This document summarizes a study to determine the heterogeneity of certified reference materials (CRMs). The researchers analyzed multiple small and large samples of various CRMs to measure sampling error and laboratory error separately. They found that CRMs exhibit both sampling error and laboratory error, contradicting the standard assumption that CRMs are homogenous. The researchers developed a method using the variance of small and large samples to calculate sampling error and laboratory error simultaneously. They conclude CRM manufacturers should provide sampling error measurements and analytical procedures should aim to optimize precision.
Data Normalization Approaches for Large-scale Biological StudiesDmitry Grapov
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance.
See here for animations used in the presentation:
http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...Randox
Randox Quality Control's latest educational guide examines Internal Quality Control, External Quality Assessment, Why laboratories should run QC, How often laboratories should run QC, Basic QC statistics and the quality control process.
The document discusses key concepts for interpreting clinical biochemical data, including:
1. Calculating measures of central tendency like the mean and measures of variability like the standard deviation and range to establish normal reference values.
2. Factors that determine the validity and reliability of analytical tests, and the importance of quality assurance and quality control.
3. Methods for describing data distribution including the coefficient of variation and using critical differences to determine the clinical significance of changes in serial measurements.
4. Concepts related to screening tests like sensitivity, specificity, and the ability to correctly identify individuals with and without the disease.
The laboratory shall determine measurement uncertainty for each measurement procedure,
in the examination phases used to report measured quantity values on patients’ samples. The
laboratory shall define the performance requirements for the measurement uncertainty of each
measurement procedure and regularly review estimates of measurement uncertainty.
Biological variation as an uncertainty componentGH Yeoh
To assist the clinical interpretation of a test result, there is a necessity to have an additional non-analytical component in the overall estimation of UM, namely the biological variation.
The document discusses quality assurance and quality control procedures for laboratory experiments and field work. It describes key elements of a quality assurance program such as trained personnel, proper analytical methods, documentation, calibration, and statistical analysis of data. The document provides references and guidelines for sampling, sample custody, analytical methods, detection limits, and using statistical tools like control charts to verify quality control.
This document summarizes a study to determine the heterogeneity of certified reference materials (CRMs). The researchers analyzed multiple small and large samples of various CRMs to measure sampling error and laboratory error separately. They found that CRMs exhibit both sampling error and laboratory error, contradicting the standard assumption that CRMs are homogenous. The researchers developed a method using the variance of small and large samples to calculate sampling error and laboratory error simultaneously. They conclude CRM manufacturers should provide sampling error measurements and analytical procedures should aim to optimize precision.
Data Normalization Approaches for Large-scale Biological StudiesDmitry Grapov
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance.
See here for animations used in the presentation:
http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/
Basic QC Statistics - Improving Laboratory Performance Through Quality Contro...Randox
Randox Quality Control's latest educational guide examines Internal Quality Control, External Quality Assessment, Why laboratories should run QC, How often laboratories should run QC, Basic QC statistics and the quality control process.
The document discusses key concepts for interpreting clinical biochemical data, including:
1. Calculating measures of central tendency like the mean and measures of variability like the standard deviation and range to establish normal reference values.
2. Factors that determine the validity and reliability of analytical tests, and the importance of quality assurance and quality control.
3. Methods for describing data distribution including the coefficient of variation and using critical differences to determine the clinical significance of changes in serial measurements.
4. Concepts related to screening tests like sensitivity, specificity, and the ability to correctly identify individuals with and without the disease.
The laboratory shall determine measurement uncertainty for each measurement procedure,
in the examination phases used to report measured quantity values on patients’ samples. The
laboratory shall define the performance requirements for the measurement uncertainty of each
measurement procedure and regularly review estimates of measurement uncertainty.
Biological variation as an uncertainty componentGH Yeoh
To assist the clinical interpretation of a test result, there is a necessity to have an additional non-analytical component in the overall estimation of UM, namely the biological variation.
The document discusses quality assurance and quality control procedures for laboratory experiments and field work. It describes key elements of a quality assurance program such as trained personnel, proper analytical methods, documentation, calibration, and statistical analysis of data. The document provides references and guidelines for sampling, sample custody, analytical methods, detection limits, and using statistical tools like control charts to verify quality control.
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 outlines the content of Module 3 of an analytical chemistry course, which covers inferential statistics. It includes lectures and practical computer sessions on confidence intervals, hypothesis testing, and statistical tests involving single and multiple samples. Students will learn about calculating confidence intervals for population means and variances, performing one-sample z-tests and t-tests, and using statistical tests like the t-test, paired t-test, and F-test for two samples. The module concludes with a midterm exam and recommends textbooks for further reading on introductory statistics and chemometrics. As homework, students will complete exercises applying these statistical concepts to practical chemistry problems involving confidence intervals, hypothesis testing, and error analysis.
