This document discusses errors in measurement and analysis. It defines absolute and relative errors as the difference between experimental and true values. Errors are classified as determinate (systemic) or indeterminate (random). Determinate errors include personal, instrumental, method, additive and proportional errors. Indeterminate errors cannot be avoided and come from unknown causes. Accuracy refers to how close a measurement is to the true value, while precision describes the reproducibility of measurements. Significant figures convey the precision or accuracy of numerical values. The document provides examples and rules for determining significant figures.
Errors in pharmaceutical analysis can be determinate (systematic) or indeterminate (random). Determinate errors are caused by faults in procedures or instruments and cause results to consistently be too high or low. Sources include improperly calibrated equipment, impure reagents, and analyst errors. Indeterminate errors are random and unavoidable, arising from limitations of instruments. Accuracy refers to closeness to the true value, while precision refers to reproducibility. Systematic errors can be minimized by calibrating equipment, analyzing standards, using independent methods, and blank determinations.
Introduction
error, accuracy, precision
Source of Errors
Types of Errors
Methods of minimizing errors
Test for rejection of data
Significant Level
Rounding of Figures
References
This document provides an overview of the Metrology (MEE 322) course. It will cover topics related to precision measurement, including mechanical measurements under strict control conditions, comparator profilometry, and tolerances and quality. There will be two tests focused on collimators and fits. Recommended books for the course are also listed. The introduction to metrology defines key terms like definitions of metrology, types of metrology including scientific and industrial, the need for inspection in manufacturing, and factors that affect the accuracy of measurements. Errors in measurement and metric units used in industry are also introduced.
Errors refer to the differences between measured and true values in measurements and experiments. It is impossible to perform analyses that are completely free of errors. Errors are caused by faulty instruments, imprecise measurements, and random variations. Methods to reduce errors include frequent calibration of instruments, analysis of known samples, and repeating measurements. Precision refers to the reproducibility of measurements and can be estimated through repeated measurements of replicate samples. Accuracy is the closeness of a measurement to the true value and is more difficult to determine than precision. There are two main types of errors: determinate errors caused by mistakes that can be avoided, and accidental errors that are difficult to control. Methods to minimize errors include calibration, using blanks, comparative analysis techniques, and repeated
Statistical analysis & errors (lecture 3)Farhad Ashraf
This document discusses statistical analysis and errors in measurement. It defines statistical analysis as dealing with numerical data using probability theory. Measurement errors can be divided into determinate (systematic) errors and indeterminate (random) errors. Determinate errors can be avoided or corrected, while indeterminate errors cannot be determined precisely but their probability can be estimated using statistical distributions like the Gaussian curve. The document also discusses concepts like significant figures, rounding off data, measures of central tendency (mean, median, mode), standard deviation, tests like F-test and T-test, quality control/quality assurance, good laboratory practices, validation of analytical methods and their parameters.
This document discusses methods for determining and classifying errors in chemical analysis. It describes two main categories of errors: systematic (determinate) errors, which can be identified and corrected, and random (indeterminate) errors, which cannot be attributed to a single cause. Determinate errors include personal errors by the analyst, errors due to faulty instruments or impure reagents, and methodic errors related to the analysis technique. Indeterminate errors are accidental and beyond the analyst's control. The document also provides examples of different types of errors and methods for minimizing systematic errors, such as running blanks, calibration, controlled determinations, and independent verification of results.
The document discusses the importance of limit tests in pharmaceutical chemistry for determining impurities, describing the principle and procedure for the limit test for chlorides which involves the chemical reaction of chlorides with silver nitrate in the presence of nitric acid to form a silver chloride precipitate, comparing the test sample to a standard solution of known chloride concentration.
This document discusses errors in measurement and analysis. It defines absolute and relative errors as the difference between experimental and true values. Errors are classified as determinate (systemic) or indeterminate (random). Determinate errors include personal, instrumental, method, additive and proportional errors. Indeterminate errors cannot be avoided and come from unknown causes. Accuracy refers to how close a measurement is to the true value, while precision describes the reproducibility of measurements. Significant figures convey the precision or accuracy of numerical values. The document provides examples and rules for determining significant figures.
