Clarissa Detomi De Albuquerque

Sébastien Curet
email: sebastien.curet@oniris-nantes.fr

Lionel Boillereaux

Clarissa Detomi

D E Albuquerque

microbial inactivation of pasteurized food products

Keywords: Microbial inactivation, Modeling, 3D-CFD, Heat transfer, Pasteurization. Nomenclature

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Prediction of microbial inactivation during thermal treatment is crucial not only to ensure the safety of food products but also to avoid overtreatment (Boillereaux, Curet, Hamoud-Agha, & Simonin, 2013). Although conventional processing technologies produce safe products, they can also lead to significant changes to the sensory and nutritional attributes of foods. In today's quality conscious world, much attention is given to producing foods that retain superior sensorial quality but must also remain safe [START_REF] Bott | Modelling microorganisms in food[END_REF][START_REF] Stratakos | Suitability, efficiency and microbiological safety of novel physical technologies for the processing of ready-to-eat meals, meats and pumpable products[END_REF].

Consequently, the determination of the microbial destruction level that a thermal treatment can deliver to a product requires both understanding of: (i) the heat transfer within the food product and (ii) the destruction kinetics of the microorganism of interest [START_REF] Valdramidis | Development of predictive modelling approaches for surface temperature and associated microbiological inactivation during hot dry air decontamination[END_REF]. The necessity of such an approach is reinforced regarding the thermal heterogeneities that occur during a pasteurization process.

A reliable inactivation parameter estimation is essential for building predictive models [START_REF] Chen | Estimating microbial survival parameters from dynamic survival data using Microsoft Excel[END_REF] and is necessary to establish safe minimal cooking-cooling conditions [START_REF] Marcotte | Modelling of cooking-cooling processes for meat and poultry products[END_REF]. The determination of these parameters has been the subject of numerous studies, either under static [START_REF] Ahmed | Heat-resistance of Escherichia Coli O157:H7 in meat and poultry as affected by product composition[END_REF][START_REF] Smith | Thermal inactivation of Salmonella spp., Salmonella typhimurium DT104 , and Escherichia coli O157 : H7 in ground beef[END_REF] or dynamic conditions [START_REF] Garre | Mathematical quantification of the induced stress resistance of microbial populations during non-isothermal stresses[END_REF][START_REF] Hassani | Induced thermotolerance under nonisothermal treatments of a heat sensitive and a resistant strain of Staphylococcus aureus in media of different pH[END_REF][START_REF] Juneja | Characterizing asymptotic D -values for Salmonella spp . subjected to different heating rates in sous-vide cooked beef[END_REF][START_REF] Valdramidis | Microbial dynamics versus mathematical model dynamics: The case of microbial heat resistance induction[END_REF][START_REF] Valdramidis | Stress-adaptive responses by heat under the microscope of predictive microbiology[END_REF]. Different models have also been proposed to describe the microbial inactivation kinetics.

Numerous studies focusing on the estimation of microbial kinetic parameters are performed in isothermal and dynamic conditions, but under ideal laboratory conditions (capillary tubes, liquid inoculum and small sample mass). In real time industrial processing, food products are often submitted to time-varying temperatures. Moreover, due to several factors affecting heat transfer, i.e. geometry, thermophysical properties or external convection, significant thermal heterogeneities can occur. In the literature, the effect of capillary tube diameters on the inactivation parameters of E. Coli, in solid food, was investigated from a two-dimensional heat transfer approach [START_REF] Chung | Influence of heat transfer with tube methods on measured thermal inactivation parameters for Escherichia coli[END_REF].

A recent study by [START_REF] Garre | Mathematical quantification of the induced stress resistance of microbial populations during non-isothermal stresses[END_REF] Another investigation, Hamoud-Agha, Curet, Simonin, & Boillereaux, (2013) proposed to estimate Dref and z-value of [START_REF] Bigelow | The logarithmic nature of thermal death time curves[END_REF] equation. These parameters were estimated for E. coli K12 inoculated into a model food under dynamic conditions (homogeneous temperature distribution due to a very small solid sample). These parameters were included in the [START_REF] Geeraerd | Structural model requirements to describe microbial inactivation during a mild heat treatment[END_REF] inactivation model coupled with a 3D-heat transfer model under microwaves to illustrate the resulting inactivation heterogeneity.

The literature is rich concerning CFD and heat transfer modelling dedicated to pasteurization of food products [START_REF] Bhuvaneswari | Heat transfer analysis of pasteurization of bottled beer in a tunnel pasteurizer using computational fluid dynamics[END_REF][START_REF] Cordioli | Computational fluid dynamics (CFD) modelling and experimental validation of thermal processing of canned fruit salad in glass jar[END_REF][START_REF] Denys | Combined CFD and experimental approach for determination of the surface heat transfer of the surface heat transfer coefficient during thermal processing of eggs[END_REF][START_REF] Dimou | 3D numerical simulation of asparagus sterilization in a still can using computational fluid dynamics[END_REF].

However, except in [START_REF] Hamoud-Agha | Microwave inactivation of Escherichia coli K12 CIP 54.117 in a gel medium: experimental and numerical study[END_REF], it is rare to find these models coupled with microbial inactivation equations and even less with real products under external dynamic conditions. These elements reflect the originality of this work proposal. In this study, a 3D-CFD and heat transfer approach is coupled with the [START_REF] Geeraerd | Structural model requirements to describe microbial inactivation during a mild heat treatment[END_REF] inactivation model.

External temperature conditions (heating rates from 1 to 13 °C/min) are applied to

Escherichia coli K12 inactivation in pre-packed ground beef. Model parameters are estimated from experimental data using the Levenberg-Marquardt algorithm [START_REF] Marquardt | An algorithm for least-squares estimation of nonlinear parameters[END_REF].

Materials and methods

Experimental section

Sample preparation

Raw ground beef (Beef Steak 5% fat, Charal ® , Cholet, France) was obtained from a local retail store. The proportion of lean meat and fat was determined on the basis of information on the package. Ground beef was provided in vacuum-packed portions of 100 g. The samples were quickly frozen in a -20 °C freezer (Servathin, Carrières-sous-Poissy, France) and stored in a freezing chamber at -20 ± 1 °C. Before each experiment, a bag of the sample was thawed for 18 h in a cooling chamber (4 °C).

Preparation of bacteria inoculum

The strain of Escherichia coli K12 (CIP 54.117,N° 11.612) used in this study was provided by the Pasteur Institute, France, in the form of a freeze-dried sample. The strain was rehydrated in 0.2 mL and inoculated in 5 mL of Brain Heart Infusion (BHI) (Panreac Applichem -Darmstadt, Germany) at 37 °C for 18 h at 150 rpm (Incubator Shaker Series -Model Excella E24, New Jersey, USA). A pre-culture was prepared with 0.1 mL of the preceding suspension and inoculated in blood agar (Columbia + 5% Sheep blood -Bio-Rad -Marnes-la-Coquette, France) at 37 °C, for 18 h. A clone of the pre-culture was then dissolved in 2 mL of sterile water (0.85% NaCl -Medium, BioMerieux). 0.1 mL of this solution was isolated in several Petri dishes of the blood agar with a sterile Pasteur pipette and incubated under the same conditions. The colonies of this second isolation were removed with a sterile swab and dissolved in 1 mL of the beef serum (Eurobio Serum Bovin, France) supplemented with 20% (v/v) glycerol and stored frozen at -80 °C (Thermo Scientific TSE -USA).

To reactivate the strain, 0.1 mL of the stocked culture was inoculated into the blood agar

Inoculation of ground beef

Raw ground beef samples (36 g) were removed aseptically from their refrigerated packaging (7 °C) and transferred into sterile polypropylene sampling pouches with 4 g of bacterial suspension. Ground beef was mixed manually to evenly distribute the inoculum throughout the sample, producing an initial cell concentration of approximately 10⁷ CFU/g. For heat treatment, 3 g of inoculated ground beef samples were aseptically placed in packaging of 11.6 mm wide x 60 mm long.

Heat treatment for microbial inactivation

As illustrated in Figure 1, each sample was inserted into the cylindrical vessel and submitted to a linear heating ramp in a programmable Peltier-based effect water bath (Hart Scientific AOIP, FC 9105, USA). After the heating treatment, the sample was quickly cooled to 20 °C by ice immersion. The temperature was measured with a thermocouple (type-K) positioned near the bottom of the sample. The data were collected by a data logger (AOIP Datalog, 91133 Ris Orangis, France) with output recordings every 1 second. To obtain the inactivation kinetics curves, eight final temperatures (50, 52, 54, 56, 58, 60, 62 and 64 °C) were considered. Six different heating rates (1, 3, 5, 7, 10 and 13 °C/min) were applied. Triplicates were carried out for each experiment.

Microbial enumeration and expression of results

The method of analysis was according to ISO 16649-1 (2001). This standard procedure specifies a horizontal method for the enumeration of β-glucuronidase-positive Escherichia coli in products intended for human consumption. It is based on a colony-count technique at 44 °C in a solid medium containing a chromogenic ingredient for detection of the βglucuronidase enzyme (ISO 16649-1, 2001).