This document discusses quality management systems in medical laboratories. It defines key terms like quality control, accuracy, precision, and quality assurance. It describes the goals of internal and external quality control. The document also explains sources of error like pre-analytical, analytical, and post-analytical phases. It provides details on Levey-Jennings charts, including how they are used to evaluate quality control results over time using statistical process control rules like Westgard rules.
This document outlines statistical quality control techniques for evaluating manufacturing and service processes. It discusses measuring and controlling process variation using variables like mean, standard deviation and control charts. Key aspects covered include process capability analysis using metrics like Cpk, acceptance sampling plans to determine quality levels while balancing producer and consumer risks, and operating characteristic curves.
Quality Control for Quantitative Tests by Prof Aamir Ijaz (Pakistan)Aamir Ijaz Brig
This document provides an overview of quality control and quality assurance processes in a chemical pathology laboratory. It discusses key terms like quality control, quality assurance, internal quality control, external quality assurance. It also describes different types of errors like random error and systematic error. The document explains statistical concepts like measures of central tendency, standard deviation, coefficient of variation. It discusses the Westgard rules for evaluating quality control results and triggering investigations into potential errors. The goal of the lecture is to describe the processes involved in quality management for chemical pathology laboratories.
Webinar: How to Develop a Regulatory-compliant Continued Process Verificatio...MilliporeSigma
Participate in the interactive webinar now: http://bit.ly/CPVWebinar
Product life cycle consists of 3 phases: Process Design, Process Performance Qualification and the last and the lengthiest Continued Process Verification (CPV). As more and more biomanufacturing processes enter commercial phases, the critical need to understand how to efficiently perform CPV programs arises.
Explore our webinar library: www.emdmillipore.com/webinars
Webinar: How to Develop a Regulatory-compliant Continued Process Verification...Merck Life Sciences
Participate in the interactive webinar now: http://bit.ly/CPVWebinar
Product life cycle consists of 3 phases: Process Design, Process Performance Qualification and the last and the lengthiest Continued Process Verification (CPV). As more and more biomanufacturing processes enter commercial phases, the critical need to understand how to efficiently perform CPV programs arises.
Explore our webinar library: www.merckmillipore.com/webinars
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.
This document discusses validation and calibration of HPLC systems. It defines validation as establishing that an analytical procedure meets requirements for its intended use through laboratory studies. A validation protocol outlines how validation will be conducted. Equipment validation demonstrates that equipment is suitable for use and comparable to routine equipment. Calibration involves demonstrating that an instrument produces results within specified limits compared to a reference standard. The document outlines parameters to validate like accuracy, precision, specificity, range, robustness and more. It provides details on testing these parameters and accepting calibration of modules like the pump, injector, detector and column heating.
Validation of lab instruments and quantitative test methods Mostafa Mahmoud
This lecture shows the procedures applied when going to validate your laboratory instruments and quantitative test methods also either FDA approved or laboratory developed tests.
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
analytical method validation and validation of hplcvenkatesh thota
The document summarizes a seminar on analytical method validation and validation of HPLC. It discusses parameters for method validation according to USP, BP, and ICH guidelines such as accuracy, precision, linearity, range, specificity, detection limit, and quantitation limit. It also covers validation of typical HPLC systems through qualification, design, installation, operational, and performance qualification. Key parameters evaluated during HPLC method validation are discussed, including system suitability tests involving retention factor, relative retention, theoretical plates, resolution, and tailing factor.
How to establish QC reference ranges - Randox QC Educational GuideRandox
Establishing QC reference ranges is an important aspect of laboratory quality. Find out how to effectively set reference ranges with our educational guide.
The document discusses the PERT (Program/Project Evaluation and Review Technique) approach for project scheduling. PERT treats task durations as probabilistic rather than deterministic, using three time estimates (optimistic, most likely, pessimistic) to model the statistical distribution of possible task durations. This allows it to better handle uncertainty compared to the Critical Path Method (CPM) and is more suitable for research and development projects with limited historical data.
Variability of clinical chemistry laboratory resultsAdetokunboAjala
Understanding the concepts associated with variability of laboratory results would help laboratorians improve the quality of laboratory service as well as aid the drive towards harmonization of laboratory quality practices.
