Agglomeration of wheat powders by a novel reverse wet agglomeration process

Keywords: Reverse agglomeration, fragmentation mechanisms, electric consumption, wheat powders, compactness, microstructure

The wet agglomeration process is implemented to improve the powders functionalities. The objective of the present work is to investigate a novel reverse wet agglomeration process for reactive powders. This novel process is conducted in two main successive phases during mechanical mixing. The first phase consists of mixing a specific amount of powder and water to produce a continuous saturated paste, called dough, by controlling the physicochemical reactivity of the biochemical components of the powder. The second stage consists of generating the agglomerates by adding the powder inside the continuous dough under mixing conditions. This agglomeration process is mainly promoted by fragmentation mechanism. We have evaluated the impact of the process conditions on the electric consumption, agglomeration yield and characteristics of the agglomerates.

Experiments were conducted using durum wheat semolina as raw materials. The results were compared to those obtained by using the classical wet agglomeration process, at same final water content of the agglomerates.

INTRODUCTION

Agglomeration is a unit operation, which is implemented to improve the powders functionalities relating to flow properties, dust generation, mixing capacity, solubility, etc., by increasing the size of structures. During agglomeration, native particles are gathered to form larger assemblies, called the agglomerates. Wet controlled growth agglomeration refers to agglomeration processes during which a liquid binder is sprayed over an agitated powder bed [START_REF] Ennis | A microlevel-based characterization of granulation phenomena[END_REF][START_REF] Iveson | Nucleation, growth and breakage phenomena in wet granulation processes : a review[END_REF]. Knowledge about the agglomeration process of food powders still remains partial, although significant scientific works have been conducted over the last 15 years [START_REF] Litster | The science and engineering of granulation processes[END_REF]Saleh and Guigon, 2009;[START_REF] Palzer | Agglomeration of pharmaceutical, detergent, chemical and food powders -Similarities and differences of materials and processes[END_REF][START_REF] Cuq | Food powder agglomeration[END_REF]. The description of the mechanisms, which are involved during the agglomeration of the food powders, still represents a real scientific stake.

The wet agglomeration of powders requires the addition of water to generate capillary bridges which adds cohesion between particles. Agglomeration mechanisms result from the spatial arrangement of the native particles with the binder components, promoting attractive interactions and links. The contribution of liquid bridges between native particles largely overtakes the physical forces and the van der Waals interactions [START_REF] Hapgood | Nucleation regime map for liquid bound granules[END_REF]. For food powders, besides the contribution of physical phenomena, the mechanisms also depend on the physicochemical reactivity of the molecules, which strengthens the adhesion forces between particles, by adding a significant contribution of the viscous forces [START_REF] Cuq | Food powder agglomeration[END_REF].

When the food powders are subjected to the addition of water, and/or to mechanical stresses, the molecules are able to undergo the glass transition and/or irreversible physicochemical changes (e.g. solubilisation, denaturation, diffusion, etc.). The process conditions, including mechanical stress, temperature, water addition, and process time, greatly determine the effective contribution of the physicochemical reactivity of the molecules [START_REF] Cuq | Food powder agglomeration[END_REF].

Based on their chemical composition, food molecules have the capacity to establish, between themselves and with the surrounding molecules, interactions of different energies. Low energy interactions include hydrogen bonds, hydrophobic interactions, and ionic interactions, while covalent bonds such as disulfide bonds represent high energy interactions. These interactions contribute to defining their capacity to react when subjected to water addition and/or heat treatments. For examples, the wheat proteins can establish continuous macromolecular network when subjected to water addition and mixing, and they can establish irreversible intermolecular covalent bonds, under heat treatments, that contribute to irreversible reticulation mechanisms [START_REF] Abecassis | Other traditional durum derived products[END_REF]. Wet agglomeration is generally described as a combination of three processes at different rates: wetting-nucleation, consolidation-growth, and attrition-breakage [START_REF] Iveson | Nucleation, growth and breakage phenomena in wet granulation processes : a review[END_REF].

The apparent rate of the agglomeration depends on the specific contribution of each processes. The growth mechanisms depend on the opposite contributions of cohesion and rupture mechanisms. The cohesion forces generate interactions between particles, while the rupture forces and the local shearing effects lead to breakage. The mechanisms of attrition and breakage help to improve homogeneity and strength of the granules by promoting consolidation, in addition to the obvious growth retardation (Reynolds et al., 2005).

Recent works have investigated the interest to consider the mechanisms of breakage and fragmentation as a major way to produce agglomerates. Rondet (2008) was the first to develop a reverse agglomeration process applied on three different powders (microcrystalline cellulose, calcium phosphate, and kaolin) classically used for pharmaceutical applications.

The first stage of the process is the preparation of a wet saturated paste by mixing the native powder with high amounts of water. The second phase consists of adding the native powder inside the paste during mixing, in order to promote the fragmentation mechanisms and generate the agglomerates with controlled water content. The authors demonstrated the reversibility of the paste transition, between the discrete granular state and the continuous paste state. The paste transition was identified at a specific range of water contents, close to the plastic limits of the powders. The agglomerates are more spherical and compact than those produced by the classical wet agglomeration process.

In 2010, a reverse wet granulation process was patented for pharmaceutical uses [START_REF] Li | Granulates, process for preparing them and pharmaceutical products containing them[END_REF]. Granules are formed by incorporating a mixture of dry powders (i.e. the excipients) into the drug-polymer slurry (i.e. the active pharmaceutical ingredient), and by drying the products. The granules formed comprise a core containing the active pharmaceutical ingredient having poor aqueous solubility.

Recently, Wade et al. (2013Wade et al. ( , 2014Wade et al. ( , 2015) ) developed a novel reverse-phase wet granulation process for pharmaceutical applications, based on the immersion of the dry powder mix, into the binder liquid. From a slurry state, the addition of the powder mass enables mechanical distribution of binder liquid to take place, until the desired particle size is obtained, through controlled breakage. The main mechanisms are supposed to be breakage of the large saturated agglomerates. The binder liquid is dispersed throughout the powder by mechanical distribution. This novel approach is supposed to have the potential advantages of eliminating the traditional variations associated to the stage of granule nucleation, and of reducing the We have evaluated the impact of the process conditions on the electric consumption, agglomeration yield and characteristics of the agglomerates. Experiments have been conducted by using durum wheat semolina as raw materials. Durum wheat semolina is a reactive powder able to form a continuous protein network, when it is wetted and mechanically sheared [START_REF] Pollini | Manufacture of pasta products[END_REF]. The physicochemical reactivity was controlled by the water content of the dough. Two conditions have been tested: low reactivity at low water content of the dough (above the plastic state) and high reactivity at high water content of the dough (above the pseudo-liquid state). The results are compared to those obtained by using the classical wet agglomeration process, at the same final water contents of the agglomerates. Semolina was first characterized using standardized methods. The water content of semolina (16.4 ±0.5 g water/100 g dry semolina) was determined according to the approved method 44-15A (AACC, 2000), by weighing after oven drying (RB 360, WC Heraeus GmbH, Hanau, Germany) at 105°C for 24 h. The total nitrogen content (TN) of semolina was determined by the Kjeldahl method, and the crude protein content (12.4 g protein/100 g dry matter) was calculated according to TN x 5.7 based on the AFNOR method V 03-050 (AFNOR, 1970).

MATERIAL AND METHODS

Raw materials

Median value of particle diameter of semolina (d 50 = 287 ±8 µm) was measured by using laser granulometry (Coulter TMLS 230, Malvern, England) at room temperature. The diameter span ((d 90 -d 10 )/d 50 ) was 1.56. The semolina true density (1.478 ±0.005 g.cm -3 ) was measured by helium pycnometry.

Liquid limit and plastic limit, which correspond to Atterberg tests used in soil science [START_REF] Holtz | An introduction to geotechnical engineering[END_REF] are carried out on semolina in accordance with the French P 94-051 and P 94-052-1 methods (AFNOR, 1993[START_REF] Afnor P | Détermination des limites d'Atterberg. Limite de liquidité. Méthode du cône de pénétration[END_REF]. For granular media, the plastic limit (w P ) is the water content which gives the transition between the solid and the plastic state. The liquid limit (w L ) is the water content which gives the transition between the plastic and the pseudoliquid state. The plastic and liquid limit values are measured at respectively 59% and 76% for the durum wheat semolina. The plasticity index (w L -w P ) is equal to 17% and allows assimilating the durum wheat semolina at a plastic organic soil like plastic silt [START_REF] Holtz | An introduction to geotechnical engineering[END_REF].

Agglomeration processes

The present work specifically investigates the effect of two different agglomeration paths (the direct classical wet agglomeration process and the reverse agglomeration process), by using a low shear planetary mixer (Fig. 1).

The direct classic wet agglomeration process is conducted by spraying water directly on the native powder inside the mixer bowl, during the mechanical mixing (Fig. 1). Water spraying is conducted until reaching the defined water content (i.e. the process water content), of the wet agglomerates (30, 40, or 50%).