Untreated inoculated ground beef and treated ground beef were aseptically removed from the packaging and transferred to a sterile filtered stomacher bag (400 mL, BagPage, St. Nom, France). The samples were diluted with 1:9 (w/w) of buffered peptone water (BK131HA, Biokar Diagnostics, France) and stomached for 2 min at 230 rpm (Stomacher Lab Blender InterscienceBagMixer® -400, Grosseron, St. Herblain, France). Decimal serial dilutions were performed in buffered peptone water. 1 mL of the initial suspension or decimal dilution was inoculated onto duplicate plates of tryptone-bile-glucuronic (TBX) medium (BK146HA, Biokar Diagnostics, France) and incubated between 18 h to 24 h at 44 °C. The presence of blue colonies is considered to be β-glucuronidase-positive Escherichia coli.

Each dish containing less than 150 typical CFU was numbered and the result was calculated as the arithmetic mean from two parallel plates. The results expressed to the base 10 logarithm of the microbial population for each temperature measurement were collected. The detection limit was fixed to 1 CFU/g of the ground beef.

Model design section

Heat transfer and computational fluid dynamics (CFD)

The experimental methodology described in section 2.1.4 was considered in a CFD-heat transfer model, with the following assumptions:

• Assumption 1: Water in the cylindrical vessel and the packaged sample were at the same initial homogeneous temperature (7 °C).

• Assumption 2: The ground beef sample was considered as being homogeneous and isotropic.

• Assumption 3: Specific heat, density and thermal conductivity of the ground beef were considered as being constant within the temperature range [START_REF] Pan | Physical and thermal properties of ground beef during cooking[END_REF][START_REF] Tsai | Thermal properties of restructured beef products at different isothermal temperatures[END_REF].

• Assumption 4: The geometry of the packaged sample was an ellipsoid-cylinder and the cross-section was considered homogeneous along the z-coordinate.

• Assumption 5: Axial symmetry was supposed for both thermal problems and fluid mechanics, limiting the size to one quarter of the experimental apparatus.

• Assumption 6: The mass transfer and the shrinkage of the sample were assumed to be negligible (in-package thermal treatment).

• Assumption 7: The package thickness was sufficiently thin to neglect its impact on the heat transfer (low thermal resistance).

The model is aimed at predicting the temperature profile when a heating rate is applied to the wall of the water bath heating cell. The model consists of two main computational domains:

the first relates to liquid water which partially fills the cylindrical cell, the second is the ground beef sample located at the centre of the cell. As illustrated in Figure 2, the sample was immersed in water and its upper surface was exposed to air surrounding the medium (natural convection). The lateral surfaces of the cylinder are modelled with a first type boundary condition (T = Tcylinder cell).

Heat transfer modelling

Heat transfer is based on the general heat equation, which depends on thermophysical properties. For the ground beef sample, the heat equation is reduced to:

( ) ( ) ( ) ( ) sample p sample sample T C div T t ρ λ ∂ = ∇ ∂ (1)
For liquid water, a convective term is added to take into account the variation of the thermophysical properties of water as a function of temperature:

( ) ( ) ( ) ( ) ( ) ( ) water p water water p water water T C C u T div T t ρ ρ λ ∂ + ∇ = ∇ ∂ r (2)
Where u r is the velocity field at any point of the 3D sample domain (u, v, w components).

The initial and boundary conditions are defined with the following mathematical expressions:

( ) 0 0 , 0, , , , 7 T T t x y z T C = = ∀ = ° (3) ( ) ( / ) ( ), , , , 0, 20 sample water air z L T h T T x y t T C z λ ∞ ∞ = ∂ = - ∀ ∀ > = °∂ (4)
The boundary condition expressed by equation 4 is applied to the upper surface of water and sample.

[ ] [ ] 0 ( , ) 0; 0, 0 , , 0, 0,

cylindrical cell for x y R and Z t dT T T t dt for x y R t for Z L  ∈ = ∀ >  = +  = ∀ > ∈   (5)
Figure 2 displays the coordinate system used to describe these mathematical expressions.

The convective heat transfer coefficient is due to natural convection between the surrounding air medium and the upper surface of the product. Free convection occurs only at the upper surface of both the water and the sample (Figure 2). An empirical correlation was conducted to compute the heat transfer coefficient on the upper cooled side of the cylinder. The air heat transfer coefficient (hair) at the surface of the vertical cylinder with its axis running vertically is the same as that from a vertical plate, so long as the thermal boundary layer is thin [START_REF] Churchill | Correlating equations for laminar and turbulent free convection from a vertical plate[END_REF].

Fluid flow modelling

The fluid flow is due to natural convection occurring within the liquid water. This convective flow is modelled from the Navier Stokes equations (Newtonian fluid with incompressible flow). Both the continuity and momentum equations are described as follows: 0 ( ) ( )

water water water u v w continuity x y z du g P u momentum dt ρ ρ µ ∂ ∂ ∂  + + =  ∂ ∂ ∂    = -∇ + ∆   r r (6) with (
)

du u u u dt t ∂ = + ∇ ∂ r r r r (7)
The associated initial and boundary conditions are given as:

[ ] 0 , , , 0 ;
a t m w a t e r P P g z a t t x y f o r z L

ρ = + = ∀ ∈ (8) [ ] 0 , , 0, 0; u at R x y t for z L = = ∀ > ∈ r (9)
For validation purposes, coupled heat transfer and fluid flow were solved for a heating rate of 10 °C /min to compare the simulation to experimental results in the same conditions.

Thermophysical properties

Thermophysical properties of the sample used for modelling purposes are listed in Table 1.

The apparent specific heat (Cp) of ground beef (5% fat) was determined using a differential scanning calorimetry (μDSC VII Evo -Setaram Instrumentations, France) at a constant pressure. Samples (~ 500 mg) were weighed in aluminium cells and sealed. A hermetically sealed empty cell was used as a reference. The samples were equilibrated at 20 °C and then heated to 65 °C, at a heating rate of 1 °C/min. The measurements were made in triplicate and the average values were determined.

The density and the thermal conductivity of sample were taken from [START_REF] Pan | Physical and thermal properties of ground beef during cooking[END_REF]. The authors reported densities of 1006-1033 kg/m 3 and thermal conductivities of 0.35-0.41 W m -1 °C-1 of ground beef (4.8% fat). These values do not change significantly as a function of temperature range (between 5 and 75 °C).

Thermophysical properties of water were considered as temperature dependent [START_REF] Green | Perry's Chemical Engineering Handbook (Eighth)[END_REF]. These properties were directly implemented in the model using polynomial interpolation functions (Table 2).

Computational details for CFD simulations

Based on the finite element method, the model solved the partial differential equations numerically, with the computational code COMSOL ® Multiphysics 5.3a. In this study, the geometry consisted of irregular shapes (ellipsoid-cylinder with axial symmetry) justifying the use of the finite element method to simulate the coupled CFD-heat transfer phenomenon.

As shown in Figure 3, the generated mesh was designed for a 3D configuration, to ensure good accuracy for the numerical resolution (522 830 tetrahedral elements with appropriate boundary layers at the near-cell wall zone). Additional mesh sensitivity studies were performed to obtain accurate number of mesh elements in relation to the computational cost.

The mesh independency to the numerical results was also verified. First, the fluid flow was solved as a stationary study in order to initialize the velocity field for a better consistent initial value.

Due to the temperature-dependent thermophysical properties of water, the coupled equations (Eq. 6 and 7) within fluid and heat transfer within the meat sample (Eq. 1) and water (Eq. 2)

were solved as a time-dependent study with a direct solver (strong coupling). To ensure a good consistent initial value, the initial time step for the resolution was fixed at 10 -7 s. Based on these computational details, the computational time did not exceed 4 hours 9 minutes and 12 seconds on a Dell ® Precision TM Workstation computer, equipped with 2×Intel ® Xeon processors (8 cores), at 2.5 GHz, with 256 GB of RAM, running on Windows ® 8 Professional, 64 bits.

Several simulations were performed depending on the heating rate being tested (from 1 to 13 °C/min).

Microbial inactivation model

The dynamic non log-linear model developed by [START_REF] Geeraerd | Structural model requirements to describe microbial inactivation during a mild heat treatment[END_REF] was selected in this study. The reduced model, without the tailing effect, consists of two coupled ordinary differential equations as follows:

max 1 1 c dN k N dt C   = -   +   (10) max c c dC k C dt = - (11)
where N represents the microbial cell density (CFU/g). In equation 10, dN dt is forced to zero once the limit of detection (1 CFU/g) is reached. kmax denotes the specific inactivation rate (s - 1 ) and Cc is related to the physiological state of cells (-). A single value for Cc(0) can be used for all experiments, since the methodology of inoculation is standardized. Consequently, a similar initial physiological state is expected for cells

The temperature effect on kinetic parameters (kmax) was expressed using [START_REF] Bigelow | The logarithmic nature of thermal death time curves[END_REF] equation:

( ) ( ) max ln10 ln10 exp ref ref k T T T D z   = -     (12)
where Dref denotes the decimal reduction time (s) at the reference temperature Tref and z-value the thermal resistance constant (°C).

