Evaluation of the spot urine sampling technique to assess urinary pseudouridine excretion in lactating dairy cows

Selostus: Mahdollisuus lyhytaikaisen virtsankeruun käyttöön lypsylehmien virtsan pseudouridiinin erityksen määrittämisessä

A simple device for sampling of volumetric forest soil cores

Summary

Variation in soil organic carbon and thickness of soil horizons within a boreal forest stand - effect of trees and implications for sampling

Summary

Cut-off importance sampling of bole volume

Summary

Inventory of sparse forest populations using adaptive cluster sampling

Abstract

Comparison of randomized branch sampling with and without replacement at the first stage

Abstract

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

Stratification of regional sampling by model-predicted changes of carbon stocks in Forested mineral soils

Selective papers of the workshop on "Development of models and forest soil surveys for monitoring of soil carbon", Koli, Finland, April 5-9 2006.

Improving quality at the preanalytical phase of blood sampling : literature review

Julkaisumaa: 158 TW TWN Taiwan