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Publication# A reliable method of extracting the rheological properties of fruit purees from flow loop data

Résumé

A method based on Tikhonov regularization is used to process the volumetric flow rate against pressure drop data of different fruit purees generated by large-scale flow loops. These data are converted into shear-rate-against-shear-stress curves. Curves from flow loops with different dia are compared to verify that they are independent of dia. They are also compared against that obtained by a conventional method. Tikhonov regularization will simultaneously extract the yield stress of the purees from the flow loop data. The results obtained by Tikhonov regularization show it to be a very efficient way of processing the flow loop data of rheologically complex foods.

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Concepts associés (27)

Publications associées (34)

Ridge regression

Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. It has been used in many fields including econometrics, chemistry, and engineering. Also known as Tikhonov regularization, named for Andrey Tikhonov, it is a method of regularization of ill-posed problems. It is particularly useful to mitigate the problem of multicollinearity in linear regression, which commonly occurs in models with large numbers of parameters.

Elastic net regularization

In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods. The elastic net method overcomes the limitations of the LASSO (least absolute shrinkage and selection operator) method which uses a penalty function based on Use of this penalty function has several limitations. For example, in the "large p, small n" case (high-dimensional data with few examples), the LASSO selects at most n variables before it saturates.

Regularized least squares

Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution. RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions.

Proximité ontologique

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