A new method for dynamic modelling of bread dough kneading based on artificial neural network

Lamrini, Bouchra; Valle, Guy Della; Trelea, Ioan-Cristian; Perrot, Nathalie; Trystram, Gilles

doi:10.1016/j.foodcont.2012.01.011

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…For other types of bread, several optimization strategies were carried out to select the best kneading, leavening or baking parameters. These optimizations employed an extensive mathematical modeling, genetic algorithms or ANN [34][35][36][37][38][39]. Therefore, to the wealth of the Carasau bread manufacturing process, the industrial bread production must be supplied with an ICT, cost-effective tool capable of allowing a real-time, continuous monitoring of the main process parameters.…”

Section: Figurementioning

confidence: 99%

“…As discussed in Sections 1 and 2, in the bread baking industry there are several environmental and operative parameters which can influence the outcome of the process [34]. For any engineered and optimal decision making process, such as in the case of ANN trained for the kneading enhancing [36], the set of information to gather from the industrial workflow (see Figure 1) must be established by addressing the company and operators [26].…”

Section: Process Parametersmentioning

confidence: 99%

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A Wireless Sensors Network for Monitoring the Carasau Bread Manufacturing Process

et al. 2019

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This work copes with the design and implementation of a wireless sensors network architecture to automatically and continuously monitor, for the first time, the manufacturing process of Sardinian Carasau bread. The case of a traditional bakery company facing the challenge of the Food-Industry 4.0 competitiveness is investigated. The process was analyzed to identify the most relevant variables to be monitored during the product manufacturing. Then, a heterogeneous, multi-tier wireless sensors network was designed and realized to allow the real-time control and the data collection during the critical steps of dough production, sheeting, cutting and leavening. Commercial on-the-shelf and cost-effective integrated electronics were employed, making the proposed approach of interest for many practical cases. Finally, a user-friendly interface was provided to enhance the understanding, control and to favor the process monitoring. With the wireless senors network (WSN) we designed, it is possible to monitor environmental parameters (temperature, relative humidity, gas concentrations); cinematic quantities of the belts; and, through a dedicated image processing system, the morphological characteristics of the bread before the baking. The functioning of the WSN was demonstrated and a statistical analysis was performed on the variables monitored during different seasons. offered by Internet-of-Things (IoT) solutions to meet sustainability (or waste reduction) criteria [2]. The food industry is managing critical changes related to consumer needs, to health and safety concerns and to the demand of food products which should be differentiated and high-quality [5]. However, the quality of these products can change suddenly during the production process; thus, leading to the need for ad hoc, reliable and real-time strategies to monitor the manufacturing process [5,6]. Therefore, to satisfy customer demands, the digital monitoring of the supply chain is required to provide a deep knowledge of the crucial production steps, in order to detect the weaknesses of and permit the optimization of the whole process, to reduce the maintenance difficulties and lower the costs [5,6]. Moreover, this digitalization trend in food industry can favor the automatic data collection, the lowering of paperwork and the enabling the development of real-time, robust feedback strategies [2]. Furthermore, the challenge of ensuring acceptable adoption costs of new information and communication technologies (ICT) by small and medium size activities calls for a reasonable and effective answers [4].As a solution to these problems, the use of wireless senors networks (WSNs) was proposed [4]. WSNs are recognized as a relevant technology of the 21st century. A WSN can be defined as a low-cost platform which connects large networks of sensors [7][8][9]. They are systems which comprise radio-frequency (RF) transceivers, sensors, micro-controller or processor and power sources [5,6]. WSNs are a novel and interesting manifestation of the IoT technology [10][11][12]....

