Novel computational tools in bakery process data analysis: a comparative study

Rousu, Juho; Flander, Laura; Suutarinen, Marjaana; Autio, Karin; Kontkanen, Petri; Rantanen, Ari

doi:10.1016/s0260-8774(02)00221-2

Cited by 19 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They illustrate the specificity of the technology of the kneading equipment and the used recipe. The extension of such works to all operations of bread-making process would allow defining tools for decision support and innovation, and to predict their impact on the properties sensory of the product (Rousu et al, 2003). Previous approach based on the incorporation of "knowhow" (Ndiaye, Della Valle, & Roussel, 2009) permits a qualitative model which considers bread-making process as a chain of various discrete operations without taking into account the dynamics of each operation.…”

Section: Introductionmentioning

confidence: 99%

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

Lamrini¹,

Valle²,

Trelea³

et al. 2012

Food Control

View full text Add to dashboard Cite

a b s t r a c tThis paper presents a dynamic model of the kneading process based on artificial neural networks. This dynamic neuronal model allows predicting the bread dough temperature and the delivered power necessary to carry out mechanical work. This neuronal technique offers the advantage of very short computational times and the ability to describe nonlinear relationships, sometimes causal, explicit or implicit, between the input and output of a system. We used the recurrent neural networks to capture the dynamic of the process. The type and the number of inputs of the neural networks, as well as the nature of the learning set, the architecture and the parameter learning technique have been studied. The comparison of the results with experimental data shows the possibility to predict the temperature and the power delivered to the dough for various operating conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Lamrini¹,

Valle²,

Trelea³

et al. 2012

Food Control

View full text Add to dashboard Cite

show abstract

“…Using data-mining approaches, SQC can provide information to identify the important factors involved in product quality. These approaches are rare in the food industry, but an example of application of several machine learning and statistical methods to predict product quality in industrial bakery processes was studied by Rousu et al (2003).…”

Section: Introductionmentioning

confidence: 99%

A decision support system to control the aeration of sponge finger batters

et al. 2006

View full text Add to dashboard Cite

“…Moreover, SVM is capable of learning in high-dimensional feature space with fewer training data. Recently, SVM has been successfully applied to numerous classification problems, such as electronic nose data (Pardo & Sberveglieri, 2002;Trihaas & Bothe, 2002) and bakery process data (Rousu et al, 2003).…”

Section: Introductionmentioning

confidence: 99%