The reverse agglomeration process is conducted with two successive steps. A part of the native semolina powder is first mixed in the mixer bowl. The first step consists of generating the continuous wet dough in the mixer bowl, by adding the defined amounts of water on the native semolina, during the mechanical mixing. Water addition is conducted until reaching the defined water contents, to produce the plastic dough (at 75% water content)

or the pseudo-liquid dough (at 110% water content) (Fig. 1). The second step corresponds to the formation of the powders by fragmentation mechanisms, lead by the addition of the native powder. The native powder was poured on the free surface of the dough and incorporated inside by the mechanical mixing. The amount of the added powder is defined to reach the final water content of the wet agglomerates (30, 40, or 50%).

We tested 9 different experimental conditions, which are associated to the 3 process paths and to the 3 final water contents of the agglomerates. The reproducibility of the process paths was checked by realizing some experiments in triplicate, at least one for each different process path. Experiments were done by using a batch planetary mixer (Kenwood Major 1200), equipped with a fixed bowl of 5 liters and a K blade that rotates around a vertical axis. All the experiments were conducted with the constant final load (1.5 kg) of the wet agglomerates and the constant rotation speed of the blade (70 rpm), to promote the conditions of low shear and constant total time of mixing (12 min). The electric consumption of the mixer was continuously measured by using a wattmeter (Wattcom ® ). Before the start of the experiments, we have conducted a stage of no-load mixing, at 70 rpm during a long enough duration (i.e.

30 min) to reach steady state conditions of the electric consumption of the mixer (Fig. 2). The initial load of the native powder (Table 1) was rapidly poured inside the mixer bowl and mixed for 2 min. During the wetting stage (Fig. 2), performed during the mixing, water was directly sprayed on the free surface of the powder bed at constant flow rate (5.25 mL.s -1 ) by using a flat spray nozzle (TPU650017, Spraying System Emani, France) connected to the water supply network. The diameter of the water droplets were estimated to 50-100 µm [START_REF] Mandato | Liquids' atomization with two different nozzles: Modeling of the effects of some processing and formulation conditions by dimensional analysis[END_REF]. In these conditions, the dimensionless spray flux was estimated to be around 0.2. This value is slightly higher than the threshold value (0.1) that distinguishes the nucleation regime by the drop controlled, in the nucleation regime map as defined by [START_REF] Hapgood | Nucleation regime map for liquid bound granules[END_REF].

For the direct process, the spraying time (30-63 sec) was defined to incorporate the necessary mass of water (Table 1) to reach the water content of the final agglomerates. After the spraying phase, the wet agglomerates were mixed until a total time of process of 12 min (Fig. 2).

For the reverse processes, the first stage of the formation of the continuous dough is conducted during a constant time of 2 min. During this stage, the spraying time was defined to incorporate the necessary mass of water (Table 1) to reach the defined water content of the continuous dough (Fig. 2). During the following fragmentation stage, the addition of the powder was conducted at constant feeding rate (8.7 g.sec -1 ) by using a weight feeder (K-SFS-24, K-TRON, New Jersey, USA), which was directly placed above the mixer bowl. The time for the addition of the powder was defined to incorporate the necessary mass of powder (Table 1) to reach the water content of the wet agglomerates. After the powder addition phase, the wet agglomerates were mixed until a total process time of 12 min.

The wet agglomerates were characterized according to their size distribution immediately after the processing. 100 g of product was sampled inside the mixer and poured over a column of 11 sieves of decreasing mesh size (10, 5.6, 2, 1.25, 0.9, 0.8, 0.71, 0.63, 0.5, 0.4, and 0.315 mm). The sieve column was moved by gentle manual stirring for 1 min to separate the agglomerate fractions. The repartition of the fractions on each sieve was measured by weighing. The size distribution was expressed as the percent of total weight. Even if the particle size distribution curves were not unimodal, the apparent median diameter (d 50 ) and apparent span ((d 90 -d 10 ) / d 50 ) of the size distribution were calculated as global apparent descriptors, that could under or over-represent the trend.

Consolidation and stabilization of the agglomerates

After the agglomeration stage, the wet agglomerates were consolidated by the steaming stage and they were stabilized by the drying stage. The stages of steaming and drying were only conducted on the wet agglomerates that were sampled between the sieves of 1.25 and 2 mm mesh size. We made this choice, because it corresponds to one of the fields of application expected for our works: the manufacturing of the agglomerated couscous grains of durum wheat [START_REF] Abecassis | Other traditional durum derived products[END_REF]. Obviously, the knowledge of the properties of this fraction does not allow describing the properties of the larger or smaller fractions.

Immediately after sieving, the agglomerates were spread as a thin layer (about 3 mm) over a stainless steel plate, inside a steam cooker of 20 L (Ravant Chaudronnerie, France). The stage of steaming was conducted for 15 min at 100°C, under saturated steam flow of 20 kg.h -1 , and at atmospheric pressure. The steamed agglomerates were immediately dried as a thin layer (about 3 mm) over stainless steel mesh, by using a pilot scale dryer (Afrem, Lyon, France).

The drying was conducted for 60 min at 70°C and at 80% relative humidity. The dried agglomerates were collected and stored inside hermetic plastic cups until characterization.

Characterization of the (wet and dried) agglomerates

Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. ( 2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10.046

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Water content -The water content was determined on 3-5 g samples, by a drying method in an oven (RB 360, WC Heraeus GmbH, Hanau, Germany) at 105°C for 24 h (AACC Method 44-15A). Mean values were determined from triplicate.

Compactness -The solid mass (m Solid ; g dry matter) was first calculated from the weight values of the agglomerates (m; g) and their water content (w; g water / g dry matter)..

݉ ௌௗ = ଵା௪ (1)
The density of the agglomerates (ρ; g.cm -3 ) was determined by using a hydrostatic balance (Precisa serie 321 LX 120A SCS, equipped with kit for determining density for solid and liquid) and using paraffin oil. The solid volume fraction of the agglomerates (ϕ; cm 3 solid matter / cm 3 ) was calculated using Eq. ( 2).

߶ = ೄ = ఘ ೄ * ሺଵା௪ሻ (2)
Where V Solid is the volume of the solid matter of the agglomerates (cm 3 solid matter) and ρ* Solid is the true density of material (1.478 ±0.005 g.cm -3 ) measured by helium pycnometry.

Mean values were determined from triplicate.

Shearing properties -Shearing properties of the wet agglomerates were measured using the FT 4 powder rheometer (Freeman Technology, Worcestershire, UK) according to Rondet et al.

(2013). Measurements were carried out in a cylindrical glass cell (diameter = 50 mm; height = 50 mm) and the FT 4 probe (with a helix of a -5° angle and a diameter of 23.5 mm). The sample was subjected to a pre-shearing stage (0.3°.s -1 on 60° with a pressure of 15 kPa) carried out with the FT 4 powder rheometer shearing probe. Four shearing cycles were carried out for various normal stresses (1.75, 1.5, 1.25, and 1 kPa), at a speed of 0.3°.s -1 . Failure shear stress was recorded for each studied normal stress. Undrained shearing tests were analysed using Mohr's representation of the plane stress state with the normal stress (σ, kPa) and the shearing stress (τ, kPa). The shearing properties were described using the Mohr-Coulomb rupture criterion.

߬ = ߤߪ + ܿ (3)
where µ is the friction coefficient (dimensionless) and c is the cohesion (kPa). The friction coefficient is the slope and cohesion is the intercept with the y-axis of the linear relationship between σ and τ. Experiments were carried out in triplicate.

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Scanning electron microscopy -The microstructure of the dried agglomerates was observed by scanning electron microscopy (JSM-T2000, JEOL, Tokyo, Japan). A sample was mounted in epoxy resin and coated by gold to provide the conductivity and the emission of secondary electron. For each sample, pictures with different magnifications were taken.

Color -A sample of dried agglomerates was densely packed in a cylindrical glass cell (6 cm diameter and 2 cm thickness) and levelled off. Color coordinates (L* lightness, a* redness, and b* yellowness) were measured directly by using a reflectance colorimeter (Konica Minolta CR -410, France) with the calibration to daily light (D65). Colours measurements were carried out in triplicate on three different samples. The arithmetic means of the coordinates were calculated together with total color difference.

Δܧ = ඥ∑ሺݔ -ݔ ሻ² (4)
Where x is L*, a* or b*, and i refers to the different products and 0 means durum wheat semolina as standard.

Swelling capacity -The swelling capacity of the dried agglomerates was measured according to [START_REF] Guezlane | Methodes d'appreciation de la qualite culinaire du couscous de blé dur[END_REF]. A sample (20 g ± 0.1 g) was placed inside a graduated 100 mL test tube (25 cm long and 3 cm diameter) containing 50 mL of distilled water. The test tube was sealed and turned upside down 10 times successively. Water at 20°C was carefully added to wash down the agglomerates stuck to the internal sidewall of the test tube, until the final volume of the mixture reached 100 mL. The test tube was then placed at room temperature for 30 min. The volume of solid hydrated "phase" was directly read on the tube.

Measurements were realized in triplicate. The swelling capacity criterion at 20°C (ml.g -1 )

corresponded to the ratio the final volume of the hydrated agglomerates (V; ml) over the initial mass of agglomerates (M; g).

(5)

Statistical analysis

The statistical significance of results was assessed using single-factor analysis of variance (ANOVA). Multiple comparisons were performed by calculating the least significant difference using Microsoft Excel 2011, at a 5% significance level.

