The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Milagros M Gomezgomez - One of the best experts on this subject based on the ideXlab platform.
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simultaneous characterisation of silver nanoparticles and determination of dissolved silver in chicken meat subjected to in vitro human gastrointestinal digestion using single particle inductively coupled plasma mass spectrometry
Food Chemistry, 2017Co-Authors: K Ramos, Lourdes Ramos, Milagros M GomezgomezAbstract:In this study, a chicken meat containing AgNPs (candidate reference material Nanolyse 14) has been used as a Model Matrix to study the fate and behaviour of AgNPs upon oral ingestion following an in vitro Model that included saliva, gastric and intestinal digestions. The behaviour of a 40nm AgNPs standard solution during the three digestion steps was also evaluated. Sample preparation conditions were optimised to prevent AgNPs oxidation and/or aggregation and to ensure the representativeness of the reported results. Total silver released from the test sample and the evaluated AgNP standard was determined by inductively coupled plasma mass spectrometry (ICPMS). The presence of both AgNPs and dissolved silver in the extracts was confirmed by single particle (SP)-ICPMS analysis. AgNPs were sized and the particle number concentration determined in the three digestion juices. Experimental results demonstrated differentiated behaviours for AgNP from the standard solution and the meat sample highlighting the relevance of using physiological conditions for accurate risk assessment. In the most realistic scenario assayed (i.e., spiked chicken meat analysis), only 13% of the AgNPs present in the reference material would reach the intestine wall. Meanwhile, other bioaccessible dissolved forms of silver would account for as much as 44% of the silver initially spiked to the meat paste.
J. Elbadraoui - One of the best experts on this subject based on the ideXlab platform.
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Minimum-Variance Pseudo-Unbiased Low-Rank Estimator for Ill-Conditioned Inverse Problems
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: I. Yamada, J. ElbadraouiAbstract:This paper presents a mathematically novel low-rank linear statistical estimator named minimum-variance pseudo-unbiased low-rank estimator for applications to ill-conditioned linear inverse problems. Based on a simple fact: `any low-rank estimator can not be a (uniformly) unbiased estimator', we introduce pseudo-unbiased low-rank estimator, as an ideal low-rank extension of unbiased estimators. The minimum-variance pseudo-unbiased low-rank estimator minimizes the variance of estimate among all pseudo-unbiased low-rank estimators, hence it is characterized as a solution to a double layered nonconvex optimization problem. The main theorem presents an algebraic structure of the minimum-variance pseudo-unbiased low-rank estimator in terms of the singular value decomposition of the Model Matrix in the linear statistical Model. The minimum-variance pseudo-unbiased low-rank estimator is not only a best low-rank extension of the minimum-variance unbiased estimator (i.e., Gauss-Markov estimator) but also a nontrivial generalization of the Marquardt's low-rank estimator (Marquardt 1970)
Isao Yamada - One of the best experts on this subject based on the ideXlab platform.
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mv pure estimator minimum variance pseudo unbiased reduced rank estimator for linearly constrained ill conditioned inverse problems
IEEE Transactions on Signal Processing, 2008Co-Authors: Tomasz Piotrowski, Isao YamadaAbstract:This paper proposes a novel estimator named minimum-variance pseudo-unbiased reduced-rank estimator (MV- PURE) for the linear regression Model, designed specially for the case where the Model Matrix is ill-conditioned and the unknown deterministic parameter vector to be estimated is subjected to known linear constraints. As a natural generalization of the Gauss-Markov (BLUE) estimator, the MV-PURE estimator is a solution of the following hierarchical nonconvex constrained optimization problem directly related to the mean square error expression. In the first-stage optimization, under a rank constraint, we minimize simultaneously all unitarily invariant norms of an operator applied to the unknown parameter vector in view of suppressing bias of the proposed estimator. Then, in the second-stage optimization, among all pseudo-unbiased reduced-rank estimators defined as the solutions of the first-stage optimization, we find the one achieving minimum variance. We derive a closed algebraic form of the MV-PURE estimator and show that well-known estimators-the Gauss-Markov (BLUE) estimator, the generalized Marquardt's reduced-rank estimator, and the minimum-variance conditionally unbiased affine estimator subject to linear restrictions-are all special cases of the MV-PURE estimator. We demonstrate the effectiveness of the proposed estimator in a numerical example, where we employ the MV-PURE estimator to the ill-conditioned problem of reconstructing a 2-D image subjected to linear constraints from blurred, noisy observation. This example demonstrates that the MV-PURE estimator outperforms all aforementioned estimators, as it achieves smaller mean square error for all values of signal-to-noise ratio.
K Ramos - One of the best experts on this subject based on the ideXlab platform.
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simultaneous characterisation of silver nanoparticles and determination of dissolved silver in chicken meat subjected to in vitro human gastrointestinal digestion using single particle inductively coupled plasma mass spectrometry
Food Chemistry, 2017Co-Authors: K Ramos, Lourdes Ramos, Milagros M GomezgomezAbstract:In this study, a chicken meat containing AgNPs (candidate reference material Nanolyse 14) has been used as a Model Matrix to study the fate and behaviour of AgNPs upon oral ingestion following an in vitro Model that included saliva, gastric and intestinal digestions. The behaviour of a 40nm AgNPs standard solution during the three digestion steps was also evaluated. Sample preparation conditions were optimised to prevent AgNPs oxidation and/or aggregation and to ensure the representativeness of the reported results. Total silver released from the test sample and the evaluated AgNP standard was determined by inductively coupled plasma mass spectrometry (ICPMS). The presence of both AgNPs and dissolved silver in the extracts was confirmed by single particle (SP)-ICPMS analysis. AgNPs were sized and the particle number concentration determined in the three digestion juices. Experimental results demonstrated differentiated behaviours for AgNP from the standard solution and the meat sample highlighting the relevance of using physiological conditions for accurate risk assessment. In the most realistic scenario assayed (i.e., spiked chicken meat analysis), only 13% of the AgNPs present in the reference material would reach the intestine wall. Meanwhile, other bioaccessible dissolved forms of silver would account for as much as 44% of the silver initially spiked to the meat paste.
I. Yamada - One of the best experts on this subject based on the ideXlab platform.
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Minimum-Variance Pseudo-Unbiased Low-Rank Estimator for Ill-Conditioned Inverse Problems
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: I. Yamada, J. ElbadraouiAbstract:This paper presents a mathematically novel low-rank linear statistical estimator named minimum-variance pseudo-unbiased low-rank estimator for applications to ill-conditioned linear inverse problems. Based on a simple fact: `any low-rank estimator can not be a (uniformly) unbiased estimator', we introduce pseudo-unbiased low-rank estimator, as an ideal low-rank extension of unbiased estimators. The minimum-variance pseudo-unbiased low-rank estimator minimizes the variance of estimate among all pseudo-unbiased low-rank estimators, hence it is characterized as a solution to a double layered nonconvex optimization problem. The main theorem presents an algebraic structure of the minimum-variance pseudo-unbiased low-rank estimator in terms of the singular value decomposition of the Model Matrix in the linear statistical Model. The minimum-variance pseudo-unbiased low-rank estimator is not only a best low-rank extension of the minimum-variance unbiased estimator (i.e., Gauss-Markov estimator) but also a nontrivial generalization of the Marquardt's low-rank estimator (Marquardt 1970)
