Asia Maamar
email: essia.maamer@sigma-clermont.fr

Maher Abdelghani
email: maher.abdelghani@gmail.com

Thien-Phu Le
email: thienphu.le@univ-evry.fr

Vincent Gagnol
email: vincent.gagnol@sigma-clermont.fr

Laurent Sabourin
email: laurent.sabourin@sigma-clermont.fr

Operational modal identification in the presence of harmonic excitation

Keywords: Operational modal analysis, transmissibility functions, stochastic subspace identification, harmonic components

with the classical stochastic subspace identification method, which is based on white noise excitation. These two methods are first applied to numerical examples and then to a laboratory test. Results validate the novel ability of the method based on transmissibility measurements to eliminate harmonics, contrary to the stochastic subspace identification approach.

Abstract

The dynamic behavior of structures can be studied by the identification of their modal parameters. Classical modal analysis methods are based on the relation between the forces applied to structures (inputs) and their vibration responses (outputs). In real operational conditions it is difficult, or even impossible, to measure the excitation. For this reason, operational modal analysis approaches which consider only output data are proposed. However, most of these output-only techniques are proposed under the assumption of white noise excitation. If additional components, like harmonics for instance, are present in the exciting force, they will not be separated from the natural frequencies. Consequently, this assumption is no longer valid. In this context, an operational modal identification technique is proposed in order to only identify real poles and eliminate spurious ones. It is a method based on transmissibility functions.

The objective of the proposed paper is to identify modal parameters in operational conditions in the presence of harmonic excitations. Identification is performed using a method based on transmissibility measurements and then

Introduction

Modal analysis [START_REF] Guillaume | Modal analysis[END_REF][START_REF] Heylen | Modal analysis theory and testing[END_REF][START_REF] Peeters | Comparative study of modal analysis techniques for bridge dynamic characteristics[END_REF] is used to identify mode shapes, natural frequencies and damping ratios under vibrational excitation. These methods are efficient tool for detecting damage in structures, controlling them, and determining their structural stability. Modal parameters are initially identified using experimental modal analysis (EMA) [START_REF] Mejri | Dynamic characterization of machining robot and stability analysis[END_REF]. This technique exploits the Frequency Response Function (FRF) of the structure, which represents the relation between the excitation and the vibrational response of the structure. Resonance frequencies appear as peaks in the measured frequency response functions. From these FRFs, modal parameters are identified using various curve-fitting techniques. Many excitation forms and experimental setups exist,their choice depending on the structure's complexity. Usually, the EMA is carried out under impact hammer and/or shaker excitation. The major drawback to experimental modal tests is that both artificially applied forces and resulting structural vibration responses need to be measured. In practice, the measurement of the exciting force is not always possible. In EMA, tests are performed at rest, and the dynamic properties of structures at rest vary from those of structures in operational conditions [START_REF] Mejri | Experimental protocol for the dynamic modeling of machining robots[END_REF], which can significantly influence the identified modal model.

For these reasons, modal identification techniques are developed and operational modal analysis (OMA) is proposed [START_REF] Masjedian | A review on operational modal analysis researches: classification of methods and applications[END_REF][START_REF] Gagnol | Modal identification of spindle-tool unit in high-speed machining[END_REF], where the modal properties are estimated from responses only. Various operational modal identification techniques are proposed, example the Stochastic Subspace Identification approach (SSI) [START_REF] Van Overschee | Subspace algorithms for the stochastic identification problem[END_REF][START_REF] Peeters | Reference-based stochastic subspace identification for output-only modal analysis[END_REF]. However, OMA methods have limitations when applied to practical cases. One limiting constraint of OMA is that the non-measured excitation of the system in operation must be a stochastic realization (white noise) [START_REF] Masjedian | A review on operational modal analysis researches: classification of methods and applications[END_REF]. This implies that if harmonic components are present in addition to random excitation, standard OMA procedures cannot be applied in a straightforward way. Harmonic components are sometimes considered as virtual modes in the identification, but when the harmonic excitation frequencies are close to eigenfrequencies, the standard OMA approaches may break down [START_REF] Mohanty | Modified sstd method to account for harmonic excitations during operational modal analysis[END_REF].

Several indicators for the separation of structural and harmonic modes in output-only modal identification are proposed. One of the most widely-used methods is based on the Probability Density Function (PDF) of harmonic and structural responses. The PDF of a structural response is a distribution with only one peak, and the PDF of a harmonic response is a distribution with two peaks. This difference was originally illustrated by Lago [START_REF] Lago | The difference between harmonics and stochastic narrow band responses. Oral presentation at the SVIBS symposium[END_REF]. Kurtosis criteria have also been used to identify harmonic components and structural modes, [START_REF]Blind separation of vibration components: Principles and demonstrations[END_REF][START_REF]The spectral kurtosis: a useful tool for characterising nonstationary signals[END_REF][START_REF] Brincker | An indicator for separation of structural and harmonic modes in output-only modal testing[END_REF][START_REF] Jacobsen | Eliminating the influence of harmonic components in operational modal analysis[END_REF][START_REF] Jacobsen | Using efdd as a robust technique for deterministic excitation in operational modal analysis[END_REF]. Kurtosis is defined as the fourth central moment of the PDF, normalized with respect to the standard deviation. In addition to the above-mentioned methods, knowledge of the damping ratios is an a priori indicator to distinguish between harmonics and structural poles. Generally, the damping ratios of real poles vary between 0.1% and 2%. This information enables modes with negative and high damping to be eliminated [START_REF] Gagnol | Modal identification of spindle-tool unit in high-speed machining[END_REF]. The Modal Assurance Criterion (MAC) [START_REF] Kim | A new method to extract modal parameters using output-only responses[END_REF] is also an effective tool to distinguish a structural mode from a harmonic one. The MAC value between a structural mode shape and a mode shape corresponding to a harmonic component will show a low correlation. Specific numerical filters have also been developed [START_REF] Randall | New cepstral methods of signal pre-processing for operational modal analysis[END_REF] in order to eliminate harmonic components from the measured response. However, in practice filters are not perfect, and if the harmonic frequency is close to resonant frequencies, the filtering will disturb the response so that the identified modal parameters are perturbed. In order to overcome the white noise excitation assumption and consequently to identify modal parameters in the presence of harmonic excitation, an operational modal identification method is proposed by Devriendt et al. [START_REF] Mohanty | Modified sstd method to account for harmonic excitations during operational modal analysis[END_REF][START_REF] Devriendt | The use of transmissibility measurements in output-only modal analysis[END_REF][START_REF] Devriendt | Identification of modal parameters from transmissibility measurements[END_REF][START_REF] Devriendt | Operational modal analysis in the presence of harmonic excitations by the use of transmissibility measurements[END_REF]. This method is based on transmissibility measurements. The significant advantage of this approach is its independence from the nature of the excitation. Consequently, the presence of harmonics will not disturb the identified modal model.

The main objective of this paper is to identify the modal parameters of structures in the presence of harmonic components using the Transmissibility Function-Based method (TFB). The decision concerning whether a particular mode is structural or not is based on a singular value decomposition of the systems transmissibility matrix. The identified eigen-parameters are then compared to those obtained using the classical SSI method, which is based on the white noise excitation assumption. The idea behind this comparison is to demonstrate that the TFB method is a particular OMA which can eliminate harmonics and identify only the real poles of the structure. The paper is organized as follows. In section 2, a brief description of the TFB and SSI techniques is provided. In section 3, the OMA technique based on transmissibility measurements is applied to a numerical model and to a cantilever beam test. Results are then compared with those obtained via the SSI technique. Finally, section 4 concludes the paper.