RESULTS

The comparison of the direct or reverse agglomeration processes was conducted by both monitoring the energy consumption during the processing and by measuring the characteristics of the wet agglomerates and of the dried agglomerates.

Electric consumption during process

The electric consumption of the mixer was measured during the successive stages of the agglomeration processes. During the first stage of no-load mixing, we observed a small peak of electric input power, followed by a progressive decrease of the electric power until it reach a constant value, in steady state conditions (data not presented). According to Rondet et al.

(2012), the electric input power during the no-load mixing is the sum of the mechanical output power for rotating the blade and of the dissipation terms (heat dissipation, motor magnetic dissipation, and motor mechanical dissipation). The measured values of the electric input power were normalized by subtracting the plateau value measured at the end of the stage of no-load mixing [START_REF] Mandato | Liquids' atomization with two different nozzles: Modeling of the effects of some processing and formulation conditions by dimensional analysis[END_REF].

During the mixing of the initial load of the native powder (Fig. 3), the normalized electric input power values are low. These values (Table 2) are correlated (R 2 = 0.988) with the weight of the native powder in the mixer tank before the water addition (Table 1), except when the initial load is too low (less than 400 g load), compared to the mixer size.

During the agglomeration stage, the curves of the normalized electric input power are specific of the process. For the direct agglomeration process (Fig. 3A), the electric input power rapidly increases during the spraying of the water (stage 3), due to the increase in cohesion between the particles (Rondet et al., 2013). The electric input power reaches a maximum value at the end of the stage of water spraying, and then progressively decreases during the final stage of mixing, and reaches a final plateau value.

For the reverse agglomeration processes (Fig. 3B-C), we first observe an increase in the electric input power during the stage of water spraying (stage 3), due to the formation of the continuous dough. For the plastic dough conditions (Fig. 3B), the power curve is characterized by a high peak and large bandwidth due to the high consistency of the dough.

For the pseudo-liquid dough conditions (Fig. 3C), the power curve is characterized by a slight increase and small bandwidth, due to the liquid behaviour and low consistency of the dough.

When starting the final stage of powder addition, we first can observe a short transitory phase

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with fluctuations of the electric input power, due to the lubricant effect of the added particles, which are not incorporated inside the dough structure. The addition of powder induces a rapid increase of the electric input power (Fig. 3B-C), due to the increase of the dry matter content of the dough: the continuous dough becomes more cohesive. Similar increases in the cohesion of wheat semolina dough with decreasing water contents were observed by Rondet et al.

(2013). They identified a maximum dough cohesion value near 52% water content (i.e. the plastic limit), which is the limit water content, at the structure transition from the continuous dough to the granular pasta. Below 52%, the decrease of the water content decreases the values of the cohesion of the discrete granular cohesive medium. During the reverse agglomeration process (Fig. 3BC), we also observe that final powder addition decreases the electric power values, due to the transition from the continuous dough to the discrete granular cohesive medium. During the final period of mixing (Fig. 3BC), the electric input power continues to slightly decrease and reaches a final plateau value.

Whatever the processes, increases in the water content (from 30 to 50%) of the wet powders increase the values of the electric power measured at the end of the process (Table 2). This is due to the increase in the cohesion between the wet agglomerates (Rondet et al., 2013).

The measured values of the normalized electric input power were used to calculate the electric energy consumption during each stage of the process (Table 2). For the direct agglomeration process, the values of the total energy consumption are low (11-27 kJ), because the products still stay in a powdered granular state with low cohesion forces, which were determined at 4 to 11 kPa for the water contents ranging from 30% to 50% (Rondet et al., 2013). These forces are low when comparing to the wheat dough with continuous protein network, whatever the end water content of the wet agglomerates.

For the reverse agglomeration processes, the mixing of the continuous dough during the stages of water addition and of fragmentation requires higher level of mechanical energy (Table 2). Whatever the water content, the total energy consumption for the liquid dough conditions (13-38 kJ) is slightly higher than those measured for the direct process. The level of viscosity of the continuous pseudo-liquid dough does not require high levels of electric energy. On the other hand, the total energy consumption for plastic dough conditions (21-61 kJ) are largely higher than those measured for the direct process. The cohesive behaviour of the plastic dough requires high level of electric energy during the stage of mixing, whatever the end water content of the wet agglomerates.

Characteristics of the wet agglomerates

To compare the agglomeration processes, we have measured the distribution of the size properties and of the hydro-textural characteristics of the wet agglomerates (diameter, water content, and compactness).

Size distribution

The agglomeration processes generate agglomerates with dispersed sizes. Whatever the processes and the water contents, the end size distribution display multimodal shapes curves (Fig. 45). The size distributions of the wet agglomerates were here described by considering four classes of agglomerates according to their diameter: small (d < 0.4 mm), medium (0.4 < d < 0.71 mm), large (0.71 < d < 1.25 mm), and very large (1.25 mm < d) agglomerates (Fig. 6). Even if the distribution curves of diameter did not display unimodal normal shape, we calculated the apparent median diameter (d 50 ) and apparent dispersion parameter (span) as global criteria (Fig. 7).

The results demonstrated a significant impact of the process on the slope of the size distribution curves (Fig. 4), whatever the water content. The reverse plastic process is characterized by the agglomerates with slightly lower values of the apparent median diameters (-90 µm) and of the apparent span, compared to the direct process (Fig. 7). On the other hand, the reverse liquid process is characterized by agglomerates with slightly higher values of the apparent median diameters (+115 µm), and lower values of the apparent span, compared to the direct process. These effects can be mainly observed at 30% and 50% content, albeit with a greater difference at 50% level. We cannot give explanation into why at 40% the D 50 for all process show similar values.

Whatever the process type, the increase of the water content induces expected increases of the apparent median diameter (Fig. 7), with lower contents of the small agglomerates and higher contents of the large agglomerates (Fig. 56). The content of the medium agglomerates is the highest at 40% water content. Increasing the water content (from 30 to 50%) does not affect the apparent diameter span values for the reverse processes (Fig. 7).

Large differences in the size distribution curves (Fig. 45) and in the apparent span values (Fig. 7) of the wet agglomerates have been found for the low water contents (30 or 40%). For the experiments conducted at 50% water content, only slight differences in the particles size distribution curves (Fig. 4C) and the apparent span values (Fig. 7) were demonstrated between the direct process and the reverse processes. There is a low impact of the process on

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the properties of the agglomerates, when the water content is close to the plastic limit and to the formation of continuous dough.

Water content distribution

As showed by Barkouti el al. (2014) for the direct agglomeration, the results demonstrate whatever the process a significant dispersion of the measured values of the water content between the four classes of the agglomerates, defined by their size (Fig. 8): the large agglomerates are more wet than the small ones, whatever the process water content (30, 40, or 50%).

Increasing the process water content (from 30 to 50%) significantly increases of the measured water content for the agglomerates of medium size (0.4 mm < d < 1.250 mm), but does not affect the water content of the small and of the large agglomerates.

For the reverse plastic process, we observe a lower dispersion of the measured water contents between the four classes of agglomerates (Fig. 8B), compared to the direct process (Fig. 8A).

For the reverse liquid process, we observe the higher dispersion of the measured water contents between the four classes of agglomerates (Fig. 8C), close to those of the direct process.

Compactness distribution

Whatever the process, the results demonstrate a significant dispersion of the measured values of the compactness between the four classes of the agglomerates, defined by their size (Fig. 9-10): the large agglomerates are less compact than the small ones, whatever the process water content. Compactness of the agglomerates is linked to their water content, in the same way that [START_REF] Barkouti | Structuration of wheat powder by wet agglomeration: Case of size association mechanism[END_REF] have showed.

The agglomerates produced by the reverse plastic process are characterized by slightly higher and less dispersed values of the compactness, compared to the direct process. The agglomerates produced by the reverse liquid process are characterized by slightly higher and more dispersed values of the compactness, compared to the direct process. This difference is certainly due to the difference between the agglomeration mechanisms. A potential future work conducted at the particle scale could provide relevant data to describe the impact of the reverse agglomeration process on the compactness values.

Increasing the process water content (from 30 to 50%) decreases the compactness values of the wet agglomerates, but does not significantly affect the dispersion of the compactness values.

Comment citer ce document : Rondet, E., Cuq

Rheological properties

The cohesion and the friction coefficient were only measured for the selected wet agglomerates (i.e. with size between 1.25 and 2 mm). The reverse processes (plastic or liquid)

give the agglomerates with higher values of the friction coefficient and lower values of the cohesion, when compared with the direct process (Fig. 11). Whatever the process, the cohesion of the bed of the wet agglomerates increases with the process water content. We did not observe significant effects of the process water content on the friction coefficient. Similar increase in the cohesion of wheat semolina dough with increasing water contents was observed and discussed by Rondet et al. (2013).

Properties of the dried agglomerates

The dried agglomerates were obtained after the stages of steaming and drying of the selected wet agglomerates (i.e. between 1.25 and 2 mm).

