Sampling of particulate materials with significant spatial heterogeneity - Theoretical modification of grouping and segregation factors involved with correct sampling errors: Fundamental Sampling Error and Grouping and Segregation Error.

There has been an extensive abuse of Gy's Formula during the entire history of applied TOS (Theory of Sampling), it being applied too liberally to almost any aggregate material conceivable for many material classes of extremely different compositions with significant (to large, or extreme) fragment size distribution heterogeneity, for example many types of municipal and industrial waste materials. This abuse regimen is for the most part characterized by lack of fundamental TOS competence and the historical context of Gy's formula. The present paper addresses important theoretical details of TOS, which become important as sampling rates increase at the conclusion of the full 'lot-to-analysis sampling pathway regarding finer details behind TOS' central equations linking sampling conditions to material heterogeneity characteristics allowing the estimation of Total Sampling Error (TSE) manifestations. We derive a new, complementary understanding of the two conceptual factors, y the grouping factor and, z, the segregation factor, intended to represent the local (increment scale) and long-range (increment to lot-scale) heterogeneity aspects of lot materials, respectively. We contrast the standard TOS exposé with the new formulation. While the phenomenological meaning and content of the new proposed factors (y and z) remains the same, their numerical values and bracketing limits are different with z now representing more realistic effects of liberation and segregation combined. This new formulation makes it easier to get a first comprehensive grasp of TOS' dealings with sampling of significantly heterogeneous materials. We believe this may present a slightly easier path into the core issues in TOS when sampling and sub-sampling gets closer to the final aliquot scale.

Grab vs. composite sampling of particulate materials with significant spatial heterogeneity--a simulation study of "correct sampling errors".

Sampling errors can be divided into two classes, incorrect sampling and correct sampling errors. Incorrect sampling errors arise from incorrectly designed sampling equipment or procedures. Correct sampling errors are due to the heterogeneity of the material in sampling targets. Excluding the incorrect sampling errors, which can all be eliminated in practice although informed and diligent work is often needed, five factors dominate sampling variance: two factors related to material heterogeneity (analyte concentration; distributional heterogeneity) and three factors related to the sampling process itself (sample type, sample size, sampling modus). Due to highly significant interactions, a comprehensive appreciation of their combined effects is far from trivial and has in fact never been illustrated in detail. Heterogeneous materials can be well characterized by the two first factors, while all essential sampling process characteristics can be summarized by combinations of the latter three. We here present simulations based on an experimental design that varies all five factors. Within the framework of the Theory of Sampling, the empirical Total Sampling Error is a function of the fundamental sampling error and the grouping and segregation error interacting with a specific sampling process. We here illustrate absolute and relative sampling variance levels resulting from a wide array of simulated repeated samplings and express the effects by pertinent lot mean estimates and associated Root Mean Squared Errors/sampling variances, covering specific combinations of materials' heterogeneity and typical sampling procedures as used in current science, technology and industry. Factors, levels and interactions are varied within limits selected to match realistic materials and sampling situations that mimic, e.g., sampling for genetically modified organisms; sampling of geological drill cores; sampling during off-loading 3-dimensional lots (shiploads, railroad cars, truckloads etc.) and scenarios representing a range of industrial manufacturing and production processes. A new simulation facility "SIMSAMP" is presented with selected results designed to show also the wider applicability potential. This contribution furthers a general exposé of all essential effects in the regimen covered by "correct sampling errors", valid for all types of materials in which non-bias sampling can be achieved.

IUPAC project: a glossary of concepts and terms in chemometrics.

A project has been initiated by the International Union of Pure and Applied Chemistry (IUPAC) to create a glossary of concepts and terms in chemometrics. This will be accomplished by consultation with the community through the means of a wiki--a web site that can be modified by users (see http://www.iupacterms.eigenvector.com/index.php?title=Main_Page). Over time new terms can be added, and consensus definitions arrived at. The definitions will be published as IUPAC recommendations.

Effect of missing values in estimation of mean of auto-correlated measurement series.

Sampling and uncertainty of sampling are important tasks, when industrial processes are monitored. Missing values and unequal sources can cause problems in almost all industrial fields. One major problem is that during weekends samples may not be collected. On the other hand a composite sample may be collected during weekend. These systematically occurring missing values (gaps) will have an effect on the uncertainties of the measurements. Another type of missing values is random missing values. These random gaps are caused, for example, by instrument failures. Pierre Gy's sampling theory includes tools to evaluate all error components that are involved in sampling of heterogeneous materials. Variograms, introduced by Gy's sampling theory, have been developed to estimate the uncertainty of auto-correlated process measurements. Variographic experiments are utilized for estimating the variance for different sample selection strategies. The different sample selection strategies are random sampling, stratified random sampling and systematic sampling. In this paper both systematic and random gaps were estimated by using simulations and real process data. These process data were taken from bark boilers of pulp and paper mills (combustion processes). When systematic gaps were examined a linear interpolation was utilized. Also cases introducing composite sampling were studied. Aims of this paper are: (1) how reliable the variogram is to estimate the process variogram calculated from data with systematic gaps, (2) how the uncertainty of missing gap can be estimated in reporting time-averages of auto-correlated time series measurements. The results show that when systematic gaps were filled by linear interpolation only minor changes in the values of variogram were observed. The differences between the variograms were constantly smallest with composite samples. While estimating the effect of random gaps, the results show that for the non-periodic processes the stratified random sampling strategy gives more reliable results than systematic sampling strategy. Therefore stratified random sampling should be used while estimating the uncertainty of random gaps in reporting time-averages of auto-correlated time series measurements.

Bioavailability of sediment-associated PCDD/Fs and PCDEs: relative importance of contaminant and sediment characteristics and biological factors.

Factors that determine accumulation of sediment-associated polychlorinated dibenzo-p-dioxins and furans and polychlorinated diphenyl ethers into semipermeable membrane devices (SPMDs) and benthic oligochaete worms (Lumbriculus variegatus) were examined. These factors included both physical-chemical and structural characteristics of the contaminants (water solubility, lipophilicity, dipole moment, molecular size, and conformation) and sediment characteristics (organic carbon content, particle size, aromaticity, and polarity of organic carbon). The results of partial least squares regression analysis indicated that lipophilicity alone is not a sufficient predictor for contaminant bioaccumulation potential, even though it is a significant contributor. It was shown that contaminant molecular size and conformation (specifically planarity/nonplanarity) as well as sediment characteristics also have a significant role. The studied factors contributed up to 63-88% of the variation in accumulation data for SPMDs and 50-65% for oligochaetes. Comparison of (bio)accumulation factors (BAF28d for oligochaetes and AF28d for SPMDs) revealed that accumulation of contaminants in oligochaetes is largely influenced by biological factors (e.g., feeding habits), while the physical-chemical nature of the process is emphasized for SPMDs.