Errors in pharmaceutical analysis can be determinate (systematic) or indeterminate (random). Determinate errors are caused by faults in procedures or instruments and cause results to consistently be too high or low. Sources include improperly calibrated equipment, impure reagents, and analyst errors. Indeterminate errors are random and unavoidable, arising from limitations of instruments. Accuracy refers to closeness to the true value, while precision refers to reproducibility. Systematic errors can be minimized by calibrating equipment, analyzing standards, using independent methods, and blank determinations.
Introduction
error, accuracy, precision
Source of Errors
Types of Errors
Methods of minimizing errors
Test for rejection of data
Significant Level
Rounding of Figures
References
This document provides an overview of the Metrology (MEE 322) course. It will cover topics related to precision measurement, including mechanical measurements under strict control conditions, comparator profilometry, and tolerances and quality. There will be two tests focused on collimators and fits. Recommended books for the course are also listed. The introduction to metrology defines key terms like definitions of metrology, types of metrology including scientific and industrial, the need for inspection in manufacturing, and factors that affect the accuracy of measurements. Errors in measurement and metric units used in industry are also introduced.
Errors refer to the differences between measured and true values in measurements and experiments. It is impossible to perform analyses that are completely free of errors. Errors are caused by faulty instruments, imprecise measurements, and random variations. Methods to reduce errors include frequent calibration of instruments, analysis of known samples, and repeating measurements. Precision refers to the reproducibility of measurements and can be estimated through repeated measurements of replicate samples. Accuracy is the closeness of a measurement to the true value and is more difficult to determine than precision. There are two main types of errors: determinate errors caused by mistakes that can be avoided, and accidental errors that are difficult to control. Methods to minimize errors include calibration, using blanks, comparative analysis techniques, and repeated
Statistical analysis & errors (lecture 3)Farhad Ashraf
This document discusses statistical analysis and errors in measurement. It defines statistical analysis as dealing with numerical data using probability theory. Measurement errors can be divided into determinate (systematic) errors and indeterminate (random) errors. Determinate errors can be avoided or corrected, while indeterminate errors cannot be determined precisely but their probability can be estimated using statistical distributions like the Gaussian curve. The document also discusses concepts like significant figures, rounding off data, measures of central tendency (mean, median, mode), standard deviation, tests like F-test and T-test, quality control/quality assurance, good laboratory practices, validation of analytical methods and their parameters.
This document discusses methods for determining and classifying errors in chemical analysis. It describes two main categories of errors: systematic (determinate) errors, which can be identified and corrected, and random (indeterminate) errors, which cannot be attributed to a single cause. Determinate errors include personal errors by the analyst, errors due to faulty instruments or impure reagents, and methodic errors related to the analysis technique. Indeterminate errors are accidental and beyond the analyst's control. The document also provides examples of different types of errors and methods for minimizing systematic errors, such as running blanks, calibration, controlled determinations, and independent verification of results.
The document discusses the importance of limit tests in pharmaceutical chemistry for determining impurities, describing the principle and procedure for the limit test for chlorides which involves the chemical reaction of chlorides with silver nitrate in the presence of nitric acid to form a silver chloride precipitate, comparing the test sample to a standard solution of known chloride concentration.
Introduction to Pharmaceutical ChemistryPriti Kokate
Chapter No. 1 from pharmaceutical chemistry , updated syllabus notes as per MSBTE
1.Introduction to pharmaceutical chemistry
Topic covers following bits
#Scope
#Objective
#Sources & Types Of Errors
#Impurities in Pharmaceuticals
#Limit Test For
*Chloride
*Sulphate
*Iron
*Heavy Metal
*Arsenic
This document outlines the key topics in Analytical Chemistry I including significant figures, types of errors, propagation of uncertainty, and systematic vs random errors. It discusses how measurements have uncertainty and errors. There are two main types of errors - systematic errors which affect accuracy and can be discovered and corrected, and random errors which cannot be eliminated and have equal chances of being positive or negative. The document also describes how to calculate the propagation of uncertainty through calculations using addition, subtraction, multiplication, division and other operations. It emphasizes keeping extra digits in calculations to properly account for uncertainty.
This document provides an overview of measurement and instrumentation topics. It defines measurement as the act of comparing an unknown quantity to a standard. Instruments are defined as devices used to determine the value of a quantity, while instrumentation refers to using instruments to measure properties in industrial processes. The document discusses types of instruments, including active vs passive, as well as different methods and standards used for measurement. It also covers sources of error in measurement, such as systematic, random, alignment, and parallax errors.