Microstructural description

The topology of the surface of the dried agglomerates was described from scanning electron microscopy (Fig. 12). The process significantly affects the superficial microstructure of the agglomerates. The agglomerates produced by the direct process (Fig. 12A) are characterized by more or less spherical shapes, with some irregularities on their surfaces. They are not dense objects, since an apparent porosity opening on the surface is observed. At the low water content, the surface of the agglomerates is made of small particles that seem to be glued to each other but still remained discernible. The increase of the water content during process gives the dried agglomerates with smooth areas on their surface. It is difficult to identify the native particles, because they are partly melted and merged, due to the specific reactivity of the hydrated wheat proteins which composes the semolina particles.

The agglomerates produced by the reverse processes (Fig. 12B-C) are characterized with heterogeneous and shapes far from sphere. When processed at low water content (30%) by the reverse processes, the agglomerates are characterized by lengthened shapes. The surface of the agglomerates is made by an apparent continuous medium, with some visible small particles glued, giving the aspect of granular roughness. The proportion of the small particles is higher on surface of the agglomerates produced by the reverse plastic process, when compared to those produced by the reverse liquid process. When processed at intermediary water content (40%) by the reverse processes, the agglomerates are characterized by original flat shapes, comparable to glitters or flakes. The shape ratio (i.e. apparent thickness / diameter) is estimated at 1/5. The surface of these agglomerates is made by the association of particles, which seems to be glued on each other.

When processed at high water content (50%) by the reverse processes, the agglomerates are characterized by heterogeneous and not spherical shapes. The surface of the agglomerates is then made by smooth areas on which some particles are merged. The proportion of the merged particles is lower on the agglomerates produced by the reverse plastic process, than on those produced by the reverse liquid process.

Compactness

Whatever the process water content of the wet agglomerates, the compactness of the dried agglomerates produced by the reverse plastic process (0.841) or by the reverse liquid process (0.848) are slightly higher than those produced by the direct process (0.831) (Fig. 13). We do not identify significant effect of the process water content on the values of compactness for the dried agglomerates.

Colour properties

The colour characteristics of dried agglomerates produced by the reverse processes are significantly different to those produced by the direct process (Fig. 13). The colour difference with wheat semolina (∆E) is close to 36.8 for the agglomerates produced by the direct process. The reverse plastic process produced agglomerates with lower values of colour difference (∆E = 33.4), due to specific values of the brightness (L*). For the reverse liquid process, the colour difference (∆E = 35.9) is close to those of the direct process.

Swelling properties

The process influences the swelling ability of the agglomerates (Fig. 13). The swelling capacity of the dried agglomerates describes their ability to interact with water molecules, which depends on the crystalline state of the starch granules, and on the ability of the porous agglomerates to swell and entrap water inside the internal voids [START_REF] Cuq | Food powder agglomeration[END_REF].

The swelling capacity of the dried agglomerates produced by the reverse plastic or liquid process (3.10 or 3.08 mL.g -1 ) are slightly higher than those measured for the direct process (2.95 mL.g -1 ). Whatever the process, the increases in the process water content of the wet agglomerates before the stage of steaming increase their swelling capacity after the drying.

DISCUSSION

The major aim of this work was to investigate the performance of an original new reverse agglomeration process (based on the fragmentation of a dough and the formation of agglomerates, by adding a dry powder), in comparison with the direct wet agglomeration process: water droplets on particles, or the novel reverse-phase wet granulation process:

particles in water layer (Wade et al., 2014).

During the reverse agglomeration process, the first stage of the elaboration of the continuous dough is conducted to initiate physicochemical mechanisms based on the reactivity of the wheat components under high water contents and mechanical shear stresses. These mechanisms generate irreversible changes of the wheat components (solubilization of proteins and fibers, formation of protein network) inside the dough [START_REF] Bloksma | Dough structure, dough rheology, and baking quality[END_REF][START_REF] Eliasson | Rheological properties of cereal proteins[END_REF].

Two conditions of dough water contents were investigated to modulate the extent of the physicochemical mechanisms: the reserve plastic process at 75% water content, with the dough of high consistency and low reactivity; the reverse liquid process at 110% water content, with the dough of low consistency and high reactivity.

The present study allows investigating the apparent electric cost of the physicochemical mechanisms during the process, and more particularly during the period of mixing of the continuous dough and of the fragmentation stage. Compared to the direct process, the electric cost of the reverse process is almost similar when considering the liquid dough conditions (Table 2). The low consistency of the liquid dough does not require overconsumption of the electric power in order to be mixed. The high molecular reactivity under the liquid dough conditions does not generate electric overcosts during the agglomeration process. The electric costs of the mixing of the continuous dough of low consistency are close to those of the mixing of the granular paste in the classic agglomeration process. On the other hand, the electric cost is higher when considering the plastic dough. The high consistency of the plastic dough requires electric overconsumption during the stage of mixing. The present work demonstrates that the ability of the continuous dough to be fragmented in small pieces is partly dependent on the characteristics of the dough. The low consistency of the liquid dough is favourable to the dispersed mechanisms of breakage: this allows the formation of pieces of dough, with high dispersion in the sizes and properties (Fig. 89). On the other hand, the high consistency of the plastic dough requires more energy for the breakage mechanisms: the high cohesion of the dough favours the effects of mixing and allows the formation of homogeneous pieces of dough, with low dispersion in the size and properties, such as water content or compactness.

Strong differences in the microstructure of the agglomerates were demonstrated when comparing the direct and reverse processes (Fig. 12). The direct process generated classical round to ellipsoid structures. The reverse processes generate particles with no spherical shapes, but with original shape close to the flakes. Our results, show different trends when compared to Rondet (2008) who found that the reverse agglomeration experiments produced "more spherical and compact (agglomerates) than those produced by the classical wet agglomeration process". These differences could be due to differences in the range of plastic and liquid behaviour of the continuous dough. In addition, their microstructure is heterogeneous due to the fact that native particles at the surface are entrapped in an apparent pasty matrix. This makes it possible to generate composite material with different components. The end use properties can thus significantly differ from those of the agglomerates obtained thanks to the direct process. It makes it possible to consider the prospect of new product development in which the added powder is different from that used to generate the dough.

The variations of the compactness and water content of the agglomerates during the process can be discussed by using the hydro-textural diagram (Ruiz et al., 2005) (Fig. 14). This diagram is limited in its upper part by the saturation curve, which represents the maximum water content that a medium of a given compactness can contain. Whatever the process, the increase in agglomerates size is concomitant with an increase in their water content and a decrease of their compactness. The results are similar to those obtained thanks to a classical agglomeration process, in the conditions of wetting and mixing which corresponding to the drop control regime of nucleation (Rondet et al., 2010). Nevertheless, the agglomerates generated in this study (and whatever the process chosen) are found to be totally saturated with water (Fig. 14). In the case of the reverse process, this result is not surprising insofar the agglomerates are generated from initially saturated dough pieces. When the direct agglomeration process is considered, the saturation of agglomerates is dependent on the conditions of wetting and mixing, that impose a mechanical dispersion regime of nucleation.

Wade et al. ( 2014) have demonstrated that the reverse-phase process is able to generate granules with low intragranular porosity when starting from dense suspension, due to the complete filling of intraparticulate pores by the binder liquid in the initial phases of the process. The addition of further powders reduces the liquid saturation of the granules, with the desired particle size being obtained through controlled breakage. Proposed advantages of the reverse-phase process over the conventional wet granulation approach include a reduced risk of uncontrolled granule growth. Wade et al. ( 2015) have indicated that the conventional process showed granule growth driven by viscous forces, whereas the reverse-phase process showed granule breakage driven by capillary forces.

CONCLUSION

The different agglomeration processes have been found to generate agglomerates with specific functional attributes, due to the physicochemical reactivity of the wheat semolina

  Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10

  uncontrolled growth of the granules. They evaluated the effects of: liquid saturation, binder liquid viscosity, and impeller speed on the physical and end-uses properties of the granules.The objective of the present paper is to investigate the novel reverse wet agglomeration process by fragmentation of a pasty state, in the specific case of reactive powders, able to form a continuous dough stabilized by a molecular protein network. Durum wheat semolina was used as reactive powder for the present study. The novel process is conducted as two main successive phases, during continuous mechanical mixing. The first stage consists of mixing a specific amount of powder and water to produce a continuous saturated paste, called the dough, by controlling the physicochemical reactivity of the biochemical components of the powder. This paste could not be considered as a granular suspension because the original granular state of the native particles is lost. While the cohesion of the granular paste is almost reversible in the previous works(Rondet, 2008), the presence of reactive proteins of the wheat (i.e. gluten) in the particles of durum wheat semolina led the formation of a dough stabilized by a biochemical molecular network and the irreversible loss of the native granular state. The second stage consists of in generating the agglomerates, by adding the dry powder inside the continuous dough, during the mixing. The fragmentation mechanisms induce dough disruption and agglomerates production.

  of industrial quality (Panzani group, France) was used as raw material for the agglomeration experiments. Semolina was stored in hermetic containers at 4°C until experiments were carried out.

S

  Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10.046

  Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10

  , B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10

  Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10of the wet agglomerates enhance the mechanisms of gelatinization of the starch granules during the steaming stage, the loss of the crystalline structures, and the formation of amorphous hygroscopic structures in the dried agglomerates.