Operational modal identification techniques

Modal identification method based on transmissibility functions (TFB)

OMA approaches are generally based on the assumption of white noise processes for operational excitations. However, this assumption is hard to respect in real situations [START_REF] Mohanty | Modified sstd method to account for harmonic excitations during operational modal analysis[END_REF][START_REF] Hermans | Modal testing and analysis of structures under operational conditions: industrial applications[END_REF]. The method based on transmissibility functions (TFB) is independent from the nature of the excitation, and solves the problem of the presence of harmonic components. The use of transmissibility functions was proposed by Devriendt et al [START_REF] Devriendt | The use of transmissibility measurements in output-only modal analysis[END_REF] as a new approach in operational modal analysis. The expression of the frequency response at a point i under an excitation at a point k is written as:

X k i (s) = H ik (s)F k (s) (1)
A transmissibility function is defined as the ratio between the motion response X k i (s) and the reference motion response X k j (s) under a single force located at k.

T k ij (s) = X k i (s) X k j (s) = H ik (s)F k (s) H jk (s)F k (s) = H ik (s) H jk (s) (2)
System poles are zero points resulting from the subtraction between two transmissibility functions measured at the same output points i and j but with two different input excitation locations k and l .

∆T kl ij (s) = T k ij (s) -T l ij (s) (3)
And consequently the poles of its inverse:

∆ -1 T kl ij (s) = 1 T k ij (s) -T l ij (s) (4)
The PolyMAX method [START_REF] Peeters | Polymax: A revolution in operational modal analysis[END_REF] is then investigated in order to calculate the system poles from ∆ -1 T kl ij (s). Generally, additional poles can be present in the ∆ -1 T kl ij (s) functions. Structural poles λ r can easily be determined by performing a singular value decomposition of the transmissibility matrix T [START_REF] Devriendt | The use of transmissibility measurements in output-only modal analysis[END_REF]. If we consider, for example, four different loading conditions k, l, m and n, the transmissibility matrix is the following:

T =       T k 1r (s) T l 1r (s) T m 1r (s) T n 1r (s) T k 2r (s) T l 2r (s) T m 2r (s) T n 2r (s) T k 3r (s) T l 3r (s) T m 3r (s) T n 3r (s) T k 4r (s) T l 4r (s) T m 4r (s) T n 4r (s) 1 1 1 1       (5)
In fact, in system poles λ r the rank of matrix T is one; consequently, σ 1 > σ 2 > σ 3 > σ 4 ≥ 0 and 1/σ 2 tends to ∞.

Stochastic subspace identification method (SSI)

The dynamic behavior of a discrete mechanical system consisting of n masses connected through springs and dampers is described by the following matrix differential equation:

Mq(t) + C 2 q(t) + Kq(t) = f (t) (6)
M, C 2 and K ∈ R n×n are the mass, damping and stiffness matrices. q(t) ∈ R n is the displacement vector at continuous time t. Vector f (t) ∈ R n is the excitation force. SSI is a method that converts a 2 nd order problem into two 1 st order problems. Equation ( 6) can be converted into the following state equation:

ẋ(t) = A c x(t) + B c f (t) y(t) = Cx(t) + Df (t) (7)
The state matrix A c in continuous time, the load matrix B c and the output matrix C are given by:

A c = 0 1 1 -M -1 K -M -1 C 2 , B c = 0 M -1 , C = 1 1 0 , x = q q
D is the feedback matrix (zero in the case of mechanical systems). The first equation in ( 7) is called the state equation and models the dynamic behavior of the system. The second equation is called the observation or output equation. Equation ( 7) can be converted to following discrete-time stochastic state-space model [START_REF] Van Overschee | Subspace algorithms for the stochastic identification problem[END_REF]:

x k+1 = Ax k + w k y k = Cx k + v k (8)
where y k = y(k∆t) is the sampled output vector, x k = x(k∆t) is the discrete state vector, w k is the process noise due to the unknown excitation of the structure, v k is the measurement noise and k is the time instant, A = exp(A c ∆t) is the discrete state matrix. In order to obtain the modal parameters, an eigenvalue decomposition (EVD) of the matrix A is performed:

A = ΨΛ d Ψ -1 (9)
Ψ ∈ C n×n is the eigenvector matrix and Λ d = diag(λ i ) ∈ C n×n is the diagonal matrix containing the discrete time eigenvalues µ i . The continuous time state equation ( 7) is equivalent to the second order matrix equation of motion [START_REF] Masjedian | A review on operational modal analysis researches: classification of methods and applications[END_REF] . Consequently, they have the same eigenvalues and eigenvectors. These can be obtained by an eigenvalue decomposition of the continuous time state matrix:

A c = Ψ c Λ c Ψ c -1 (10)
Where Λ c = diag(λ c ) is a diagonal matrix containing the continuous time complex eigenvalues and Ψ c contains the eigenvectors. The continuous time complex eigenvalues λ c are found from the discrete time eigenvalues λ i by:

λ c = ln(λ i ) ∆t ( 11
)
where ∆t is the sampling time. The eigenvalues of A c occur in complex conjugated pairs and are written as:

λ c , λ * c = -ξ c ω c ± jω c 1 -ξ 2 c ( 12
)
Natural frequencies f c and damping ratios ξ c are identified from:

f c = ω c 2π = |λ c | 2π , ξ c = | (λ c )| |λ c | (13)
The modal matrix Φ can be obtained from the eigenvector matrix Ψ using the relation:

Φ = CΨ (14)
3. Applications

Numerical model

In order to validate the numerical implementation of the transmissibility function-based method, a numerical model was considered and modal identification was performed in the case of impact hammer excitation and then in the case of a combination between harmonic and random excitations for comparison purposes.

Impact hammer excitation

A two-degree-of-freedom (DoF) system is considered for numerical validation and is illustrated in Figure 1 with its mechanical properties. Two impact hammer excitations were applied to both masses of the system separately. For each loading condition, responses in displacement are obtained by the integrating motion equations with the Range-Kutta algorithm. The mass displacement responses, which are sampled at N = 4000 points with a period ∆t = 0.01s, are presented in Figure 1.

c 1 k 1 m 1 c 2 k 2 m 2 6
x 2 (t) In this case, two transmissibility functions are calculated with respect to the frequency response of each mass of the studied system under the two different applied forces. In Figure 2a, the two transmissibility functions T 1 12 (s) and T 2 12 (s) cross each other at two different points, which represent the system's poles. The subtraction between two transmissibility functions under two different input excitations on each of the two masses is:

6 x 1 (t) m 1 = m 2 = 1 kg c 1 =
T 1 12 (s) = X 11 (s) X 21 (s) , T 2 12 (s) = X 12 (s) X 22 (s) (15)
∆T (s) = T 1 12 (s) -T 2 12 (s) (16)
Systems poles are poles of ∆ -1 T (s). In Figure 2b, two peaks of the system's poles are observed at ∆ -1 T (s). The frequency domain estimator, PolyMAX, is then investigated for the function ∆ -1 T (s), in order to identify the damping ratio and the natural frequency of the system's poles. Modal parameters are 1 with their exact values.

The modal parameters identified by the transmissibility function-based method are quite close to exact ones, thus confirming the numerical implementation of the TFB method. The damping ratio is more influenced by the nature of the excitation than the natural frequency. This explains the good correlation between TFB and exact values as regards the determination of natural frequencies.