Parameter estimation procedure

The approach consists in estimating the model parameters of Equation 12 by minimizing the residual sum of squares (RSS):

( ) ( )

2 exp 1 0 0 1 og log n i simu i i N t dv N t v RSS l N N =     = -       ∫ ∑ (13)
where ti are the times at which the thermocouple located in the sample reached the expected the temperatures (respectively 50, 52, 54, 56, 58, 60, 62 and 64 °C), n is the number of experimental data points, Nsimu is the volume-averaged value of the simulated cell density at ti. Nexp is the volume-averaged value of the triplicate experimental cell density at ti. V is the volume of the sample and N0 is the initial cell density.

To minimize the RSS, the Levenberg-Marquardt algorithm (lsqnonlin available in Matlab ® 7.10) was used. This algorithm is based on the least-squares minimization technique and is an improvement of the Gauss-Newton algorithm. In addition, confidence intervals at 95% were calculated for the estimated parameters using nlparci function available in Matlab ® 7.10.

Methodology used for model comparison and selection

Considering the model proposed by [START_REF] Geeraerd | Structural model requirements to describe microbial inactivation during a mild heat treatment[END_REF], it appears that parameters Cc(0), Dref and z-value can be estimated to fit the experimental data. Nevertheless, it is necessary to evaluate the relevance of the number of adjusted parameters. In this contribution, a non-linear least squares approach based on minimization of the RSS (residual sum of squares) is used. It evaluates the well-known Akaike Information Criterion (AIC) [START_REF] Akaike | A New Look at the Statistical Model Identification[END_REF], which provides a means for model selection. AIC is established as follows, on the assumption that the residues distribution is Gaussian:

2 2 ln ( ) AIC p LL = - ( 14
)
Where p is number of independently adjusted parameters within the model and LL is the maximum likelihood. If the AIC criterion is used only with the aim of comparing models, the following simplified expression is used:

* 2 2 RSS AIC p σ = + (15)
When the residual sum of squares are almost equal for two different models, the minimum AIC estimation selects the model with the lowest number of parameters according to the "principle of parsimony" [START_REF] Yamaoka | Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations[END_REF]. In addition, a variance σ 2 was calculated for each heating rate from the experimental results obtained in triplicate.

Results and discussion

Specific heat of ground beef

The apparent specific heat (Cp) of ground beef at 5% fat was determined from 20 to 65 °C. As can be observed in Figure 4, the Cp values were found to be quite constant on the temperature range. The average measured value (Cp = 3.69 ± 0.01 kJ kg -1 K -1 ) is close to the reported one in the literature for meat product [START_REF] Zhang | Dielectric and thermophysical properties of meat batters over a temperature range of 5-85 °C[END_REF]). The Cp average value was integrated directly into the heat transfer model.

Heat transfer and CFD model validation

To validate the modelling approach, the experimental temperatures measured as a function of time in both the water and the sample were compared to the one simulated from the CFD-heat transfer model. Figure 5 illustrates the case for a heat treatment at 10 °C/min. The standard deviation of the experimental data was about ± 0.3 °C, corresponding to the measuring accuracy of the thermocouple used. Figure 5 depicts the linear setting temperature. The agreement between experimental and model temperatures of the water and the sample is noticeable. Non-negligible thermal delays (about 26 s and 57 s) can be observed respectively between the water and the sample and the set temperature and the sample, justifying the interest of such an approach when heating rates are applied.

The CFD modelling enabled prediction of the velocity fields due to the natural convection problem within a static enclosure. The Navier-Stokes equation was solved by considering a laminar flow. The norm of the velocity field, for example at 10 °C/min, is depicted in Figure 6 for different processing times. Fluid motion occurs (illustrated by black arrows in Figure 6), because of the temperature dependent thermophysical properties of water. Recirculation of the heating fluid leads to velocity gradients, with a maximum value of 2.5 mm/s around the sample (norm of the velocity field). The Reynold number was calculated considering this maximum fluid velocity of 10 °C/min with a value of 9.4. This result clearly demonstrates a natural convection of water within a confined space, in which the flow regime is laminar.

Once the CFD approach is validated, the model illustrates the temperature gradients when heating rates are applied. Figure 7 illustrates the temperatures at the final time of simulation for the six different heating rates considered. Simulation was stopped when the simulated temperature of the corresponding point to the thermocouple position reached 64 °C.

For all treatments the hottest zone was located at the surface of the sample, except for the upper central part, due to contact with ambient air (cooling). The coldest zone was along the central axis of the product. The temperature gap between the hottest and coldest zones increased with the applied heating rates, so 4.3, 6.2, 8.1, 9.8, 12.4, and 15.8 °C for 1, 3, 5, 7, 10 and 13 °C/min, respectively.

To confirm the effect of temperature heterogeneities on the volumetric microbial inactivation, equations 10, 11 and 12 were used. The simulation of the 3D local temperature distribution in the meat sample (CFD and heat transfer model solved by COMSOL ® and transposed to MATLAB ® ) was performed to quantify the 3D local microbial inactivation. For example, under a thermal treatment at 10 °C/min, the numerical result illustrated a thermal cartography with hot and cold points. These temperature heterogeneities lead to heterogeneous local microbial inactivation represented by the hatched area in Figure 8.

In Figure 8, a total inactivation can be seen for the hottest point after 5.8 minutes. For the coldest point, the maximum inactivation achieved was of 2 Log10, at the end of treatment. However, it is not possible to experimentally validate these local microbial inactivation data, at every geometrical point of the domain. Consequently, the use of a mathematical model to describe the local microbial inactivation during a pasteurization process remains a relevant tool. More, it confirms the importance of considering the volumetric distribution of temperature in the meat product to accurately predict microbial inactivation.

Estimation of kinetic parameters of inactivation

Various results can be found in the literature concerning evaluation of kinetic parameters (Dref, Tref and z-value) under isothermal conditions. In a study of heat resistance of Escherichia coli 0157:H7 inoculated into 7% fat ground beef, [START_REF] Ahmed | Heat-resistance of Escherichia Coli O157:H7 in meat and poultry as affected by product composition[END_REF]

obtained a

Dref, by linear regression, of 684 s (at 55 °C) and 27 s (at 60 °C), and a z-value equal to 4.78 °C. In the same strain, but in ground beef with 4.8% fat, [START_REF] Smith | Thermal inactivation of Salmonella spp., Salmonella typhimurium DT104 , and Escherichia coli O157 : H7 in ground beef[END_REF] calculated by first-order kinetics a Dref equal to 73 s (at 58 °C) and 19 s (at 61 °C), for a z-value of 3.79 °C.

These variations might be attributed to differences in the strains of E. coli used, the fat content of meat, along with differences in recovery methods and sample size [START_REF] Smith | Thermal inactivation of Salmonella spp., Salmonella typhimurium DT104 , and Escherichia coli O157 : H7 in ground beef[END_REF][START_REF] Stringer | Thermal inactivation of Escherichia coli 0157 : H7[END_REF]. In both studies, the values of Dref differ greatly whereas zvalue remains in the same order of magnitude.

In this study, ground beef with 5% fat was used and the heating rates described above were applied. Despite these dynamical conditions, in a first approach, the parameters Dref = 73 s, zvalue = 3.79 °C and Tref = 58 °C obtained by [START_REF] Smith | Thermal inactivation of Salmonella spp., Salmonella typhimurium DT104 , and Escherichia coli O157 : H7 in ground beef[END_REF] in isothermal conditions and A second approach consisted in estimating the model parameters from the dynamic experiments. First, the triplet parameters Cc(0), Dref and z-value were estimated. [START_REF] Poschet | Assessing the optimal experiment setup for first order kinetic studies by Monte Carlo analysis[END_REF] showed that the uncertainties on Dref and z-value are minimal when Tref is equal to the middle of the lethal experimental temperature range. As Dref is closely linked to Tref, this value was fixed at 58 °C, as defined in [START_REF] Smith | Thermal inactivation of Salmonella spp., Salmonella typhimurium DT104 , and Escherichia coli O157 : H7 in ground beef[END_REF]. The Cc(0) value was estimated with a lower bound equal to zero. In table 3, Cc(0) is not significantly different from zero for all heating rates. Consequently, equation ( 11) can be removed and the [START_REF] Geeraerd | Structural model requirements to describe microbial inactivation during a mild heat treatment[END_REF] model becomes a classical first order inactivation model.

With the Bigelow model, the Dref and z-values were estimated (Table 4). It should be noted that these AIC * values are lower than the AIC * values obtained in previous estimate procedure with three parameters (Table 3). The confidence intervals for Dref increase for extreme heating rates (10 and 13 °C/min), certainly due to the large thermal heterogeneities. Another explanation is based on [START_REF] Van Derlinden | Progress on quantitative approaches of thermal food processing[END_REF], where the authors discussed the link between parameter identifiability and the shape of the temperature profile.

The results of z-value indicated in Table 4 are consistent with those presented by [START_REF] Smith | Thermal inactivation of Salmonella spp., Salmonella typhimurium DT104 , and Escherichia coli O157 : H7 in ground beef[END_REF], for a similar product at 58 °C (z-value = 3.79 °C). Indeed, this value (3.79 °C) is almost included in the confidence intervals and will thus be considered hereafter.

With the estimation limited to Dref, the confidence intervals are widely reduced, even if they remain significant for heating rates of 10 and 13 °C /min (12.52 s and 8.75 s). These results are summarized in Table 5. Compared to Thereby, for one estimated parameter, the Dref values remain in the same order of magnitude.