This document discusses experimental errors in scientific measurements. It defines experimental error as the difference between a measured value and the true value. Experimental errors can be classified as systematic errors or random errors. Systematic errors affect accuracy and can result from faulty instruments, while random errors affect precision and arise from unpredictable fluctuations. The document also discusses ways to quantify and describe experimental errors, including percent error, percent difference, mean, and significant figures. Understanding experimental errors is important for analyzing measurement uncertainties and improving experimental design.
1. Systematic errors affect the accuracy of results and are caused by factors like improper instrument calibration, faulty methodology, or personal biases.
2. Random errors affect precision and result from unpredictable factors that cause random scatter in measurements.
3. Various statistical analyses can be used to determine systematic and random errors in experimental data, including calculating measures of central tendency, variability, and confidence limits. Propagation of errors must also be considered.
This document discusses various concepts related to errors and accuracy in chemical analysis. It defines different types of errors like gross errors, systematic errors, and random errors. It explains how to classify errors based on their origin and how to minimize different types of errors. The document also covers key statistical concepts like mean, median, standard deviation, normal distribution, precision and accuracy that are important for understanding errors in chemical analysis.
Today's Topic Errors - Introduction, Sources of Errors, Types of Errors, Minimization of Errors, Accuracy, Precision, Significant Figures in Pharmaceutical Analysis subject in B.pharmacy 1st year as per JNTUA Syllabus...
This document discusses types of errors that can occur in chemical analysis and sampling. There are two main categories of error: determinate and indeterminate. Determinate errors can be further broken down into constant and proportional determinate errors. Indeterminate errors are random errors that occur due to unpredictable fluctuations. Various statistical analyses can be used to evaluate precision and accuracy in chemical analysis, including calculating the mean, median, standard deviation, and using control charts. Proper statistical analysis is important for understanding the reliability of results from chemical experiments.
METROLOGY & MEASUREMENT Unit 1 notes (5 files merged)MechRtc
Metrology is the science of measurement. It is concerned with establishing standards of measurement, measuring errors and uncertainties, and ensuring uniformity of measurements. Metrology has applications in industry, commerce, and public health/safety. It functions to maintain standards, train professionals, regulate manufacturers, and conduct research to improve measurement methods and accuracy. Proper measurement requires standards, instruments, trained personnel, and control of environmental factors that could influence results. Sources of error include the measuring system and process itself as well as environmental and loading factors. Accuracy depends on the operator, temperature, measurement method, and instrument deformation.
This document discusses sources of errors, types of errors, and techniques to minimize errors in experimental measurements and chemical analysis. It defines accuracy as closeness to the true value and precision as reproducibility of results. The main types of errors are systematic errors which can be determined and avoided, random errors which are accidental, and gross errors. Sources of error include improper sampling, preparation, instrumentation, calibration, reporting, calculations, and environmental factors. Methods to reduce errors include instrument calibration, running blanks, controls, standard additions, independent methods, parallel determinations, and amplification for small amounts.
Introduction to analysis- Pharmaceutical AnalysisSanchit Dhankhar
QUALITY CONTROL (QC)
SIGNIFICANT FIGURE
CONCEPT OF ERROR
ACCURACY
PRECISION
CALIBRATION OF ANALYTICAL INSTRUMENTS
DIFFERENT METHOD FOR EXPRESSING CONCENTRATION
FUNDAMENTAL OF VOLUMETRIC ANALYSIS
STANDARD DEVIATION
NORMAL DISTRIBUTION CURVE
DEFINATION:
Quality control (QC) is a procedure or set of procedures intended to ensure that a manufactured product sticks to a defined set of quality criteria or meets the requirements of the client or customer.
OR
A system for verifying and maintaining a desired level of quality in an existing product or service by careful planning, use of proper equipment, continued inspection, and corrective action as required.
Evaluation of Quality
Raw materials and API
Physical Tests
Raman and IR Spectroscopy
Assay (HPLC and Titration)
Drug Product
HPLC
Dissolution
Packaging components
Appearance
Loss on Drying
Retains
At label conditions
Retain time determined by regulatory guidelines
Raw materials 12 Years
Finished products 10 Years
Definition:
Significant figures are the reliable digits in a number or measurement which are known with certainty.
Rules:
ALL non-zero numbers (1,2,3,4,5,6,7,8,9) are ALWAYS significant.
ALL zeroes between non-zero numbers are ALWAYS significant.
ALL zeroes which are SIMULTANEOUSLY to the right of the decimal point & at the end of the number are ALWAYS significant.