Combination of white noise excitation and fixed harmonic excitation

In order to evaluate the influence of the presence of harmonic components on the exciting force, and to check the efficiency of the TFB method, a combination of harmonic excitation and Gaussian white noise excitation with zero mean is applied to each mass separately. The frequency of the applied harmonic excitation is equal to 15 Hz. The Fast Fourier Transform (FFT) of the simulated temporal response is shown in Figure 3. Three peaks combining structural and harmonic modes are present in the FFT curve. The harmonic component frequency is 15 Hz, and the two other modes are structural. In Figure 4, the T 1 12 (s) and T 2 12 (s) curves cross each other on the system's poles. In [START_REF] Devriendt | Operational modal analysis in the presence of harmonic excitations by the use of transmissibility measurements[END_REF], it was demonstrated that the ratio between two frequency responses under the same input force eliminates the harmonic component. Thus these harmonic components will not be present in ∆ -1 T (s) . However, ∆ -1 T (s) may contain additional poles that are not related to the system's dynamics, as can be seen on the curve of ∆ -1 T (s) in Figure 5. It is important to verify whether these peaks on the curve of ∆ -1 T (s) are real structural modes or not.

Step 1: Singular value decomposition of the transmissibility matrix T At the system poles, the rank of the proposed matrix T is one. Consequently, σ 2 tends to zero and the peaks of 1/σ 2 indicate the system's poles. Step 2: Stabilization diagram Stabilization diagrams showing the stability of the poles as a function of ∆ -1 T (s) and as a function of increasing model order were used to distinguish the spurious modes from the physical poles. Figure 6 shows such a stabilization diagram. The green point indicates that a pole is stable with respect to damping ratio and natural frequency, and the red point indicates that a pole is unstable with respect to damping ratio and/or natural frequency. Here it is clear that the two poles at frequencies demonstrate the novel ability of the TFB technique to eliminate harmonics, modal parameters are also identified using the SSI method. Results obtained from the two different approaches are presented in Table 2 and compared. The times consumption of the proposed TFB approach and the classical SSI method are respectively 3.544 s and 2.834 s. In the stabilization diagram obtained with the SSI method, shown in Figure 7, three poles are estimated.

The SSI is a robust method, but we cannot really distinguish between structural and non-structural poles, and this generally represents a difficulty in the operational modal identification field. With the transmissibility-based method, the harmonic mode is automatically eliminated, which is not the ------0.05 --case for SSI, that estimates it with a low damping value. However, the modal identification of the real structural pole parameters shows a good correlation, especially on the natural frequencies.

Laboratory experimental test 3.2.1. Experimental modal analysis

In order to check the efficiency of the OMA method based on transmissibility measurements, an experimental test, shown in Figure 8, was carried out for a cantilever beam. A cantilever beam 0.757 m in length, 0.103 m in width and 0.008 m in height is considered to identify its modal parameters. First, an analytical computation of the first five natural frequencies of the considered beam was performed, using the following equation:

f i = 1 2π √ 12 α 2 i E ρ h L 2 (17)
with: Height: h=0.08 m Length: L=0.757 m Young's modulus: E=70000 MPa Density: ρ=2698 Kg/m 3 α 0 =1.8751, α 1 =4.695, α 2 =7.85, α i+1 =(2i +1)/2, for i >2 In order to obtain a reference modal model, an EMA was performed using an impact hammer at well-defined locations on the beam. Time responses were recorded using six PCB Piezoelectric accelerometers, equally distributed over the full length of the beam, as shown in Figure 8. LMS TEST.Lab software was used to acquire the responses from the accelerometers. Modal parameters were identified using the PolyMAX modal identification method implemented in the LMS TEST.Lab software. From the stabilization diagram shown in Figure 9, modal parameters of the studied cantilever beam were identified. The method based on transmissibility measurements is then applied to the measured data response. The results obtained by the two different methods are shown in Table 4. Results show a good correlation when comparing the values of the natural frequencies and the damping ratios. In order to compare the mode shapes identified by the two different methods, the modal assurance criterion is calculated. The modal assurance criterion (MAC) shows a very good correlation between the five structural mode shapes obtained by these two different techniques (Coefficient of correlation greater than 0.9).

Operational modal analysis

In addition to the random impact hammer shocks, the beam is now excited by harmonic excitations provided by a motor turning at 851 rpm. The motor is fixed to the second point of the beam, as can be seen in Figure 11. Experimental tests were carried out under random impact hammer shocks in the presence of harmonic excitation. Data responses were measured using six accelerometers, equally distributed over the full length of the beam, as described in the EMA section. Operational modal analysis was performed The stabilization diagram is obtained using the PolyMAX method implemented in the LMS TEST.Lab software. Here we find that the distinction between harmonics and structural poles is difficult. It is therefore not possible to decide if a pole is real or spurious. The same difficulty was found when identifying the modal parameters of the excited beam with the SSI method, which identifies both structural and harmonic poles.

As explained in section1, in order to select the correct poles a singular value decomposition of the transmissibility matrix is performed, and the second singular value of T(s) should be close to 0 for this systems poles.

T =         T 1 12 (s) T 2 12 (s) T 3 12 (s) T 4 12 (s) T 1 32 (s) T 2 32 (s) T 3 32 (s) T 4 32 (s) T 1 42 (s) T 2 42 (s) T 3 42 (s) T 4 42 (s) T 1 52 (s) T 2 52 (s) T 3 52 (s) T 4 52 (s) T 1 62 (s) T 2 62 (s) T 3 62 (s) T 4 62 (s) 1 1 1 1         (18)
Through the EMA of the studied cantilever beam, three structural poles are identified in the frequency band [0 200 Hz], at frequencies of 10.57 Hz, 67.51 Hz and 188.99 Hz, respectively. Furthermore, in Figure 13, it is shown that in the considered frequency band, the three dominant peaks coincide with the first three natural frequencies. The PolyMAX technique is then investigated to identify modal parameters from the transmissibility ∆ -1 T (s) functions. Results obtained from the two different TFB and SSI approaches are presented in Table 5. The times consumption of the proposed TFB approach and the classical SSI method are respectively 7.519 s and 2.492 s. Similarly to the first studied case, only structural modes are selected and estimated by the method based on transmissibility measurements. Modal parameter identification appears to be reasonably robust in relation to the determination of the natural frequencies and vibration mode shapes. However, the damping ratio identified by both SSI and PolyMAX methods is strongly influenced by the presence of harmonics. The modal assurance criterion (MAC) shows a very good correlation between the structural mode shapes obtained using these three different techniques (Coefficient of correlation greater than 0.8).

Conclusions

In this paper, an operational modal identification method is proposed for ambient vibration testing in the presence of harmonics. This operational modal identification method, based on transmissibility functions (TFB), is first applied to a two-degree-of-freedom system in order to validate its numerical implementation, and then investigated via an experimental beam test. Identified modal parameters are compared to those found by a classical OMA approach based on the white noise excitation assumption, namely the SSI method. In the two different cases, the time consumption of the TFB approach is higher than that of the SSI method. This can be explained by the additional stage of the signals preprocessing. It should be noted that the efficiency of the proposed TFB method also depends on the modal identification capacity from transmissibility functions. Even these functions are independent of excitations nature, the TFB could be thus less efficient when modal density is high as in mid-frequency range. The main advantage of the TFB method is its independence from the nature of the excitation and its novel ability to eliminate harmonic components. Consequently, it provides an accurate prediction of modal parameters in the presence of harmonic excitations. Thus, it opens possibilities for application to more complicated structures in the presence of harmonic excitations under operational conditions.

