The document summarizes the results of a within-laboratory analytical quality control exercise conducted with 30 laboratories analyzing standard solutions of nutrients from February 1997 to 1998. It found that while 70% of laboratories participated, the response time was slow and only around 50% of requested analyses were completed. Reasons for incomplete participation included unfamiliarity with quality control procedures, lack of necessary equipment and chemicals, and workload. The document evaluates the precision of results using coefficient of variation, finding some results less precise than quality control goals from published sources. It emphasizes the importance of quality control for obtaining reliable data.
Quality assurance and quality control programs are necessary to ensure the reliability and accuracy of analytical environmental data. An inter-laboratory study by the EPA showed wide variation in nutrient concentration measurements between laboratories. Measurement of total dissolved solids and electrical conductivity also showed significant variation between laboratories. Shewhart control charts can be used to monitor the statistical control of analytical procedures and identify sources of random and systematic error by tracking the spread and displacement of results from control samples over time. Control limits on the charts indicate thresholds for corrective action to maintain method accuracy.
Quality assurance and quality control programs are necessary to ensure the reliability and accuracy of analytical environmental data. An inter-laboratory study by the EPA showed wide variation in nutrient concentration measurements between laboratories. Measurement of total dissolved solids and electrical conductivity also showed significant variation between laboratories. Shewhart control charts can be used to monitor the statistical control of analytical procedures and identify issues by tracking results from quality control samples against mean values and standard deviations. Key aspects of a quality assurance program include sample handling procedures, standardized analytical methods, analyst training, instrument maintenance, calibration procedures, analytical quality control tests, data management, and control chart monitoring.
qualification of analytical instruments..M pharmacy 1st year.validationSohailPattan
The document discusses the qualification and calibration of analytical instruments like electronic balances and UV-Visible spectrophotometers. It provides details on the various tests and parameters to be checked during qualification of these instruments to ensure they meet performance requirements. These include tests for baseline flatness, wavelength accuracy, transmittance, absorbance, emission lines, stray light, resolution and photometric linearity. The document also outlines the recommended frequency of qualification and calibration.
Calibration establishes the relationship between instrument measurements and known standard values through a series of steps. Key aspects of calibration include identifying instruments and sources, following calibration procedures, documenting results, accounting for sources of error, and ensuring traceability to national standards. Calibration procedures vary based on instrument type, but generally involve evaluating instrument performance, establishing calibration curves using certified reference materials at multiple concentration levels, and quantifying samples based on the calibration curves.
Calibration of analytical instruments is important to ensure they are accurate and precise. It involves comparing an instrument's measurements to a reference standard to determine any adjustments needed. Regular calibration helps verify that instruments are suitable for their intended purposes in pharmaceutical analysis. It is necessary to comply with quality standards and regulations. The calibration process involves using traceable standards and documented procedures to evaluate instruments and certify their performance is within specified tolerance limits. Maintaining calibration records provides a history of each instrument's accuracy over time.
Principles and Instrumentation of QC Equipments by Sourav SharmaSourav Sharma
1. A conductivity meter works by applying an alternating current between two electrodes and measuring the resulting voltage. It uses this measurement to calculate conductance and then conductivity based on the cell constant.
2. Conductivity is a measure of a solution's ability to conduct an electric current and is proportional to the total dissolved ions in the solution. It is a common method used for quality control.
3. Modern conductivity cells use a 4-pole design which applies current to outer electrodes and measures voltage between inner electrodes to minimize polarization and interferences. This provides more reproducible results.
This document discusses quality assurance in haematology. It defines quality and introduces the concepts of quality control and quality assurance. Quality control aims to minimize errors through statistical sampling and verification of consistent performance. Quality assurance ensures reliable test results through adherence to standards within and outside the laboratory. This includes internal quality control, external quality assessment, and standardization using reference materials and methods. Several examples are provided of potential pre-analytical errors in sample collection, transport, and handling that can affect test results. Adherence to proper procedures is emphasized to avoid issues like hemolysis, clotting, and dilution.
Quality assurance and quality control programs are necessary to ensure the reliability and accuracy of analytical environmental data. An inter-laboratory study by the EPA showed wide variation in nutrient concentration measurements between laboratories. Measurement of total dissolved solids and electrical conductivity also showed significant variation between laboratories. Shewhart control charts can be used to monitor the statistical control of analytical procedures and identify sources of random and systematic error by tracking the spread and displacement of results from control samples over time. Control limits on the charts indicate thresholds for corrective action to maintain method accuracy.
Quality assurance and quality control programs are necessary to ensure the reliability and accuracy of analytical environmental data. An inter-laboratory study by the EPA showed wide variation in nutrient concentration measurements between laboratories. Measurement of total dissolved solids and electrical conductivity also showed significant variation between laboratories. Shewhart control charts can be used to monitor the statistical control of analytical procedures and identify issues by tracking results from quality control samples against mean values and standard deviations. Key aspects of a quality assurance program include sample handling procedures, standardized analytical methods, analyst training, instrument maintenance, calibration procedures, analytical quality control tests, data management, and control chart monitoring.
qualification of analytical instruments..M pharmacy 1st year.validationSohailPattan
The document discusses the qualification and calibration of analytical instruments like electronic balances and UV-Visible spectrophotometers. It provides details on the various tests and parameters to be checked during qualification of these instruments to ensure they meet performance requirements. These include tests for baseline flatness, wavelength accuracy, transmittance, absorbance, emission lines, stray light, resolution and photometric linearity. The document also outlines the recommended frequency of qualification and calibration.
Calibration establishes the relationship between instrument measurements and known standard values through a series of steps. Key aspects of calibration include identifying instruments and sources, following calibration procedures, documenting results, accounting for sources of error, and ensuring traceability to national standards. Calibration procedures vary based on instrument type, but generally involve evaluating instrument performance, establishing calibration curves using certified reference materials at multiple concentration levels, and quantifying samples based on the calibration curves.
Calibration of analytical instruments is important to ensure they are accurate and precise. It involves comparing an instrument's measurements to a reference standard to determine any adjustments needed. Regular calibration helps verify that instruments are suitable for their intended purposes in pharmaceutical analysis. It is necessary to comply with quality standards and regulations. The calibration process involves using traceable standards and documented procedures to evaluate instruments and certify their performance is within specified tolerance limits. Maintaining calibration records provides a history of each instrument's accuracy over time.
Principles and Instrumentation of QC Equipments by Sourav SharmaSourav Sharma
1. A conductivity meter works by applying an alternating current between two electrodes and measuring the resulting voltage. It uses this measurement to calculate conductance and then conductivity based on the cell constant.
2. Conductivity is a measure of a solution's ability to conduct an electric current and is proportional to the total dissolved ions in the solution. It is a common method used for quality control.
3. Modern conductivity cells use a 4-pole design which applies current to outer electrodes and measures voltage between inner electrodes to minimize polarization and interferences. This provides more reproducible results.
This document discusses quality assurance in haematology. It defines quality and introduces the concepts of quality control and quality assurance. Quality control aims to minimize errors through statistical sampling and verification of consistent performance. Quality assurance ensures reliable test results through adherence to standards within and outside the laboratory. This includes internal quality control, external quality assessment, and standardization using reference materials and methods. Several examples are provided of potential pre-analytical errors in sample collection, transport, and handling that can affect test results. Adherence to proper procedures is emphasized to avoid issues like hemolysis, clotting, and dilution.
This document provides information on quality control and quality assurance in medical laboratories. It defines key terms like quality control, quality assurance, and quality assessment. It describes variables that can affect result quality and sources of errors. Random errors are unpredictable variations while systematic errors create biases. The document outlines Westgard rules, which are used to evaluate analytical runs and detect random and systematic errors. Steps for resolving quality control problems and minimum criteria for determining when results are out of control are also discussed.
The document discusses calibration, including defining calibration as checking the accuracy of measuring instruments against a standard. It describes various calibration laboratories and standards in India such as NPL, ERTL, and ETDC. It explains the importance, purpose, and types of calibration, as well as requirements for calibration management systems and common instrument calibrations.
Analytical method validation as per ich and usp shreyas B R
Analytical method validation is a process of documenting/ proving that an analytical method provides analytical data acceptable for the intended use.After the development of an analytical procedure, it is must important to assure that the procedure will consistently produce the intended a precise result with high degree of accuracy. The method should give a specific result that may not be affected by external matters. This creates a requirement to validate the analytical procedures. The validation procedures consists of some characteristics parameters that makes the method acceptable with addition of statistical tools.
Calibration and Quality controls of automated hematology analyzerPranav S
This document discusses calibration and quality control of automated hematology analyzers. It begins with a brief history of hematology and automation in the field. Ensuring accurate results through quality assurance is important, involving preventative, assessment, and corrective activities like standardization, internal quality control, and external quality proficiency testing. Proper documentation, training, environment control, and management of preanalytical variables are among the general requirements for quality control and calibration. Calibration must be performed on new or repaired analyzers, and internal and external quality controls help monitor and ensure accuracy of results.
The analyst is required to analyze a number of QC samples throughout the run where there are decisions to be made based on a window of acceptance for each QC sample analyzed.
This document discusses key principles of calibration, including defining calibration as comparing a measuring instrument to a higher accuracy standard. It describes maintaining traceability through an unbroken chain of comparisons to national standards and evaluating uncertainty through factors that affect accuracy. The key characteristics of a calibration discussed are specifying a tolerance, using an accuracy ratio of 4:1 between the instrument and standard, and documenting traceability.
Third party quality controls are important to ensure accurate patient test results. They provide an independent assessment of analytical performance compared to controls from instrument or reagent manufacturers. The document describes several examples where Randox third party controls detected errors that dedicated manufacturer controls failed to identify, including instrument faults, procedural errors, and shifts in results between reagent batches. Regulatory bodies recommend using third party controls over manufacturer controls to independently monitor testing quality.
This document provides information on method validation. It begins with definitions of method validation and discusses why method validation is important. It states that method validation is required for non-standard methods, laboratory-designed methods, standard methods used outside their intended scope, and when modifications have been made to standard methods. The document discusses who is responsible for carrying out method validation and the extent of validation studies required for different types of analytical applications and circumstances. It provides information on various method performance characteristics that are evaluated during validation including accuracy, precision, sensitivity, selectivity, limits of detection and determination, linearity, specificity, repeatability, reproducibility, and robustness. The document concludes with sections that should be included in a validation plan and report.
This document describes a human liquid ready-to-use stable multi-analyte control containing 27 different analytes including antibody isotypes, complement components, and other specific proteins. It reports that the control material has an open vial stability of 30 days when stored between 2-8 degrees Celsius and a shelf life stability of at least 2 years under the same storage conditions based on measurements on various automated systems. The control is concluded to be a convenient ready-to-use material for clinical applications that standardizes the test menu and reduces errors.
This document provides information on calibrating and qualifying various analytical instruments. It discusses the importance of calibration and qualification to ensure proper functioning and accurate results. It describes the different types of qualification including design, installation, operational and performance qualification. It then provides details on specific calibration procedures for various instruments like electronic balances, pH meters, UV-Vis and IR spectrophotometers, and HPLC. The calibration procedures ensure the instruments meet parameters for accuracy, resolution, wavelength verification and flow rate consistency.