This thesis aims to determine the heterogeneity of reference materials used in geochemical analysis. Reference materials are assumed to be homogeneous, but some variation in results could be due to sampling error from heterogeneity rather than just laboratory error. The researcher measures the concentration of elements in both small and large samples of reference materials. Using statistical analysis, this allows determining the magnitudes of sampling error and laboratory error simultaneously. The results indicate that reference materials do exhibit some heterogeneity, so sampling error contributes to total variation. Knowing the sampling error is important for accurately assessing analytical quality using reference materials.
The document discusses various quality control procedures that are important for ensuring accurate and reliable laboratory test results. It covers topics like calibration checks, control charts, quality control rules, corrective actions, and external quality assessment. The key aspects emphasized are the need for documented quality control protocols, monitoring control results for errors, and taking actions to correct any issues identified.
Analytical method development and validation are one of the very imp aspects in Drug testing and approval process.Here I tried to explain the same with my experience.
The document discusses analytical method validation. It describes the key steps in analytical method validation including method development, qualification, transfer to quality control, periodic monitoring and revalidation if changes are made. It outlines the key parameters that are evaluated in validation including accuracy, precision, specificity, linearity, range, limit of detection, limit of quantitation and robustness. Approaches for evaluating each parameter are provided with examples. The importance of validation for ensuring reliable test methods as required by regulatory agencies is also highlighted.
Global Soil Partnership efforts to promote soil governance from the global to...Soils FAO-GSP
Webinar on soil governance and launch of SoiLEX
13 January 2021 | 15:00 to 16:30 CET online (Zoom platform).
Mr Hugo Bourhis, International Consultant, FAO GSP
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Participate in the interactive webinar now: http://bit.ly/CPVWebinar
Product life cycle consists of 3 phases: Process Design, Process Performance Qualification and the last and the lengthiest Continued Process Verification (CPV). As more and more biomanufacturing processes enter commercial phases, the critical need to understand how to efficiently perform CPV programs arises.
Explore our webinar library: www.emdmillipore.com/webinars
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Participate in the interactive webinar now: http://bit.ly/CPVWebinar
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Explore our webinar library: www.merckmillipore.com/webinars
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.
This document discusses validation and calibration of HPLC systems. It defines validation as establishing that an analytical procedure meets requirements for its intended use through laboratory studies. A validation protocol outlines how validation will be conducted. Equipment validation demonstrates that equipment is suitable for use and comparable to routine equipment. Calibration involves demonstrating that an instrument produces results within specified limits compared to a reference standard. The document outlines parameters to validate like accuracy, precision, specificity, range, robustness and more. It provides details on testing these parameters and accepting calibration of modules like the pump, injector, detector and column heating.
Validation of lab instruments and quantitative test methods Mostafa Mahmoud
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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
analytical method validation and validation of hplcvenkatesh thota
The document summarizes a seminar on analytical method validation and validation of HPLC. It discusses parameters for method validation according to USP, BP, and ICH guidelines such as accuracy, precision, linearity, range, specificity, detection limit, and quantitation limit. It also covers validation of typical HPLC systems through qualification, design, installation, operational, and performance qualification. Key parameters evaluated during HPLC method validation are discussed, including system suitability tests involving retention factor, relative retention, theoretical plates, resolution, and tailing factor.
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Understanding the concepts associated with variability of laboratory results would help laboratorians improve the quality of laboratory service as well as aid the drive towards harmonization of laboratory quality practices.
This thesis aims to determine the heterogeneity of reference materials used in geochemical analysis. Reference materials are assumed to be homogeneous, but some variation in results could be due to sampling error from heterogeneity rather than just laboratory error. The researcher measures the concentration of elements in both small and large samples of reference materials. Using statistical analysis, this allows determining the magnitudes of sampling error and laboratory error simultaneously. The results indicate that reference materials do exhibit some heterogeneity, so sampling error contributes to total variation. Knowing the sampling error is important for accurately assessing analytical quality using reference materials.
The document discusses various quality control procedures that are important for ensuring accurate and reliable laboratory test results. It covers topics like calibration checks, control charts, quality control rules, corrective actions, and external quality assessment. The key aspects emphasized are the need for documented quality control protocols, monitoring control results for errors, and taking actions to correct any issues identified.
Analytical method development and validation are one of the very imp aspects in Drug testing and approval process.Here I tried to explain the same with my experience.
The document discusses analytical method validation. It describes the key steps in analytical method validation including method development, qualification, transfer to quality control, periodic monitoring and revalidation if changes are made. It outlines the key parameters that are evaluated in validation including accuracy, precision, specificity, linearity, range, limit of detection, limit of quantitation and robustness. Approaches for evaluating each parameter are provided with examples. The importance of validation for ensuring reliable test methods as required by regulatory agencies is also highlighted.