1 Ns/m c 2 = 1 Figure 1 :

 211 Figure 1: Two-degree-of-freedom system and simulated responses under impact hammer excitation on mass 1

Figure 2 :

 2 Figure 2: (a) Transmissibility functions T 1 12 and T 2 12 , (b) ∆ -1 T under impact hammer excitation

Figure 3 :

 3 Figure 3: Fast Fourier Transform of the simulated response

Figure 4 :

 4 Figure 4: (a) Transmissibility functions T 1 12 and T 2 12 , (b) ∆ -1 T under a combination of harmonic and white noise excitation

Figure 5 :

 5 Figure 5: Selection of system poles by means of singular value decomposition

Figure 6 :

 6 Figure 6: Selection of system poles through a stabilization diagram by the TFB method

Figure 7 :

 7 Figure 7: Stabilization diagram obtained from the SSI method

Figure 8 :

 8 Figure 8: Description of the cantilever beam and position of the accelerometers

Figure 9 :

 9 Figure 9: Stabilization diagram from an EMA

Figure 10 :Figure 11 :

 1011 Figure 10: Mode shape comparison-MAC values

Figure 12 :

 12 Figure 12: Sum of the measured cross-power spectra and stabilization diagram using the PolyMAX method

Figure 13 :

 13 Figure 13: Selection of the system's poles by means of singular value decomposition

Figure 14 :

 14 Figure 14: Modal assurance criterion for mode shapes identified using the SSI, TFB and PolyMAX methods

Table 1 :

 1 Identified modal parameters from the TFB method and exact values

	Mode Natural frequency (Hz) Damping ratio (%)
		Exact	TFB	Exact	TFB
	1	3.55	3.51	1.12	1.36
	2	8.71	8.78	2.73	2.47
	reported in Table

  3.54 Hz and 8.72 Hz are real structural poles, and that the other peaks are spurious ones. In order to

		30
	Model order	10 20
		0	0	2	4	6	8	10	12	14	16	18	20
							Frequency (Hz)

Table 2 :

 2 Identified modal parameters using SSI and TFB methods

	Mode Natural frequency (Hz)	Damping ratio (%)
		Exact SSI	TFB	Exact SSI	TFB
	1 st	3.55	3.58	3.54	1.12	1.21	1.24
	2 nd	8.71 8.717	8.72	2.73	2.02	2.68
	3 rd	15	15

Table 3 :

 3 Exact natural frequency values of the cantilever beam

	Mode Natural frequency (Hz)
	1 st	10.8
	2 nd	67.8
	3 rd	189.85
	4 th	371.5
	5 th	614.8

Table 4 :

 4 Modal parameters identified using EMA

	Mode Natural frequency (Hz) Damping ratio (%)
		PolyMAX	TFB	PolyMAX TFB
	1 st	10.569	10.57	1.05	1.059
	2 nd	67.751	67.51	0.74	0.841
	3 rd	185.959	188.99	1.59	1.547
	4 th	387.542	388.92	0.78	0.763
	5 th	626,917	626,51	0,57	0,578

Table 5 :

 5 Modal parameters of the cantilever beam, identified through experimental and operational modal analysis

	Component	Identified frequency (Hz)	Damping ratio (%)	Nature
		PolyMAX SSI	TFB	PolyMAX SSI	TFB Structural
	1 st	10.14	10.22 10.12	1.12	7.85 1.128 Harmonic
	2 nd	14.10	14.46 ---	0.10	0.04 ---Harmonic
	3 rd	---	25.93 ---	---	7.9 ---Harmonic
	4 th	43.69	43.52 ---	0.66	6.12 ---Harmonic
	5 th	63.70	63.67 67.751	2.66	9.05	2.20	Structural
	6 th	184.10	183.1 185.957	0.94	0.72	0.39	Structural

Acknowledgments

This work was sponsored by the French government research program through the IMobS3 Excellence Laboratory, by the European Union through the Regional competitiveness program and by the Auvergne region.


Patricia Paulin

Packkia-Doss

Sylvie Chevallier

Akash Pare

Alain Le- Bail
email: alain.lebail@oniris-nantes.fr

Effect of Supplementation of Wheat Bran on Dough Aeration and Final Bread Volume

Keywords: wheat bran, kneading, rheology, expansion rate, pore size

One of the best ways to increase the fiber content in the diet is supplementing wheat bran in bread. Wheat bran is known to affect the cellular structure of the dough, resulting in lower final bread volume. The present study was focused on analyzing the effect of wheat bran in throughout the bread making process; the results were compared with the recipe without bran. Wheat bran at different level viz., 0%, 8 %, 16% and 24% w/w on flour basis was added into the wheat flour for the preparation of sandwich bread. Results showed that wheat bran significantly (p<0.05) affected the dough kneading parameters. Dough properties like stickiness and extensibility decreased with respect to increase in bran quantity.

Unexpectedly, wheat bran enhanced the aeration of dough during mixing and the expansion rate of dough during fermentation. However, wheat bran decreased the specific volume of bread upto 10.81%, which was confirmed using 2D image analysis method where it was observed as the wheat bran alters the pore size distribution in bread crumb.

Introduction

Wheat bran is rich in fiber and is an important source of fiber for diet and nutritional concerns. Increased intake of dietary fiber plays beneficial role in the reduction of chronic diet related disorders namely cardiovascular disease, obesity, diabetes, colorectal cancer and constipation [START_REF] Lairon | Dietary fiber intake and risk factors for cardiovascular disease in French adults[END_REF][START_REF] Rodriguez | Dietary fibre from vegetable products as source of functional ingredients[END_REF]. In the last two decades, consumers started to prefer dietary fiber rich foods due to its health benefits [START_REF] Drzikova | Dietary fibre-rich oat-based products affect serum lipids, microbiota, formation of short-chain fatty acids and steroids in rats[END_REF].

Wheat is the most commonly available cereal crop in the world and wheat bran is the major by-product from the wheat industry. Generally, white bread prepared from refined wheat flour is consumed in western countries which leads to reduced intake of dietary fiber. The main aim of supplementing wheat bran in bread is to meet the consumer demand, in order to increase the dietary fiber level in bread. Addition of wheat bran considerably affected the dough properties and final bread qualities [START_REF] Ognean | Wheat fiber as additives in reduced calories wheat bread -Part one: Dough and bread properties[END_REF]. Therefore, it is necessary to analyse the effect of wheat bran on dough and bread.

Bread is considered as one of the oldest processed food. Each ingredient used in bread has its own function throughout the bread making process and also it enhances the quality such as freshness, softness and mouth feel of the final product. Therefore, a proper balance of ingredients should be acquired to produce high-quality bread [START_REF] Giannou | Quality and safety characteristics of bread made from frozen dough[END_REF]. Bread making can be divided into three main stages namely kneading, fermentation and baking.

