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Aflatoxins (AFs) are toxic fungal secondary metabolites that are commonly detected in food commodities. Currently, there is a lack of generic methods capable of determining AFs both at postharvest stages in agricultural products and preharvest stages, namely, the agricultural soil. Here, we present a simple and reliable method for quantitative analysis of AFs in soil and food matrices at environmentally relevant concentrations for the first time, using the same extraction procedure and chromatography, either by HPLC-FLD or LC-MS. AFs were extracted from matrices by ultrasonication using an acetonitrile/water mixture (84:16, v + v) without extensive and time-consuming cleanup procedures. Food extracts were defatted with n-hexane. Matrix effects in terms of signal suppression/enhancement (SSE) for HPLC-FLD were within ±20% for all matrices tested. For LC-MS, the SSE values were mostly within ±20% for soil matrices but outside ±20% for all food matrices. The sensitivity of the method allowed quantitative analysis even at trace levels with quantification limits (LOQs) between 0.04 and 0.23 μg kg-1 for HPLC-FLD and 0.06-0.23 μg kg-1 for LC-MS. The recoveries ranged from 64 to 92, 74 to 101, and 78 to 103% for fortification levels of 0.5, 5, and 20 μg kg-1, respectively, with repeatability values of 2-18%. The validation results are in accordance with the quality criteria and limits for mycotoxins set by the European Commission, thus confirming a satisfactory performance of the analytical method. Although reliable analysis is possible with both instruments, the HPLC-FLD method may be more suitable for routine analysis because it does not require consideration of the matrix.

This work presents an approach for both dis- tributed synthesis and control for a network of discrete-time constrained linear systems without central coordinator. Every system in the network is dynamically coupled to a number of neighboring systems and it is assumed that communication among neighbors is possible. A model predictive controller based on distributed optimization is introduced, by which every system in the network can compute feasible and stabilizing control inputs online. Stability of the closed-loop network of systems is guaranteed by introducing local terminal cost functions and sets, which together satisfy invariance conditions in a distributed way. This includes in particular that the local terminal sets are not static but evolve over time. It is shown that synthesis of both quadratic terminal cost functions and corresponding terminal sets can be done by distributed optimization. Finally, closed-loop performance of the proposed controller is demonstrated on a coupled array of inverted pendulums.