ALL zeroes which are to the left of a written decimal point and are in a number >= 10 are ALWAYS significant.
Minimization of errors, accuracy, precission, significant figures.Nidhi Sharma
This document discusses several methods for minimizing errors and improving accuracy and precision in measurements and analysis. It defines key terms like accuracy, precision, and significant figures. Some key points:
1. Systematic errors can be reduced through calibration of instruments, using control determinations with standard substances, blank determinations to remove impurities, and independent verification of methods.
2. Accuracy refers to how close a measurement is to the true value, while precision refers to the repeatability of measurements.
3. Significant figures include all digits known with certainty plus one estimated digit, excluding leading or some trailing zeros. They indicate the precision and uncertainty of a measurement.
Errors in chemical analysis can be random or systematic. Random errors cause imprecise results while systematic errors lead to inaccurate results by introducing bias. Common sources of systematic error include faulty instrumentation, non-ideal chemical behaviors in analytical methods, and personal biases of experimenters. Systematic errors can be detected through frequent calibration of instruments, analysis of reference standards, independent verification methods, blank determinations, and evaluation of results from varying sample sizes. Controlling for systematic errors is important for obtaining reliable analytical data.
Accuracy precision and significant figuresnehla313
This document defines accuracy, precision, and significant figures in analytical chemistry.
Accuracy refers to how close a measurement is to the true value, while precision describes the reproducibility of repeated measurements. A measurement can be precise without being accurate.
There are two methods to determine accuracy - absolute and comparative. The absolute method uses samples of known composition, while the comparative method uses secondary standards. Precision is measured by tests of repeatability and reproducibility.
The number of significant figures indicates the certainty of a measurement and must be considered when performing calculations to avoid losing accuracy. Rules are provided for determining significant figures in addition, subtraction, multiplication, division and other operations.
Analytical Errors and Validation of Analytical procedures.pdfAbdiIsaq1
1. The document discusses analytical errors and validation of analytical procedures.
2. There are two main types of analytical errors - determinate (systematic) errors which have a definite cause, and indeterminate (random) errors which cannot be attributed to a single cause.
3. Validation of analytical procedures is important to demonstrate that a method is suitable for its intended purpose. Common parameters validated include linearity, range, accuracy, precision, selectivity, and robustness.
Analytical Errors and Validation of Analytical procedures.pdfAbdiIsaq1
1. The document discusses analytical errors and validation of analytical procedures.
2. There are two main types of analytical errors - determinate (systematic) errors and indeterminate (random) errors. Systematic errors can be identified and accounted for, while random errors are unpredictable.
3. Validation of analytical procedures is important to demonstrate that a procedure is suitable for its intended purpose. Common parameters validated include linearity, range, accuracy, precision, selectivity, and robustness. Validation helps ensure quality and reliability of analytical results.
Errors - pharmaceutical analysis -1, bpharm 1st semester, notes, topic errors
full details and answer about error
TN DR MGR UNIVERSITY
by Kumaran.M.pharm, professor
This document discusses metrology, which is the science of measurement. Precise and accurate measurements are needed for quality inspection during manufacturing. There are two types of errors in measurement: systematic errors, which are constant due to issues like faulty instruments, and random errors, which are unpredictable due to external factors. Systematic errors cannot be eliminated by repeated measurements but can be reduced by calibration, while random errors can be minimized through repetition and require statistical analysis to characterize. The objectives of metrology are to ensure components meet specifications and identify sources of error.
The document discusses different types of errors that can occur in measurement. It describes gross errors, systematic errors like instrumental errors and environmental errors, and random errors. It also defines key terms used to analyze errors like limit of reading, greatest possible error, and discusses analyzing measurement data using statistical methods like the mean, standard deviation, variance and histograms. Measurement errors can occur due to issues like parallax, calibration, limits of the measuring device, and are analyzed statistically.
This document discusses various redox titration methods including permanganometry, dichrometry, cerimetry, iodimetry, and bromatometry. It defines oxidation, reduction, and redox reactions. It explains how to calculate equivalent weights of oxidizing and reducing agents and different methods to detect the endpoint of a redox titration including using internal indicators, self indicators, external indicators, and instrumental methods. It provides examples of applications for each type of redox titration.