Data Analysis Of An Analytical Method Transfer ToDwayne Neal
To provide the basis for a PDA task force discussion to arrive at a consensus of best industry practices for data analysis of method transfers. The discussion is also relevant to method validation activities.
Quality Control for Point of Care Testing - White PaperRandox
Point of care testing (POCT) refers to testing that is performed near or at the site of a patient with the result leading to a possible change in the care of the patient. Over the past few years, the popularity and demand of POCT has been growing rapidly. This should come as no surprise as there are many advantages to POCT, for example, the added convenience of being able to obtain a rapid result at the patient’s bedside, thus allowing immediate action, saving time and improving the potential outcome for the patient.
Acusera Verify Linearity Verification - June 17 LT674Randox
Our Acusera Linearity Sets are perfect for laboratories wishing to challenge the full reportable range of their instruments. With sets available for Roche & Beckman analysers, our sets are sure to meet all your laboratory and regulatory requirements.
Internal quality control (IQC) in coagulation labAnkit Raiyani
In the haematology laboratory it is essential to ensure that the right test is carried out on the right specimen and that the correct results are delivered to the appropriate recipient without delay.
Quality control (QC) is defined as measures that must be included during each assay run to verify that the test is working properly.
Internal quality control (IQC) is monitoring the haematology test procedures to ensure continual evaluation of the reliability of the daily work of the laboratory with validation of tests before reports are released
Acusera Third Party Quality Controls for Medical Laboratories Randox
Randox is a leading provider of true third party quality controls for laboratories. Their controls offer the most accurate target values based on data from over 23,000 labs, the most consistent material between lots, and unrivalled stability that meets or exceeds claims. Many controls are 100% human material, important for immunoassay methods. As true third party controls, they provide an unbiased assessment of performance across different instruments and methods, reducing the need for multiple controls. Randox controls also allow for consolidation, reducing the number of individual controls required through their wide coverage of analytes.
This document outlines the policy and procedure for method verification at Presbyterian Laboratory Services. It defines key terms and describes the process for selecting, approving, and verifying new testing methods before reporting patient results. The verification process involves evaluating a method's precision, accuracy, linearity, reference range, and potential for carryover through statistical analysis of test results using quality controls, patient samples, and known standards. The goal is to ensure new methods meet performance standards before clinical use.
Acusera 24.7 Interlaboratory Data Management - June 17 LT244Randox
Acusera 24.7 Live Online version 2.0 is here! Faster, smarter and more efficient than ever before, this interlaboratory data management program will be sure to help streamline internal QC in all laboratories regardless of size.
Internal quality control in clinical laboratories hematology(2)NAZAR ABU-DULLA
This document discusses internal quality control in hematology laboratories. It begins with an introduction of the author, Nazar Ahmed Mohamed Abd-Alla, which notes his qualifications and experience in hematology and laboratory administration.
The document then outlines topics that will be covered, including quality definitions, types of errors, specimen handling, method selection, calibration, documentation, and quality programs. It discusses sources of errors like pre-analytical, analytical, and post-analytical errors. It also covers quality assurance, method validation, and specifications for proper specimen collection, transport, and acceptance or rejection. The goal is to provide reliable and accurate test results through effective quality control.
Improving Laboratory Performance Through Quality Control - The role of EQA in...Randox
Randox Quality Control's five simple steps to QC success. The second education guide from Randox QC for clinical laboratory staff. The guide will examine how EQA works, benefits of EQA and what a laboratory should look for when choosing an EQA scheme.
Improving Laboratory Performance Through QC - CommutabilityRandox
This document discusses the importance of using commutable quality control materials in laboratories. It states that approximately 70% of clinical decisions are based on laboratory test results, so reliable quality control is needed. Non-commutable controls can lead to unnecessary shifts in quality control values when reagent batches change. In contrast, commutable controls will perform consistently and reflect actual patient sample performance. The document also describes a case study that demonstrates how a laboratory's quality control values remained stable between reagent batch changes when using Randox commutable controls, unlike with their previous non-commutable controls.
This document outlines surface water monitoring procedures and maintenance norms for various types of stations and laboratories in India. It provides maintenance cost estimates for:
1. Standard and Autographic Rain Gauge stations, including costs for civil works, consumables, and staffing. The estimated annual cost is Rs. 5,750 for SRG stations and Rs. 8,200 for ARG stations.
2. Full Climate stations, including costs for civil works, equipment maintenance, consumables, and staffing. The estimated annual cost is Rs. 56,000.
3. GD (Gauge Discharge) stations of various types, including wading, bridge/cableway, and boat outfit stations. Annual maintenance costs are
Mh gw relative study of ground water dynamics in earth quake affected area in...hydrologywebsite1
The document provides information from a baseline hydrogeological survey conducted in an area affected by the 1993 Latur earthquake in Maharashtra, India. The objectives of the survey were to study changes in groundwater dynamics after the earthquake, assess current groundwater quality, and develop recommendations to improve conditions. Methodology included well inventories, piezometer and observation well installation, aquifer testing, and water level monitoring. Preliminary results found seasonal water level fluctuations and suggested further study of the aquifer system was needed to understand impacts of the earthquake and develop management solutions.
This document provides information on quality control and quality assurance in medical laboratories. It defines key terms like quality control, quality assurance, and quality assessment. It describes variables that can affect result quality and sources of errors. Random errors are unpredictable variations while systematic errors create biases. The document outlines Westgard rules, which are used to evaluate analytical runs and detect random and systematic errors. Steps for resolving quality control problems and minimum criteria for determining when results are out of control are also discussed.
The document discusses calibration, including defining calibration as checking the accuracy of measuring instruments against a standard. It describes various calibration laboratories and standards in India such as NPL, ERTL, and ETDC. It explains the importance, purpose, and types of calibration, as well as requirements for calibration management systems and common instrument calibrations.
Analytical method validation as per ich and usp shreyas B R
Analytical method validation is a process of documenting/ proving that an analytical method provides analytical data acceptable for the intended use.After the development of an analytical procedure, it is must important to assure that the procedure will consistently produce the intended a precise result with high degree of accuracy. The method should give a specific result that may not be affected by external matters. This creates a requirement to validate the analytical procedures. The validation procedures consists of some characteristics parameters that makes the method acceptable with addition of statistical tools.
Calibration and Quality controls of automated hematology analyzerPranav S
This document discusses calibration and quality control of automated hematology analyzers. It begins with a brief history of hematology and automation in the field. Ensuring accurate results through quality assurance is important, involving preventative, assessment, and corrective activities like standardization, internal quality control, and external quality proficiency testing. Proper documentation, training, environment control, and management of preanalytical variables are among the general requirements for quality control and calibration. Calibration must be performed on new or repaired analyzers, and internal and external quality controls help monitor and ensure accuracy of results.
The analyst is required to analyze a number of QC samples throughout the run where there are decisions to be made based on a window of acceptance for each QC sample analyzed.
This document discusses key principles of calibration, including defining calibration as comparing a measuring instrument to a higher accuracy standard. It describes maintaining traceability through an unbroken chain of comparisons to national standards and evaluating uncertainty through factors that affect accuracy. The key characteristics of a calibration discussed are specifying a tolerance, using an accuracy ratio of 4:1 between the instrument and standard, and documenting traceability.
Third party quality controls are important to ensure accurate patient test results. They provide an independent assessment of analytical performance compared to controls from instrument or reagent manufacturers. The document describes several examples where Randox third party controls detected errors that dedicated manufacturer controls failed to identify, including instrument faults, procedural errors, and shifts in results between reagent batches. Regulatory bodies recommend using third party controls over manufacturer controls to independently monitor testing quality.
This document provides information on method validation. It begins with definitions of method validation and discusses why method validation is important. It states that method validation is required for non-standard methods, laboratory-designed methods, standard methods used outside their intended scope, and when modifications have been made to standard methods. The document discusses who is responsible for carrying out method validation and the extent of validation studies required for different types of analytical applications and circumstances. It provides information on various method performance characteristics that are evaluated during validation including accuracy, precision, sensitivity, selectivity, limits of detection and determination, linearity, specificity, repeatability, reproducibility, and robustness. The document concludes with sections that should be included in a validation plan and report.
This document describes a human liquid ready-to-use stable multi-analyte control containing 27 different analytes including antibody isotypes, complement components, and other specific proteins. It reports that the control material has an open vial stability of 30 days when stored between 2-8 degrees Celsius and a shelf life stability of at least 2 years under the same storage conditions based on measurements on various automated systems. The control is concluded to be a convenient ready-to-use material for clinical applications that standardizes the test menu and reduces errors.
This document provides information on calibrating and qualifying various analytical instruments. It discusses the importance of calibration and qualification to ensure proper functioning and accurate results. It describes the different types of qualification including design, installation, operational and performance qualification. It then provides details on specific calibration procedures for various instruments like electronic balances, pH meters, UV-Vis and IR spectrophotometers, and HPLC. The calibration procedures ensure the instruments meet parameters for accuracy, resolution, wavelength verification and flow rate consistency.
Data Analysis Of An Analytical Method Transfer ToDwayne Neal
To provide the basis for a PDA task force discussion to arrive at a consensus of best industry practices for data analysis of method transfers. The discussion is also relevant to method validation activities.
Quality Control for Point of Care Testing - White PaperRandox
Point of care testing (POCT) refers to testing that is performed near or at the site of a patient with the result leading to a possible change in the care of the patient. Over the past few years, the popularity and demand of POCT has been growing rapidly. This should come as no surprise as there are many advantages to POCT, for example, the added convenience of being able to obtain a rapid result at the patient’s bedside, thus allowing immediate action, saving time and improving the potential outcome for the patient.
Acusera Verify Linearity Verification - June 17 LT674Randox
Our Acusera Linearity Sets are perfect for laboratories wishing to challenge the full reportable range of their instruments. With sets available for Roche & Beckman analysers, our sets are sure to meet all your laboratory and regulatory requirements.
Internal quality control (IQC) in coagulation labAnkit Raiyani
In the haematology laboratory it is essential to ensure that the right test is carried out on the right specimen and that the correct results are delivered to the appropriate recipient without delay.
Quality control (QC) is defined as measures that must be included during each assay run to verify that the test is working properly.
Internal quality control (IQC) is monitoring the haematology test procedures to ensure continual evaluation of the reliability of the daily work of the laboratory with validation of tests before reports are released
Acusera Third Party Quality Controls for Medical Laboratories Randox
Randox is a leading provider of true third party quality controls for laboratories. Their controls offer the most accurate target values based on data from over 23,000 labs, the most consistent material between lots, and unrivalled stability that meets or exceeds claims. Many controls are 100% human material, important for immunoassay methods. As true third party controls, they provide an unbiased assessment of performance across different instruments and methods, reducing the need for multiple controls. Randox controls also allow for consolidation, reducing the number of individual controls required through their wide coverage of analytes.
This document outlines the policy and procedure for method verification at Presbyterian Laboratory Services. It defines key terms and describes the process for selecting, approving, and verifying new testing methods before reporting patient results. The verification process involves evaluating a method's precision, accuracy, linearity, reference range, and potential for carryover through statistical analysis of test results using quality controls, patient samples, and known standards. The goal is to ensure new methods meet performance standards before clinical use.