  Comment citer ce document : Rondet, E.,Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10of the reverse agglomeration process is thus not proportional to the extent of physicochemical mechanisms involving the wheat components. The reverse agglomeration process can thus be managed to promote different irreversible physicochemical reactions without negative impacts on the electric energy costs.The present work demonstrates that the characteristics of the continuous dough are important factors during the reverse agglomeration process, because they directly contribute to the physical mechanisms involved in the formation of the agglomerates. They impact more particularly the relative dispersion of the individual properties of the agglomerates.(i) The low consistency of the liquid dough is favourable to the mixing efficiency of the mixer, in order to quickly generate homogeneous dough at the end of the initial stage of the addition of water. On the other hand when considering the plastic dough with the high consistency, more process time is required during the initial stage of mixing in order to obtain a homogeneous dough.However, the present results do not demonstrate the lower dispersion of the individual properties of the agglomerates produced by the liquid reverse process, when compared with the reverse plastic dough process (Fig.8-9). The capability of the mixer to homogenize the dough thus does not seem to affect the dispersion of the individual properties of the agglomerates.(ii) The final stage of the addition of power during the reverse process is conducted in order to promote the controlled breakage mechanisms of the continuous dough. The breakage mechanisms are generated by adding the native particles under mechanical stresses to produce the wet agglomerates. The wet agglomerates are made by the association of small pieces of dough and of the dry native particles. It should be pointed out that the spans of size distributions obtained by using the reverse process are narrower than that obtained for direct agglomeration process. The breakage mechanisms during the reverse process are thus not less efficient in order to produce grains with homogeneous characteristics, compared to the growth mechanisms during the direct process. The reverse process makes it possible to increase the agglomeration yields of the process.Wade et al. (2014Wade et al. ( , 2015) ) also indicated that size distribution of the granules may be simpler to be controlled in the reverse-phase granulation process. The size of the granules progressed towards a unimodal distribution indicating that improved mechanical dispersion occurred. Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10

  Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10

Figure 1 :

 1 Figure 1: Schematic description of the three investigated agglomeration processes, direct process (A), reverse plastic process (B), or reverse liquid process (C), as a function of the process water content.

Figure 2 :

 2 Figure 2: Schematic description of the successive stages during the three investigated agglomeration processes: direct process (A) or reverse processes (B).

Figure 3 :

 3 Figure 3: Typical normalized electric input power curves monitored during the agglomeration of wet agglomerates at 30% water content. With the direct agglomeration process (A), the reverse plastic process (B), and the reverse liquid process (C).

Figure 4 :

 4 Figure4: Impact of the agglomeration process (direct process, reverse plastic process, or reverse liquid process), and of the process water content (at 30% (A), 40% (B), or 50% (C) water content), on the measured particle size distribution curves of the wet agglomerates.

Figure 5 :

 5 Figure 5: Impact of the process water content (at 30, 40, or 50%) and of the agglomeration process (direct (A), reverse plastic (B), or reverse liquid (C) process) on the measured particle size distribution curves of the wet agglomerates.

Figure 6 :

 6 Figure 6: Mass proportion for the 4 classes of the wet agglomerates, as a function of the process water content (at 30, 40, or 50%) and the process type: direct process (A), reverse plastic process (B), or reverse liquid process (C).

Figure 7 :

 7 Figure 7: Impact of the process water content on the apparent median diameter (d 50 ) (A) and apparent span diameter dispersion (B) of the wet agglomerates produced by the different processes.

Figure 8 :

 8 Figure 8: Water content values for the 4 different classes of the wet agglomerates, as a function of the process water content (at 30, 40, or 50%) and the process type: direct process (A), reverse plastic process (B), or reverse liquid process (C).

Figure 9 :Figure 10 :

 910 Figure 9: Compactness values for the 4 classes of the wet agglomerates, as a function of the process water content (at 30, 40, or 50%) and the process type: direct process (A), reverse plastic process (B), or reverse liquid process (C).

Figure 11 :

 11 Figure 11: Impact of the process water content on the rheological properties (cohesion and friction coefficient) of the selected wet agglomerates (1.25 < diameter < 2 mm) produced by the different processes.

Figure 12 :

 12 Figure 12: Microstructure by SEM of the dried agglomerates produced by the different process (direct, reverse plastic, or reverse liquid process) at the different process water content (at 30, 40, or 50%).

Figure 13 :

 13 Figure13: Impact of the process water content on the properties (compactness, color characteristics and the swelling properties) of the dried agglomerates, produced by the different processes.

Figure 14 :

 14 Figure 14: Hydro-textural description (compactness vs. measured water content) of the wet agglomerates and of the dried agglomerates, produced by the different processes (direct, reverse plastic, or reverse liquid processes).

Figure 1 :Figure 2 :

 12 Figure 1: Schematic description of the three investigated agglomeration processes, direct process (A), reverse plastic process (B), or reverse liquid process (C), as a function of the process water content.

Figure 3 :

 3 Figure 3: Typical normalized electric input power curves monitored during the agglomeration of wet agglomerates at 30% water content. With the direct agglomeration process (A), the reverse plastic process (B), and the reverse liquid process (C).

Figure 4 :

 4 Figure4: Impact of the agglomeration process (direct process, reverse plastic process, or reverse liquid process), and of the process water content (at 30% (A), 40% (B), or 50% (C) water content), on the measured particle size distribution curves of the wet agglomerates.

Figure 5 :

 5 Figure 5: Impact of the process water content (at 30, 40, or 50%) and of the agglomeration process (direct (A), reverse plastic (B), or reverse liquid (C) process) on the measured particle size distribution curves of the wet agglomerates.

Figure 6 :

 6 Figure 6: Mass proportion for the 4 classes of the wet agglomerates, as a function of the process water content (at 30, 40, or 50%) and the process type: direct process (A), reverse plastic process (B), or reverse liquid process (C).

Figure 7 :

 7 Figure 7: Impact of the process water content on the apparent median diameter (d 50 ) (A) and apparent span diameter dispersion (B) of the wet agglomerates produced by the different processes.

Figure 8 :

 8 Figure 8: Water content values for the 4 different classes of the wet agglomerates, as a function of the process water content (at 30, 40, or 50%) and the process type: direct process (A), reverse plastic process (B), or reverse liquid process (C).

  Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10

Figure 9 :

 9 Figure 9: Compactness values for the 4 classes of the wet agglomerates, as a function of the process water content (at 30, 40, or 50%) and the process type: direct process (A), reverse plastic process (B), or reverse liquid process (C).

Figure 10 :

 10 Figure 10: Impact of the process water content on the compactness of the selected wet agglomerates (1.25 < diameter < 2 mm), produced by the different processes.

Figure 11 :

 11 Figure 11: Impact of the process water content on the rheological properties (cohesion and friction coefficient) of the selected wet agglomerates (1.25 < diameter < 2 mm) produced by the different processes.

Figure 12 :Figure 13 :

 1213 Figure 12: Microstructure by SEM of the dried agglomerates produced by the different process (direct, reverse plastic, or reverse liquid process) at the different process water content (at 30, 40, or 50%).

Table 1 :

 1 Description of the experimental conditions (initial load of powder, mass of added water, and mass of added powder) defined for the successive stages of the agglomeration processes at the different process water content. Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10.046

		Process water	Initial load of Mass of added Mass of added	Total final
		content (% db)	powder (g)	water (g)	powder (g)	Load (g)
	Direct	30	1347	153	--	1500
		40	1250	250	--	1500
		50	1167	333	--	1500
	Reverse	30	307	154	1039	1500
	(75%)	40	500	250	750	1500
		50	667	333	500	1500
	Reverse	30	191	153	1156	1500
	(110%)	40	314	252	934	1500
		50	417	333	750	1500

Table 2 :

 2 Mechanical energy consumption (kJ) measured during the successive stages of the agglomeration processes at the different process water content.

		Process		Mechanical energy consumption (kJ)
		water content	Total	Initial load	Water addition	Fragmen-tation
		(% db)	Process	mixing	stage	stage
	Direct	30	11.3 (± 0.6) a	0.76 (± 0.07) e	10.4 (± 0.9) de	-
		40	12.9 (± 0.8) b	0.60 (± 0.05) d	12.3 (± 1.2) e	-
		50	27.0 (± 1.6) d	0.57 (± 0.06) d	26.4 (± 2.4) f	-
	Reverse	30	25.6 (± 1.5) d 0.64 (± 0.06) de	7.2 (± 0.8) c	13.1 (± 1.4) a
	(75%)	40	34.3 (± 2.0) e	0.11 (± 0.02) b	9.1 (± 1.0) d	24.9 (± 2.6) b
		50	60.6 (± 3.5) f	0.26 (± 0.03) c	10.8 (± 1.0) de	49.3 (± 5.3) d
	Reverse	30	13.2 (± 0.8) b	0.05 (± 0.01) a	1.4 (± 0.2) a	11.8 (± 1.2) a
	(110%)	40	16.7 (± 1.0) c	0.31 (± 0.03) c	3.3 (± 0.4) b	13.1 (± 1.4) a
		50	37.7 (± 2.2) e	0.07 (± 0.01) a	10.2 (± 1.1) d	36.6 (± 4.2)

c Comment citer ce document : Rondet, E., Cuq, B., Cassan, D., Ruiz, T. (2016). Agglomeration of wheat powders by a novel reverse wet agglomeration process. Journal of Food Engineering, 173, 92-105. DOI : 10.1016/j.jfoodeng.2015.10.046

Acknowledgments

The authors sincerely acknowledge Miss Macarena Soledad Silva Castro for her significant contribution in the experimental work.