Consequently, the average of the previously estimated Dref values (Table 5) of 47 s was used.

The predicted inactivation curves obtained in this case are presented in black continuous line in Figure 9. They are very close to inactivation results obtained with each estimated Dref (red dotted line is superimposed on black continuous line in figure 9). Hence, for Dref values ranging from 40 to 50 s, considering a unique value of 47 s does not significantly affect the inactivation predictions.

Finally, results reveal that the kmax expression with temperature dependence proposed by

Bigelow is sufficient to fit experimental data obtained in dynamic conditions with large temperature heterogeneities (>15 °C for 13 °C /min), provided that these heterogeneities are considered in the prediction model. Constant values for Tref, z and Dref can be considered for all heating rates in the range 1 to 13 °C /min. Additionally, Tref and z-values of literature [START_REF] Smith | Thermal inactivation of Salmonella spp., Salmonella typhimurium DT104 , and Escherichia coli O157 : H7 in ground beef[END_REF] were used with only a fitting of Dref.

Conclusion

The contribution of this work lies in the integration of experimental and numerical approaches dedicated to the real-food pasteurization process including both thermal and inactivation kinetics with high heating rates.

The originality of this paper lies (i) in the integration of three-dimensional heat transfer conditions in parameter estimations applied to the inactivation model and (ii) in the application of extreme heating rates to challenge the validity of the Geeraerd modelling approach developed for isothermal conditions. The heat transfer model was validated during blank tests, and the volume-average of logarithmic inactivation was considered to be comparable with experimental data. In addition, the experiments were carried out on real ground beef, and not in capillary tubes, as usually proposed. The inactivation model was coupled with heat transfer to design a 3D simulator to predict the spatial distribution of logarithmic inactivation of E. coli in a meat sample.

Such a 3D simulator illustrates how the temperature gradients can provoke large inactivation heterogeneities, and thus the necessity to consider them in realistic pasteurization conditions.

The satisfactory agreement between model and experiments allows coupling of the modelling approach with process control procedures, allowing energy supply to reach the expected microbial inactivation while limiting temperature heterogeneities. Such an objective could be reached for example by using combined energy sources with volumetric heating to avoid overtreatment at the surface of the food product (microwaves, ohmic heating).

  presented a mathematical model to describe the nonisothermal microbial inactivation process. The model was dedicated to Escherichia coli inactivation under linear heating rates of 1, 5, 15, 35 and 40 °C/min, from 35 to 70 °C. The authors developed a mathematical model taking into account the thermotolerance of the microbial cells. The model was able to describe the experimental data using a unique set of model parameters.

Cc( 0 )

 0 = 0.23, as reported in[START_REF] Hamoud-Agha | Microwave inactivation of Escherichia coli K12 CIP 54.117 in a gel medium: experimental and numerical study[END_REF], were used in the[START_REF] Geeraerd | Structural model requirements to describe microbial inactivation during a mild heat treatment[END_REF] model. Results are illustrated in Figure9. Black circles illustrate the experimental data in triplicate, whereas the predictions obtained using the aforementioned parameters are represented by broken blue lines. Note that the model underestimates the experimental inactivations, but this underestimation has the same order of magnitude whatever the heating rate.

  Table 4, quite similar or lower values of the AIC * were obtained. It appears that adjusting Dref is sufficient to fit the experiments without degrading the AIC* values. Moreover, it also reduces the parameter uncertainties as verified with 95% confidence intervals.

Table 1 .

 1 Thermophysical properties of ground beef.

	Properties	Value	Unit	Source
	Apparent specific heat (Cp) 3.69 ± 0.01	kJ kg -1 K -1	Experimental
	Thermal conductivity (λ)	0.35	W m -1 °C-1	Pan and Singh (2001).
	Density (ρ)	1006	kg/m 3	Pan and Singh (2001).

Table 2 .

 2 Thermophysical properties of water as a function of temperature (Kelvin).

	Functions of the properties*

Table 3 .

 3 Estimated values of Dref, z-value and Cc(0), for Tref = 58 °C. Standard deviation of experimental data (σ 2 ), 95% confidence intervals (CI) and simplified Akaike Information Criterion (AIC * ).

	Heating rate (°C /min)	σ 2	Dref (s) CI Dref	z-value (°C)	CI z-value	Cc(0)	CI Cc(0)	AIC *
	1	0.24	33.1	3.8	1.96	1.41	0	0.30	11.8
	3	0.20	48.9	6.8	3.81	0.63	0	0.21	12.2
	5	0.23	41.7	5.3	7.32	1.53	0.11	0.15	10.2
	7	0.34	45.7	8.1	3.59	0.61	0.21	0.18	9.3
	10	0.50	56.2	22.4	6.19	2.3	0.08	0.17	11
	13	0.63	74.7	33.1	2.99	0.69	0.04	0.15	9.2

Table 4 .

 4 . Standard deviation of experimental data (σ 2 ), 95% confidence intervals (CI) and simplified Akaike Information Criterion (AIC * ).

	Heating rate (°C /min)	σ 2	Dref (s)	CI Dref	z-value (°C)	CI z-value	AIC *
	1	0.24	33.1	3.8	1.96	1.41	8.8
	3	0.20	48.9	6.8	3.81	0.63	9.2
	5	0.23	40.8	5.1	6.70	1.41	7.2
	7	0.34	31.4	12.4	4.80	0.81	7.2
	10	0.50	62.1	23.2	5.98	2.65	8.2
	13	0.63	73.1	32.6	3.02	0.71	6.6

Table 5 .

 5 Estimated values of Dref (z = 3.79 and Tref = 58 °C). Standard deviation of experimental data (σ 2 ), 95% confidence intervals (CI) and simplified Akaike Information Criterion (AIC * ).

	Heating rate (°C/min)	σ 2	Dref (s)	CI Dref	AIC *
	1	0.24	40.1	0.03	9
	3	0.20	47.5	0.38	6.2
	5	0.23	50.0	0.59	8.2
	7	0.34	50.0	0.48	3.6
	10	0.50	50.1	12.52	6.8
	13	0.63	46.7	8.75	3.6

Acknowledgments

The authors are grateful to the National Council for Scientific and Technological Development (CNPq, Brazil) for C. D. Albuquerque's PhD scholarship and financial support (process number 232767/2014-9).


Philippe Renault

Influence of calcium to silica ratio on H2 gas production in calcium silicate hydrate

INTRODUCTION

In France, some of low and intermediate level wastes issued from the dismantling of nuclear facilities are conditioned in calcium silicate cements to ensure the stability and the confinement of the radioactivity for disposal and future geological storage. From an industrial point of view, cement-based materials are good candidates to store large quantities of nuclear waste as they are inexpensive, easy to produce and can be tailored to reach various different properties such as soundness, strength. However, when cements are submitted to ionizing radiation, hydrogen gas is released and its potential reactivity has drawn great attention for the safety of nuclear waste disposal [1] . In order to not exceed the lower flammability limit (LFL) [1] , the hydrogen release from the cementitious packages must be limited and quantified. Since it is a material with various possible compositions and a complex nano-and micro-structure [2], [3] , the radiolytic process leading to hydrogen release in such material is challenging and not well understood.

The behaviour of cement paste [4], [5], [6], [7], [8] under irradiation is well described in the literature in terms of the radiolysis of pore water and therefore the radiolytic hydrogen emission. Their stability under irradiation up to several GGy of absorbed doses makes choice materials for packaging waste. In addition, a decrease in mechanical strength is explained by the decomposition of hydrates, the appearance of amorphous phases and the dissociation of chemically bound water [9] . However, the mechanism of H 2 production is not completely understood and recent studies [9], [START_REF] Chartier | Influence of formulation parameters of cement based materials towards gas production under gamma irradiation[END_REF] have shown that, beside the pore water, the water of hydration ("bound") of different hydrates contributes as well to the radiolytic hydrogen production.

In such hydrated compounds, radiolysis is not well described and the literature is quite limited. In minerals, ionizing irradiation induces electronic defects that promote H° radicals and oxygen center holes.

The H° radicals should be trapped in the structure, recombine with oxygen center holes or recombine with each other to form dihydrogen gas H 2 . The probability of each process depends on the crystal structure, the acidity, the mobility and the content of hydroxide groups, the nature and the content of charge compensative cations and the amount of crystallization water.

In other porous structures such as, aluminum hydroxides [9] , zeolite [START_REF] Frances | [END_REF] , controlled-pore glasses [12] , geopolymer [13] , and clays [14], [15], [16], [17] , the water radiolysis has been already carefully studied using gamma-rays. In clays, water radiolysis studies showed strong modifications of the mechanisms involved with respect to bulk water because of confinement and interfacial phenomena that conducted to dramatic effects on reaction rates and radiolytic mechanisms [18], [19], [20], [21] .

Moreover, few knowledge remains on the radiolysis of the water at the interface of hydrates and how it contributes to the stability of the cementitious material under irradiation. According to some authors [22], [23], [24], [25], [16] , some specific effects should be expected in these materials such as energy transfers or diffusion of transient radiolytic species in solid and/or at the water/solid interface. C-S-H, which is the main product of the hydration of Portland cement, in radiolytic hydrogen release, needed obviously to be payed attention to.

