scholarly journals Quality Control Criteria for Analysis of Organic Traces in Water

2002 ◽  
Vol 2 ◽  
pp. 1040-1043
Author(s):  
José L. Martinez Vidal ◽  
Antonia Garrido Frenich ◽  
Francisco J. Egea González

The aim of this paper is the discussion of quality control (QC) criteria for environmental monitoring of organic contaminants at trace levels in water. In addition, QC criteria in the identification and confirmation of target analytes have been considered.

2004 ◽  
Vol 4 (2) ◽  
pp. 23-30
Author(s):  
K. Connell ◽  
M. Pope ◽  
K. Miller ◽  
J. Scheller ◽  
J. Pulz

Designing and conducting standardized microbiological method interlaboratory validation studies is challenging because most methods are manual, rather than instrument-based, and results from the methods are typically subjective. Determinations of method recovery, in particular, are problematic, due to difficulties in assessing the true spike amount. The standardization and validation process used for the seven most recent USEPA 1600-series pathogen monitoring methods has begun to address these challenges. A staged development process was used to ensure that methods were adequately tested and standardized before resources were dedicated to interlaboratory validation. The interlaboratory validation studies for USEPA Method 1622, for Cryptosporidium, USEPA Method 1601 for coliphage, and USEPA Method 1605 for Aeromonas assessed method performance using different approaches, due the differences in the nature of the target analytes and the data quality needs of each study. However, the use of enumerated spikes in all of the studies allowed method recovery and precision to be assessed, and also provided the data needed to establish quantitative quality control criteria for the methods.


Kybernetes ◽  
2017 ◽  
Vol 46 (5) ◽  
pp. 876-892 ◽  
Author(s):  
Parisa Fouladi ◽  
Nima Jafari Navimipour

Purpose This paper aims to propose a new method for evaluating the quality and prioritizing of the human resources (HRs) based on trust, reputation, agility, expertise and cost criteria in the expert cloud. To evaluate some quality control (QC) factors, a model based on the SERVQUAL is used. Design/methodology/approach The aim of this paper is to offer a fast and simple method for selecting the HRs by the customers. To achieve this goal, the ranking diagram of different HRs based on the different criteria of QC is provided. By means of this method, the customer can rapidly decide on the selection of the required HRs. By using the proposed method, the scores for various criteria are evaluated. These criteria are used in the ranking of each HR which is obtained based on the evaluation conducted by previous customers and their colleagues. First, customers were asked to select their needed criteria and then by constructing a hierarchical structure, the ranking diagram of different HRs is achieved. Using a ranking system based on evaluating the quality of the model, satisfy the customer needs to be based on the properties of HRs. Also, an analytical hierarchical process-based ranking mechanism is proposed to solve the problem of assigning weights to features for considering the interdependence between them to rank the HRs in the expert cloud. Findings The obtained results showed the applicability of the radar graph using a case study and also numerically obtained results showed that a hierarchical structure increases the quality and speed rating of HR ranking than the previous works. Originality/value The suggested ranking method in this paper allows the optimal selection due to the special needs of any given customer in the expert cloud.


2021 ◽  
pp. 546-554
Author(s):  
Carol D. Ryff ◽  
Jennifer Morozink Boylan ◽  
Julie A. Kirsch

We challenge the view that “one is better than none” on grounds that single-item assessments perpetuate a simplistic view of well-being, which is out of touch with how the field has progressed over recent decades. We also question blanket advocacy for measures in the absence of substantive scientific questions that require thoughtful engagement with the prior literature to make sound measurement choices. Substantive illustrations, invoking research on well-being and health in different cultural and socioeconomic contexts, are provided. Quality control is also essential in making sound measurement choices. Numerous contenders fail at this juncture because they have no conceptual foundation and also lack rigorous psychometric analyses documenting their empirical credibility. Another critical element in adjudicating measurement quality is extent of prior usage: evidence that the measures have taken hold in the scientific community, indicated by citation counts and number of published studies. We conclude that all such quality control criteria were inadequately addressed or missing in the measurement recommendations put forth in Chapter 17.


2005 ◽  
Vol 32 (6Part10) ◽  
pp. 2000-2000
Author(s):  
P Dunscombe ◽  
C Arsenault ◽  
G Mawko ◽  
J Bissonnette ◽  
H Johnson ◽  
...  

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