Tutorial review of error evaluation in experimental water research at the example of membrane filtration

Experimental water research lacks clear methodology to estimate experimental error. Especially when natural waters are involved, the characterization tools bear method-specific artifacts while the varying environmental conditions prevent regular repeats. This tutorial review identifies common mistakes, and proposes a practical procedure to determine experimental errors at the example of membrane filtration. Statistical analysis is often applied to an insufficient number of repeated measurements, while not all error sources and contributions are considered. This results in an underestimation of the experimental error. Variations in relevant experimental parameters need to be investigated systematically, and the related errors are quantified as a half of the variation between the max and min values when standard deviation is not applicable. Error of calculated parameters (e.g. flux, pollutant removal and mass loss) is estimated by applying error propagation, where weighing contributions of the experimental parameters are considered. Appropriate judgment and five-time repetition of a selected experiment under identical conditions are proposed to validate the propagated experimental error. For validation, the five repeated data points should lie within the estimated error range of the error bar. The proposed error evaluation procedure is adaptable in experimental water research and intended for researchers to identify the contributing factors of an experimental error and carry out appropriate error quantification and validation. The most important aim is to raise awareness of the necessity to question error methodology and reproducibility of experimental data, to produce and publish high quality research.

Delta checks

Delta check is an electronic error detection tool. It compares the difference in sequential results within a patient against a predefined limit, and when exceeded, the delta check rule is considered triggered. The patient results should be withheld for review and troubleshooting before releasing to the clinical team for patient management. Delta check was initially developed as a tool to detect wrong-blood-in-tube (sample misidentification) errors. It is now applied to detect errors more broadly within the total testing process. Recent advancements in the theoretical understanding of delta check has allowed for more precise application of this tool to achieve the desired clinical performance and operational set up. In this Chapter, we review the different pre-implementation considerations, the foundation concepts of delta check, the process of setting up key delta check parameters, performance verification and troubleshooting of a delta check flag.

Interference in ELISA

ELISA is a well-established technique used worldwide to quantify analytes present in a diverse milieu of biological samplings. It is especially important to clinicians who rely on the accuracy and precision of the test to administer patient care. Those results are to be held with great scrutiny since the assay is subject to error caused by interfering substances found in the sample matrix. In this chapter, we examine the nature of such interferences and discuss approaches to identify and offer remedies to remove the interference and validate the assay.

Relationship between biological variation and delta check rules performance

OBJECTIVES

The performance of delta check rules has been considered to be dependent on the biological variation characteristics of the analyte of interest. The assumed relationships have not been formally studied. The mathematical relationship between biological variation and delta check rules is explored in this study.

DESIGN AND METHODS

From the mathematical model for absolute difference delta check, the threshold for specificity and sensitivity are observed to be normalized differently. For specificity, the threshold is normalized by the within-subject biological variation (expressed as a coefficient of variation, CV_i), whereas for sensitivity the threshold is normalized by the between-subject biological variation (expressed as a coefficient of variation, CV_g). This highlights the different roles the two biological variations play in affecting the absolute difference distribution for correct and switched patient samples. Analogous to absolute difference delta checks, for relative difference delta checks, the expressions for specificity and sensitivity are scaled by CV_i and CV_g, respectively. However, the expressions are independent of μ_g(the average of the population).

RESULTS

A comparison between the mathematical model and empirical/ historical laboratory data obtained from patients was conducted for both absolute and relative difference delta checks. In general it was found that the specificity obtained from the historical laboratory data was less than the model predicted values, while on the other hand, good correspondence was obtained between the experimental sensitivity and predicted sensitivity.

CONCLUSIONS

The difference in within-subject biological variation in different patients may contribute to the observed discrepancy in predicted and empirical delta check performance.

Spurious Results in Mineral and Electrolyte Analysis

Assessment of health status and the course of treatment of patients are often determined by results obtained from analysis of blood parameters. Errors in results can occur and cause inappropriate interpretation of laboratory data and, therefore, disease states. Electrolytes and minerals are particularly prone to spurious results; therefore, it is critical that factors influencing inappropriate resulting be recognized, and steps taken to minimize them.

A short history, principles, and types of ELISA, and our laboratory experience with peptide/protein analyses using ELISA

Playing a critical role in the metabolic homeostasis of living systems, the circulating concentrations of peptides/proteins are influenced by a variety of patho-physiological events. These peptide/protein concentrations in biological fluids are measured using various methods, the most common of which is enzymatic immunoassay EIA/ELISA and which guide the clinicians in diagnosing and monitoring diseases that inflict biological systems. All the techniques where enzymes are employed to show antigen-antibody reactions are generally referred to as enzymatic immunoassay EIA/ELISA method. Since the basic principles of EIA and ELISA are the same. The main objective of this review is to present an overview of the historical journey that had led to the invention of EIA/ELISA, an indispensible method for medical and research laboratories, types of ELISA developed after its invention [direct (the first ELISA method invented), indirect, sandwich and competitive methods], problems encountered during peptide/protein analyses (pre-analytical, analytical and post-analytical), rules to be followed to prevent these problems, and our laboratory experience of more than 15 years.

Consequences of using pooled versus individual samples for designing environmental monitoring sampling strategies

Choosing an appropriate sampling strategy for chemical analysis within environmental monitoring includes the important decision of whether to sample and store individual or pooled samples. This choice impacts on future analyses from Environmental Specimen Bank samples. A number of advantages exist to support using either individual or pooled samples for temporal trend studies. However, it is important to know the total and analytical variance to be able to design the best sampling strategy. Statistical power in temporal or spatial studies is determined by the random/unexplained sample variation. The relationship between chemical analytical error and other sources of variation, as well as the cost for collection, preparation of samples and chemical analysis, will determine the number of individuals in each pool, and the number of pools that should be analysed to achieve high cost efficiency and good statistical power. Various scenarios of different numbers of individual samples, different numbers of pooled samples containing various numbers of individual specimens, the relationships between chemical analytical error and other sources of sample variance, have been compared by simulating random sampling from computer generated populations using realistic measures of variation from ongoing monitoring activities. These results offer guidance in the design of a cost-efficient, statistically sound sampling strategy.