According to Jennings [26], [27] , the crystal chemistry of C-S-H should be modelled taking into account the "Feldman-Sereda" and "Power and Brownyard" hypothesis.

Structural relations among tobermorites and C-S-H have been studied by many workers [28] . The C-S-H are amorphous and layered structures that could be compared to the tobermorite [29] . In tobermorite, each sheet is composed of silicate chains that are connected with each other by calcium atoms as demonstrated by 29 Si MAS NMR measurements (Figure 1) [30], [31] . In tobermorite, two oxygens from non-bridging tetrahedral coordinate to calcium ions

[28c], [32] . , and others that do not (called 'bridging' tetrahedra (B)) [14] .

In C-S-H, crystallization water is located in an interlayer space. The interlayer space varies with the C/S ratio and the interlayer distance increase from 9 to 14 Å [33], [34] . Consequently, water molecules are placed in a confined environment where the radiolysis of water can be strongly affected.

However, one must also consider that the layers associate to form grains of few nm.

A last radiolysable type of water is the chemically bound one, present as the form of hydroxide.

The purpose of the present work is to understand the water radiolysis in the C-S-H and how the solid/water interface can modify the radiolytic mechanisms: different C-S-H with varying C/S ratio (from 0.8 to 1.6) at controlled relative humidity of 60% are submitted to gamma irradiation.

MATERIALS AND METHODS

Synthesis of C-S-H

C-S-H samples were synthesized from pozzolanic reaction of Samples were filtered using a Büchner funnel and a filter paper (0.22 µm, Millipore) and rinsed with water and freeze-dried for 4 days. It should be noted that the use of organic solvent (isopropanol) to stop the hydration is not applied because of its potential contribution to radiolytic hydrogen production. Then, the samples were cured in desiccator chamber with 60% relative humidity under a nitrogen atmosphere at room temperature for 4 weeks until their weight stabilized. The relative humidity was controlled by a sodium bromide saturated salt (ca. 60% RH).

Sample Characterization

X-Ray Diffraction

To check the purity of C-S-H samples, X-Ray diffraction experiments were performed on a Panalytical X'Pert Pro diffractometer (Bruker)

with an RX tube using a copper anticathode (λKα1 = 1.54056 Å). The diffractometer was operated at 45 kV and 40mA. Measurements were made at a 2θ range from 5° to 70° with steps of 0.017° and a measurement time equivalent to 80 seconds per step. The detector is an Xcelerator (linear detector with120 points aligned detectors). The diffraction patterns were analysed using the Eva 21.0 software (Bruker).

Thermogravimetry analysis

Thermogravimetric analysis was performed to describe the dehydration process of C-S-H using a STA 409 PC Luxx ® (Netzsch) from 25°C to 1000°C at a heating rate of 10 °C/min, with two hours at 300°C and two hours at 500°C. The analysis is conducted under nitrogen flux at 50 ml/min. Alumina crucibles were used and the sample weight was approximately 50 mg. Data were processed using Proteus Thermal Analysis (Netzsch). The derivative theromogravimetric (DTG) curve permits to determine the different loss of weight over the range of temperature. Acquisitions were performed on freeze-dried samples, equilibrated at 60% of relative humidity.

Nitrogen gas adsorption and desorption

Nitrogen gas adsorption-desorption cycles at 77K were determined using a Micromeritics ASAP 2020 surface area and porosity analyser.

Samples were initially outgassed by using 0.3 g of each material under vacuum at 100 °C for 48 h to obtain the dry sample before analysis.

The specific surface area data were obtained using Brunauer-Emmett-Teller (BET) between 0.1 and 0.3 of relative pressure P/P0. using an average dose rate of 600 Gy/h at 25 ± 1°C up to 100 and 200 kGy of absorbed doses. For each absorbed dose, two independent samples were irradiation.

IRRADIATION EXPERIMENTS AND GAS ANALYSIS

The amount of dihydrogen produced during γ-irradiation was evaluated by using a micro-gas chromatography (490 Micro-GC, Agilent) equipped with a compression system (SRA Instruments) to

RESULTS

Sample chemical composition

The experimental C/S ratios were indirectly checked by chemical analysis of the solution at the end of the synthesis (

X-Ray powder Diffraction

The purity of C-S-H samples was controlled by X-Ray powder Diffraction (XRD). The experimental patterns show that all the samples present the diffraction peaks corresponding to the tobermorite structure [35] (Figure 2). For nominal C/S ratio from 0.80, to 1.14, the diffractograms do not reveal other peaks. Then, the pozzolanic reaction is complete and the C-S-H are considered pure.

It is noticed that, for samples with C/S ratio at 1.30 and 1.40, additional peaks appear that are attributed to a small amount of portlandite and calcite. The presence of portlandite and calcite is quantified by TGA analysis in Table 2. The main differences between the various C-S-H XRD patterns with different C/S ratio acquired at 60% RH are the position and the intensity of the peak at 7.2-9.1° and 30° (2θ). The position of the first peak corresponds to the (001) reflection and reveals the interlayer distance using the structural model described by Grangeon et al [35] .

At C/S ratio of 1.40, 1.30, 1.14, 0.97 and 0.80 ratio, the (001) reflection evolves from 10.0 to 12.7 Å that is in good agreement with previous study [33] . (Figure 3) Noticingly, the interlayer distance vary very little between C/S of 1.1 and 1.4

Thermogravimetry analysis

Table 2 summarized the results of TGA and the thermograms were shown in Figure 4.

All the TGA analyses showed extremely similar curves. Two steps of decomposition are observed. The first one (from 30 to 300°C) corresponds to the loss of the sum of pore water, crystallization water and hydroxides. We must notice that the amount of water trapped into the material vary less than the corresponding interlayer distance (the amount of water decreases by 10% table 1, when the distance decreases by 30%)

The second step (from 300 to 500°C) corresponds to the decomposition of few amounts of portlandite (Ca(OH) 2 ). In the third step, the weight loss (from 500 to 750°C) results from the decomposition of carbonates. Note that temperature boundaries are approximate and depend on the heating rate and on how the water interacts with solid surfaces. Hydration state is determined by thermogravimetry analysis in order to quantify the water and the portlandite amounts and to detect the eventual carbonation. The appearance of portlandite is probably due to the further hydration process during the storage. Such results can be interpreted by the fact that the organic solvent is not applied in our protocol. The formation of carbonation could be due to the well-known reason of the existence of the atmospheric CO 2 . The rate of carbonation of C-S-H is increasing with the pH.

As a consequence, the amount of portlandite and carbonate increase with increasing C/S ratio, which is the common observation in the previous literature.

Nitrogen adsorption/desorption

From the different C-S-H samples, the isotherms were all type-IV according to IUPAC, which is characteristic of a mesoporous material. The H3-type hysteresis loop is linked with the non-rigid nature of the adsorbent and the location of the characteristic shoulder (0.42P 0 ) is consistent with the destabilization of condensate at the limiting P/P0 value. The type H3 hysteresis loops are typically given by the adsorption of nonpolar gases by montmorillonite clays [36], [37] and the aggregates of other platy particles. At C/S ratio 0.8, C-S-H has the highest specific surface area which is 226 m 2 /g. Then, with increasing C/S ratio from 0.80 to 1.14, the specific surface area decreases. This could be explained by an increase of the grain size from C-S-H 0.8 to C-S-H 1.2, which has been interpreted by Roosz [38] , as an increase of the number of layers stacked along c axis. At higher ratios, C-S-H 1.6 displays a higher value (183 m 2 /g) than C-S-H 1.2. This could be due to the precipitation of portlandite nanoparticles and detected by TGA and XRD, during the drying step, which tends to increase the surface area of the C-S-H 1.6. according to a platelike morphology [38] . When hydrates were exposed to gamma radiation, the H 2 gas was produced. Radiolytic yields were presented in Table 3. It is shown from the figure that radiolytic hydrogen production decreases with increasing C/S ratio from 0.8 to 1.4 (Figure 7), the radiolytic hydrogen production from portlandite and calcite could be neglected under gamma irradiation comparing to C-S-H because their effects are under uncertainty.

Hydrogen production under gamma irradiation

As reference, the molecular yield of dihydrogen obtained under γirradiation of bulk aqueous solutions, in the presence of 10 -3 M bromide to protect the molecular hydrogen from OH radical attack, is 4.4 × 10 -8 mol J -1 [39], [40] .

The first results show that all the values of G norm (H 2 ) in C-S-H (that is in the absence of any radical scavenger) are approximately 3 to 6 times higher than G(H 2 ) in bulk water. These yields are important and show that the radiolytic phenomena that occur in water confined in C-S-H are different to those in bulk water. But they are not exceptionally important and remain compatible either with a scavenging/ trapping of the oxidative species [9] leading to a preferential recombination of reductive species (1-3) and/ or to an efficient energy transfer from the material to the neighbouring OH groups (4-6).

Indeed, energy transfers in hydroxide systems are usually associated with electron migration from the material towards surface OH groups

The second result is the decrease of the yields when the ratio C/S increases.

In clays, the interlayer distance was a major parameter in controlling the H 2 production [16] , with a non monotoneous G(H 2 ) evolution with this distance.