Acusera 24.7 Interlaboratory Data Management - June 17 LT244Randox
Acusera 24.7 Live Online version 2.0 is here! Faster, smarter and more efficient than ever before, this interlaboratory data management program will be sure to help streamline internal QC in all laboratories regardless of size.
Internal quality control in clinical laboratories hematology(2)NAZAR ABU-DULLA
This document discusses internal quality control in hematology laboratories. It begins with an introduction of the author, Nazar Ahmed Mohamed Abd-Alla, which notes his qualifications and experience in hematology and laboratory administration.
The document then outlines topics that will be covered, including quality definitions, types of errors, specimen handling, method selection, calibration, documentation, and quality programs. It discusses sources of errors like pre-analytical, analytical, and post-analytical errors. It also covers quality assurance, method validation, and specifications for proper specimen collection, transport, and acceptance or rejection. The goal is to provide reliable and accurate test results through effective quality control.
Improving Laboratory Performance Through Quality Control - The role of EQA in...Randox
Randox Quality Control's five simple steps to QC success. The second education guide from Randox QC for clinical laboratory staff. The guide will examine how EQA works, benefits of EQA and what a laboratory should look for when choosing an EQA scheme.
Improving Laboratory Performance Through QC - CommutabilityRandox
This document discusses the importance of using commutable quality control materials in laboratories. It states that approximately 70% of clinical decisions are based on laboratory test results, so reliable quality control is needed. Non-commutable controls can lead to unnecessary shifts in quality control values when reagent batches change. In contrast, commutable controls will perform consistently and reflect actual patient sample performance. The document also describes a case study that demonstrates how a laboratory's quality control values remained stable between reagent batch changes when using Randox commutable controls, unlike with their previous non-commutable controls.
This document outlines surface water monitoring procedures and maintenance norms for various types of stations and laboratories in India. It provides maintenance cost estimates for:
1. Standard and Autographic Rain Gauge stations, including costs for civil works, consumables, and staffing. The estimated annual cost is Rs. 5,750 for SRG stations and Rs. 8,200 for ARG stations.
2. Full Climate stations, including costs for civil works, equipment maintenance, consumables, and staffing. The estimated annual cost is Rs. 56,000.
3. GD (Gauge Discharge) stations of various types, including wading, bridge/cableway, and boat outfit stations. Annual maintenance costs are
Mh gw relative study of ground water dynamics in earth quake affected area in...hydrologywebsite1
The document provides information from a baseline hydrogeological survey conducted in an area affected by the 1993 Latur earthquake in Maharashtra, India. The objectives of the survey were to study changes in groundwater dynamics after the earthquake, assess current groundwater quality, and develop recommendations to improve conditions. Methodology included well inventories, piezometer and observation well installation, aquifer testing, and water level monitoring. Preliminary results found seasonal water level fluctuations and suggested further study of the aquifer system was needed to understand impacts of the earthquake and develop management solutions.
A teacher from Teach for India describes teaching 29 underprivileged 2nd grade students with low language and math skills. Through testing at the beginning and end of the year, the teacher found that while the students made progress, there were still large gaps between their achievement levels and what is expected for their grade. The teacher is requesting funds to purchase a projector to use innovative audio-visual teaching methods to try and bridge this 1.5 year achievement gap in the coming school year.
The document is a recruitment page for Teach For India, which aims to eliminate educational inequity in India. It summarizes that 10-90% of Indian children do not complete primary or secondary school. It recruits top university graduates and professionals for a two-year teaching fellowship placing them in under-resourced schools, providing training and support. Fellows have achieved test score increases of 30-160% for their students. Long-term, alumni become leaders across sectors continuing to advocate for educational equity in India. The deadline to apply is January 17, 2010.
Mp gw ground water quality in jabalpur urban area with emphasis on transport ...hydrologywebsite1
The final report summarizes the results of a groundwater quality study conducted in Jabalpur, Madhya Pradesh, India from 2009 to 2014. The study focused on assessing contamination in the Omti Nalla drainage system and leakage from sewage systems into groundwater. Water quality parameters such as nitrates, sulfates, bacteria, and heavy metals were analyzed at 60 monitoring wells over the study period. The results were used to evaluate spatial and temporal variations in groundwater quality, identify sources of contamination, and inform future water management plans.
The document appears to be a local news article about a traffic accident in Etawah, India on October 19th, where a bus carrying 3642 passengers collided with a truck, resulting in injuries. The accident occurred on the Etawah-Farrukhabad highway when the bus collided with the truck near Lallianzuala village. 42 people were killed and 37 others were injured in the accident.
Data analysis is a process that involves gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. It describes several major techniques for data analysis, including correlation analysis, regression analysis, factor analysis, cluster analysis, correspondence analysis, conjoint analysis, CHAID analysis, discriminant/logistic regression analysis, multidimensional scaling, and structural equation modeling.
Quality assurance and quality control programs are necessary to ensure the validity and reliability of analytical environmental data. Several studies have shown large variations in results for identical samples analyzed by different laboratories. AQC programs establish procedures for sample collection, analysis, calibration, quality control checks, and data reporting. Key aspects include standard methods, analyst training, instrument maintenance, calibration verification, internal quality control samples, and inter-laboratory sample exchanges to check for accuracy. Control charts can be used to monitor results and identify any loss of statistical control that could indicate errors have been introduced. Both precision and accuracy are important to consider when evaluating results.
Validation is the process of demonstrating that an analytical procedure is suitable for its intended use. It was first proposed in the 1970s by FDA officials to improve pharmaceutical quality. Validation activities focus on manufacturing processes and ensure quality is built into every step. The goal of validation is to demonstrate that a process will consistently produce the expected results. It includes qualification of equipment and training of personnel. The entire production process and individual objects within it undergo validation. Validation helps ensure accurate measurements, adherence to quality standards, and compliance with regulations. It is important for process optimization, reduced costs and failures, improved efficiency, and meeting requirements for product approval and introduction. A validation master plan provides an overview of all validation activities and establishes performance standards.
This document discusses the findings of an inter-laboratory analytical quality control exercise conducted with 42 water testing laboratories in India. The exercise tested the laboratories' ability to accurately measure 9 water quality parameters in 2 synthetic samples.
The key findings were that only 15 laboratories reported results for all 9 parameters, and the percentage of accurate results ranged from 36.8% to 57.1% depending on the parameter. Comparison to a previous quality control exercise showed similar or lower accuracy levels. The document concludes with recommendations to improve laboratories' analytical capabilities and ensure more consistent and accurate water quality monitoring across India.
This document provides an overview of analytical method validation. It discusses key method performance characteristics like accuracy, precision, sensitivity, selectivity and limits of detection. It explains how these characteristics are evaluated through experiments like linearity assessment, specificity testing, and precision determination. The document also covers validation parameters like repeatability, reproducibility and reliability which are established by testing the method under different conditions.
The document discusses various aspects of validation including calibration, process validation, analytical method validation, and cleaning validation. It defines validation as a systematic approach to collecting and analyzing data to give reasonable assurance that a process will consistently produce results within predetermined specifications. It describes the scope, organization, and types of validation including process, analytical method, cleaning, and computer system validation. It also discusses key aspects of analytical method validation like accuracy, precision, specificity, linearity, repeatability, intermediate precision, and robustness.
Quality control in a virology laboratory.pdfsamwel18
The document discusses quality control in virology laboratories. It defines quality control as measures taken during each assay to ensure tests are working properly, quality assurance as the overall program ensuring results are correct, and quality assessment as external evaluations of laboratory performance. It emphasizes the importance of quality control, quality assurance, and quality assessment programs in producing accurate and consistent diagnostic test results. Key aspects of these programs discussed include monitoring laboratory staff, equipment, record keeping, transcription accuracy, and following standard operating procedures. Quality control specifically refers to measures taken during each assay like using controls and following test protocols.
This document summarizes guidelines for validating analytical methods as outlined by the International Council for Harmonisation (ICH). It discusses key aspects of method validation that should be considered, including specificity, linearity, range, accuracy, precision, detection limit, quantitation limit, robustness, and ruggedness. Specific procedures for establishing each parameter are provided. The objective of validation is to demonstrate that the analytical method is suitable for its intended purpose by consistently producing reliable results. Validation helps ensure the quality, safety, and efficacy of pharmaceutical products.
Analytical method- Content, Development, validation, Transfer & Life Cycle Ma...Md. Mizanur Rahman Miajee
This document discusses analytical method validation and provides guidelines on developing and validating analytical methods according to regulatory standards. It outlines the key components that should be included in an analytical method as well as considerations for method development such as selecting stationary and mobile phases, operating parameters, and evaluating method performance characteristics during development. The document also discusses best practices for transferring validated analytical methods between laboratories.
White Paper Quality Control for Point of Care TestingRandox
Point of care testing (POCT) refers to testing that is performed near or at the site of a patient with the result leading to a possible change in the care of the patient.
Quality control (QC) is a procedure or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. QC is similar to, but not identical with, quality assurance (QA).
QC IN clinical biochemistry labs and hospitals
This document discusses quality assurance in mycobacteriology laboratories. It describes the three main components of a quality assurance system as quality control, external quality assessment, and quality improvement. Quality control procedures should address pre-analytical, analytical, and post-analytical phases of testing. Monitoring performance indicators such as contamination rates, turnaround times, and proficiency testing scores helps to evaluate laboratory performance and identify areas for improvement.
The document discusses validation of analytical methods used in cleaning validation. It covers parameters assessed in analytical method validation like specificity, linearity, range, accuracy, precision, LOD, LOQ. It also discusses method validation, cleaning validation, levels of cleaning, cleaning process validation, typical analytical procedures and their applicability. Key aspects of validation covered include equipment and personnel qualification, microbiological considerations, documentation, sampling, rinsing, rinse samples, detergents used and establishment of acceptable limits.
The document discusses quality control in clinical laboratories. It begins by describing how automated analyzers have modernized clinical laboratories by generating large numbers of test results quickly through integrated technologies. Quality control is then discussed, noting that statistical quality control (SQC) involves monitoring a minimum number of samples to check quality rather than individually checking every sample. SQC can be applied to clinical laboratories by using control samples rather than patient samples. The document outlines key terms used in quality control such as precision, accuracy, random errors, and systematic errors. It describes the use of internal quality control, using daily quality checks with laboratory equipment and materials, and external quality control using periodic proficiency testing with external organizations.
In tech quality-control_in_clinical_laboratoriesMillat Sultan
The document discusses quality control in clinical laboratories. It begins by describing how automated analyzers have modernized clinical laboratories by generating large numbers of test results quickly through integrated technologies. Quality control is then discussed, noting that statistical quality control (SQC) involves monitoring a minimum number of samples to check quality rather than individually checking every sample. SQC can be applied to clinical laboratories by using control samples rather than patient samples. The document outlines key terms used in quality control such as precision, accuracy, random errors, and systematic errors. It describes the use of internal quality control, using daily quality checks with laboratory equipment and materials, and external quality control using periodic proficiency testing with external organizations.