This document provides instructions for preparing 0.1 N solutions of various acids and bases. It describes weighing out the appropriate amounts of the substances based on their molecular weights and normalities, and diluting them with distilled water in volumetric flasks. The solutions are standardized by titrating them against standardized solutions of oxalic acid, sodium hydroxide, potassium dichromate, and arsenic trioxide to determine their actual normalities.
Introduction to Pharmaceutical ChemistryPriti Kokate
Chapter No. 1 from pharmaceutical chemistry , updated syllabus notes as per MSBTE
1.Introduction to pharmaceutical chemistry
Topic covers following bits
#Scope
#Objective
#Sources & Types Of Errors
#Impurities in Pharmaceuticals
#Limit Test For
*Chloride
*Sulphate
*Iron
*Heavy Metal
*Arsenic
This document outlines the key topics in Analytical Chemistry I including significant figures, types of errors, propagation of uncertainty, and systematic vs random errors. It discusses how measurements have uncertainty and errors. There are two main types of errors - systematic errors which affect accuracy and can be discovered and corrected, and random errors which cannot be eliminated and have equal chances of being positive or negative. The document also describes how to calculate the propagation of uncertainty through calculations using addition, subtraction, multiplication, division and other operations. It emphasizes keeping extra digits in calculations to properly account for uncertainty.
This document provides an overview of measurement and instrumentation topics. It defines measurement as the act of comparing an unknown quantity to a standard. Instruments are defined as devices used to determine the value of a quantity, while instrumentation refers to using instruments to measure properties in industrial processes. The document discusses types of instruments, including active vs passive, as well as different methods and standards used for measurement. It also covers sources of error in measurement, such as systematic, random, alignment, and parallax errors.
This document discusses experimental errors in scientific measurements. It defines experimental error as the difference between a measured value and the true value. Experimental errors can be classified as systematic errors or random errors. Systematic errors affect accuracy and can result from faulty instruments, while random errors affect precision and arise from unpredictable fluctuations. The document also discusses ways to quantify and describe experimental errors, including percent error, percent difference, mean, and significant figures. Understanding experimental errors is important for analyzing measurement uncertainties and improving experimental design.
1. Systematic errors affect the accuracy of results and are caused by factors like improper instrument calibration, faulty methodology, or personal biases.
2. Random errors affect precision and result from unpredictable factors that cause random scatter in measurements.
3. Various statistical analyses can be used to determine systematic and random errors in experimental data, including calculating measures of central tendency, variability, and confidence limits. Propagation of errors must also be considered.
This document discusses various concepts related to errors and accuracy in chemical analysis. It defines different types of errors like gross errors, systematic errors, and random errors. It explains how to classify errors based on their origin and how to minimize different types of errors. The document also covers key statistical concepts like mean, median, standard deviation, normal distribution, precision and accuracy that are important for understanding errors in chemical analysis.
Today's Topic Errors - Introduction, Sources of Errors, Types of Errors, Minimization of Errors, Accuracy, Precision, Significant Figures in Pharmaceutical Analysis subject in B.pharmacy 1st year as per JNTUA Syllabus...
This document discusses types of errors that can occur in chemical analysis and sampling. There are two main categories of error: determinate and indeterminate. Determinate errors can be further broken down into constant and proportional determinate errors. Indeterminate errors are random errors that occur due to unpredictable fluctuations. Various statistical analyses can be used to evaluate precision and accuracy in chemical analysis, including calculating the mean, median, standard deviation, and using control charts. Proper statistical analysis is important for understanding the reliability of results from chemical experiments.
METROLOGY & MEASUREMENT Unit 1 notes (5 files merged)MechRtc
Metrology is the science of measurement. It is concerned with establishing standards of measurement, measuring errors and uncertainties, and ensuring uniformity of measurements. Metrology has applications in industry, commerce, and public health/safety. It functions to maintain standards, train professionals, regulate manufacturers, and conduct research to improve measurement methods and accuracy. Proper measurement requires standards, instruments, trained personnel, and control of environmental factors that could influence results. Sources of error include the measuring system and process itself as well as environmental and loading factors. Accuracy depends on the operator, temperature, measurement method, and instrument deformation.
This document discusses sources of errors, types of errors, and techniques to minimize errors in experimental measurements and chemical analysis. It defines accuracy as closeness to the true value and precision as reproducibility of results. The main types of errors are systematic errors which can be determined and avoided, random errors which are accidental, and gross errors. Sources of error include improper sampling, preparation, instrumentation, calibration, reporting, calculations, and environmental factors. Methods to reduce errors include instrument calibration, running blanks, controls, standard additions, independent methods, parallel determinations, and amplification for small amounts.