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Section: Figurementioning

confidence: 99%

Section: Process Parametersmentioning

confidence: 99%

A Wireless Sensors Network for Monitoring the Carasau Bread Manufacturing Process

et al. 2019

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“…In addition, the ANN technique permits the study of relationships between the input variables and of the outputs of the process using a limited number of experimental run (Bingöl, Hercan, Elevli, & Kılıç, 2012), being commonly applied for modeling and optimization of various processes. ANNs have been used in prediction of some parameters in various processes (Alvarez, 2009;Chakraborty & Shrivastava, 2019;Kashaninejad, Dehghani, & Kashiri, 2009;Koji c et al, 2019;Lamrini, Della Valle, Trelea, Perrot, & Trystram, 2012;Sablani, Baik, & Marcotte, 2002) and food quality evaluation (Broyart & Trystram, 2003;Funes et al, 2015;Goyache et al, 2001) or to predict dough rheological properties (farinograph peak, extensibility, and maximum resistance) from the torque developed during mixing (Ruan, Almaer, & Zhang, 1995). Other studies revealed that ANNs were used to predict the parameters of farinograph (Abbasi, Emam-Djomeh, & Seyedin, 2011), extensograph, alveograph, and fundamental rheological properties of dough from several physicochemical properties of flour (Abbasi et al, 2012;Abbasi, Emam-Djomeh, & Ardabili, 2014;Abbasi & Mohammadifar, 2015).…”

Section: Introductionmentioning

confidence: 99%

Dough bread from refined wheat flour partially replaced by grape peels: Optimizing the rheological properties

Mironeasa

2019

J Food Process Engineering

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The valorization of grape peels by‐product to supplement wheat bread has immense potential, particularly in refined wheat flour (RWF) to increase the dietary fibers. The main aims of this study were successful modeling of dough rheological properties using predictive models, investigating the effect of factors, grape peels flour (GPF) variety, level and particle size (PS) of GPF added in RWF on responses, and finally, optimizing the formulation with respect to dough rheological behavior. The rheological properties of grape peels–wheat composite flour formulated with GPF from two variety and increasing levels of GPF (0–9%) in the presence of different PS (large, L > 500 μm; medium, 200 μm > M < 500 μm, and small, S < 200 μm) of GPF was evaluated using farinograph, alveograph, and dynamic oscillation measurements. The artificial neural network models (R2 > .82) developed to predict dough rheological properties highlighted an improved estimation and predictive capabilities compared with response surface methodology models (R2 > .77). The multiobjective optimization approach allowed anticipation of the optimal value for each response in terms of dough rheological properties as function of GPF from white variety of small PS, which replaced RWF at level of 3.81%. Practical applications In the composite flour formulation to produce effectiveness and nutritive bread, optimizing dough rheology in relation to the formulation factors represents the actual need for the bread‐making industry. Dough behavior during mixing (water absorption, dough stability, dough development time, and degree of softening), alveograph parameters (dough tenacity, extensibility, and deformation energy), and dynamic rheological properties (elastic modulus, viscous modulus, loss tangent, and complex viscosity) are considered essential to optimization of grape peels–wheat composite flour formulation. The current study revealed the efficacy of modeling and optimization between response surface and artificial neural network techniques for the grape peels–wheat flour dough rheological properties. The information given from this study would help the industry to develop new bread‐making products with the desired particle size with optimum functional and nutritional properties.

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“…., 9) the dynamic evolution of the 'screening-and-optimization' dataset (Hadiyanto et al, 2008). The inherent messiness of the replicated multi-response dataset is demonstrated in order to justify the necessity for the suggested hybridization of a distribution-free treatment by an intelligent processor (Milliken and Johnson, 2004;Lamrini et al, 2012). The required replicate compression will cast the problem to the menacing unreplicated-saturated condition which is convincingly resolved while overcoming reliance on the ambiguous sparsity assumption (Besseris, 2012).…”

Section: Doementioning

confidence: 99%

Profiling multiple static and transient puff-pastry characteristics with a robust-and-intelligent processor

Besseris

2015

Journal of Food Engineering

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A new method for dynamic modelling of bread dough kneading based on artificial neural network

Cited by 21 publications

References 25 publications

A Wireless Sensors Network for Monitoring the Carasau Bread Manufacturing Process

A Wireless Sensors Network for Monitoring the Carasau Bread Manufacturing Process

Dough bread from refined wheat flour partially replaced by grape peels: Optimizing the rheological properties

Profiling multiple static and transient puff-pastry characteristics with a robust-and-intelligent processor

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