CLEANING VALIDATION for M.pharm and industry personabhishek pandey
YOU CAN EASY WAY TO UNDERSTAND A PROCESS AND ANLYTICAL METHOD OF CLEANING VALIDATION
Cleaning validation is the methodology used to assure that a cleaning process removes residues of the active pharmaceutical ingredients of the product manufactured in a piece of equipment, the cleaning aids utilized in the cleaning process and the microbial attributes.[1] All residues are removed to predetermined levels to ensure the quality of the next product manufactured is not compromised by waste from the previous product and the quality of future products using the equipment, to prevent cross-contamination and as a GMP requirement.
The U.S. Food and Drug Administration (FDA) has strict regulation about the cleaning validation. For example, FDA requires firms to have written general procedures on how cleaning processes will be validated. Also, FDA expects the general validation procedures to address who is responsible for performing and approving the validation study, the acceptance criteria, and when revalidation will be required. FDA also require firms to conduct the validation studies in accordance with the protocols and to document the results of studies.The valuation of cleaning validation is also regulated strictly, which usually mainly covers the aspects of equipment design,cleaning process written, analytical methods and sampling. Each of these processes has their related strict rules and requirements. Regarding to the establishment of limits, FDA does not intend to set acceptance specifications or methods for determining whether a cleaning process is validated. But some limits that have been mentioned by industry include analytical detection levels such as 10 PPM, biological activity levels such as 1/1000 of the normal therapeutic dose and organoleptic levels.[2][3][4]
Cleaning Validation in the context of Active Pharmaceutical Ingredient manufacture may be defined as: "The process of providing documented evidence that the cleaning methods employed within a facility consistently controls potential carryover of product (including intermediates and impurities), cleaning agents and extraneous material into subsequent product to a level which is below predetermined levels".
Total Quality Management (TQM) by Dr Anurag YadavDr Anurag Yadav
Total quality management principles aim to improve patient care through monitoring laboratory work to detect deficiencies and correct them. Errors can occur in preanalytical, analytical, and postanalytical phases, and quality control procedures help control variables and ensure accuracy. Calibration, precision, accuracy, linearity, and detection limits are important analytical concepts, and factors like equipment, reagents, personnel, and documentation must be controlled and monitored to minimize errors and ensure quality.
The document provides an overview of the Clinical Laboratory Improvement Amendments (CLIA). It discusses how CLIA was established in 1988 to set quality standards for clinical laboratory testing to ensure accurate test results. It describes the different complexity levels for tests - waived, moderate and high. High complexity tests pose the most risk and have the strictest CLIA standards, including requirements for personnel qualifications, quality control, proficiency testing, patient test management, and quality assessment. Laboratories must be certified under CLIA and comply with these quality standards based on the complexity of tests they perform.
This document discusses quality assurance and quality control programs for waste water analysis laboratories. It outlines key differences between quality assurance and quality control such as their focus on process vs. product and being proactive vs. reactive. It also provides details on quality assurance documentation and measures including calibration, reference materials, and proficiency testing. For quality control, it describes validation and verification of analytical methods, determination of method detection limits, initial and ongoing demonstration of analyst capability, control charts, and corrective actions. Specific procedures are defined for determining method detection limits, accuracy, precision, linearity, and reporting verification results.
This document discusses concepts of change control, out of specifications (OOS), and out of trends (OOT) in pharmaceutical quality assurance. It defines change control as a procedure to review, verify, regulate, manage, approve and control changes made to systems or processes. OOS refers to test results that fall outside pre-defined acceptance criteria, while OOT describes results that do not follow expected trends. The document outlines procedures for investigating and managing changes, OOS, and OOT to ensure product quality and compliance with regulations.
Similar to Download-manuals-water quality-wq-manuals-within-labaqcfindings-1stround (20)
The World Bank conducted a final supervision mission in May 2014 to review a water resources project in Chhattisgarh, India. The project aimed to strengthen water resource management institutions and expand hydrological monitoring networks. Over 90% of allocated funds had been spent as of March 2014, with additional expenditures expected through May 2014. Key achievements included upgrading data centers, installing rain and groundwater monitoring equipment, conducting trainings, and publishing water resources data. The project improved availability of hydrological data for use in planning irrigation projects, infrastructure design, and other development activities in Chhattisgarh.
The document summarizes the Hydrology Project-II being implemented in Punjab, India. Key points:
- The Rs. 46.65 crore project aims to improve water resource data collection and management. Around 80% of the work and funding has been used.
- Networks to monitor groundwater, surface water, and rainfall have been installed across 700, 25, and 81 stations respectively. Digital equipment transmits data in real time.
- Three data centers have been constructed to store and analyze water data. A state data center in Mohali will house various water resource offices and laboratories.
- Observed hydrological data will be shared with state agencies, CGWB, and other users to inform water
The document provides an overview of the World Bank Monitoring Mission for the Hydrology Project Phase II in India from May 06-09, 2014. It summarizes the key achievements and post-project plans for each of the implementing agencies. The agencies include 13 state organizations and 8 central agencies. The objectives of HP-II were to extend and promote the sustained use of hydrological information systems to improve water resources planning and management. The estimated cost was Rs. 631.83 crore with funding from the World Bank. Several agencies had completed construction of data centers, monitoring equipment installations, and pilot studies. Plans after the project included continuing maintenance and operations, staff training, and further developing applications.
This document summarizes the progress and completion of the Odisha Hydrology Project-II. The key points are:
1) The project had a total revised cost of Rs. 13.46 crore and ran from April 2006 to May 2014 to strengthen surface water data collection and decision support systems in Odisha.
2) Financial progress shows that Rs. 891.04 crore was spent out of the total revised cost of Rs. 1346 crore. Major components included installing a real-time data acquisition system and developing decision support systems for drought monitoring and conjunctive surface and groundwater use.
3) Key achievements were establishing the concept for a real-time data acquisition system,
The document summarizes a review meeting for the Hydrology Project Phase II in Madhya Pradesh, India. The project involves establishing surface water and groundwater monitoring stations. For surface water, 24 river gauge stations and 52 meteorological stations were set up across three river basins. For groundwater, 3750 observation wells and 625 piezometer wells were established. The project period was from 2004-2014 with a total cost of Rs. 24.67 crores. Major achievements included upgrading monitoring stations, establishing new stations, and developing decision support systems for reservoir management and groundwater planning. Lessons learned and plans for continuing activities after the project are also discussed.
The document provides information on the financial targets and achievements of a hydrological project in India. It summarizes that as of March 2014, expenditure was Rs. 304.959 crores out of the revised target of Rs. 399.808 crores. It also describes various components of the project including institutional strengthening activities conducted, the development of decision support systems and real-time data systems for river basins, and studies carried out on optimizing monitoring networks and evaluating the impacts of water allocation changes. Lessons learned included the need for stronger central-state linkages and continued consultant support to meet project goals.
The document summarizes two hydrology projects in Kerala, India from 1996-2004 and 2006-2014. It provides financial details and physical progress updates on the projects, including building construction, staff hiring, equipment procurement, and the establishment of data dissemination and decision support systems. Key accomplishments include the development of applications to study conjunctive use, artificial recharge, reservoir operation, and more. Lessons learned include the benefits of integrated surface and groundwater management and adopting techniques from other agencies.
The document summarizes the Hydrology Project-II implemented in Goa between 2006-2014 with funding from the World Bank. The key aspects include:
- Establishment of 11 river gauge stations, 4 automatic weather stations, and 6 automatic rain gauge stations to improve surface water and hydro-meteorological data collection.
- Installation of 47 open wells and 57 piezometers to monitor groundwater levels across 9 river basins in Goa.
- Construction of a new data center and level II+ laboratory to store, analyze and disseminate hydrological data to support water resource management and planning.
- Capacity building initiatives including training of over 200 local staff on hydrological monitoring and data management.
This document provides expenditure details and progress updates for the Phase II (2006-2014) implementation of the Narmada, Water Resources, Water Supply and Kalpsar Department in Gujarat, India. It outlines spending on civil works, goods, consultancy, and trainings. It also describes the physical progress made in consolidating hydrological data, raising awareness, implementing decision support systems, and conducting purpose-driven studies. Proposals are made for continuing certain activities in potential Phase III of the project.
Central Water and Power Research Station (CWPRS) in Pune saw several advancements under the World Bank's Hydrology Project II (HP-II) including:
1) Technical trainings for over 100 CWPRS officers in areas like water resources planning, climate change impacts, and more.
2) Infrastructure upgrades including a Supervisory Control and Data Acquisition system, laboratory equipment, and renovated buildings.
3) Research activities such as optimizing stream gauge networks in Maharashtra's Bhima river basin and hydrographic surveys of the Tawa reservoir.
4) Over Rs. 4 crore was spent on civil works, equipment, trainings and other costs aligned with the goals of
The document summarizes the major activities and achievements of the Central Pollution Control Board's Hydrology Project-II regarding water quality monitoring. Some of the key points include:
- Installation of 10 real-time water quality monitoring stations on the Ganga and Yamuna rivers
- Development of a GIS-based water quality web portal to visualize historical and current water quality data
- Organization of 30 training workshops on water quality monitoring that reached over 750 laboratory staff
- Renovation of the CPCB water laboratory and development of water quality criteria and standards
The project aims to continue activities like annual maintenance of monitoring stations and the web portal, as well as propose new initiatives for the next phase including nationwide water pollution
The document describes BBMB's Real Time Decision Support System (RTDSS) project. The objectives are to incorporate advanced data acquisition and communication systems to help with operational management of Bhakra and Beas reservoirs. The system collects telemetry data from over 80 stations, including rainfall, water levels, snow levels. It also downloads satellite data and forecasts. The data is analyzed using hydrological and hydrodynamic models to forecast reservoir inflows and water levels to help with flood control and water distribution.
The World Bank conducted a final supervision and completion mission for the Hydrology Project in Andhra Pradesh from May 7-8, 2014. The project aimed to strengthen surface water data collection networks and build institutional capacity for hydrological data management and use. Key achievements included establishing 25 additional data collection stations, procuring IT equipment, developing a project website, and providing training. Expenditures totaled Rs. 4.13 crore against the revised project cost of Rs. 8.92 crore. Moving forward, the document discusses continuing project activities in Andhra Pradesh and potential areas of focus for a phase III of the Hydrology Project.
This document provides a summary of the financial progress and achievements of the Gujarat - Ground Water hydrology project. Some key points:
- Total projected cost is 176.32 crore INR, of which 169.11 crore (96%) has been spent as of March 2014.
- Major activities include upgrading the piezometer network, procuring equipment like DWLRs, GIS data, and training programs.
- Key outcomes are improved groundwater data availability and monitoring networks, as well as awareness raising and decision support systems.
- Lessons learned include the importance of data quality control, coordination, and training to improve groundwater management.
The document describes methods for hydrological observations including rainfall, water level, discharge, and inspection of observation stations. It contains sections on ordinary and recording rainfall observation, ordinary and recording water level observation, observation of discharge using current meters and floats, and inspection of rainfall and water level observation stations. The document was produced by the Ministry of Construction in Japan.
This document provides guidance on how to review monitoring networks. It begins with an introduction on the objectives and physical characteristics that networks are based on. It then discusses the types of networks, including basic, secondary, dedicated, and representative networks. The document outlines the steps in network design, which include assessing data needs, setting objectives, determining required network density, reviewing the existing network, and conducting a cost-effectiveness analysis. Specific guidance is provided on reviewing rainfall and hydrometric networks.