Introduction to analysis- Pharmaceutical AnalysisSanchit Dhankhar
QUALITY CONTROL (QC)
SIGNIFICANT FIGURE
CONCEPT OF ERROR
ACCURACY
PRECISION
CALIBRATION OF ANALYTICAL INSTRUMENTS
DIFFERENT METHOD FOR EXPRESSING CONCENTRATION
FUNDAMENTAL OF VOLUMETRIC ANALYSIS
STANDARD DEVIATION
NORMAL DISTRIBUTION CURVE
DEFINATION:
Quality control (QC) is a procedure or set of procedures intended to ensure that a manufactured product sticks to a defined set of quality criteria or meets the requirements of the client or customer.
OR
A system for verifying and maintaining a desired level of quality in an existing product or service by careful planning, use of proper equipment, continued inspection, and corrective action as required.
Evaluation of Quality
Raw materials and API
Physical Tests
Raman and IR Spectroscopy
Assay (HPLC and Titration)
Drug Product
HPLC
Dissolution
Packaging components
Appearance
Loss on Drying
Retains
At label conditions
Retain time determined by regulatory guidelines
Raw materials 12 Years
Finished products 10 Years
Definition:
Significant figures are the reliable digits in a number or measurement which are known with certainty.
Rules:
ALL non-zero numbers (1,2,3,4,5,6,7,8,9) are ALWAYS significant.
ALL zeroes between non-zero numbers are ALWAYS significant.
ALL zeroes which are SIMULTANEOUSLY to the right of the decimal point & at the end of the number are ALWAYS significant.
ALL zeroes which are to the left of a written decimal point and are in a number >= 10 are ALWAYS significant.
Minimization of errors, accuracy, precission, significant figures.Nidhi Sharma
This document discusses several methods for minimizing errors and improving accuracy and precision in measurements and analysis. It defines key terms like accuracy, precision, and significant figures. Some key points:
1. Systematic errors can be reduced through calibration of instruments, using control determinations with standard substances, blank determinations to remove impurities, and independent verification of methods.
2. Accuracy refers to how close a measurement is to the true value, while precision refers to the repeatability of measurements.
3. Significant figures include all digits known with certainty plus one estimated digit, excluding leading or some trailing zeros. They indicate the precision and uncertainty of a measurement.
Errors in chemical analysis can be random or systematic. Random errors cause imprecise results while systematic errors lead to inaccurate results by introducing bias. Common sources of systematic error include faulty instrumentation, non-ideal chemical behaviors in analytical methods, and personal biases of experimenters. Systematic errors can be detected through frequent calibration of instruments, analysis of reference standards, independent verification methods, blank determinations, and evaluation of results from varying sample sizes. Controlling for systematic errors is important for obtaining reliable analytical data.
Accuracy precision and significant figuresnehla313
This document defines accuracy, precision, and significant figures in analytical chemistry.
Accuracy refers to how close a measurement is to the true value, while precision describes the reproducibility of repeated measurements. A measurement can be precise without being accurate.
There are two methods to determine accuracy - absolute and comparative. The absolute method uses samples of known composition, while the comparative method uses secondary standards. Precision is measured by tests of repeatability and reproducibility.
The number of significant figures indicates the certainty of a measurement and must be considered when performing calculations to avoid losing accuracy. Rules are provided for determining significant figures in addition, subtraction, multiplication, division and other operations.
Analytical Errors and Validation of Analytical procedures.pdfAbdiIsaq1
1. The document discusses analytical errors and validation of analytical procedures.
2. There are two main types of analytical errors - determinate (systematic) errors which have a definite cause, and indeterminate (random) errors which cannot be attributed to a single cause.
3. Validation of analytical procedures is important to demonstrate that a method is suitable for its intended purpose. Common parameters validated include linearity, range, accuracy, precision, selectivity, and robustness.
Analytical Errors and Validation of Analytical procedures.pdfAbdiIsaq1
1. The document discusses analytical errors and validation of analytical procedures.
2. There are two main types of analytical errors - determinate (systematic) errors and indeterminate (random) errors. Systematic errors can be identified and accounted for, while random errors are unpredictable.
