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Multi-tissue partial volume estimation in MRI images is investigated with a viewpoint related to spectral unmixing as used in hyperspectral imaging. The main contribution of this paper is twofold. It firstly proposes a theoretical analysis of the statistical optimality conditions of the proportion estimation problem, which in the context of multi-contrast MRI data acquisition allows to appropriately set the imaging sequence parameters. Secondly, an efficient proportion quantification algorithm based on the minimisation of a penalised least-square criterion incorporating a regularity constraint on the spatial distribution of the proportions is proposed. Furthermore, the resulting developments are discussed using empirical simulations. The practical usefulness of the spectral unmixing approach for partial volume quantification in MRI is illustrated through an application to food analysis on the proving of a Danish pastry. [ABSTRACT FROM AUTHOR]

The effects of the number of fat layers (4–162 layers) on expansion and loss of CO 2 and water in Danish pastries (yeasted puff pastry) were studied at each processing step. Proving contributed to most of the expansion (200–300%) and no specific role of fat layers was evidenced at this stage; this was confirmed by measurements of CO 2 released. Relative expansion during baking ranged from 20% to 65% for 4 to 32 fat layers and remained unchanged above 48 layers. Images cross sections of Danish pastry showed that bubbles were few for 4 fat layers, but very large (3–4 cm) and elongated in the direction of layering. For high numbers of fat layers, bubbles were numerous, smaller and round (2–7 mm). The loss of water vapor retention during baking in relation to fragmentation of fat layers during sheeting explained the leveling-off of expansion and the decreasing size of bubbles. [ABSTRACT FROM AUTHOR]

The mechanism commonly assumed to explain the exceptional lift of laminated doughs is that fat layers act as a barrier to steam produced during baking. Successive folding and sheeting steps increase the number of fat layers and the lift, up to a point (48 fat layers in our study) where fat layers would suddenly rupture. Void spaces in the fat layers were characterized on confocal laser scanning microscopy images of Danish pastry from 8 to 64 fat layers: the greater the number of fat layers, the greater the average number of void spaces, following a power law from 16 layers. At 64 fat layers, Danish pastry appeared to be over-laminated. The size of small void spaces (2-50μm) was shown to be independent of the number of fat layers. The number of large void spaces was linearly dependent on the number of fat layers. These results failed to show any transition at 48 fat layers. [ABSTRACT FROM AUTHOR]

We studied the potential of various machine learning and statistical methods in the prediction of product quality in industrial bakery processes. The methods included classification and regression tree, decision list, neural network, support vector machine and Bayesian learning algorithms as well as statistical multivariate methods. Our data originated from two industrial bakery processes: a sourdough rye bread and a Danish pastry process. In our studies, the Naive Bayesian algorithm turned out to be the best classifier building algorithm while the partial least squares (PLS) method was the best regression method. The prediction accuracy of these models improved significantly by pruning the original set of variables. In this study, two response variables could be predicted on a level that justifies further study: rye bread pH could be predicted with high accuracy with Naive Bayesian Classifier, and Danish pastry height could be predicted with a moderately high correlation with PLS. [Copyright &y& Elsevier]