This document provides information and instructions for conducting correlation and spectral analysis. It includes definitions of autocovariance, autocorrelation, cross-covariance, and cross-correlation functions. It also defines variance spectrum and spectral density functions. The document provides examples of applying these analytical techniques to time series data, including monthly rainfall and daily water level data. It demonstrates how these techniques can be used to identify periodicities and correlations in hydrological time series data.
This document provides guidance on statistical analysis of rainfall and discharge data. It discusses graphical representation of data including histograms, line diagrams, and cumulative frequency diagrams. It also covers measures of central tendency, dispersion, skewness, kurtosis and percentiles. The document emphasizes that hydrological time series must meet stationarity conditions to be suitable for statistical analysis and discusses evaluating and accounting for trends and periodic components when analyzing rainfall and discharge data.
This document provides operational details for groundwater data processing and analysis in India. It outlines the monitoring networks for water levels, quality, and hydro-meteorology. It describes the geological structures, soil types, typical groundwater issues, and the organizational setup of the responsible groundwater agency. The agency collects various dynamic data through monitoring networks to estimate groundwater resources and inform management recommendations in an annual groundwater yearbook.
This document provides an operations manual for water quality analysis. It discusses good laboratory practices and quality assurance protocols that should be followed, including proper handling of chemicals, cleaning of glassware, measurement techniques, and maintenance of laboratory equipment. Standard analytical procedures for over 30 water quality parameters are also described. The manual establishes guidelines for sample receipt, storage, analysis, and reporting results to ensure reliable and comparable water quality data.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
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1. Findings within-laboratory AQC, first round 1997 February 1998
HYDROLOGY PROJECT
TechnicalAssistance
Table of Contents
1 Quality Assurance...................................................................................................... 1
1.1 Need for quality Assurance .................................................................................... 1
1.2 Quality assurance programme ............................................................................... 2
1.3 Definitions and basic statistics ............................................................................... 3
1.4 Interpretations of Shewhart control charts.............................................................. 4
2 Report on within-laboratory AQC exercise.................................................................. 6
2.1 Response/reasons ................................................................................................. 6
2.2 Discussion of results .............................................................................................. 6
2.3 Potential sources of error ....................................................................................... 9
2.4 Interpretation of reported Shewhart control charts................................................. 9
2.5 Calculating revised limits when continuing the exercise ...................................... 13
2.6 Errors that cannot be detected by within-lab AQC................................................. 13
3 Inter-laboratory AQC exercise................................................................................... 14
3.1 Objectives............................................................................................................. 14
3.2 Results of AQC conducted by Central Pollution Control Board............................. 14
List of Parameters covered...................................................................................... 15
Methodology ............................................................................................................ 15
Findings ................................................................................................................... 15
Suggestions ............................................................................................................. 16
4 Planning of first round inter-laboratory AQC ............................................................. 17
4.1 Co-ordinating Laboratory...................................................................................... 17
4.2 Test samples........................................................................................................ 17
4.3 Purity of material used to prepare standard solutions.......................................... 17
4.4 Errors in preparing the test solution or samples................................................... 17
4.5 Determinand stability and contamination ............................................................. 18
4.6 Participating laboratories...................................................................................... 18
4.7 Proposal ............................................................................................................... 18
2. Findings within-laboratory AQC, first round 1997 February 1998
HYDROLOGY PROJECT 1
TechnicalAssistance
1 Quality Assurance
1.1 Need for quality Assurance
Many studies have shown that analytical results are often subject to serious errors,
particularly at the low concentrations encountered in water analysis. In fact, the errors
may be so large that the validity of actions taken regarding management of water quality
may become questionable.
Nutrients, N and P, in very small concentrations can cause eutrophication of
waterbodies. An analytical quality control exercise (AQC) exercise conducted by United
States Environmental Protection Agency (US-EPA) showed a wide variation in results
when identical samples were analysed in 22 laboratories:
Nutrient Concentration,
mg/L
Range of results,
mg/L
Ammonia 0.26
1.71
0.09 - 0.39
1.44 - 2.46
Nitrate 0.19 0.08 - 0.41
Total phosphorus 0.882 0.642 - 1.407
It is seen that the range of values reported are significantly large, ±50% for ammonia and
±100% for nitrates, compared to the actual concentrations. Therefore, the need for
nutrient control programme and its results become difficult to assess.
Many laboratories under Hydrology Project (HP) report total dissolved salts (TDS)
calculated from the electrical conductivity (EC) value:
TDS, mg/L = A x EC, µS/cm
where A is a constant ranging between 0.55 and 0.9 depending on the ionic composition
of salts dissolved in the water.
An inter-laboratory AQC exercise conducted by Central Pollution Control Board (CPCB)
showed that for measurement of EC of a standard solution, out of 44 participating
laboratories only 34% reported values in the acceptable range. See Figure 1.
Thus, the reliability of iso-concentrations of TDS in groundwaters, drawn based on data
of several laboratories may become questionable on two counts; use of an arbitrary
value for the constant A and variation in inter-laboratory measurements.
These examples amply demonstrate the need for quality assurance (QA) programmes.
3. Findings within-laboratory AQC, first round 1997 February 1998
HYDROLOGY PROJECT 2
TechnicalAssistance
1.2 Quality assurance programme
The QA programme for a laboratory or a group of laboratories should contain a set of
operating principles, written down and agreed upon by the organisation, delineating
specific functions and responsibilities of each person involved and the chain of
command. The following sections describe various aspects of the plan.
Sample control and documentation: Procedures regarding sample collection,
labelling, preservation, transport, preparation of its derivatives, where required, and the
chain-of-custody.
Standard analytical procedures: Procedures giving detailed analytical method for the
analysis of each parameter giving results of acceptable accuracy.
Analyst qualifications: Qualifications and training requirements of the analysts must be
specified. The number of repetitive analyses required to obtain result of acceptable
accuracy also depends on the experience of the analyst.
Equipment maintenance: For each instrument, a strict preventive maintenance
programme should be followed. It will reduce instrument malfunctions, maintain
calibration and reduce downtime. Corrective actions to be taken in case of malfunctions
should be specified.
Calibration procedures: In analyses where an instrument has to be calibrated, the
procedure for preparing a standard curve must be specified, e.g., the minimum number
of different dilutions of a standard to be used, method detection limit (MDL), range of
calibration, verification of the standard curve during routine analyses, etc.
Analytical quality control: This includes both within-laboratory AQC and inter-laboratory
AQC.
Under the within-laboratory programme studies may include: recovery of known
additions to evaluate matrix effect and suitability of analytical method; analysis of
reagent blanks to monitor purity of chemicals and reagent water; analysis of sample
blanks to evaluate sample preservation, storage and transportation; analysis of
duplicates to asses method precision; and analysis of individual samples or sets of
samples (to obtain mean values) from same control standard to check random error.
Inter-laboratory programmes are designed to evaluate laboratory bias.
It may be added that for various determinands all of the AQC actions listed may not be
necessary. Further, these are not one time exercises but rather internal mechanisms for
checking performance and protecting laboratory work from errors that may creep in.
Laboratories who accept these control checks will find that it results in only about 5
percent extra work.
4. Findings within-laboratory AQC, first round 1997 February 1998
HYDROLOGY PROJECT 3
TechnicalAssistance
AQC is:
• an internal mechanism for checking your own performance
• protecting yourself from a dozen of errors that may creep into analytical work
• to avoid human errors in routine work
• practised by responsible chemists
• not useless work
• common practice in certified laboratories
AQC is NOT:
• much work
• to be carried out for each and every routine sample
• consultants checking and reporting the quality of your work
• a one time exercise to be forgotten soon
Data reduction, validation and reporting: Data obtained from analytical procedures,
where required, must be corrected for sample size, extraction efficiency, instrument
efficiency, and background value. The correction factors as well as validation procedures
should be specified. Results should be reported in standard units. A prescribed method
should be used for reporting results below MDL.
An important aspect of reporting the results is use of correct number of significant
figures. In order to decide the number of significant digits the uncertainty associated with
the reading(s) in the procedure should be known. Knowledge of standard deviation will
help in rounding off the figures that are not significant. Procedures regarding rounding off
must be followed.
1.3 Definitions and basic statistics
Bias: Bias is a measure of systematic error. It has two components, one due to method
and the other due to laboratory use of method.
Precision: Precision is a measure of closeness with which multiple analyses of a given
sample agree with each other.
Random error: Multiple analyses of a given sample give results that are scattered
around some value. This scatter is attributed to random error.
Accuracy: Combination of bias and precision of an analytical procedure, which reflects
the closeness of a measured value to the true value.
Frequency distribution: Relation between the values of results of repetitive analyses of
a sample and the number of times (frequency) that a particular value occurs.
Mean: Mean is the central value of results of a set of repetitive analyses of a sample. It
is calculated by summing the individual observations and dividing it by the total number
of observations.
Normal distribution: Normal distribution is a frequency distribution, which is
symmetrical around the mean. In a normal distribution 95.5% and 99.7% of the
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observations lie in ± two times standard deviation and ±three times standard deviation
range around the mean, respectively. See also Figure 2.
Standard deviation: Standard deviation is a measure of spread of results of repetitive
analyses of a sample around its mean value. It is a measure of precision of the analytical
method. It is calculated by taking square root of sum of squares of deviation of the
observations from the mean divided by the number of observations minus one.
Coefficient of variation: Comparison of standard deviation values for results of
repetitive analysis, of two samples having different concentration of the determinand,
may sometimes give wrong conclusion regarding precision of the measurement.
Coefficient of variation (CV), which is calculated as CV = standard deviation/mean X 100,
is a better parameter for such comparison. For example, for results of two sets of
analyses, performed on two different samples, if the mean values are 160 and 10 mg/L
and standard deviations are 8 and 1.5 mg/L, respectively, comparison of standard
deviation would indicate lower precision for the first set of observations (standard
deviation 8 mg/L), while the CV values work out to be 5 (8/160 X 100) and 15 (1.5/10 X
100) percents respectively. Indicating a better precision for the second set of
observations.
1.4 Interpretations of Shewhart control charts
If a set of analytical results is obtained for a control sample under conditions of routine
analysis, some variation of the observed values will be evident. The information is said
to be statistically uniform and the analytical procedure is said to be under statistical
control if this variation arises solely from random variability. The function of a control
chart is to identify any deviation from the state of statistical control.
Shewhart control chart is most widely used form of control charts. In its simplest form,
results of individual measurements made on a control sample are plotted on a chart in a
time series. The control sample is analysed in the same way as the routine samples at
fixed time intervals, once or twice every week, or after 20 to 50 routine samples.
Assuming the results for the control sample follow the Normal frequency distribution, it
would be expected that only 0.3% of results would fall outside lines drawn at 3 standard
deviations above and below the mean value called upper and lower control limits, UCL
and LCL, respectively. Individual results would be expected to fall outside these limit so
seldom (3 out of 1000 results), that such an event would justify the assumption that the
analytical procedure was no longer in statistical control, i.e., a real change in accuracy
has occurred.