During mixing and kneading, mechanical energy is applied to convert flour mixture and water into a homogenous viscoelastic dough. Effect of fiber in the properties of dough and bread mainly depends on the particle size, composition and amount of fiber added [START_REF] Ktenioudaki | Recent advances in the development of high-fibre baked products[END_REF]. [START_REF] Banu | Effect of the addition of wheat bran stream on dough rheology and bread quality[END_REF] found that different w/w concentration (i.e., 3% to 30%) of wheat bran significantly affected the rheological properties of dough. It has been concluded that refined wheat flour with 25% wheat bran has higher nutritional value with ash content similar to whole wheat flour and causes minimum damage to the bread quality. [START_REF] Calderon-Dominguez | Rheological changes of dough and bread quality prepared from a sweet dough: Effect of temperature and mixing time[END_REF] noted that the mixing time and temperature affected the dough extensible characteristics. The air bubbles incorporated into the dough during mixing acts as a nucleation site during proving. However, at the time of fermentation these bubbles will get inflated with carbon dioxide which gives foam like aerated structure to the dough [START_REF] Bloksma | Dough structure, Dough rheology, and baking quality[END_REF]. The final loaf volume and bread texture depends on the stability and growth of these bubbles [START_REF] Sliwinski | On the relationship between gluten protein composition of wheat flours and large-deformation properties of their doughs[END_REF]. The two basic mechanisms involved in the bubble destabilization during proving are disproportionation and coalescence.

Disproportionation is the process of migration of carbon dioxide present in the smaller bubbles to larger bubbles due to Laplace pressure difference whereas coalescence is caused by the rupture of thin dough films which leads to gas loss and formation of irregular and coarse crumb structure [START_REF] Kokelaar | Surface rheological properties of bread dough components in relation to gas bubble stability[END_REF].

The dough expansion [START_REF] Zhang | Effects of crust constraints on bread expansion and CO 2 release[END_REF] and gas retention capacity [START_REF] Gomez | Effect of dietary fibre on dough rheology and bread quality[END_REF] during fermentation depends on the rheological properties such us elasticity and viscosity of the cell membrane in the dough. Both physical and chemical mechanism of wheat bran involved in affecting the properties of gluten network. The interaction between gluten and fiber prevented free expansion of dough during fermentation [START_REF] Ragaee | Effects of fiber addition on antioxidant capacity and nutritional quality of wheat bread[END_REF].

Different level of wheat bran in the dough affected guten re-aggregation during mixing. This lead to coalescence or disproportionation of the gas cells during fermentation and, hence, resulted in reduced gas retention in the dough. It has been proposed to use coarse bran rather than fine bran because the interaction between fine bran and gluten was more which adversely affected the gluten network [START_REF] Noort | The effect of particle size of wheat bran fractions on bread quality-Evidence for fibre-protein interactions[END_REF]. [START_REF] Zhou | Phytochemicals and antioxidant properties in wheat bran[END_REF] and [START_REF] Li | Effect of water migration between arabinoxylans and gluten on baking quality of whole wheat bread detected by magnetic resonance imaging (MRI)[END_REF] believed that arabinoxylan gels, formed through oxidative gelation of ferulic acid, affected the gluten network and resulted in lower baking quality of bread. [START_REF] Zhang | Wheat bran particle size effects on bread baking performance and quality[END_REF] evidenced that bread with medium particle sized wheat bran (mean particle size 415 µm) had significantly higher specific volume than bread with coarse (609 µm) or fine (278 µm) bran.

Baking is the most important part of bread making process which converts dough into bread by applying heat [START_REF] Khater | Heat and mass balance for baking process[END_REF]. Major role of baking is to alter the sensory properties of foods and to destroy enzymes and microorganisms [START_REF] Fellows | Food Processing Technology-Principles and Practice[END_REF].

While baking, fiber directly interacted with the structural elements of the three dimensional gluten networks and ruptured the starch-gluten matrix resulting in the interconnection of adjacent gas cells [START_REF] Gan | Gas cell stabilisation and gas retention in wheat bread dough[END_REF]. Generally, wheat bran is rich in α-amylase, which directly causes damage to the gluten network and indirectly to the baking quality with the loss of crumb texture and loaf volume [START_REF] Every | Amylase, falling number, polysaccharide, protein and ash relationships in wheat millstreams[END_REF]. When the level of extruded wheat bran increased from 15% to 20% (w/w) in bread, the final volume of bread as well as porosity was decreased [START_REF] Basinskiene | Effects of enzymes and extruded wheat bran in fibre-enriched bread baking[END_REF]. The specific volume of the bread decreased and in turn the density may increase from 0% to 40% depending on the bran type and level of bran added in the recipe as indicated by [START_REF] Katina | Influence of wheat bran on wheat dough rheology and subsequent texture of bread. vancouver Talmud toran[END_REF]. Incorporating fermented wheat bran in bread formulation significantly reduced 20% of the bread's specific volume and also affected the physical and sensory properties of baked bread [START_REF] Hassan | Effect of fermentation and particle size of wheat bran on the antinutritional factors and bread quality[END_REF]. This study aims to asses the effect of adding different levels (w/w) of wheat bran viz., 8%, 16% and 24% to the sandwhich bread. It helps to optimize the formulation by understanding the effect of wheat bran on dough porosity at the end of mixing and kneading process and final bread volume.

Materials and Methods

Raw materials

The wheat flour used for this study T65 (0.62%-0.65% ash content (Whitley, 2006)) was provided by Minoterie Girardeau, France. Other ingredients such as wheat bran, water, salt (Les Salins du Midi -France), sugar (Beghin Say -France), purple ibis (bread flour improver, Lesaffre -France), soybean flour (Giraud -France), milk powder (skimmed milk-Regilait, France), dry yeast (Saf instant, Lesaffre -France) and colza oil (Transgourmet, France) were also added in order to increase the taste and final quality of bread. The formulation protocol of sandwich bread was given in Table 1.

Generally, most of fibrous materials are hydrophilic in nature. An increased level of pentosans in fiber leads to higher water absorption property [START_REF] Hoseney | Functional properties of pentosans in baked foods[END_REF]. [START_REF] Rosell | Influence of hydrocolloids on dough rheology and bread quality[END_REF] stated that the water binding capacity of fiber enriched dough is higher, because, more number of hydroxyl groups present in the fiber structure permits to hold higher amount of water through hydrogen bonds. Therefore, the water required to hydrate the mixture increased with increase in the level of wheat bran added in the recipe. Thus, the amount of water required to be added in the mixture with respect to wheat bran was determined by using the the following formula.

∆ = (1)
Where, ∆ Hydration= 1.041

The delta hydration coefficient value (1.014) was determined by making DSC tests on the free (freezable) water, in order to keep it constant when wheat brans were added to the dough.

LOCATION OF TABLE 1

Dough preparation and bread making procedure

The water temperature was adjusted in such a way that the cumulative sum of the temperature of flour, ambience and water should be equal to 50°C. Prototype planetary spiral mixer (VMI, Montaigu Vendee, France) was used to knead the dough. There were two different stages carried out during dough mixing: slow speed mixing (50 RPM for the gyratory motion of the spiral hook and 6 RPM for the rotational motion of the mixer's bowl)

for 5 min followed by fast speed mixing (120 RPM for the gyratory motion of the spiral hook and 10 RPM for the rotational motion of the mixer's bowl) for 5.35 min. The temperature inside the bowl of the planetary mixer was maintained by cooling water which was supplied by the heat exchanger fixed inside the mixing system. The bowl temperature was fixed as 20°C, to maintain uniform dough temperature at the end of mixing. The power required to mix the dough and dough temperature with respect to mixing time were recorded automatically. The dough development time was determined by overmixing the dough. The optimum time required to develop the dough was found by plotting the graph between dough mixing time and maximum power consumed by mixer during mixing. The optimum mixing time of the dough varied with amount of bran added to the recipe. After mixing, bulk of the dough was divided into small pieces of 560 g (weight determined according to the final weight of bread) and shaped into balls. Then, the shaped dough was allowed to rest for 20 min. This rested dough was sheeted and moulded into rolls by passing through the dough moulder (Deleume L'Artisane® 2004 AVE.BP, Saint-Eloi, France) in order to maintain uniform size and shape of dough, by fixing constant roller speed of 208 RPM and roller gap of 2.8 mm. The moulded dough was fermented (Hengel AR68, Parigny, France) at a temperature of 37°C and 80% relative humidity. Finally, the fermented dough was baked in oven (MIWE CO 1.1208, Germany) at a temperature of 220°C for 20 min.