The problem in the interpretation of our result is that three types of water must be considered here [38] .

-the interlayer water -the external water, adsorbed as a multilayer at the considered RH -the surface hydroxyl groups

The amount of external water and the grain size decreases when C/S increases, whereas the amount of interlayer water and hydroxyl groups are thought to increase [38] .

This obviously suggests that the radiolyzable sites (ROH in equation 5) are located on the surface of the grains. Indeed, the Figure 7 show that the specific surface and the G(H 2 ) are well connected., Studies at different relative humidities will be probably necessary to confirm this fact.

However, the variation of C/S change also the nature of the material itself, with Ca 2+ ions increasing, changes in electronic densities or in bridging and non-bridging oxygens.

Sophie Le Caer et al. [4] studied cement paste and point out that the water-rich interlayer regions with Ca 2+ ions act as electrons traps that promote the formation of H 2 . But we don't have any evidence in our case that C-S-H hydrates richer in Ca 2+ ions promote more efficiently H 2 formation.

The fate of the oxidative species (the hole in reaction 4) remain largely unknown in CSH. However, as no oxygen production was measured here, they remain probably trapped as accumulating defects within the material. To further understand this part of CSH radiolysis, radiation induced transient species in C-S-H should be probed by Electron Spin Resonance spectroscopy (ESR).

CONCLUSIONS

The production of radiolytic dihydrogen different C-S-H samples submitted to y-irradiation has been quantified. First results confirm an enhancement of hydrogen production in C-S-H hydrates compared to that in bulk water and an evolution of hydrogen production with varying the effective C/S ratio. Indeed, the greater specific surface of low C/S ratio involves a greater hydrogen release. But the radiolytic mechanisms involved require more studies and the influence of various parameters such as porosity, water content, and secondary minor phases should be investigated.

Figure 1 .

 1 Figure 1. Single layer of 1.4-nm tobermorite illustrating dreierketten chains. The chains have a kinked pattern where some silicate tetrahedra share O-O edges with the central Ca-O layer (called 'paired' tetrahedra (P)), and others that do not (called

  amorphous silica (SiO 2 , AEROSIL 380, Degussa) and CaO in excess of water. Reagent grade calcium carbonate (CaCO 3 , VWR Company) was calcined at 960 °C for 24 hours to obtain calcium oxide, then it is stored in vacuum desiccator. Stoichiometric amounts of CaO and SiO 2 were mixed in order to produce C-S-H samples having C/S ratio of 0.8, 1.0, 1.2, 1.4 and 1.6. The materials were mixed with ultrapure water (Milli-Q 18 MΩ) at a water / solid mass ratio of 50 in 1 L highdensity polyethylene (HDPE) bottles. The bottles were shaken on a rotating rack for 1 month. The synthesis was conducted at room temperature (i.e., 25°C). Once the desired time was reached, the remaining calcium and silicon ions concentrations in the solution were determined by ICP-AES.

provide 1

 1 bar of pressure at the input of the µ-GC injector. The channel used for hydrogen analysis contains a Molecular Sieve 5A PLOT column under argon at 28 psi pressure and a micro thermal conductivity detector (µ-TCD). The oven and detector temperatures were respectively 90 and 120 °C, with an acquisition time of 2 minutes.The hydrogen production yield, denoted as the G-value and expressed in mol/J, corresponds to the amount of dihydrogen produced per amount of absorbed energy. For each C-S-H sample, apparent hydrogen radiolytic yields G(H 2 ) have been assessed by calculating the slope of the hydrogen production as a function of the absorbed dose. Normalized apparent hydrogen radiolytic yields G norm (H 2 ), expressed in mol/J, were calculated by normalizing apparent hydrogen radiolytic yields by the experimentally measured initial water mass fraction. Yields standard deviations are estimated to be less than 10 %.

Figure 2 .Figure 3 .

 23 Figure 2. Powder XRD patterns of the different C-S-H samples (From bottom to top: 0.80, 0.97, 1.14, 1.30 and 1.40 CSH RH=60%).

Figure 4 .

 4 Figure 4. TG and DTG curves of 0.80, 0.97, 1.14, 1.30 and 1.40 C/S ratio CSH samples at RH 60%. DTG curves are given to illustrate the water loss and the detection of minor phases such as portlandite and calcium carbonate.

Figure 5 .Figure 6 .

 56 Figure 5. Nitrogen adsorption/desorption isotherms of 1.0 C/S ratio CSH sample.

Figure 7 .

 7 Figure 7. The normalized H2 radiolytic yields (in blue colour), the specific surface area (in black colour) and the interlayer distance (in red colour) as a function of the

Table 2 .

 2 Chemical composition in weight percentages calculated from thermogravimetry analysis of different C/S ratio samples cured at 60% RH.

Table 1

 1

	), the

Table 1 .

 1 Real compositions of the synthesized phases.

	[CaO] (mmol/L)	151.7	167.4	179.87	188.3	187.3
	[SiO2] (mmol/L)	190.3	172.4	157.19	144.5	133.7
	Experimental C/S ratio	0.80	0.97	1.14	1.30	1.40
	pH value of the solution	11.3	12.1	12.2	12.5	12.5

Table 3 .

 3 Hydrogen radiolytic yields released from Gamma irradiated samples hydrated at 60% at room temperature.

		G(H2) (mol/J×10 -7 )	G(H2) (mol/J×10 -7 )
		(calculated with	(calculated with
	Effective C/S ratio	respect to the total	respect to the energy
		energy received by the	received solely by
		system)	water)
	0.80	0.61±0.06	3.23±0.32
	0.97	0.58±0.06	3.11±0.31
	1.14	0.49±0.05	2.85±0.29
	1.30	0.42±0.04	2.44±0.24
	1.40	0.36±0.04	2.13±0.21

normalized to water content(10 -7 mol.J -1 ) Effective C/S ratio


Benjamin Audiard
email: benjamin.audiard@cepam.cnrs.fr

Isabelle Thery-Parisot
email: isabelle.thery@cepam.cnrs.fr

Thierry Blasco
email: thierry.blasco@upmc.fr

Pierre-Jean Texier
email: pj.texier@gmail.com

Giovanna Battipaglia
email: giovanna.battipaglia@unicampania.it

Anoud Bani-Hani
email: anoud.bani-hani@zu.ac.ae

Munir Majdalweieh

Aisha Alshamsi

The 6th International Workshop on Machine Learning and Data Mining for Sensor Networks (MLDM-SN) Online Authentication Methods Used in Banks and Attacks Against These Methods

Keywords: Authentication, banks, comparison matrix, characteristics, attack vectors, methodology, UAE, password

Crossing taxonomic and isotopic approches in charcoal analyses to reveal past climates.

Robust methodology in charcoal analyses has strong potential in the reconstruction of past environments. However, charcoal assemblages from Pleistocene contexts, particulart those that formed under varying climatic conditions during glaciation and mostly composed of the genus Pinus, can neither express changes in forest density, nor can they reveal climatic conditions.. Here, we explore the potential of a combined approach using taxonomic and δ13C isotopic study of archaeological charcoal from middle Paleolithic sequence of La Combette. In order to investgate past climate and/or environmental conditions, we focus on charcoal of Pinus spp. from infra stratigraphic units at the site that showed a high potential for isotope analysis. Those isotopic results are consistent with other paleoenvironnemental data (anthracology, micromorphology, palynology, etc.) that are manifested at different time scales. In some cases, isotopic data provide, more accurate information than that derived from taxonomic identifications. This is due to the quicker physiological response of the plants compared to the long-term changes in forest biodiversity under climatic pressure.

Subsequently, this first application of cross taxonomic and isotopic approach offers promising and original results and must be applied to different context (low to high taphonomic process ). Lastly, the degree of asynchrony or synchrony between taxonomic and isotopic findings allows us to rethink Neanderthal fuel management and mobility patterns and to explain the correlation between Neanderthal occupations of the rock shelter in view of climatic conditions.

Introduction

Online Banking or E-banking is the practice of initiating banking transactions through the internet [START_REF] Bhatt | Online Banking[END_REF]. Major Banks currently offer different online banking services to their customers. For example, it reduces physical visits to the bank, it enables serving more customers at a fraction of the cost and solves the issue of customer's convenience [START_REF] Mannan | Security and Usability: The Gap in Real-World Online Banking[END_REF]. As Bhatt [START_REF] Bhatt | Online Banking[END_REF] posted too, online banking can help the customers to check his/her balance, make deposits, withdrawals, transactions, and even pay the bills from anywhere [START_REF] Mannan | Security and Usability: The Gap in Real-World Online Banking[END_REF].

This research is focus on online authentication methods, so our main concern is on the first two factors what the user knows and what he/she has. The authentication methods related to biometric characters, something the user is, will not be discussed in this paper. Authentication through the use of a username & password (i.e., something the user knows) only considers a single-factor authentication. While, depending on something the user possesses (i.e., the card) combined with something the user knows (i.e., PIN, password) considers two-factor authentication or multifactor authentication. Authentication methods that depend on more than one factor (multifactor authentication) are stronger, more reliable and more difficult to compromise than single-factor methods [START_REF] Council | Authentication in an Internet Banking Environment[END_REF]. "As online transactions require new authentication methods, banks are trying to introduce new approaches in order to prevent attacks being successful and to increase security" [START_REF] Cepis | Authentication approaches for online-banking[END_REF].