Version postprint

Comment citer ce document : Rondet, E., Cuq The process conditions control the properties of the agglomerates.

The agglomeration process is mainly promoted by fragmentation mechanism.


Thierry Pelaccia

Germain Forestier

Cédric Wemmert

Deconstructing the diagnostic reasoning of human versus artificial intelligence

Artificial intelligence (AI) is o en presented as the future of medical practice. The concept of AI was developed in the s and has been defined as "the use of a computer to model intelligent behaviour with minimal human intervention." [ ] It is an alternative to human intelligence, particularly as a replacement for the diagnostic skill of physicians. For several years, the scientific literature and lay media have commented that nonhuman intelligence could equal or even exceed human intelligence in diagnostic tasks [ ]. Human intelligence is evident in the concept of clinical reasoning [ ], which has been defined as "the internal mental processes that a physician uses when approaching clinical situations" [ ]. This central component of physicians' competence, once honed, allows them to make diagnoses [ ]. In medicine, clinical reasoning is o en understood from the perspective of cognitive psychology's information process theory [ ]. Artificial intelligence may refer to several di erent methods. Most AI diagnostics are based on machine learning algorithms that are "intelligent" enough to handle di icult and complex problems; algorithms rely on human intelligence for their creation [ ]. Recently, substantial progress has been made in this field through the resurgence of neural networks -a family of methods of machine learning -and particularly deep neural networks [ ]. Herein, we focus mainly on machine learning (specifically deep neural networks). We analyze the di erences in the ways humans and AI approach diagnostic reasoning to argue that human reasoning will not become obsolete in medical diagnosis. This is the author's version of an article published in the Canadian Medical Association Journal. The final authenticated version is available online at: https://doi.org/10. 1503/cmaj.190506 How do humans and AI perform diagnostic tasks and learn to make diagnoses?

Both humans and AI learn through repeated exposure to clinical cases, referred to as "experiences" for human intelligence and "examples" for AI. For both to develop, feedback, based on the intervention of an expert, is important. A physician solves most clinical problems in an intuitive and deductive way, whereas AI problem-solving depends on access to and analytical and deductive processing of large quantities of data that relate to the case.

Deductive versus inductive; intuitive versus analytical

To learn to make diagnoses, medical students must organize their experiences of many clinical cases in long-term memory [ ]. However, in addition to broad-ranging experience, the development of expertise requires understanding of context and the way in which disease is presented in that context; this is crucial to being able to solve new cases through a generalization process [ ]. Immediate, appropriate feedback on decision-making consolidates knowledge and enables future clinical reasoning [ ].

Physicians mainly use a hypothetico-deductive approach to make diagnoses [ ]. A er generating diagnostic hypotheses early, they spend most of their diagnostic time testing them by collecting more data. This approach is underpinned by cognitive processes that, according to the dual-process theory, can be either intuitive or analytical [ ]. Intuition -sometimes referred to as "pattern recognition" -is a process that works automatically and subconsciously [ , ]. It allows humans to generate diagnostic hypotheses early by taking a few pieces of information, associating them and comparing the result with patterns stored in long-term memory [ ]. These patterns are built through academic and clinical learning experiences, particularly repeated confrontation with similar situations [ ]. Intuition allows humans to consider only a few solutions -the most likely in the context -among all those that could be considered given the available data. This approach is essential given the limited capacity of the human brain to process information. Most researchers agree that intuitive processes are the main source of generation of diagnostic hypotheses for humans [ ].

Machine learning, however, depends on the development of an algorithm that "learns" important features from a data set known as a "training set" to then make predictions about other unknown data [ ]. For the learning to occur, data used for training must be labelled according to their association with the solution; these data are referred to as the "ground truth." For example, a patient's physiologic data must be associated with a label indicating whether the patient is sick or healthy. The ground truth is provided by a human expert (most o en a physician), either directly (e.g., image annotations) or through documents (e.g., clinical reports). Thus, unlike humans, who know thousands of small pieces of information (often referred to as "common sense"), AI is limited to the specific information provided for a specific task. Furthermore, for every new task, AI systems must usually start from scratch.

Artificial intelligence systems are composed of a model (representing the learned knowledge), a decision function (making it possible to answer to the problem when a new input is given) and an evaluation metric (to evaluate the quality of

Data

Physicians need very few data (i.e., to pieces of contextual or clinical information) to generate diagnostic hypotheses through intuition [ , ]. Subsequently, and to verify the hypotheses generated, additional data guided by the hypotheses are collected through the interview, clinical examination and additional tests. Human intelligence will transform data collected during the patient interview into something that can be processed through "semantic transformation" [ ]. For example, clinicians might transform "the first time" into "inaugural," or "several episodes" into "iterative." Most AI systems do not model intuition and therefore require substantial data to make a relevant diagnosis [ ]. This is why AI is presently most e ective in situations where all the data of the problem to be solved are immediately accessible, such as in medical imaging. Artificial intelligence also requires data transformation, but in AI this a much more complex and time-consuming process. Through data integration or data preprocessing, the data must be transformed to be computational, which means that all information needs to be digitized and categorized to be interpreted by the machine. This is one of AI's great challenges [ ].

How do humans and AI misdiagnose?

The rate of diagnostic errors in medical practice is estimated at about %-%, depending on the specialty [ ]. This translates into more than million misdiagnoses annually in the United States alone [ ]. Cognitive biases are considered to be the cause of most diagnostic errors [ ] and many biases have been reported in the medical scientific literature [ ]. Premature closure bias (i.e., the tendency to stop considering other hypotheses a er reaching a diagnosis) is considered to be the most common [ ]. Three other common biases are anchoring bias (the tendency to focus early on or more salient features of the initial presentation of the problem and failure to change this first impression in the light of data gathered later), availability bias (the tendency to consider diagnoses that are easy to remember, o en because they have recently been made, as more likely) and confirmation bias (the tendency to consider only confirmatory data in relation to the generated hypothesis, while ignoring or underestimating contradictory data) [ ].

In most instances, the error rate for AI can be calculated accurately by comparing the results provided by the AI model to expected results (considered to be the truth) [ ]. Errors in AI are not comparable to human errors as they mostly result from problems that arise during the learning step, usually poor training data quality or an irrelevant evaluation metric [ ]. Having a data set that expresses the entire variety of the data and the real associations between them, and that does not contain misclassified examples and does not present any bias that could lead the AI to learn false assumptions, is essential. Other sources of errors, imprecisions or uncertainty could include the use of an inappropriate model (e.g., unable to represent the knowledge to learn) or poor experimental design (e.g., stopping learning too early).

What evidence supports the role of AI in medical diagnosis?

Artificial intelligence was shown to be capable of classifying skin cancers with a level of performance comparable to that of dermatologists when it was trained using a data set of nearly images and then tested on its ability to distinguish between common cancers and between a benign and a malignant lesion [ ]. Artificial intelligence was able to detect diabetic retinopathy just as well as ophthalmologists, while providing more consistent interpretation, high sensitivity and specificity, and an instantaneous result, following training using a data set of nearly retinal images and validation using further data sets [ ]. In an evaluation of more than deep-learning algorithms, diagnostic algorithms were shown to be better than histopathologists at diagnosing breast cancer metastases to lymph nodes in images of tissue sections when human specialists and AI were similarly time constrained [ ]. An AI algorithm trained on a data set of more than images was better than specialist radiologists at detecting pneumonia using chest radiographs [ ]. A machine-learning framework was trained to perform better than emergency medical dispatchers in recognizing cardiac arrest in emergency phone calls [ ].

What are the criticisms of AI in medical diagnosis?

Many studies conducted in the field of medical AI have been criticized for lack of scientific rigour, an unsatisfactory evaluation process or insu icient information reported in the methods [ ]. Moreover, the scientific literature skews toward publishing successful projects, whereas failures are rarely reported on blogs or consumer articles, if they are reported at all. These concerns undermine trust in AI.

A recent article [ ] described essential characteristics for trusting AI systems: fairness (training data and models must be free of bias to avoid unfair treatment of certain groups of patients), robustness (AI systems should be safe and secure), explainability (decisions provided by AI must be understandable by their users) and transparency (AI systems should include details of their development, deployment and maintenance). Explainability is perhaps the most challenging issue to solve. Although it is usually possible to explain physicians' reasoning and the origin of their decisions, many of the most powerful AI methods (e.g., deep neural networks) are o en criticized for being a "black box" [ ]. Currently, machine learning on medical data most o en takes the form of retrospective analysis of large routinely collected data sets with careful scrutiny of the results proposed by the AI.

An active and fast-growing field of AI seeks to make AI decisions explainable and understandable by users, with any preliminary research studies being conducted to reach this goal [ -]. Another challenge is to propose robust machinelearning methods [ ]. Meta-learning [ ] and transfer learning [ ] are promising avenues of research to help AI "remember" something and to learn "how to learn."