3. Validation of analytical procedures is important to demonstrate that a procedure is suitable for its intended purpose. Common parameters validated include linearity, range, accuracy, precision, selectivity, and robustness. Validation helps ensure quality and reliability of analytical results.
Errors - pharmaceutical analysis -1, bpharm 1st semester, notes, topic errors
full details and answer about error
TN DR MGR UNIVERSITY
by Kumaran.M.pharm, professor
This document discusses metrology, which is the science of measurement. Precise and accurate measurements are needed for quality inspection during manufacturing. There are two types of errors in measurement: systematic errors, which are constant due to issues like faulty instruments, and random errors, which are unpredictable due to external factors. Systematic errors cannot be eliminated by repeated measurements but can be reduced by calibration, while random errors can be minimized through repetition and require statistical analysis to characterize. The objectives of metrology are to ensure components meet specifications and identify sources of error.
The document discusses different types of errors that can occur in measurement. It describes gross errors, systematic errors like instrumental errors and environmental errors, and random errors. It also defines key terms used to analyze errors like limit of reading, greatest possible error, and discusses analyzing measurement data using statistical methods like the mean, standard deviation, variance and histograms. Measurement errors can occur due to issues like parallax, calibration, limits of the measuring device, and are analyzed statistically.
This document discusses various redox titration methods including permanganometry, dichrometry, cerimetry, iodimetry, and bromatometry. It defines oxidation, reduction, and redox reactions. It explains how to calculate equivalent weights of oxidizing and reducing agents and different methods to detect the endpoint of a redox titration including using internal indicators, self indicators, external indicators, and instrumental methods. It provides examples of applications for each type of redox titration.
This document provides instructions for preparing 0.1 N solutions of various acids and bases. It describes weighing out the appropriate amounts of the substances based on their molecular weights and normalities, and diluting them with distilled water in volumetric flasks. The solutions are standardized by titrating them against standardized solutions of oxalic acid, sodium hydroxide, potassium dichromate, and arsenic trioxide to determine their actual normalities.
Pharmaceutical analysis involves techniques to identify, quantify, and purify chemical substances and separate mixtures. It can be qualitative, determining presence/absence, or quantitative, determining concentration. Techniques include chemical methods like volumetric titration, gasometry, and gravimetry. Physicochemical methods use instruments to detect changes in physical properties. Microbiological analysis tests antibiotic efficacy by microbial growth inhibition. Biological analysis estimates potency by comparing biological effects in tissues/animals to standards. Pharmaceutical analysis has applications in quality control/assurance, analytical research, environmental surveys, and pollution monitoring.
Pharmaceutical analysis involves techniques to identify, quantify, and purify substances as well as separate mixture components. [1] It uses qualitative and quantitative methods like chemical, physicochemical, microbiological, and biological analyses. [2] Common chemical techniques include volumetric (e.g., acid-base titration), gasometric, and gravimetric analysis. [3] Instrumental methods rely on measuring properties like electrical conductance and optical density. Microbiological analysis examines microbial growth inhibition. Biological analysis estimates potency using animal or tissue samples. Pharmaceutical analysis has applications in quality control, research, pollution monitoring, and more.
This document discusses pharmacopoeias, which are official collections of standards for drugs that are published by national authorities. It provides information on the purpose of pharmacopoeias in controlling drug quality and ensuring public health. Several major pharmacopoeias are identified, including the Indian Pharmacopoeia, US Pharmacopoeia, and European Pharmacopoeia. The document also describes pharmacopoeial monographs, which provide standards for individual drug substances, and limit tests, which are used to control small amounts of impurities in drugs. As an example, the limit test for chloride is outlined, which uses silver nitrate to precipitate chloride ions and compare the sample to a standard solution.
Impurities in pharmaceutical substances can come from several sources such as raw materials, reagents, manufacturing processes, equipment, storage conditions, and potential adulteration. Proper selection and purification of raw materials, reagents, solvents like distilled water, and equipment like stainless steel vessels can help minimize impurities in final products. Control of factors like temperature, pH, mixing, and storage conditions are also important to prevent the introduction of impurities during manufacturing and over time. Testing final products for impurities helps ensure quality and safety.
The skin is the largest organ and its health plays a vital role among the other sense organs. The skin concerns like acne breakout, psoriasis, or anything similar along the lines, finding a qualified and experienced dermatologist becomes paramount.