Background

By strengthening the security and protocols of online banking, it would prevent unrestricted access to private information and further strengthen the relationship with customers. A key step in protecting the information is authentication. Username and password are the most common type of authentication [START_REF] Barkadehi | Authentication systems: A literature review and classification[END_REF].

However, authentication through single factor authentication like username and password is considered weak. Similarly, partial password is form of password authentication. Unlike the regular password, the system would request random characters from the password [25]. Furthermore, A newer form of authentication that has been integrated in many authentication systems. Secret image or identifiable picture uses an image in place of a password [11].

Password and PIN are vulnerable to a variety of insidious threats such as dictionary attack, brute force attack and password guessing attack. The goal of the attacks is to find the username and password to gain unauthorized access. In addition to that, keyloggers are another threat that can capture all the keystrokes done on the keyboard by the user [START_REF] Harmandeep | Cybercrimes: A proposed taxonomy and challenges[END_REF]. It is noteworthy to mention that keyloggers can have an advanced feature that enables the attacker to capture the screen of the victim [START_REF] Reyes | Securing one time password (OTP) for multi-factor out-of-band authentication through a 128-bit blowfish algorithm[END_REF]. However, not all form of attacks would require advanced skills. Some attacks such as social engineering and phishing depends more on the manipulation skills of the attacker to trick victims into revealing information [START_REF] Sharma | Analysis of QKD multifactor authentication in online banking systems[END_REF]. Similarly, with shoulder suffering and hardware (observation) attack, it would require the attacker to have better more observational skills.

Methodology

The aim of this research is to examine common existing online authentication methods. In addition, this study examines the current authentication threats and attacks against these methods. Then, discuss and analyze the methods used in six banks operating in UAE, which for will be referred to as Bank A, Bank B, Bank C, Bank D, Bank E, and Bank F. Discussion and analysis section are based on the data captured through observation while navigating through the internet banking services of the targeted banks and from the user's aspect who has the privilege (credentials) to access the online services of the banks. Jong [START_REF] Jong | Online authentication methods[END_REF] evaluated the authentication methods in his paper based on three primary subjects, which are characteristics, attack vectors and user acceptance. We are interested mostly in characteristics and attack vectors, and we are going to use the two matrices belong to these two subjects only to evaluate the authentication methods. Based on the Comparison Matrix Characteristics of Authentication Methods used in Jong's paper [START_REF] Jong | Online authentication methods[END_REF], the characteristics of authentication methods used in these six banks, for example, requirement of additional Hardware and Software, Complexity, Scalability, Portability and System requirements will be compared and evaluated. Comparison Matrix Attack Vectors of Authentication Methods will be used also to represent the probability to succeed the attack for each authentication method discussed earlier, for example, shoulder Surfing, Keylogger, Screen Capturing, Brute force attack, Guess attack, Dictionary attack, Hardware Observation attack, Social engineering, Phishing attack, MITM attack, MITB attack, Network Sniffing and Short Access [START_REF] Jong | Online authentication methods[END_REF].

Online Banking Authentication Methods

The objective of this section is to review the common different online authentication methods used in banks around the world. The Comparison Matrix Characteristics of Authentication Methods done by Jong shows the authentication methods and their characteristics based on a scale from 1 to 5, where higher is better. Using this matrix, we can evaluate the specified authentication methods and point out their strengths and weaknesses. We did a small modification in the matrix by adding new suitable authentication methods that need to be evaluated, which are (PIN, Secret Image, Smart card, USB token and Security questions) * and deleting some that are not relevant. At the end, we got a matrix with 13 authentication methods. In addition, some characteristics have been deleted from the characteristics' row in the matrix, which are Login time, Acquisition cost, Deployment cost and Operating cost. The reason behind removing them is because it's difficult to estimate acceptable values for these specific characteristics. A short explanation for the new authentication methods is found in Table 1 and a short explanation for each characteristic is found in Table 2. Authenticating by requesting some random characters from the user's password instead of the full password [25]. Secret Image*

The user would have to select one memorable image from a group of random images to verify his identity [11]. Smart Card* It has an inbuilt memory with the user's credentials and only requires a password to gain access [START_REF] Sharma | A lightweight multi-factor secure cuser authentication scheme for cloud-IoT applications[END_REF]. USB token* It is similar to a smart card. USB token is a secure way to provide authentication by providing credentials [START_REF] Jiang | Identity authentication scheme of cloud storage for user anonymity via USB token[END_REF]. OTP manual, automatic, synchronous, asynchronous OTP automatic OTP synchronous OTP a-synchronous

The problem with password is that users tend to use short and common combinations to avoid memorizing [START_REF] Barkadehi | Authentication systems: A literature review and classification[END_REF]. Using one-time passwords that are valid for one time use only could solve this problem and reduce the risk. Onetime password is valid for one time use and a new password is generated for the next authentication process [START_REF] Reyes | Securing one time password (OTP) for multi-factor out-of-band authentication through a 128-bit blowfish algorithm[END_REF]. Security Questions* Security questions are used to authenticate users when they forgot or lost their passwords [START_REF] Anvari | Generating security questions for better protection of user privacy[END_REF].

Bookmark authentication

Bookmark authentication depends on a bookmark the user creates during the registration process. It's often sent to the new customer via emails, so he/she can add it easily [START_REF] Jong | Online authentication methods[END_REF].

* new authentication methods

Table 2. Explanation of Characteristics.

Characteristics Explanation

Current Authentication Threats and Attacks

The objective of this section is to review the common threats and attacks that the previous authentication methods are vulnerable to. The Comparison Matrix Attack vectors of Authentication Methods done by Jong shows the attack vectors on online authentication methods used by hackers based on a scale from 1 to 5 (see table 3), where higher is a better resistance against the attack. Using this matrix, we can measure and determine whether the authentication method is more vulnerable to a specific attack or represent higher probability to succeed this attack than other methods or not. The attack vectors are similar to the ones mentioned in Jong's study, no addition or elimination was necessary since the author covers the most common authentication threats and attacks as shown in (Table 4).

Table 3 Likely to succeed the attack [START_REF] Jong | Online authentication methods[END_REF].

Based on Jong's study, online banking authentication systems are vulnerable to several different threats and attacks: Table 4 shows the description of the Attack vectors.

Attack Vector Description

Shoulder Surfing Is an observation technique that can be done by looking at someone while they enter their password or PIN [START_REF] Jiang | Identity authentication scheme of cloud storage for user anonymity via USB token[END_REF]

Keylogger

Is a hardware or a software that runs in the background and records all the keystrokes done by the user [START_REF] Harmandeep | Cybercrimes: A proposed taxonomy and challenges[END_REF].

Screen Capturing Is used by attackers to get both the user's keystrokes and screenshots of the victim's computer screen [START_REF] Reyes | Securing one time password (OTP) for multi-factor out-of-band authentication through a 128-bit blowfish algorithm[END_REF].

Brute force attack

Is a method to crack passwords by generating all the possible passwords and tries them [START_REF] Harmandeep | Cybercrimes: A proposed taxonomy and challenges[END_REF].

Guess Attack

Is when attacker knows a little about the target's social life and try to guess the victim's password [START_REF] Harmandeep | Cybercrimes: A proposed taxonomy and challenges[END_REF].

Dictionary Attack

A technique that uses words from the dictionary to find the password combination [START_REF] Harmandeep | Cybercrimes: A proposed taxonomy and challenges[END_REF]

Hardware Observation

This attack involves trying to look inside the hardware and its risk determined on the cost and the profit [START_REF] Jong | Online authentication methods[END_REF].

Social Engineering

Is the art of convincing people to reveal confidential information like passwords for malicious use [START_REF] Sharma | Analysis of QKD multifactor authentication in online banking systems[END_REF].

Phishing

It works by imitating a trusted well-known website and tricking users into entering their credentials [START_REF] Bhadane | State of research on phishing and recent trends of attacks. I-Manager[END_REF].

MITM Attack

When an attacker insert himself between the victim and a legitimate connection [START_REF] Rahim | Man-In-The-Middle-Attack Prevention Using Interlock Protocol Method[END_REF].

MITB Attack

It uses a malware to create a realistic browsing environment for the victim to gather personal information [START_REF] Grau | TIM lecture series -the business of cybersecurity[END_REF].

Network Sniffing

It is used by attackers to sniff confidential information such as usernames and passwords [START_REF] Diyeb | Ethical Network Surveillance using Packet Sniffing Tools: A Comparative Study[END_REF].

Short access

It describes the possibilities of an attacker to get short access to the computer like an unlocked computer [START_REF] Jong | Online authentication methods[END_REF].