Wheat bran particle size analysis

A Laser Diffraction Granulometer -Malvern Instrument (SA Parc club de l'universite, France) was used to determine the particle size distribution of the wheat bran. This instrument can detect particle size ranges from 0.06 µ to 3.4 mm. A laser beam is passed through wheat bran to measure the angular variation in intensity of light scattered from the bran (dry channel). The light scattered from larger particle at smaller angle and from smaller particle at larger angle. The particle size was reported as volume equivalent to sphere diameter. A sample of 5 to 10 g was required to carry out the analysis. The results were expressed as D 10 , D 50 and D 90 based on volume distribution of the particles, which represented the size of fine (D 10 ), median (D 50 ) and coarse (D 90 ) bran. From these values, specific surface area and span value of the bran particles were determined. Specific surface area is defined as the total surface area (4πr 2 ) per unit volume of a particle.

Specific surface area (m 2 /m 3 ) =

.

"# $ % & (2)
where r denotes radius of the total volume distribution of the bran fractions and v represents the total volume of the bran. The span value denotes the width of the particle size distribution i.e., distance between two points which are equally spaced from the center of the particle. It was calculated by using the following formula:

Span = ' () ' *) ' +)
(3)

Dough properties

Dough extensibilty

The dough extensibility and resistance to extension was measured by using TA.XTplus Texture Analyser-Stable Microsystems with Kieffer dough and gluten extensibility rig [START_REF] Collar | Optimization of hydrocolloid addition to improve wheat bread dough functionality: a response surface methodology study[END_REF]. After mixing, a small amount of non-yeasted dough was made into small strips of 5 cm length by using dough form and clamp. Then, the dough strip was placed over the sample holder and inserted into the rig. The test was performed with a 5 kg load cell, at a pre-test speed of 2 mm/s, test speed of 3.3 mm/s, post-test speed of 10 mm/s with distance of 80 mm, trigger force of 5 g and trigger distance of 2 mm. Once, the trigger force has been attained the hook, it will start to extend the dough until it gets ruptured.

Thus, the extensibility of dough (mm) (distance to which the maximum force is experienced by the sample before it ruptures) and resistance to extension (N) (Maximum force required to stretch a sample until it ruptures) were determined by using in-built programme in the exponent software. Ten such measurements were taken for each dough sample.

Dough stickiness

The stickiness of the dough was measured by using TA.XT plus Texture Analyser-Stable Microsystems with the SMS/Chen-Hoseney dough stickiness rig (A/DSC) [START_REF] Grausgruber | Analysis of repeated stickiness measures of wheat dough using a texture analyzer[END_REF]. A small amount of dough (1 g) was placed inside the chamber and tightly closed with the lid. Then, the dough was extruded through the small holes in the lid by rotating the internal screw and allowed to rest for 30 seconds. The perspex cap was placed over the sample surface to minimize the moisture loss. Then, the cover was removed and the dough stickiness rig was placed under the 25 mm perspex cylindrical probe attached to the load cell of the texture analyser. Now, the test was performed with a 5 kg load cell, at a pretest speed of 0.5 mm/s, test speed of 0.5 mm/s, post-test speed of 10 mm/s, applied force of 40 g, contact time of 1 s, return distance of 4 mm and trigger force of 5 g. Finally, the dough stickiness values were recorded in the force-time graph. Three parameters such as dough stickiness (N) (measure of maximum positive force), work of adhesion (g.s) (positive area under the curve) and dough strength or cohesiveness (mm) (distance to which the sample is extended towards the returning probe) were measured using the in-built programme in the exponent software. Ten such measurements were taken for each dough sample.

Dough porosity

The apparent density of the dough was determined by using oil displacement method. [START_REF] Ktenioudaki | Monitoring the dynamic density of wheat dough during fermentation[END_REF] proposed a similar method in which dough sample was immersed in xylene using a double cup system. The apparent density measurement setup works on the basis of Archimedes' principle, i.e., oil displacement method. This set up consists of a glass beaker filled with known density of 500 ml of colza oil, a stand to hold the sample inside the oil and a weighing balance. The weight of the dough in air (m 1 ) and in oil (m 2 ) (Colza oil;

density-916 kg/m 3 ) was noted down. From these values, the apparent density of the dough was calculated from the following equation:

Volume of dough (V D ) = , ' -. + , 0 (4)
Volume of gas free dough (, ' -. ) =

1 * 2 3 -.
(5)

Volume of gas (V gas ) =

4 % 5 3 -. ×7 89:

7 89:

(6) Whereas, m 2 = , ' -. ρ < + V 0 ρ < (7)
Where, > ' -. = density of gas free dough, kg/m 3 , ρ oil = density of oil, kg/m 3 , V gas = volume of gas in dough, m 3 , , ' -. = volume of gas free dough, m 3 , V D = volume of dough, m 3

True density of each ingredients in the recipe was measured by using helium gas pycnometer (AccuPyc 1330 Pycnometer, USA). Then, the density of the degassed dough (kg/m 3 ) was calculated by applying mass, m i and density of all the ingredients, ρ i in the following formula:

> '0 =∑(m i *ρ i )/∑m i (8)
True density of the dough without bran and with bran was calculated as 1299.9 kg/m 3 and 1393.5 kg/m 3 . Finally, the dough porosity was determined by using the following equation [START_REF] Sahin | Size, shape, volume and physical attributes[END_REF].

Porosity ? = @ ABC @ ABC D@ E AF = G - I BJJ I E AF (9)
Where, ρ app = Apparent density of dough, g/mL, ρ Dgf = Gas free dough density, g/mL

Dough behavior during fermentation

Fermentation time is the time required to tripling the initial volume of the dough. The dough fermentation time and expansion rate was determined where a small amount of dough was placed in a cylindrical flask and a displacement transducer (VRVT050/E/TM, Penny & Giles, UK) was introduced into the flask which was directly connected with the data logger.

Data logger records the change in height of the dough with respect to time during fermentation. The volume expansion of dough was supposed to be equal to CO 2 produced by the yeast during fermentation assuming that no CO 2 gas escaped from the dough (closed dough porosity, and negligible mass exchange with the ambiance). From these values, a graph showing the volume of CO 2 produced during fermentation as function of time was obtained. The linear expansion phase of the curve was fitted with a linear trendline and the slope gave the rate of expansion of dough during fermentation in cm 3 /min.

Quality characteristics of breads

Specific volume

The baked bread was allowed to cool for an hour. Then, the mass of the bread was noted down and the volume was determined by using volumeter (TexVol BVM-L370LC, France). Bread specific volume (m 3 /kg) was calculated from the ratio of volume and mass of bread.

Bread crumb porosity

The true density of the bread was determined by using helium gas pycnometer and apparent density was determined by the ratio of bread weight and volume. From these values, the bread crumb porosity was calculated.