Lecture 6 -- Memory 2015.pptlearning occurs when a stimulus (unconditioned st...AyushGadhvi1
learning occurs when a stimulus (unconditioned stimulus) eliciting a response (unconditioned response) • is paired with another stimulus (conditioned stimulus)
Summer is a time for fun in the sun, but the heat and humidity can also wreak havoc on your skin. From itchy rashes to unwanted pigmentation, several skin conditions become more prevalent during these warmer months.
5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.
Co-Chairs, Val J. Lowe, MD, and Cyrus A. Raji, MD, PhD, prepared useful Practice Aids pertaining to Alzheimer’s disease for this CME/AAPA activity titled “Alzheimer’s Disease Case Conference: Gearing Up for the Expanding Role of Neuroradiology in Diagnosis and Treatment.” For the full presentation, downloadable Practice Aids, and complete CME/AAPA information, and to apply for credit, please visit us at https://bit.ly/3PvVY25. CME/AAPA credit will be available until June 28, 2025.
Nano-gold for Cancer Therapy chemistry investigatory projectSIVAVINAYAKPK
chemistry investigatory project
The development of nanogold-based cancer therapy could revolutionize oncology by providing a more targeted, less invasive treatment option. This project contributes to the growing body of research aimed at harnessing nanotechnology for medical applications, paving the way for future clinical trials and potential commercial applications.
Cancer remains one of the leading causes of death worldwide, prompting the need for innovative treatment methods. Nanotechnology offers promising new approaches, including the use of gold nanoparticles (nanogold) for targeted cancer therapy. Nanogold particles possess unique physical and chemical properties that make them suitable for drug delivery, imaging, and photothermal therapy.
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
DECLARATION OF HELSINKI - History and principlesanaghabharat01
This SlideShare presentation provides a comprehensive overview of the Declaration of Helsinki, a foundational document outlining ethical guidelines for conducting medical research involving human subjects.
Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
2. Unit I
Error
Definition of error
Error is the difference between true value or standard value and observed
value.
Types of error
Error
Determinate error Indeterminate error
Personal error Instrumental error reagent error Error in method
3. Determinate error
These errors are known to analyst. By preplanning and careful
working it can be avoided or kept at minimum
1. Personal error – Caused due to personal mistakes or by
carelessness of analyst. Careful working of analyst can
eliminate these type of error.
Example – If analyst wrongly calculating the weight of
NaOH required to produce 0.1N NaOH.
2 Instrumental error - Caused due to defect in instrument, may
cause due to faulty and uncalibrated glass wares, apparatus.
These error can be removed by causing good quality
apparatus and calibrated glasswares, apparatus and
instruments.
4. Example – 5ml pipette is used to take sample solution for
analysis bur pipette take out only 4.8ml due to construction
defect thus error of 0.2 ml will introduced.
3. Reagent error - Many reagents and compounds are not in
pure form they contain impurities.
4. Error in method - Selection of wrong method or responsible
for this type of error
Indeterminate or random error
These errors are called as accidental error, cause of this type of
error may or may not be known and due to unknown cause
they cannot be removed.
5. Methods of minimising error
Errors can be minimised by using following methods
1) By calibration of apparatus
Error can be minimised by calibrating all the instruments
and appropriate correction are applied to original
measurement.
2) Control determination
Standard substance is used in experiment in identical
experimental condition to minimise error.
3) Blank determination
By omitting sample determination is carried out in
identical condition.
6. 4) Parallel determination
Instead of single determination duplicate or triplicate
determination is carried out to minimise error.
Accuracy and precision
Accuracy
Accuracy is the degree of agreement between measured value and
true value. The term accuracy refers to how near the observed
value is to standard value.
Precision
Precision is defind as the degree of agreement between replicate
measurement of the same quantity.
It is the repeatability of the result. Precision may be expressed as
standard value so the precision refers to nearness between several
measurements
of the same quantity.
7. Significant figure
Significant figure of a number are those digits that carry
meaning contributing to its measurement.
The rules for identifying significant figures when writing or
interpreting numbers are as follows.
1) All non zero digits are considered significant.
For example – 27 has two signiicant figures 2 and 7.
2) Zero appearing anywhere between two non zero digits are
significant
For example – 308.896 have six significant figures 3, 0, 8,
8,9,6
8. 3) Leading zeros are not significant 0.00056 has two
significant figures 5 and 6.
4) Trailing zero in number containing decimal point are
significant.
For example – 33.55276 has seven significant figures
3,3,5,5,2,,7,6