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Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Good laboratory practices. Internal quality control by z score approach
1. First Meeting of the Regional Soil
Laboratory Network for Eurasia and
Europe
Good Laboratory Practices
Internal quality control by z-score approach
J Coutinho
University of Trás-os-Montes e Alto Douro
Portugal
2. Framework
At the end of the day, we are supposed to know if we
are confident about our the daily results:
• to be used in research projects, or;
• to be published in scientific journals, or;
• to be used to take political decisions, or;
• to be sold to our clients.
3. For that decision, we need Internal Quallity Control
(IQC) for each batch of samples and for each parameter
analised.
4. Z-score
Upperadmissible limit
Lower admissible limit
Upperwarning limit
Lower warning limit
The daily Internal QC may be based on the z-score (or standard score) , which
is a measure of how many standard deviations below or above the
population mean a raw score is.
Z-score or Shewhard chart
5. The daily Internal QC may conducted by the use of:
• a reference sample
expensive, especially for laboratories with high number of samples (30
g/day x 230 working days/year ≈ 7 kg/year!);
• internal standards samples
preferably 2-3 samples with different values for most of the analyzed
parameters.
6. In order to use a z-score (which is a dimensionless quantity), we need to
know the mean (m) and the standard deviation (s) of the population:
Z-score = (x – m) / s
or
Z-score = (x – m*) / s*
7. • For reference samples, both m and s are provided;
• For internal standards samples, we need to establish those values.
How?
8. Option A
establish both values of m and s in our own laboratory,
with 10 or more replications obtained with the same
timeframe of the normal analysis. Nevertheless, we need
to remember that we only get data related with
repeatibility (precision),
but we need to be aware that we are ignoring the
accuracy of the true value, since the value of m may be
bias. The values of one specific laboratory may have an
higher precision (low s) but they can be systematically
higher or lower than the true value.
9. Option B
to provide sample(s) for PT programs (at the national,
subregional ou regional scale) and use the same
sample(s) as internal standards.
This way, and free of charge, we can obtain values of m
and s (or m* and s * ) for our internal standards.
10. In some cases circunstancies we may want:
• to evaluate the ”quality” of the s to be used
and/or
• to establish an estimated value for s
Estimation of s
For this purpose, we can use the coeficient of variation (CV), also known as the relative
standard deviation (%RSD):
%RSD (or CV) = (s / m) x 100
If we establish an expected value for %RSD, then we can estimate the (new) value of s
sestimated = (%RSDexpected * m) / 100
11. How to calculate (or establish) the expected %RSD
Option A
for soil parameters expressed in SI units, the Equation of Horwitz(1) can be used
%RSDR = 2(1-0.5logC)
where where C is the concentration expressed as powers of 10 (e.g., 1 mg kg-1 = 10-6)
and %RSDR is the CV between-laboratories
(1) William Horwitz/FDA), 1982, Analytical Chemistry, 54 (1) 67-76
12. The between-laboratory %RSDR at
• 1 mg kg-1 is 16 % (24);
• 100 mg kg-1 is 8 %;
• 10 g kg-1 is 4 %;
• 100 g kg-1 is 2.8 %
The within-laboratory RSD (%RSDr)
should ordinarily be one-half to two-
thirds the %RSDR
Then, within-laboratory %RSDr at
• 1 mg kg-1 is about 8 to 10 %;
• ....
• 100 g kg-1 is about 1.4.to 1.9 %
13. How to calculate (or establish) the expected %RSD
Option B
for soil pH values (non-expressed in SI units)
We can consider the %RSDr value of 3.17% adopted by the
Association of Official Agricultural Chemists (AOAC), which equals the
median of %RSDr obtained in the validation of pH methods for 40
mineral, saline and organic soils by 53 laboratories(2).
(2) Y.P. Kalra (1995). Journal AOAC international, 78 (2): 310-324
18. • 1 value out of ±3 s: the probability is < 0,3%;
• 2 values in 3 consecutive values out of ±2 s: the normal probability was exceded;
• 4 values in 5 consecutive values out of ± 1 s: the normal probability was exceded;
• 15 consecutive values inside ±1 s: the actual standard deviation is lower than the
expected;
• 9 consecutive values in the same side of m: a sistematic deviation is occurrring;
• 8 consecutive values out of ± 1 s: evidence of a bimodal distribution;
• 6 consecutive values going up or going down: evidence of a nonrandom trend;
• 14 consecutive values alternating up and down: evidence of a time series affecting
the data;
Deviation tests recommended by ISO 8258
But z-scores are also usefull of internal QC in the medium-term evaluation
19. The decisions about daily or medium-term internal QC are
more sounded if we use 2 – 3 internal references