Bread crumb pore size distibution

The bread was sliced (Sofraca, France) after storing at 4°C for a period of 24 h. The pore size distribution in the bread crumb was analysed by scanning the bread slices with the flat bed scanner (HP Scanjet G4010-HP, USA). This scanner delivers the scanned image instantaneously. The scanned image was analysed by using imageJ® software (version 1.51j8). Initially, the software was calibrated then the image was duplicated and converted into 8 bit gray scale image. A mask for the image was created for applying threshold.

Thresholding is one of the most important step, in which the gray scale image was segmented and converted into binary image. The major advantage of obtaining this binary image is to reduce the complexity of the data and to simplify the process. Auto local threshold method provided in the software was used for image segmentation. Morphological filters such as erosion and dilation, opening and closing were used for structuring the pore area.

Morphological reconstruction is another major part of image processing which was used for constructing an image without altering the original shape of the pores present in the bread slice. Then, particle analysis was performed for analyzing the bread crumb structure. The selected parameters measured in the particle analysis were the pore area and total pore count.

The range of pore size and circularity of the pores to be analysed was fixed and the analysed values were exported to Microsoft Office Excel (version 12.0.6612.1000), to generate primary information of the pores. Atlast, image calculator performed arithmetic and logical operations between the original and duplicate images to deliver a final labelled RGB image in the new window. A macro file was created in order to analyse uniformly and to determine the pore area easily throughout the study.

Statistical analysis

The data was statistically analysed by using one-factor ANOVA with three replication at 5% level of significance with the help of STATGRAPHICS XVI. Fisher's least significant difference (LSD) method was followed to determine significant difference between the bran incorporated recipes.

Results and discussion

Wheat bran particle size distribution

Particle size distribution is the number of particles that can be categorized into various size ranges. The specific surface area and width of the particle was calculated from particle size distribution values (Table 2). The median diameter (D 50 ) of the wheat bran was observed as 750 µm which specifies that 50% of the volume of particles falls greater than the D 50 value and 50% of the volume of particles falls lesser than the D 50 value. [START_REF] Noort | The effect of particle size of wheat bran fractions on bread quality-Evidence for fibre-protein interactions[END_REF] quoted that wheat bran particle surface area increased with decrease in particle size i.e., when the median diameter of the wheat bran (D 50 ) was 491.5 µm and 47.9 µm then the specific surface area was noted as 40,626 m 2 /m 3 and 590,063 m 2 /m 3 . Thus, the increased surface area of bran leads to more interaction between wheat bran and gluten network.

LOCATION TABLE 2

Dough kneading parameters

Dough kneading parameters such as dough development time, maximum power consumption and specific energy input required during mixing (Table 3) were calculated by using the data recorded during mixing.

The dough developmental time is the optimum mixing time at which maximum energy is required to develop the gluten network. The gluten network started to breakdown upon further mixing. The dough developmental time was found to be 5.4 min for the recipe with 0% bran and 7.1 min for the recipe with 24% bran which indicated that the dough developmental time increased with addition of wheat bran. It was also observed that recipe with 24% wheat bran showed significant (p<0.05) change in dough development time.

Similarly, [START_REF] Almeida | Effect of adding different dietary fiber sources on farinographic parameters of wheat flour[END_REF] and Sanz [START_REF] Penella | Effect of wheat bran and enzyme addition on dough functional performance and phytic acid levels in bread[END_REF] stated that the slower water absorption property of wheat bran and dilution of gluten protein, delays the formation of gluten network.

The maximum power consumed during mixing is the maximum power required to develop the gluten network. Further mixing weakened the gluten network which causes linear decrease in the power value. The power consumption depends on the type and amount of bran added in the recipe. Wheat bran disrupts and weakens the gluten structure [START_REF] Pomeranz | Fiber in breadmaking--effects on functional properties[END_REF]. So, less power was required to mix the bran enriched recipe. The power consumption was 322.7 W in the recipe with 0% bran and 281.7 W in the recipe with 24% bran. There was significant (p<0.05) difference between all the recipes except between 16% and 24% wheat bran added recipe. The specific energy input is the energy required to mix per unit mass of the dough. Specific energy input was minimum 419.2 J/kg in the recipe with 24% bran and maximum 554.6 J/kg in the recipe without bran. It was observed that the wheat bran significantly (p<0.05) affected the specific energy input required during mixing.

Dough porosity

The effect of different proportions of wheat bran on dough porosity was given in the Table 3. Dough porosity depends on the type and quantity of ingredients used in the recipe and on the mixing parameters. The highest porosity value was 25.6% observed in recipe with 24% bran and lowest value was observed as 14.6% in the recipe with 0% bran. It could be explained by understanding that once the amount of wheat bran was increased, the dough mixing time and dough porosity after mixing also increased and the specific energy input was decreased. Campbell et al., (2008a) investigated the effect of wheat bran on dough aeration and concluded that the wheat bran increased dough aeration during mixing. This causes an increased air entrapment into the dough and hence results in higher dough porosity.

LOCATION TABLE 3

Dough extensibility and stickiness

Dough extensibility is an important parameter in bread making process. It predetermines the dough raising quality. The values of extensibility (mm) and resistance to extension (N) were measured after mixing. Table 4 indicates that wheat bran decreased dough extensibility and resistance to extension from 64.7 mm to 55.8 mm and 0.32 N to 0.15 N.

Thus, the increased concentration of wheat bran in the recipe reduced the dough strength and extensibility property, by interrupting the starch gluten network (Gómez et al. 2011) and minimized the force required to rupture the dough.

Dough stickiness is a combination of adhesion, the interface between dough and surface, and cohesion, the interface within the dough. Therefore, both surface and rheological properties of dough plays major role in stickiness. Dough stickiness should be considered for handling and machinability property of dough. Table 4 shows that addition of wheat bran to the recipe significantly (p<0.05) reduced the dough stickiness and adhesiveness.

LOCATION TABLE 4

Dough behavior during fermentation

The effect of wheat bran on dough behaviour during fermentation was given in the Table 5. It was observed that the bran decreased dough fermentation time to 58.3 min and increased expansion rate of dough during fermentation to 1.5 cm 3 /min for the recipe with 24% bran. The faster expansion of dough during fermentation was owing to the fact that bran enriched dough produced CO 2 gas more rapidly than the control sample. This might be due to the presence of higher amount of simple sugars, glucose and fructose, as these sugars are consumed earlier by yeast than maltose. [START_REF] Ralet | Influence of extrusion-cooking on the physico-chemical properties of wheat bran[END_REF][START_REF] Macarthur | Comparison of oat and wheat carbohydrates. I. Sugars[END_REF].

Quality characteristics of bread

Specific volume of bread

The bread volume depends on the volume expansion of dough during fermentation and on the baking parameters. The effect of wheat bran on bread specific volume was shown in the Table 5. The maximum specific volume was 3.7 mL/g observed in recipe with 0% bran and minimum volume was 3.3 mL/g observed in recipe with 24% bran. Wheat bran physically disrupted the foam structute of the dough and minimized retention of gas during fermentation. This lead to reduction in bread loaf volume [START_REF] Mosharraf | Effect of hydrothermaled bran on physicochemical, rheological and microstructural characteristics of Sangak bread[END_REF][START_REF] Polaki | Fiber and hydrocolloid content affect the microstructural and sensory characteristics of fresh and frozen stored bread[END_REF][START_REF] Majzoobi | Effect of different levels and particle sizes of wheat bran on the quality of flat bread[END_REF].