Future directions

Several studies have shown the extent to which AI can be used to make and support diagnosis in medicine. Since current evidence supports the e ectiveness of AI for only a small selection of diagnostic tasks and human experts remain able to learn and diagnose a wide array of conditions, human intelligence would seem to remain essential to diagnosis for now. However, the consistency with which AI can be trained to perform diagnoses when exposed to similar data independent of context -with errors fixable by improving the quality of data supplied for learning -supports the continued development of AI diagnostics. Physicians'reasoning has been shown to be sensitive to factors such as fatigue, sleep deprivation, interruptions, cognitive overload, noise or psycho-emotional status [ ], and to be influenced by cognitive biases [ ] with human error impossible to eliminate entirely and even di icult to reduce substantially [ ]. AI is becoming, and will continue to develop to be, a useful tool to mitigate human error and improve quality in medical practice. Yet the idea that AI is able to learn on its own and will replace physicians is a myth that needs to be deconstructed [ , ]. The potential of AI in medicine can be realized only if it is designed by the collaborative human intelligence of a physician and a data scientist [ ].

Because human and artificial intelligences are di erent and complementary, it is unlikely that AI will entirely replace the physician in the resolution of clinical problems. Artificial intelligence will be among the tools available to physicians seeking to make a diagnosis, to help with reasoning, reduce diagnostic uncertainty and augment shared decision-making, which also involves other health professionals and the patient. Diagnostic uncertainty is common in medical practice [ ]. Ar-tificial intelligence can enable physicians to favour one diagnostic hypothesis over another or to generate hypotheses that they had not previously considered.

The tasks facing stakeholders in the development of AI, among whom physicians will play a central and essential role, will be improving the quality and accessibility of medical data that can be used as a source of learning for AI while carefully respecting ethical considerations; being able to explain the results produced by AI to human intelligence; overcoming physicians'resistance related to fears of being downgraded when certain diagnostic tasks no longer rely solely on their intelligence; and training medical students early on in the integration of AI tools into their diagnostic practice, which implies extracting themselves from a historical and firmly rooted posture of the physician-centred diagnostic process [ ]. Under these conditions, AI can assume its place as a routine tool in medical practice.

  Centre for Training and Research in Health Sciences Education, Faculty of Medicine, University of Strasbourg; Hôpitaux universitaires de France the answer provided by AI compared with the ground truth). In AI, acquired knowledge can be stored in di erent ways. Deep neural networks are composed of layers of interconnected artificial neurons forming a "model." The architecture of the network and the weights associated with each connection represent a "decision function." From an input (e.g., a histopathological image), the neural network provides a prediction as an output (e.g., cancer or not cancer). To learn, the algorithm automatically optimizes its solution by calculating an evaluation metric function, which is basically the di erence between the output proposed by the algorithm and the ground truth. In deep neural networks, the error computed by the evaluation metric is back-propagated through the layers of the network, and the algorithm modifies the weights of the connections between the neurons. The process is iterated until the algorithm proposes accurate outputs on the training set. Problem solving by AI is thus di erent from the hypothetico-deductive approach used by humans. Intuitive reasoning is di icult to model or simulate as it is based on experience that bypasses a conscious "orderly sequential analysis" of a situation, which is the core of an algorithm. Therefore, AI uses an analytical approach in an inductive mode (i.e., it systematically moves from data toward the solution) [ ]. Although humans understand cause-and-e ect relations, these are not yet modelled in AI. This subject has been studied for a long time in AI, but it is only recently that first attempts to define an AI that "thinks like a human" have been proposed [ ].

Strasbourg, Strasbourg, France Institute of Research in Computer Science, Mathematics, Automation and Signal (Forestier), Université de Haute-Alsace, Mulhouse, France The Engineering Science, Computer Science and Imaging Laboratory, University of Strasbourg, Illkirch,

Acknowledgment

This article is partly based on a lecture given by the first author at the congress of the French National Society of Internal Medicine on June , , and published in the congress proceedings.


Stephan Shrader

Sihem Ben

MULTILEVEL INTEGRATION OF EXPLORATION UNITS : BEYOND THE AMBIDEXTROUS ORGANIZATION

How firms combine incremental and radical innovation? The ambidextrous model suggests to differentiate exploration units from exploitation units. We show the importance of integration between these entities in outlining the tensions between them. We suggest that integration takes place on a multilevel basis and relies on various mechanisms.

INTRODUCTION

Several authors have stressed that large multidivisional firms are efficient in exploiting existing product lines but face obstacles when exploring new businesses and technologies (Dougherty, 1992 ;Leonard-Barton, 1992). Several organizational designs were suggested for innovating organizations from autonomous (Christensen, 1998), to integrated (Iansiti, 1997) and hybrid (Tushman and O'Reilly, 1997) or modular (Galunic and Eisenhardt, 2001). We focus on the hybrid ones, the ambidextrous model, that suggest the establishment of specific units dedicated to the exploration of radical innovations and separated from the units in charge of the exploitation of the established products. The ability to balance and combine these two different processes, exploration and exploitation, in one firm is a critical challenge but few works analyze it at a fined-grained level. Recent works on the ambidextrous model (O'Reilly and Tushman, 2004;Benner and Tushman, 2004;Smith and Tushman, 2005) underscore the importance of strategic integration of these units, identifying this as the responsibility of the top management without elaborating on how they should bring it about. We developed an in-depth case study in a large multidivisional automotive supplier that wanted to enhance its growth through innovation and created a unit dedicated to the development of radical innovations. In this research we further characterize the ambidextrous organizational model by focusing on the activities performed by the unit in charge of radical innovations. Furthermore, we looked at the tensions associated to these activities and the effective forms of integration between this entity and those in charge of the established products. The paper shows how firms can enable an exploratory entity to exist separately from exploitation entities and yet be integrated. Another contribution is to link the literature analysing the dynamic of reconfiguration of resources (Galunic and Eisenhardt, 1996;2001) to the work on the ambidextrous organizations.

THEORETICAL BACKGROUND

On the basis of the seminal work of Burns and Stalker (1961) and Abernathy and Utterback (1978) who shed light on the specificities of organization dedicated to innovation, Tushman and O'Reilly (1997) apply the opposition between exploration and exploitation proposed by March (1991). They distinguish the "exploiting units" whose operations follow a certain routine allowing incremental innovation from the "exploratory units", which are less finalized and formalized and whose mission is to generate radical innovation. Thus, according to Tushman and O'Reilly (1997), the generation of radical innovation is the core mission of the exploratory units.

Some work underline (Christensen, 1998) the separation of the entities dedicated to the development of new businesses such as the disruptive innovation. However, this separation can in some situations lead to the isolation of the structures focused on exploration which fail to have their idea accepted and to draw on the resources of the firm's other structures.

Other authors suggest that exploratory activity does not need to be separated and can be developed within the existing structure by autonomous entrepreneurs (Leonard-Barton, 1995). The benefits of integrating various areas of expertise for innovation were highlighted by Clark andFujimoto (1991) andIansiti (1997). However, recent work (Charue-Duboc and Midler, 2000;Ben Mahmoud-Jouini, 2004) underscored that cross functional and integration teams fall short of developing radical innovations.

Henderson and Clark (1990) highlighted that in order to achieve architectural and radical innovations, the organization must be re-designed and resources recombined because these innovations call into question the very structure of the organization. In the same line of thought, Galunic andEisenhardt (1996, 2001) stressed the firms' need to continuously restructure the divisions in order to adapt to rapid technological changes and market trends anchoring the superiority of the multidivisional form in this capability. But, with the exception of the pioneering work of Burgelman (1983Burgelman ( , 2002)), research studies that provide a detailed analysis of resource recombination processes and their management within the organization are few and far between.

The most recent works on the ambidextrous organization (Benner and Tushman, 2003;O'Reilly and Tushman, 2004) stress the importance of the separation of the exploratory and exploitation units and at the same time their integration that they assign to the top management. Gibson and Birkinshaw (2004) develop an alternative view with the concept of contextual ambidexterity: the behavioural capacity to simultaneously demonstrate alignment and adaptability.

Our research aims at understanding how ambidexterity can be actually achieved. We explain how firms enable exploratory entities and exploitative unit to exist separately and yet be integrated. We analyze the dynamic of resources reconfiguration that enables the ambidextrous organization.

RESARCH SITE AND METHOD

To address this question, we developed a longitudinal study in a firm that created a new entity in order to enhance radical innovation.

Research Site

Our field work took place within one of the ten largest suppliers of the automotive industry which works with the main car manufacturers throughout the world and operates in all main geographic zones. The company is a large multidivisional firm : each division is dedicated to a functionally consistent product line. To stand out from its competitors and ensure growth in this highly competitive market, in January 2004, Domauto (a pseudonym), created a new organizational structure designed to enhance radical innovation and innovative synergies across its divisions. A new entity, the "powertrain efficiency domain of innovation" henceforth referred to as PTE, was structured in a separate unit existing alongside the previous organization. The objective for this new entity was to offer manufacturers innovative products and services, based on integrating components and exploiting synergies between the products and the industrial capabilities of the five contributing divisions the powertrain (PWT) scope in Domauto. This new unit was responsible for exploring innovations to enhance the efficiency of the powertrain, in particular radical innovations. Its mission was defined as follows: "to offer automobile manufacturers solutions capable of anticipating their most stringent requirements in the powertrain".

