Use of neutron activation analysis and LC–ICP-MS in the development of candidate reference materials for As species determination

Arsenic (As) is widely found in marine organisms, mainly as organic arsenical compounds. With the introduction of regulations for As species in foodstuffs and for environmental control, it has become more important to quantify the amount of arsenic species present. To address this concern new reference materials (RMs) for validation and quality assurance of As speciation measurements are being developed, including a tuna fish tissue and a robalo liver tissue. Instrumental neutron activation analysis (INAA) is used in this work as a proven analytical tool for As determinations and for validating the As mass fraction determined by liquid chromatography coupled to inductively coupled mass spectrometry (LC–ICP-MS) in extracts of the candidate RMs and other fish samples including certified RMs. Various methods for the extraction of water-soluble As species were evaluated. The best results were acquired after methanol/acetone/water extraction yielding in 93 % extractable As in the tuna RM. This procedure was used for the LC–ICP-MS studies. The results demonstrate that INAA can account for 100 % of the distribution of As species in analytical processes. It complements LC–ICP-MS for the validation of the characterization of As species in the development of RMs for such species. Excellent agreement of measured results in certified RMs with the reported values confirms the validity of the applied procedures.     

Instrumental neutron activation analysis,a technique for measurement of Se,Hg, Fe,Zn, K,Mn, Br,and the Hg:Se ratio in brain tissue samples with results from the Memory and Aging Project (MAP)

An INAA method for measurement of Se, Hg, Fe, Cr, Zn, Mn, K, and Br in autopsy cerebellum, anterior putamen, white matter, mid-frontal cortex, and inferior temporal lobe. Se, Hg, Fe, Cr, and Zn were measured autopsy samples collected from participants of the Memory and Aging Project. The first study examined the association between seafood consumption, brain Hg and Se, Apolipoprotein E (APOE-ε4) status, and brain neuropathology. Following the initial study, the samples were archived. A subsequent method was developed to measure Mn, K, and Br in the archived brain tissue samples.     

Feasibility study for the preparation of a tuna fish candidate reference material for total As determination

This work describes the evaluation of several parameters for the preparation of a tuna fish candidate as a reference material (RM) in order to measure the total As mass fraction by slurry sampling graphite furnace atomic absorption spectrometry (SLS-GF AAS) and slurry sampling hydride generation atomic absorption spectrometry (SLS-HG AAS). The main parameters investigated were the homogeneity, analyte segregation and composition during material production. For candidate RM preparation, tuna fish was collected at a local market, cleaned, freeze-dried and treated using different procedures as follows: (1) ground in a cutting mill and separated in different particle sizes (2) ground in cryogenic mill. The mass fraction of As in the cryogenically ground sample was (4.77?±?0.19)?μg?g^?? for SLS-GF AAS and (4.61?±?0.34)?μg?g^?? for SLS-HG AAS. The accuracy of the procedures was checked with tuna fish certified reference material (BCR 627) with recoveries of 102 and 94% for SLS-GF AAS and SLS-HG AAS, respectively. The homogeneity factor was calculated for different pretreatment procedures and for particle sizes in the range of 500??50?μg, indicating good homogeneity, except for raw fish. There was no observed analyte segregation and no losses, no contamination and no changes in the microdistribution of material during preparation.     

A Generalized Conditional Gradient Method for Dynamic Inverse Problems with Optimal Transport Regularization

We develop a dynamic generalized conditional gradient method (DGCG) for dynamic inverse problems with optimal transport regularization. We consider the framework introduced in Bredies and Fanzon (ESAIM: M2AN 54:2351–2382, 2020), where the objective functional is comprised of a fidelity term, penalizing the pointwise in time discrepancy between the observation and the unknown in time-varying Hilbert spaces, and a regularizer keeping track of the dynamics, given by the Benamou–Brenier energy constrained via the homogeneous continuity equation. Employing the characterization of the extremal points of the Benamou–Brenier energy (Bredies et al. in Bull Lond Math Soc 53(5):1436–1452, 2021), we define the atoms of the problem as measures concentrated on absolutely continuous curves in the domain. We propose a dynamic generalization of a conditional gradient method that consists of iteratively adding suitably chosen atoms to the current sparse iterate, and subsequently optimizing the coefficients in the resulting linear combination. We prove that the method converges with a sublinear rate to a minimizer of the objective functional. Additionally, we propose heuristic strategies and acceleration steps that allow to implement the algorithm efficiently. Finally, we provide numerical examples that demonstrate the effectiveness of our algorithm and model in reconstructing heavily undersampled dynamic data, together with the presence of noise.