998. Non-invasive monitoring of dough mixing by near infrared spectroscopy. Journal of Cereal Science 27, 61–69. Wesley, I.J., Larsen, N., Osborne, B.G., Skerritt, J.H., 2002. Monitoring of dough properties. United States Patent 6,342,259 B1. Wesley, I.J., Uthayakumaran, S., Anderssen, R.S., Cornish, G.B., Bekes, F., Osborne, B. G., Skerritt, J.H., 1999. A curve-fitting approach to the near infrared reflectance measurement of wheat flour proteins which influence dough quality. Journal of Near Infrared Spectroscopy 7, 229–240. Williams, P.C., Jaby El-Haramein, J., Ortiz-Fereiro, G., Srivastava, J.P., 1988. Preliminary observations on the determination of wheat strength by nearinfrared reflectance. Cereal Chemistry 65, 109–114. Williams, P.C., 2002. In: Chalmers, J.M., Griffths, P.R. (Eds.), Near-Infrared Spectroscopy of Cereals in Handbook of Vibrational Spectroscopy, vol. 5. Wiley, Chichester, pp. 3693–3719. Wilson, A.J., Newberry, M.P., 1995. Dough probe investigations. New Zealand Food Technology 30, 36–40. Yu, P., 2005. Protein secondary structures (a-helix and b–sheet) at a cellular level and protein fractions in relation to rumen degradation behaviours of protein: a new approach. British Journal of Nutrition 94, 655–665. Zardetto, S., 2005. Potential applications of near infrared spectroscopy for evaluating thermal treatments of fresh egg pasta. Food Control 16, 249–256.