PTE is led by a high status manager (the former R&D Senior Executive of a division which produces components within the scope of the PWT). PTE relies on a working group of 10 persons referred to as "the team" comprising, for each contributing division, representatives from R&D and marketing functions. Two other persons were dedicated to PTE on a full-time basis: one to monitor the projects launched by PTE and the other to manage the marketing function.

PTE has positively influenced the capacity of the firm to foster radical innovation. Using the Henderson and Clark (1990) typology, among the innovation tracks generated by PTE, 29 were radical innovations, 2 were architectural, 4 were modular and more than 5 were incremental.

PTE can be regarded as an exploratory entity operating within the PWT scope. It is responsible for managing innovation within this scope and for medium and long-term growth through innovation. On the other hand, Domauto's divisions can be regarded as exploitation units. They are organized into business units, each one dedicated to a functionally consistent product line. The creation of the PTE alongside the divisions gave Domauto several of the characteristics of an ambidextrous organization. This company offered the researcher a setting to tackle their research question.

Research method

We developed our field study in this new PTE entity. This allowed us to observe and analyze the operational activities of the new entity as well as its numerous relationships with the divisions.

Data was collected over a period of 24 months from January 2004 until December 2005 and started with the creation of PTE. During the first six months, the authors spent one day onsite each week, then three days a month over the following 18 months. Data was collected from multiple sources: observation, interviews and reviews of documents. Observations consisted of passive participation (sitting in) at each PTE team meeting over the two years. During these meetings, we focused on discussions between the team members and on the progress of PTE activities. In total, we attended and documented 53 "team meetings" amounting to a total of 353 hours with a mean presence at these meetings of 8 persons of the company. Observations also involved sitting in on project meetings when exploratory projects were launched after the first six months. The "team" meeting was our principal place of observation because it provided a valuable database of all the activities undertaken by PTE, the obstacles encountered, and the relationship between the new entity and the divisions.

According to the paradigm of grounded research, (Glaser and Strauss, 1967;Miles and Huberman, 1984;Eisenhardt, 1989), and according to the research tradition within our research centre, our analysis drew on detailed field notes, interview notes, transcripts of meetings and company documents. Our orientation with regard to data analysis was inductive and the aim was to generate insights into how this new entity managed the exploration process and overcame interdependency issues. Qualitative analysis is an inherently dynamic and ongoing process and we conducted multiple readings of field notes, minutes of meetings and documentation to create categories and identify recurring themes. We proceeded iteratively, with the early stages being more open-ended than the later ones.

DATA ANALYSIS

PTE's activities typology and the benefits of separation

We identified six types of activities carried out by this entity during this period. The first activity is identifying innovation tracks and structuring and managing projects to explore these innovations. We differentiate two main types of exploratory projects: developing innovative products within the new scope, and incorporating several innovations developed separately by the divisions into a prototype or Democar. The second activity is drawing up a technological strategy on the new scope, based on an understanding of technological and market trends. This selection of planned exploratory projects represents PTE's technological roadmap. The third activity is developing interactions with targeted customers and experts on specific technological issues associated with the PWT scope which is larger than the divisions' one. This led to interactions with different customer representatives and at different stages of the car development process. The fourth activity is organizing external communications on the new scope, for example for World Auto Shows. The fifth activity is analyzing potential acquisition and partnership targets. Finally, PTE was also involved in proposing reorganizations and resource redeployments across divisions in order to rationalize product lines where different divisions had developed similar products or technologies or to enhance visibility for customers. The activities were generally carried out in connection with a technical object. The detailed analysis of these activities showed the extent to which separation from the divisions was necessary to carry out the exploration mission of PTE.

Interdependencies and tensions between the exploratory entity and the divisions

While separating PTE from the divisions appeared to bring benefits, at the same time, many interdependencies existed between the exploratory entity and the exploitation units. These interdependencies gave rise to tensions that have occasionally been mentioned in the literature but never been described in detail. We identified four types of tensions. The first occurs when the exploratory unit has to share scarce resources, like experts, with the divisions in order to develop its exploratory projects. This competition between the divisions' development projects and PTE's exploratory projects appears also on other topics : the technological strategies of the exploitation entities and the exploratory entity can be incompatible. It is the case, for example, when some technologies are pushed by a division and rejected by the exploratory entity or vice versa. This leads to a second type of tension. A third type of tension emerges when there is incompatibility between the image that the two entities want to promote towards the customer. Finally, PTE created new market and technological knowledge that could be useful for both of these entities. However, the utilisation and management of this knowledge base can be conflictual.

DISCUSSION

Multilevel integration of exploration units

Integration was required to overcome these tensions and take advantage of complementary aspects between exploratory and exploitative units. Our work suggests that both separation and integration between the entities are required; We reconcile the views of authors recommending autonomous designs for innovation organization with those in favour of integrated. Our results are consistent with the recent thesis put forward by Westerman et al. (2006), arguing that "strategic contingencies of uncertainty and interdependence created conflicting signals for managing integration and separation" and that "contrary to many theoretical recommendations none of the modes is purely autonomous or purely integrated".

We highlighted the various integration strategies developed by an exploratory unit to deal with interdependencies and overcome the tensions associated to the separation integration duality. Four levels of integration were outlined: the top management, the PTE leader himself, the division VPs and divisions senior R&D executives and the middle managers of the divisions. Hence, integration is not carried out purely at the level of corporate senior management (Tushman and O'Reilly, 1997) and does not rely solely on ambidextrous individuals (Gibson and Birkinshaw, 2004). The corporate management level in particular corporate R&D VP and the Exec VP to whom the PTE leader reports is the first of four integration levels. The second level is the PTE leader that plays a major role in integration through his frequent informal contacts with the divisions and his initiative in inviting experts and senior managers from the divisions to important events organized by PTE. According to that, he can be compared to an ambidextrous individual. Through their participation in various committee meetings, the division VPs and divisions senior R&D executive contribute towards integrating their divisions' strategies with that of PTE. Finally, the middle managers of the divisions participating in the PTE team meeting and the members of the exploratory projects contribute to integrating the exploratory units to the divisions.

Our analysis also shows four types of integration mechanisms:

-permanent cross-divisional working groups, -ad hoc cross-divisional committee meetings, -assigning transversal subjects to a high status actor such as the PTE leader, giving him de facto authority and responsibility for integration, -specific ambidextrous individuals such as the PTE leader or the PTE team members involved in the activities and working for their division. The behavioral orientation of key individuals towards connecting disconnected people has recently been emphasized by Obstfeld (2005) and predicts involvement in innovation.

Resources reconfiguration

Besides the fact that the exploratory entity itself is the result of resources reconfiguration this entity contributes to the resources recombination considered by Galunic and Eisenhardt (2001) as a competitive advantage of the multidivisional firm facing a rapidly changing environment. Two activities of PTE relate to this issue: recommending and analyzing partnerships or acquisition targets and redeploying resources across divisions. In these two types of resources reconfiguration, PTE appeared to be an important actor in preparing, proposing and implementing it although this reconfiguration did not directly affect the organizational boundaries of the exploratory entity itself. Galunic and Eisenhardt (2001) place responsibility for this restructuring at senior management level. Our longitudinal field work enabled us to demonstrate that these changes are not carried out solely at the instigation of senior managers. However, they are prompted by an actor capable of identifying and examining this type of decision and enjoying a certain level of autonomy and legitimacy on the basis of a cross-divisional position, technological and market knowledge. In relation to the above-mentioned activities, PTE has developed a unique technological and market knowledge. Resources reconfiguration based on this new knowledge are therefore driven by different and complementary motivations to those of senior management as well as divisions.

CONCLUSION

Perspectives on organizational design for innovating firms have tried to tackle the challenge of balancing exploration and exploitation. The ambidextrous model and the reconfiguration of resources are one of them but very little detailed investigation of how organization actually achieves ambidexterity and reconfiguration has been conducted. The aim of this paper is to link these two research trends and understand how firms actually enable exploratory and exploitative entities to exist separately and yet be integrated. Our study raises important issues for both theory and practice.

It complements the rapidly growing literature on ambidexterity in providing empirical evidence of what organizational design principles are appropriate in the context of technological innovation. We suggest that both separation and integration of exploratory and exploitative entities are required. We highlight the interdependencies between the exploratory entity and the divisions and the tensions that arose.

We provide a framework to analyze how integration is achieved. We emphasize the multilevel dimension of integration and outline four levels. We also underscore four types of integration mechanisms. We show that these various integration strategies were combined.

This analysis of integration is of direct managerial importance as it shifts from the question of should exploration be structurally separated to how dealing with interdependencies and achieving integration between exploratory and exploitative entities.

We have only touched upon the interesting issue of resource reconfiguration and how it relates to the exploratory entity.

By grounding our study in detailed data of an exploratory entity operation collected on site we also complement the literature from a methodological perspective. Over the past years most studies have relied on interviewing and survey data methodologies that tend to lose some of the richness of the underlying phenomena. On-site observation allowed us to discover new phenomena on interdependencies between exploration and exploitation entities.

This article analyzes integration strategies within the firm across its divisions. However we believe that our findings are of relevance also for integration across the boundaries of the firm and could expand the concept of integration to inter-firm relationships. This should be explored in further research.