Two lines are inserted on the chart at 2 standard deviations above and below the mean
value called upper and lower warning limits, UWL and LWL, respectively. If the method
is under control, approximately 4.5% of results may be expected to fall outside these
lines. This type of chart provides a check on both random and systematic error gauged
from the spread of results and their displacement, respectively
Standard Methods lists the following actions that may be taken based on analysis results
in comparison to the standard deviation.
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Control limit: If one measurement exceeds the limits, repeat the analysis immediately. If
the repeat is within the UCL and LCL, continue analyses; if it exceeds the action limits
again, discontinue analyses and correct the problem.
Warning limit: If two out of three successive points exceeds the limits, analyse another
sample. If the next point is within the UWL and LWL, continue analyses; if the next point
exceeds the warning limits, discontinue analyses and correct the problem.
Standard deviation: If four out of five successive points exceed one standard deviation,
or are in increasing or decreasing order, analyse another sample. If the next point is less
than one standard deviation away from the mean, or changes the order, continue
analyses; otherwise discontinue analyses and correct the problem.
Central line: If six successive points are on one side of the mean line, analyse another
sample. If the next point changes the side continue the analyses; otherwise discontinue
analyses and correct the problem.
Figure 3 to Figure 7 illustrate the cases of loss of statistical control for analysis of
individual samples based on the above criteria.
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2 Report on within-laboratory AQC exercise
2.1 Response/reasons
Out of 30 laboratories nominated for the exercise 21 responded, an overall degree of
participation of exactly 70%. The degree of participation for state organisations is
somewhat higher:
type of lab degree of participation
CWC 5 out of 8 nominated (60%)
CGWB 4 out of 7 nominated (57%)
state
laboratories
12 out of 15 nominated (80%)
None of the laboratories in Orissa of either state or central agencies responded.
In terms of requested parameters (30 laboratories times 4 parameters =120) the actual
response of 61 reported parameters is only slightly higher than 50%. This is mainly due
to lack of functioning spectrophotometer for NO2 analysis. The central organisations’
response was better with respect to NO2
-
. Some laboratories spontaneously analysed
NO3
-
, F-
or Cl-
instead.
Our biggest concern is the response time. Only one laboratory was able to respond
within the timeframe envisaged by the consultants (2 months after receiving the AQC
booklet). Most laboratories needed a lot of pushing and persuasion before the work
started.
We can think of the following reasons for this below 100% performance:
1. the topic was new and not yet appreciated
2. the topic was misunderstood and too much work was envisaged
3. the topic was found difficult by some, -e.g. in terms of statistics involved
4. the necessary equipment for the tests was not functioning properly or missing, such
as balance
5. the necessary chemicals for the tests were not readily or at all available
6. the workload of some laboratories was too high
7. there were two evident errors in the booklet provided (in the NO2
-
analytical method)
2.2 Discussion of results
The most important parameter to evaluate in the results is the precision. The statistical
term to evaluate precision is standard deviation. The numerical value of the standard
deviation depends on the average concentration (standard deviation also has the unit of
concentration). Numerical values of standard deviations of low concentration solutions
are usually smaller than those of solutions with higher concentrations. The precision of
measurement for low concentration solutions is generally lower and therefore the
numerical value of standard deviation is not a universal measure for precision. Therefore
the coefficient of variation or normalised standard deviation (CV = standard
deviation/mean x 100) will be used to evaluate precision. This is particularly useful when
comparing results of analysis for samples having different concentrations.
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Before evaluating the results one should answer the question ‘what is the desired
precision for an analyses?’. In fact this question should be answered by the so called
‘data users’. The use of the data determines the required precision, e.g. detection of
trends may require more precise results (in order to actually detect small changes in the
cause of time) than checking water for use (a rough comparison with a standard).
Laboratory staff should always ask for the purpose for which they are performing the
requested test.
As a minimum goal for precision, however, the precision that can be obtained by
correctly and adequately following the method prescribed by the APHA Standard
Methods for the examination of water and wastewater may be adopted (see Table 1).
Table 1 Coefficient of variation for Total Solids, Total Hardness and Electrical Conductivity
from three sources: Standard Methods (1995), Central Pollution Control Board New
Delhi (1992-1997) and US-Environmental Protection Agency (around 1980). The
underlined numbers are the proposed precision goals.
TS EC TH
average cv average cv average cv
Reference mg/L % umho/cm % mg/L %
Standard
Methods
15 33 - 1 to 2 610 2.9
242 10
1707 0.76
293 7.2
EPA 58.1 23.1 119 14.6 299 0.9
298 7.4 10.4 300 2.2
CPCB 68.3 6.9 171 2.3 47.17 8.4
145.4 5.3 214 2.5 61.43 4.3
152 8.5 231 1.9 65.25 6.2
171.8 5.7 256 2.6 75.25 4.5
238.1 3.0 294 1.8 84.08 6.3
244.9 5.4 305 2.0 84.71 3.9
249.7 3.6 401 2.6 111.92 2.9
472.3 2.8 478 1.5 135.00 3.7
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Results of the first round of the within-laboratory AQC exercise are shown in shown in
Table 2 and Figure 8 to Figure 10. The ‘goal’ CV values from Table 1 are represented in
the figures by arrows indicating the range.
Table 2 Results from the laboratories that participated in the first round within laboratory AQC.
Results for TS
Compared to CV values reported by Standard Methods (7-10%) practically all are within
the minimum precision requirement (only 3 laboratories are outside this acceptable
range). Results from CPCB, US-EPA and the majority of HP laboratories indicate that a
goal of 3-7% is achievable.
The precision for TS analysis strongly depends on the concentration of the sample, as
can be seen from Table 1. Therefore, the analyst can modify the method (by drying
successive portions of the sample when concentration is low) according to the needs or
to the (known) limitations of the balance available.
The distribution of CV values is skewed, Figure 8, (the average value of 5.2% is higher
than the median value of 4%). This is what one would expect since low values of CV are
unlikely considering the analytical method used to determine the TS concentration. In
this method laboratories try to weigh, say, 10mg dried residue. To perform better than
1% would require a balance that is capable of weighing with an accuracy higher than
0.10mg. Apparently, most of the balances in the participating laboratories do not perform
up to that level.
Results for EC
Except one lab all laboratories work within the precision goal set by Standard Methods
(2 %), Figure 9. This is a remarkable result because consultants feel that this is a
stringent precision goal that is only achievable under ideal conditions. This feeling is
supported by the, higher CV values reported by CPCB and US-EPA (>10%) although the
latter might be partly caused by old equipment.
Especially techniques that depend on instrument reading, the precision will depend
strongly upon the time between the successive readings (were all 20 readings taken in
one hour or did it take several days?).
In inter-laboratory tests, EC is on of the parameters that shows a high bias.
Results for TH
Only three laboratories reported CV values outside Standard Methods limits, Figure 10.
The largest class (7 laboratories) reported 0-1% of precision. This precision seems to be
very high compared to EPA, Standard Methods and CPCB. This high precision is not
achievable when the method as prescribed is carried out by standard laboratory
glassware. Three laboratories have used the standard CaCO3 solution instead of the ten
times diluted control sample resulting in 0.6% precision. For the other 4 laboratories no
explanation can be given.
The expected precision of the TH determination according to the prescribed procedure is
estimated in appendix A. The effect of the accuracy of the burette and the pipette gives
an estimated CV of 1%. The largest contribution comes from the 2.5mL EDTA titration by
Lab Id TS EC TH NO2
avg sd cv avg sd cv avg sd cv avg sd cv
1 146.1 7.7 5.3 √ 256.4 8.7 3.4 × 99.2 4.1 4.1 × 0.417 0.018 4.3 √
2 149.37 4.8 3.2 √ 283 5.39 1.9 √ 99.4 5.2 5.2 ×
3 157.4 3.19 2.0 √ 325 5.2 1.6 √ 10.03 0.035 0.3 √
4 202.3 11.08 5.5 √ 330.9 2.23 0.7 √ 776.5 4.12 0.5 √
5 147.7 21.76 14.7 × 1415.6 13.46 1.0 √ 100.24 5.79 5.8 ×
6 137 9.33 6.8 √ 285 4.35 1.5 √ 101.2 1.24 1.2 √
7 147.78 4.14 2.8 √ 1418.3 7.91 0.6 √ 101.6 2.48 2.4 √
8 142.7 2.66 1.9 √ 280 4.4 1.6 √ 98.15 5.1 5.2 ×
9 148.8 3.5 2.4 √ 99.9 0.7 0.7 √
10 156.8 6.47 4.1 √ 285 5.27 1.8 √ 101.4 0.51 0.5 √
11 136 13.92 10.2 × 304 2.5 0.8 √ 99.64 1.632 1.6 √ 0.128 0.002 1.6 √
12 150 3.8 2.5 √ 283 1.4 0.5 √ 99.4 2.91 2.9 √ 100 3.7 3.7 √
13 146 6.57 4.5 √ 1411 1.68 0.1 √ 999.4 6.65 0.7 √ 1 0.008 0.8 √
14 146.8 0.72 0.5 √ 283.67 1.19 0.4 √ 99.8 2.7 2.7 √ 0.985 0.012 1.2 √
15 136.0 10.5 7.7 × 274.10 2 0.7 √ 93.4 0.9 1.0 √
16 140.0 5.3 3.8 √ 276.10 3 1.1 √ 95 0.9 0.9 √
17 149.8 2.1 1.4 √ 284.00 2.9 1.0 √ 99.5 2.8 2.8 √
18 165.4 23.1 14.0 × 289.8 2.7 0.9 √ 100.7 1.5 1.5 √
19 298 3.95 1.3 √ 102 2.39 2.3 √
20 288.6 5.59 1.9 √ 99.2 0.81 0.8 √
21 83.59 2.049 2.5 √
22 280.0 6.32 2.3 × 70.9 1.9 2.7 √
23 710 1.2 0.2 √ 104.7 1.1 1.1 √
avg 5.18 1.21 2.18 2.32
median 3.96 1.02 1.64 1.56
max 202.30 23.10 14.73 1418.30 13.46 0.00 999.40 6.65 5.78 3.70 4.32
min 136.00 0.72 0.49 256.40 1.19 0.00 10.03 0.04 0.35 0.00 0.80
n 18 18 18 21 21 21 21 21 21 5 5 5
√ = meeting the precision goal × = not meeting the precision goal
avg =average value, sd=standard deviation, cv=coefficient of variation (sd/avg*100)
Control solutions: TH=100mg/L; EC=283-290umho/cm at 25C; TS=149mg/L and NO2=1.0mg/L.
Bold numbers indicate a different solution w as used (e.g. stock).
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the burette. This calculation does not include the effect of all other sources of error
mentioned and therefor the actual precision must be less and CV value higher than 1%.
