Food and feed materials characterization, risk assessment, and safety evaluations can only be ensured if QC measures are based on valid analytical data, stemming from representative samples. The Theory of Sampling (TOS) is the only comprehensive theoretical framework that fully defines all requirements to ensure sampling correctness and representativity, and to provide the guiding principles for sampling in practice. TOS also defines the concept of material heterogeneity and its impact on the sampling process, including the effects from all potential sampling errors. TOS's primary task is to eliminate bias-generating errors and to minimize sampling variability. Quantitative measures are provided to characterize material heterogeneity, on which an optimal sampling strategy should be based. Four critical success factors preceding analysis to ensure a representative sampling process are presented here.
The presence of mycotoxins, in particular aflatoxin B1 , can cause significant health problems as well as severe economic loss, and are therefore regulated with respect to maximum acceptable concentration for various feed- and foodstuffs. International regulatory authorities have recognized the importance of representative sampling, and sampling guidelines that only partly comply with the Theory of Sampling have been formulated. Practical guidance regarding sampling, including correct design and operation of sampling devices and explanations on how to develop sufficient sampling protocols are lacking in current guidelines. These are critical practicalities of main importance, especially when dealing with trace concentrations and/or concentrations that are irregularly distributed, as for mycotoxins. Heterogeneity characterization, which is necessary to be able develop valid sampling protocols or validation assessments of existing sampling operations, is currently not mentioned in the existing guidelines. This paper explains all critical practicalities with respect to sampling of mycotoxins for 1-D and 3-0 feed decision units. Correct design and use of sampling and mass reduction devices, as well as structural guidelines for correctly designing experimental heterogeneity characterizations are presented, allowing evaluation of sampling representativeness and determination of optimal number of increments per composite sample. Practical examples are given on how to extract information from variographic analysis and replication experiments, based on an extensive field trials performed to determine aflatoxin levels in various feed components.
Industrial energy efficiency measures are proving financially viable, but the implementation rate is stagnating. This results in the need to develop a comprehensive and standardized methodology to assess the multiple benefits of energy efficiency measures in an industrial context. However, a comprehensive methodology to assess the multiple benefits of energy efficiency measures are omitted. The methodology, as presented in this study, was developed and validated based on nine case studies performed between 2016 and 2018 in the Swiss industrial sector. The aim is to close this gap with the introduction of a three-phase standard methodology, applicable to a wide range of industrial processes and energy efficiency measures. The three phases are further split into individual steps, each pursuing a specific goal in order to facilitate the implementation of energy efficiency measures. The first phase, the delimitation, aims at defining the system boundaries of the considered industrial process(es). The second phase, the assessment, involves the identification, the quantification, and the monetization of multiple benefits, as well as the qualitative assessment of non-monetizable multiple benefits. The last phase, the evaluation, focusses on the integration of the obtained results into the financial valuation of the energy efficiency measure and, therefore, on the cash flow analysis and the determination of the payback time under consideration of the monetizable multiple benefits. The study has shown that the consideration of monetizable multiple benefits may reduce the payback time of energy efficiency measures by up to 40-85%.
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