Bread crumb porosity

Bread pores are the air pockets present in the baked bread. Carbon-di-oxide created this interconnected void structure during fermentation process. The degree of porosity plays major role in crumb texture and appearance. The crumb porosity was maximum (79.4%) in recipe with 0% bran and minimum (73.7%) in recipe with 24% bran as shown in Table 5. The bread crumb porosity decreased with an increase in bran content. Similar trend was observed in specific volume of bread. [START_REF] Ognean | Technological and sensorial effects of sorghum addition at wheat bread[END_REF] stated that crumb porosity was related to specific volume of bread. Analysis of variance showed that different level of wheat bran significantly affected the bread crumb porosity.

LOCATION TABLE 5

Effect of wheat bran on pore distribution in bread crumb

Scanned 8 bit images of the bread slices with different bran content is shown in Fig. 1a. From these images, it appears clearly that there was differences in the crumb structure. These images were analysed to quantify these differences by calculating the pore surface area distribution in bread crumb. The effect of wheat bran on pore surface area distribution of bread crumb was visualized by plotting mid area of the pores as a function of cumulative frequency of pores present in the surface of bread slice as shown in the Fig. 1b.

From the figure, it was observed that in recipe with 0% bran, most of the pores present in the mid area ranging between 0.5 mm 2 -25.5 mm 2 , whereas in recipe with 24% bran the pores are present in the range of 0.5 mm 2 -44.5 mm 2 . This vast difference in pore surface area distribution may be due to the effect of wheat bran. The bran alters the actual pore structure of the bread crumb. The pore surface area of the bread crumb depends on the turnover of gas during mixing, growth of bubbles during proving and homogeneity of crumb porosity (Campbell et al., 2008b). It was observed that the bread crumb median pore area increased with increase in bran content. The focused segment of cumulative frequency of bread crumb pore surface area was shown in the Fig. 1c. Thus, the pore area distribution varied depending on the amount of wheat bran added in the recipe. [START_REF] Katina | Influence of wheat bran on wheat dough rheology and subsequent texture of bread. vancouver Talmud toran[END_REF] studied the effect of wheat bran in bread crumb morphology by 2D image anlysis and concluded that crumb evenness and finesse were decreased depending on the addition of different level of bran.

Conclusion

From the above work, it was concluded that supplementing different level (w/w) of wheat bran in the recipe significantly (p<0.05) affected the properties of dough and bread.

Wheat bran delayed the formation of gluten network. So, the dough developmental time was increased with 24% bran content. At the same time, bran decreased the maximum power consumed and specific energy input required during mixing by weakening the gluten structure. Wheat bran increased dough aeration during mixing and hence resulted in higher dough porosity which was an unexpected result not shown in previous literature.

Wheat bran reduced dough extensibility and resistance to extension. It has been observed that the recipe with 24% bran content decreased dough stickiness and adhesiveness and increased cohesivess. During fermentation, yeast fed on the fermentable sugars present in the bran and rapidly produced CO 2 gas which resulted in increased expansion rate of dough and reduced fermentation time.

Wheat bran increased the carbondioxide production in dough during fermentation which caused decrease in specific volume of bread and crumb porosity. The specific volume of bread and crumb porosity decreased by 10.81% and 7.18% respectively as compared with the recipe with 0% bran. Similarly, by image analysis method it was confirmed that wheat bran altered the pore area distribution in the bread crumb.

Fig. 1a .Fig. 1c .

 1a1c Fig. 1a. Scanned 8 bit image of bread slice. (A) 0%, (B) 8% (C) 16% and (D) 24% wheat bran

Table 1

 1 Formulation protocol of sandwich bread.

	Ingredients	Recipe 1	Ingredient (g/per 100g of flour) Recipe 2 Recipe 3	Recipe 4
		(0% bran)	(8% bran)	(16% bran)	(24% bran)
	Flour	100.00	100.00	100.00	100.00
	Wheat bran	0.00	8.00	16.00	24.00
	Dry yeast	1.33	1.33	1.33	1.33
	Sugar	4.99	4.99	4.99	4.99
	Salt	1.81	1.81	1.81	1.81
	Powder milk	1.50	1.50	1.50	1.50
	Purple ibis (improver)	0.49	0.49	0.49	0.49
	Soya flour	0.26	0.26	0.26	0.26
	Rapeseeds oil	4.01	4.01	4.01	4.01
	Water	56.06*	64.39*	72.72*	81.04*
	Total	170.44	186.78	203.11	219.43

*Amount of water required to hydrate the bran added dough was determined using ∆ Hydration coefficient (calculated as 1.014)

Table 2

 2 Wheat bran particle size distribution, specific surface area and span value.

		D 10 (µm)	D 50 (µm)	(µm) D 90	particle (m 2 /m 3 ) Specific surface area of	Span
	Wheat bran	130±21.65 750±16.19 1873±34.72	51,545	2.32

Table 3

 3 Effect of wheat bran on dough kneading properties and porosity

	Wheat	Dough development	Maximum power	Specific energy	Dough
	bran (%)	time(min)	consumption (W)	input (J/kg)	porosity (%)
	0	5.4±0.2 a	322.7±6.4 a	554.6±14.3 a	14.6±0.00 a
	8	5.4±0.1 a	303.0±7.9 b	480.1±13.1 b	18.6±0.01 b
	16	5.6±0.3 a	290.3±2.5 c	442.5±18.8 c	23.9±0.02 c
	24	7.1±0.1 b	281.7±3.8 c	419.2±26.7 c	25.6±0.01 c
	Values are expressed as Mean ± standard deviation followed by same letters are not significantly
	different (p > 0.05)

Table 4

 4 Effect of wheat bran on dough extensibility and stickiness

	Wheat	Resistance to	Extensibility	Stickiness	Dough strength/	Work of
	bran (%)	extension, force	(mm)	(N)	Cohesiveness	Adhesion (g.
		(N)			(mm)	s)
	0	0.32±0.03 a	60.9±8.2 a	0.59±0.02 a	1.9±0.2 a	5.4±0.6 a
	8	0.22±0.02 b	64.7±3.1 a	0.50±0.02 b	1.9±0.2 a	4.8±0.7 b
	16	0.17±0.02 c	62.6±3.1 a	0.45±0.02 c	2.1±0.2 a	4.5±0.5 bc
	24	0.15±0.02 c	55.8±5.5 b	0.38±0.03 d	2.4±0.3 b	4.1±0.6 c
	Values are expressed as Mean ± standard deviation followed by same letters are not significantly
	different (p > 0.05)

Table 5

 5 Effect of wheat bran on dough fermentation and bread quality

	Wheat	Fermentation	Expansion rate	Bread specific volume	Bread crumb
	bran (%)	time (min)	(cm 3 /min)	(mL/g)	porosity (%)
	0	65.9±1.5 a	1.4 ±0.03 a	3.7±0.12 a	79.4±0.003 a
	8	66.6±0.7 a	1.4 ±0.02 a	3.5±0.05 b	77.9±0.004 b
	16	62.9±1.3 b	1.5 ±0.04 ab	3.4±0.02 b	76.6±0.006 c
	24	58.3±1.8 c	1.5±0.05 b	3.3±0.04 c	73.7±0.002

d

Values are expressed as Mean ± standard deviation followed by same letters are not significantly different (p > 0.05)

Acknowledgement

This publication is financially supported by ONIRIS-GEPEA and by the "MIXI-LAB" ANR project (LABCOM "MIXI-LAB" -Project ANR-15-LCV3-0006-01). Thanks to Luc Guihard for his technical assistance for this entire research work.