2.3 Potential sources of error
Potential sources of error affecting precision of TS analysis
i. balance precision and calibration
ii. handling of glassware/paper to avoid addition of weight from other sources
iii. weighing while dish is still hot (air turbulence)
iv. small amounts of solid dried results in accumulating errors due to differential method
v. large amount of solids (water-trapping crust formation)
vi. hygroscopic solids require prolonged drying
vii.poor storage during cooling in desiccator
viii.human errors
Potential sources of error affecting precision of TH analysis
i. balance precision
ii. standardisation of titrant on day of analysis using primary standard
iii. reading of burette during titration: a sample volume between 5 and 20mL is optimal
for a 50mL (0.1mL readability)
iv. reagent blank correction (titration of distilled water)
v. indicator in blank and standard should be of same quality
vi. preparation of dilutions (volumetric flask)
vii.determination of endpoint: colour change (light conditions, white background)
viii.determination of endpoint: speed of adding the last drops
ix. correct pH (buffer solution) during titration
Potential sources of error affecting precision of EC measurement
i. calibration of the instrument
ii. accuracy in preparation of the calibration solution (0.01M KCl)
iii. quality of the de-mineralised water used for the calibration solution
iv. measurement of the sample temperature and temperature correction
v. condition of the conductivity cell
vi. procedure (rinsing, temperature equilibrating, temperature correction etc.)
2.4 Interpretation of reported Shewhart control charts
Laboratories 2, 3, 6, 8, 10, 13, 14, 17, 18, 21 and 23
The exercise was not carried out according to the instructions provided in the booklet.
These laboratories performed the first 20 analysis on their control sample (analysis
spread over a two week period)and used these data to calculate warning and action
limits for the construction of the Shewhart control chart.
Unfortunately, they did not analyse another 10 or so analyses in the next say 10 days.
Instead, the same 20 data that were used to calculate the warning and control limits
were plotted in the chart. Per definition, exceeding the control limits is very rare in this
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approach. Careful reading of instructions and appreciating the merits of the exercise is
essential for the future.
Therefore, the actual exercise for these laboratories is yet to start!
Laboratory 20
This laboratory has carried out 20 repetitive analysis on a sample from one of their
regular monitoring sites (river water). We like to stress that this is not needed for any
AQC exercise. Only control samples (artificial solutions prepared by the laboratory itself)
should be analysed in a within laboratory AQC exercise.
Repeated analysis on a natural sample from the field is less suitable for evaluation of
method precision because of potential disturbance of the sample with time.
Laboratory 7
Commented that 10 times diluting the stock for EC (2826 umho/cm) will not exactly lead
to a value of 283 as suggested in the AQC booklet.
If a solution is diluted, say ten times, the EC value of the diluted solution will by higher
than the EC based on the dilution factor. In other words, the same ions contribute more
to the EC in a diluted solution. At higher salt levels, the ionic strength causes screening.
The effect is of practical importance. If a 0.02M yields 2765 umho/cm (standard
methods) a 0.002M solution will be around 296 umho/cm at 25°C, about 10% higher
than expectations based on dilution only.
It is however important to realise that for a within laboratory exercise this bias is not
relevant since only precision is under investigation.
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Statistical control of analytical procedure
Interpretation of the charts of all laboratories is summarised in Table 3.
Table 3 Results of Shewhart control charts for all laboratories that participated in the
within laboratory AQC
Lab TS EC TH
1 √ T √
2 T √ √
3 A A A
4 √ √ √
5 √ A A
6 A T, A √
7 T T √
8 √ √ T
9 T - T
10 T √ √
11 √ C, A, W √
12 √ T, W √
13 √ T √
14 √ √ √
15 √ C A
16 √ √ √
17 √ √ √
18 √ T √
19 - √ √
20 - A √
21 - - -
22 - √ √
23 - - -
Number of times out
of control (all
laboratories)
6
18
33%= %55
20
11
= %26
24
5
=
Number of times
failed more than one
criterion
%17
6
0
=
3
11
27%=
0
5
0%=
√ = under statistical control
C = 2 successive points out of Control (Action)l Limit
W = 3 out of 4 successive points out of Warning Limit
S = 5 out of 6 successive points out of standard deviation limit
T = 5 out of 6 successive points having the same trend
A = 7 successive points on one side of the average
- = parameter not reported or insufficient repetitions for statistical
evaluation
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Laboratory 10 – TS (Figure 11)
The concentration of TS is decreasing in time and more than 5 points do show the same
trend so the analysis is not under statistical control and the problem(s) should be
corrected. High values of TS may indicate poor drying of the sample, e.g. caused by too
low oven temperature (oven out during weekend and nights?) or too long stay of dried
samples in poorly functioning desiccator etc.
Laboratory 13 – TS
This laboratory uses 50ml sample for drying. By weighing 50ml of the control solution
(146mg/L), the amount of solid remaining after drying is only 50/1000x146 = 7.3mg.
Standard Methods prescribes a minimum yield of 10mg!
With a good analytical balance, with an error of ± 0.1mg, a relative error of 0.14% can be
achieved in this case. In this laboratory the error of the analytical balance is much higher
however (estimated at ±0.4 to 0.5mg based on the raw data provided by the laboratory,
See also Appendix B). If the laboratory is aware of the performance of their balance and
the propagation of errors during differential weighing methods, the relative error can be
reduced by 50% by drying double the amount of sample volume.
Some laboratories are aware of the -poor- quality of their this because they vary the
sample volume from 100ml up to 200ml (two successive portions of 100ml). Even then
the result is not as precise as it should be: CV = 3.5%. Probably the balance has an error
of more than ±0.5mg in these cases.
Laboratory 5 –TH (Figure 12)
The distribution (all 10 below the average of 100.5 mg/L) suggests that the analysis is no
longer under statistical control. The precision of the 10 data plotted in the chart is much
higher than the precision of the first 20 analysis on which the warning and control limits
of the Shewhart control chart are based (around 5%).
Since the precision for this analysis in this lab is the lowest among all participating
laboratories it is likely that a systematic error is involved somewhere (change of analyst,
solutions, indicator). The laboratory itself should investigate this prior to taking part in an
inter-laboratory programme.
Laboratory 15 – TH (Figure 13)
The concentration of the control solution prepared by the laboratory (100 mg/L) is not
found back in the average of the first 20 titrations (93.4 mg/L). The next 10 titrations tend
to be all below this average (negative trend line). If a laboratory does not find back the
concentration of a solution prepared in its own laboratory, the analysis is not carried out
correctly! Most probably, the EDTA solution is not standardised against the Standard
CaCO3 solution!
More than seven successive points are below the central line, the problem should be
corrected.
Laboratory 14 – TH (Figure 14)
The chart shows a very symmetric pattern with only discrete variations of ±4mg/L. Four
mg/L corresponds to a burette reading of ±0.1mL, whereas a reading precision ±0.05ml
is achievable. Moreover, the 16 data plotted are also used for constructing the chart so
the actual AQC exercise has yet to start in fact.
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Laboratories 4, 11 and 15 – EC (Figure 15)
These laboratories have done 20 repetitive analyses for EC in a short time (on one day?)
and find a very low CV of 0.8%, 0.7% and 0.7% respectively. When the control sample
was checked within a period of a month afterwards, this very high precision could not be
maintained. A more realistic precison (e.g. based upon the last 10 measurements)
should replace the control and warning limits on the control chart.
2.5 Calculating revised limits when continuing the exercise
Warning and control limits should be recalculated periodically. Especially when new
techniques are introduced, the precision improves when experience is gained with the
technique. A good time for recalculating the control and warning limits is at the time
when the control chart is full and a new graph has to be created anyway. At this point,
use the 20 most recent data on the old chart for construction of LCL, LWL, average,
UWL and UCL.
2.6 Errors that cannot be detected by within-lab AQC
i. balance bias (malfunctioning)
ii. improper storage between drying and weighing (desiccator)
iii. human errors (e.g. blowing through a pipette, short person reading a high positioned
burette)
iv. old EC-cells that are not in good condition (e.g. not platinised regularly)
A laboratory on its own cannot detect many sources of bias. A good example to illustrate
this is the total hardness method. If the analytical balance in a lab always reads 10% too
much all solution prepared will have a 10% higher concentration: the Standard CaCO3
solution, the EDTA titrant and also the control sample containing CaCO3. This error can
only be detected by analysing a sample prepared by a laboratory with a correctly
functioning balance. The current lab will underestimate the concentration of such a inter-
laboratory sample by 10% because their EDTA titrant is ’10% too strong’.
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3 Inter-laboratory AQC exercise
3.1 Objectives
The within-laboratory exercise of individual measurements does not tell much about
bias. It focuses mainly on precision and whether the system is under statistical control.
Only in some circumstances it may point towards freshly introduced bias, for example
the total solids values of individual measurements being consistently on one side of the
previously calculated mean.
The main objectives of an inter-laboratory AQC exercise are:
1. To test for possible bias in measurements in a laboratory.
2. To provide direct evidence of comparability of results among laboratories in a common
water quality-monitoring programme such as Hydrology Project. Some related objectives
and benefits are listed below:
• to assess the status of analytical facilities and capabilities of concerned laboratories.
• to identify the serious constraints (random & systematic) in the working environment
of laboratories.
• to provide necessary assistance to the concerned laboratories to overcome the
short comings in the analytical capabilities.
• to validate the water quality monitoring data.
• to promote scientific and analytical competence of the concerned laboratories to the
level of excellence for better output.
• to enhance the internal and external quality control of the laboratories in an
organised manner.
3.2 Results of AQC conducted by Central Pollution Control Board
The Central Pollution Control Board (CPCB) is monitoring water quality at 180 stations
under GEMS, MINARS, GAP, NRCD programs through various State Pollution Control
Boards (SPCBs). The water samples are being analysed in central or regional
laboratories of SPCBs for 22 parameters. In order to obtain reliable and accurate
analytical data, CPCB has started regular AQC exercises with the concerned laboratories
from 1991 onwards.
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List of Parameters covered
1. Conductivity 2. Total dissolved solids (TDS)
3. Fixed dissolved solids (FDS) 4. Total Hardness
5. Calcium 6. Magnesium
7. Sodium 8. Potassium
9. Chloride 10. Fluoride
11 Sulphate 12. Nitrate - N
13. Ammonical - N 14. Total Kjeldahl nitrogen(TKN)
15. Phosphate P 16. Boron
17. Chromium hexavalent 18. Chemical oxygen demand (COD)
19. Biochemical oxygen demand (BOD)
The above listed 19 parameters are covered in 2 groups of exercises in one year period,
to make it as one full round. As on 31st March, 1997, four rounds of exercises were
completed covering all 19 parameters.
Methodology
Two synthetic samples labelled as A & B each of 1 litre volume, prepared in laboratory
by adopting standard procedures and precautions, are distributed to all participating
laboratories by courier service to avoid any transport delay. Samples were also analysed
in CPCB laboratory for arriving at “reference value” for comparison and to estimate the
acceptable limits of the reported values. The acceptability of results was determined
using “Youden 2 sample plot” method. See Figure 16.
Findings
The findings of nine exercises conducted during 1991 to 1997 by CPCB by involving
various laboratories of SPCBs, Pollution Control Committees and laboratories
recognised under E.P. Act are summarised below.
• The number of laboratories participating in AQC programme has increased from 20 in
1991 to 70 in 1997.
• At present, 52 laboratories of Pollution Control Boards and 18 other environmental
laboratories recognised under E. P. Act are participating in the program.
• The response of laboratories was always more that 80% and the maximum was in the
9th
AQC exercise as 94.2 %.
• The overall performance of all the 4 rounds of exercises carried out in 8 slots during
1992 to 1997 covering 19 parameters in terms of laboratories found within the
acceptable limits for all the 19 parameters is shown in Figure 1
• A perusal of this Figure 1 indicates that in general performance of these laboratories
for titrimetric methods of analysis is better than colorimetric and complex type
analyses.