Discussion and Analysis

As mentioned earlier, discussion and analysis section are based on the data captured through observation while navigating through the internet banking services of the targeted banks and from the user's aspect who has the privilege (credentials) to access the online services of these banks. In Table 5, we will discuss about the authentication methodology for each bank:

Note: The inserted score/value for each authentication method is based on scale 1-5, where higher is better as explained in Table 2 (Explanation of Characteristics [START_REF] Jong | Online authentication methods[END_REF]). Bank C 1. Enter his/her username to proceed. 2. After, user will be asked to answer the correct security image chosen during registration with the bank online service along with mandatory fields for entering both the password and Token password (RSA). 3. Similar to UserID, user can use the physical keyboard or the virtual keyboard (optional) to enter his/her password. To enter the token password (RSA), user should have the hardware token (RSA Secure ID) that generates that password. 4. Bank C uses RSA SecureID 700, a small durable device that can be connected easily to any key ring and is very convenient for the user. Every 60 seconds, a new code generated by the RSA SecureID AES algorithm is displayed ("Technische Daten," n.d.

Bank C use 5 authentication methods out of 13 methods available in the matrix, which are 1. Username and password, 2. Virtual Keyboard (optional), 3. Secret Image, 4. OTP Synchronous (RSA hardware token) and 5. Security Questions.

Bank D 1. Enter his/her username to proceed. 2. After, user will be asked to answer the security question correctly and enter the partial password correctly to be able to log in. The security question never changes or selected randomly among different questions, the same question is asked by the bank each time Bank D use 3 authentication methods out of 13 methods available in the matrix, which are 1. Username and password, 2. Partial Password and Security Questions.

Refer to Appendix A -Comparison Matrices

Bank E 1. User enters the User ID that he/she created during the registration and press login button (If the user has the recent version of JAVA application installed on his/her PC or laptop, then he/she will be able to successfully log in after entering the correct password) "In case the user PC/laptop is not updated with latest version of JAVA, then he/she will receive a SMS in his/her mobile phone from the bank". This SMS notifies the user to install JAVA and includes a temporary access password valid for 5 minutes.

Bank E uses 4 authentication methods out of 13 methods available in the matrix, which are 1. Username and password, 2. PIN, 3. OTP Automatic (SMS) and 4. Security Questions.

2. User can enter this password and log in to Bank E internet banking service. Bank E depends mainly on One Time Password (OTP) for authentication and protection of the user. Bank F 1. Enter his/her username to proceed Bank F depends on Short Messages Service (SMS) to notify the user with any action or update happened in the account.

Bank F uses 2 authentication methods out of 13 methods available in the matrix, which are 1. Username/password and 2. Virtual keyboard (optional).

Findings

The evaluation shows that virtual keyboard is the default keyboard to enter the password in Bank A Internet banking, while other banks make the virtual keyboard optional. Bank B depends on One Time Password (OTP automatic) that sends to the user's cell phone via a Short Message Service (SMS) when making transactions, while other banks depend on security questions for that purpose. Bank C is the only bank among our sample that depends on Token password (RSA) to authenticate the users by distributing hardware token (RSA Secure ID) to them. In addition, partial password is a good authentication method used by Bank D. User will be asked to enter random characters of the password each time he/she tries to log in. On the other hand, Bank E depends mainly on One Time Password (OTP automatic) for authentication and protection of the user. The bank sends SMS with a temporary password to the user's mobile phone to allow him/her to make most transactions. Although, Bank F doesn't use unique authentication method like other banks and depends mainly on username and password, the bank keeps notify the user with any update occurs in the account via SMS. Through navigation and observation, the shared authentication method used by most of the targeted bank is username/password and virtual keyboard (optional).

Implications for practice and future research

As per the results mentioned earlier, banks could use different kinds of security methods to strengthen their online authentication systems. Depending on one authentication method only, for example username/password combination, considers weak and not sufficient like before. Implementing a defense-in-depth strategy by adding different protection layers/challenges for the user makes the job of an intruder or attacker more difficult.

Our sample consists of only six banks because of the time restriction. Soon, we are thinking about adding international banks and including online services of UAE government agencies. As we all know, Dubai has started working on eGovernment Strategy to make all government services available online. We are going to use both matrices (characteristics and attack vectors) to evaluate the authentication methods of these agencies. Our methodology depends on the observation only; in future we can conduct survey (questionnaire) to know customers' opinions and to evaluate the authentication methods based on that as Jong [START_REF] Jong | Online authentication methods[END_REF] did also using the third Matrix (User Acceptance).

Limitation

In this research, we didn't cover all the authentication methods that involve using cell phones. Today, many banks offer good telephone services and allow users to make transactions on their cell phones for example, Al-Barq cell phone services of Bank A.

Conclusion

Today, authentication of the user is required in all online services. Day by day, governmental agencies and companies emerge their services to the internet for the public. And since the findings from matrices are not restricted to evaluate online banking, these governmental agencies can use the matrices to find the best authentication methods that suit their needs. Banks were our first choice to be evaluated because the main concern for banks is to protect the confidentiality and integrity of the information. If the authentication methods used are advanced, then they can reduce the threat of unauthorized access. If the authentication method is very good and less vulnerable to attack, then they can ensure that only authorized people can access the secret information and they are the only ones who can make modification. The guidance of Federal Financial Institutions Examination Council [START_REF] Council | Authentication in an Internet Banking Environment[END_REF] emphasized on the point that Financial Institutions which adopt online banking systems should have effective and reliable methods to authenticate their customers before granting them access to particular services. An effective authentication System is necessary to avoid financial loss and reputation damage through fraud, identity theft, disclosure of customer information, corruption of data, or unenforceable agreements.

Comparison Matrices

Below are two examples of the comparison matrices for Bank A and Bank D.

  Hardware Does the solution require additional hardware? (1= card + reader), (2 = hardware token), (3 = cell phone), (4 = paper or card), (5 = none) Additional Software Does the solution require additional software? (1= application), (2 = cookie), (3 = n/a yet), (4 = Flash/Java enabled), (5 = none) Complexity How complex is to implement the solution? (1 = very difficult), (2 = difficult), (3 = medium), (4 = easy), (5 = very easy) Scalability Can the solution grow? (1 = very difficult), (2 = difficult), (3 = medium), (4 = easy), (5 = very easy) Portability Is the solution portable? (In your pocket or to another computers/smart phone) (1 = very difficult), (2 = difficult), (3 = medium), (4 = easy), (5 = very easy) System Requirements (1= card + reader), (2 = compatible cell phone), (3 = normal cell phone), (4 = Flash/Java enabled), (5 = normal computer)

Table 1 .

 1 Comparison Matrix Characteristics of Authentication Methods[START_REF] Jong | Online authentication methods[END_REF].

	Authentication Method	Explanation
	Username & password

Authenticating the user by what he knows which the username and password [3]. PIN* Is a personal identification number. It is commonly assigned to bank customers to use for transactions of ATM [19]. Virtual Keyboard It's a keyboard shown on the screen. The user needs to enter his password by clicking on the proper characters [18]. Partial Password

Table 5

 5 Discussion about the authentication methodology for each bank. In case user forgot his/her login password, he/she will need to use Al-Barq 4-digits Telephone Banking PIN (TPIN). 5. if the user wants to make a transaction, for example, online payment or transfer money to a new account, he/she needs to answer the security question successfully to be able to do so. Enter his/her username to proceed. 2. After, user will be asked to either answer the security question correctly (one out of 5 questions randomly displayed) or selecting the correct security image. Selection depends on the method the user chose during registration with the bank online service. 3. Any transaction, like transferring money from your bank's account to another account or making online payments, requires the user to enter the One Time Password that sent to his/her cell phone via a Short Message Service (SMS). That One Time Password is a temporary password and valid for a short time (timer).

	Bank	Login

Table 6

 6 Bank A.

Table 7

 7 Bank D.

								Attack vectors
	Authentic	Shoul	Keylog	Screen	Bru	Gue	Diction	Hardwar	Soci	Phishi	MIT	MIT	Netw	Sho	Tot
	ation	der	ger	Capturi	te	ss	ary	e	al	ng	M	B	ork	rt	al
	Methods	Surfin		ng	forc	Atta	Attack	Observa	Eng		attac	atta	Sniffi	acce	Sco
		g			e	ck		tion	.		k	ck	ng	ss	re
					atta			Attack
					ck
	Username	3	1	4	2	2	1	5	3	1	1	2	1	3	29
	&
	password
	Virtual	1	5	1	2	2	1	5	3	3	1	3	3	3	33
	Keyboard
	(optional)
	Secret	3	5	1	2	2	5	5	3	1	1	2	1	3	34
	Image*
	OTP	4	4	4	5	5	5	5	5	4	4	4	4	3	56
	automatic
	Security	3	1	4	1	1	1	5	1	1	1	2	1	3	25
	Questions*
								Attack vectors
	Authentic	Shoul	Keylog	Screen	Bru	Gue	Diction	Hardwar	Soci	Phishi	MIT	MIT	Netw	Sho	Tot
	ation	der	ger	Capturi	te	ss	ary	e	al	ng	M	B	ork	rt	al
	Methods	Surfin		ng	forc	Atta	Attack	Observa	Eng		attac	atta	Sniffi	acce	Sco
		g			e	ck		tion	.		k	ck	ng	ss	re
					atta			Attack
					ck
	Username	3	1	4	2	2	1	5	3	1	1	2	1	3	29
	&
	password
	PIN*	1	1	4	1	1	1	5	1	1	1	2	1	3	23
	OTP	4	4	4	5	5	5	5	5	4	4	4	4	3	56
	automatic
	Security	3	1	4	1	1	1	5	1	1	1	2	1	3	25
	Questions*