Distribution-based maximum likelihood estimation methods are preferred for estimating Salmonella concentration in chicken when contamination data are highly left-censored

Postprocessing methods based on minimum detectable amount and method blank for data reporting of particle count and refining estimation of matrix spike recovery in environmental microplastics analysis

Data handling that converts the raw data into a deliverable dataset is a necessary step in any analytical work. This procedure involves applying detection limits to shaping the raw data to form a deliverable dataset. The detection limit for microplastics analysis is the minimum detectable amount (MDA) that can be calculated from the particle counts of procedural blank samples following the rules of the Poisson distribution. Currently, there is a lack of adequate data reporting guidance encompassing the MDA for microplastics analysis. The goal of this study was to establish a robust protocol for processing count-based raw data using the particle counts of the MDA and the procedural blank. Utilizing the dataset of an interlaboratory comparison exercise, effectiveness of the protocol was elaborated to generate a deliverable dataset and to accurately define the matrix spiking recoveries. The guidance was applied to the raw data of all size fractions (1 - >500 μm), four individual size fractions (>500, 212-500, 20-212, 1-20 μm), and two morphologies (fiber and non-fiber). Six possible data reporting scenarios were identified, with the raw data ranging well above the MDA to below the critical value. One-third (12 of 34) of the raw data for all size fractions needed blank-MDA corrections. The mean values of the spiking recoveries decreased by up to 10 % after performing the data reporting guidance. Application of this suggested data reporting guidance may be beneficial for high quality data for microplastics analysis.

Entropy-metric estimation of the small data models with stochastic parameters

The formalization of dependencies between datasets, taking into account specific hypotheses about data properties, is a constantly relevant task, which is especially acute when it comes to small data. The aim of the study is to formalize the procedure for calculating optimal estimates of probability density functions of parameters of linear and nonlinear dynamic and static small data models, created taking into account specific hypotheses regarding the properties of the studied object. The research methodology includes probability theory and mathematical statistics, information theory, evaluation theory, and stochastic mathematical programming methods. The mathematical apparatus presented in the article is based on the principle of maximization of information entropy on sets determined as a result of a small number of censored measurements of "input" and "output" entities in the presence of noise. These data structures became the basis for the formalization of linear and nonlinear dynamic and static models of small data with stochastic parameters, which include both controlled and noise-oriented input and output measurement entities. For all variants of the above-mentioned small data models, the tasks of determining the optimal estimates of the probability density functions of the parameters were carried out. Formulated optimization problems are reduced to the forms canonical for the stochastic linear programming problem with probabilistic constraints.