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Suggestions
• Since overall performance of the AQC Exercises reveals that colorimetric and complex
type of analysis are not up to the expectation, it is necessary to give more attention
towards those methods of analysis to reduce possible analytical errors.
• As the performance of the most of analytical parameters were found lacking in
accuracy, it is necessary to take corrective measures.
• Improvement in within laboratory AQC is to be made with reference to selection of
method, grade of chemicals, glassware, analytical balance and preparation of control
charts.
• Known reference samples are to be provided to participating laboratories for
improving within laboratory AQC.
• Inter-laboratory AQC programme for participating laboratories is to be conducted
regularly and all the laboratories should participate regularly to asses the analytical
competence among various laboratories with a view to take necessary corrective
measures for reducing analytical errors.
• Training programme on AQC with special emphasis on hands-on training on internal
AQC programme is to be organised.
• Laboratory visits should be performed to advise necessary corrective measures.
• Central and Regional level workshops should be organised to sort out analytical
problems.
It is suggested that laboratories that are not participating in MINARS & GEMS
programmes may also participate in the AQC programme conducted by CPCB in order to
improve the analytical capability and performance.
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4 Planning of first round inter-laboratory AQC
4.1 Co-ordinating Laboratory
The co-ordinating laboratory distributes identical portions of the same standard solution
or sample to each participating laboratory, which analyses the portion it receives.
Results from the different laboratories are analysed by the co-ordinating laboratory to
estimate the bias of results of each laboratory.
Thus, it is essential that a laboratory able to act in this co-ordinating role is available and
has sufficient time and resource for the very careful work involved. Such a co-ordinating
laboratory should be a member of the working group of analysts. On satisfactory
completion of the tests, any of the participating laboratories may then act as co-
ordinating laboratory
4.2 Test samples
The objective of distributing a solution or sample is that each participating laboratory
should receive and analyse a portion containing the same concentration of the
determinand. For standard solutions, the co-ordinating laboratory should know this
concentration to accuracy appreciably better than that required of normal analytical
results otherwise the results of the exercise will be worthless. The need for great care in
the preparation and distribution of solutions cannot, therefore, be over-emphasised.
Generally, it will often be desirable for the co-ordinating laboratory alone to make
preliminary tests to ensure that its procedures do achieve the above requirement.
4.3 Purity of material used to prepare standard solutions
The chemicals used to prepare solutions should be of standard quality whose purity is
guaranteed by a written specification; 99.5% or better purity is usually adequate. High
purity water (de-ionised or distilled) is generally satisfactory, but absence of the
determinand in such water should not be assumed.
4.4 Errors in preparing the test solution or samples
In preparing a standard solution, it is useful for two analysts independently to calculate
the weight of standard material required in making up the desired volume of solution. A
second analyst should check the balance readings. When the standard material is
weighed, and also independently calculated, the weight of material is taken.
All apparatus used must be scrupulously clean and, in particular, free from traces of the
determinand of interest. Great care must be taken to avoid contamination of materials
and apparatus before and during the preparation. Manipulations such as quantitative
transfers and diluting solutions to a graduation mark must be conducted with the utmost
care.
When the standard solution has been prepared a question arises whether the
concentration of the solution should be checked by analysis. The approach
recommended is to prepare the solution as a primary standard using all the classical
precautions associated with such a preparation. The freshly prepared solution should be
analysed for the determinand of interest, a sufficient number of replicates being made for
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the purposes of stability testing. The estimate of initial concentration also serves as a
check for gross errors in the preparation. The true concentration for the collaborative test
should, however, be taken as the nominal concentration of the solution as a primary
standard and not the analytical result obtained in the concentration check.
4.5 Determinand stability and contamination
When the distribution is carried out, several portion of solution should be retained at the
co-ordinating laboratory for stability checks, and for replacements if required. The
solution should be stored in containers of the type used in the distribution and under the
storage conditions specified to participating laboratories. The concentration of the
determinand of interest should be checked at the end of the collaborative exercise and
should not have changed significantly from the initial value. For most determinands, this
usually means 1% of the nominal concentration, and sufficient replicate analyses should
be made to achieve that precision.
It is vitally important that the concentration of the determinand of interest in the samples
should be stable throughout the period of the tests, and a preservative may some times
be added to ensure this stability. However, some preservatives may cause interference
in certain analytical methods, and so the possible effect of any proposed preservative on
all methods of analyses must always be investigated carefully before the preservative is
used.
The material of which sample bottles are made should neither absorb nor release the
determinand, and bottles must be scrupulously cleaned to be free of the determinand of
interest. Particular care is necessary for many trace impurities to ensure that bottle
stoppers and caps are not a source of contamination.
4.6 Participating laboratories
The participating laboratories should be thoroughly familiar with the recommended
analytical procedure. They should have satisfactorily completed a within-laboratory
exercise for the determinand producing results of acceptable precision.
The participating laboratories can easily assess sources of bias resulting from the use of
impure chemicals, poor quality distilled water and sub-standard. If such errors are
detected, they should be removed before starting the inter-laboratory exercise.
4.7 Proposal
The exercise will be started by sending two samples by courier to the participating
laboratories. The laboratories are requested to analyse both samples for various
parameters. Each sample should be analysed in duplicate. The results must be reported
within three weeks after receipt of the bottles. Within two weeks after receipt of the
results the laboratory will be informed about the result (within limits / outside limits). The
results of the performance of all laboratories will be reported after wards. Consultants
offer assistance in solving analytical problems if so appears necessary.
Central Pollution Control Board (CPCB) laboratory at New Delhi was identified as the
reference co-ordinating laboratory. The laboratory is conduction such programmes for
the last several years for many laboratories in the country.
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Figure 1 The overall performance of all the 4 rounds of exercises carried out by CPCB in 8 slots during 1992 to 1997 covering 19
parameters. Laboratories found within the acceptable limits for all the 19 parameters.
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Normal distribution with high precision
0
5
10
15
20
25
16-18 18-20 20-22 22-24 24-26 26-28 28-30 30-32 32-34 34-36 36-38 38-40
TH (mg/L)
noofobservations
Norm aldistribution w ith low precision
0
5
10
15
20
25
16-18 18-20 20-22 22-24 24-26 26-28 28-30 30-32 32-34 34-36 36-38 38-40
TH (m g/L)
noofobservatio
Figure 2 Example of two normal distributions with the same mean value, the upper one being
more precise (having a lower standard deviation and CV)
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Figure 3 Example of loss of statistical control by the Control Limit criterion
Error! Not a valid link.
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Figure 4 Example of loss of statistical control by the Warning Limits criterion
Error! Not a valid link.
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Figure 5 Example of loss of statistical control by the Standard Deviation criterion
Error! Not a valid link.
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Figure 6 Example of loss of statistical control by the Trend criterion
Error! Not a valid link.
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Figure 7 Example of loss of statistical control by the Average (Central Line) criterion
Error! Not a valid link.
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Figure 8 CV values for TS analysis for HP laboratories in the within laboratory AQC
Coefficient of Variation (SD/AVG*100)
Total Solids (TS)
0
1
2
3
4
5
6
7
0-1 1-3 3-5 5-7 7-9 9-11 11-13 13-15
CV classes (%)
nosoflabsinclass
Standard
Methods
CPCB
US-EPA
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Figure 9 CV values for EC analysis for HP laboratories in the within laboratory AQC
Coefficient of Variation (SD/AVG*100)
Electrical Conductivity (EC)
0
1
2
3
4
5
6
7
0.0-0.5 0.5-1.0 1.0-1.5 1.5-2.0 2.0-2.5 2.5-3.0 3.0-3.5
CV classes (%)
nosoflabsinclass
CPCB
10 to15%
US-EPA
Standard
Methods
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Figure 10 CV values for TH analysis for HP laboratories in the within laboratory AQC
Coefficient of Variation (SD/AVG*100)
Total Hardness (TH)
0
1
2
3
4
5
6
7
8
9
0-1 1-2 2-3 3-4 4-5 5-6
CV classes (%)
nosoflabsinclass
US-EPA
Standard
Methods
CPCB
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Figure 11 Shewhart control chart for TS by laboratory 10
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Figure 12 Shewhart control chart for TH by laboratory 5
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Figure 13 Shewhart control chart for TH by laboratory 15
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Figure 14 Shewhart control chart for TH by laboratory 14
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Figure 15 Shewhart control chart for EC by laboratory 15
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Figure 16 Youden plot (specimen) of CPCB results for within laboratory exercise
Youden plot CPCB (specimen)
0
5
10
15
10 15 20 25 30 35 40
Sam ple A (m g/L)
SampleB(mg/L)
B+3σ
B-3σA-3
A+3
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Appendix A
Estimation of error in TH
burette accuracy (50mL type) is ± 0.05mL
pipette accuracy (15mL type) is 0.03mL
EDTA titrant = 0.01M (standardised)
standard Ca solution: 0.01M = 1000 mg/L
control sample is 10x diluted standard Ca solution = 100mg/L
Procedure: take 25mL of control sample and titrate this with approximately 2.5mL EDTA until
colour changes.
Formula: TH
A
mL
B= × ×1000 gives TH in mg/L
where A = mL EDTA titrated for sample
mL = millilters of sample titrated
B = mgCaCO3 equivalent to 1.0 mL of EDTA
Estimation of the error in TH of the control sample:
Total error TH = %ErrorA + %ErrormL + %ErrorB
%ErrorA = 15mL ± 0.03 -> 0.2% (pipette)
%ErrormL = 2.5mL ± 0.05 ->2% (burette)
Determination of B is again a titration and therefor:
%ErrorB-A = 15mL ± 0.03 ->0.2% (pipette
%ErrorB-ml = 15mL ± 0.05 ->0.3% (pipette
Sum of all four errors = 0.2 + 2 + 0.2 +0.3 = ±2.8%! The estimated CV is then ±1.0%, assuming
%error = 3 σ. The largest contribution comes from the 2.5mL EDTA titration by the burette. A
larger sample volume requiring larger EDTA titrant quantity reduces the relative error.
Note that this is a theoretical calculation based upon accuracy’s of burette and pipette only. The
actual precision is probably less because of other potential sources of error mentioned (e.g.
caused by judging the colour change).
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Appendix B
Effect of sample volume on precision for Total Solids analysis
Based on 100mL Based on 50mL sample
Dish mg 14000 ± 0.5 14000 ± 0.5
dish range mg 13999.5 to14000.5 13999.5 to14000.5
dish + solids mg 14015 ±0.5 14007.5 ±0.5
dish + solids range mg 14014.5 to 14015.5 14007.0 to 14008.0
differences mg 14, 16, 14, 15 7.5 8.5 6.5 7.5
average weight mg 15 ± 1.0 7.5± 1.0
% error - ±6.7% ± 13.5%
sample volume ml 100 ± 2.0 50 ± 2.0
% error ±2.0% ±4%
average concentration mg/l 150 150
differences mg/l 142.9 137.3 163.3 156.9 135.4 125.0 177.1 165.5
error (%) 8.7% 17.4%
cv (%) 2.9% 5.8%