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
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...
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...
Neutralization curves in acid base analytical titrations, indicators.nehla313
Neutralization curves in acid base analytical titrations, indicators,
strong acid strong base
weak acid strong bse
strong acid weak base
weak acid and weak base
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
Pharmaceutical Inorganic chemistry UNIT-V Radiopharmaceutical.pptx
Isotopes Types of decay
Alpha rays, which could barely penetrate a piece of paper
Beta rays, which could penetrate 3 mm of aluminium
Gamma rays, which could penetrate several centimetres of lead
Units of Radioactivity:
Measurement of Radioactivity
The measurement of nuclear radiation and detection is an important aspect in the identification of type of radiations (, , ) and to assay the radionuclide emitting the radiation, suitable detectors are required. The radiations are identified on the basis of their properties.
e.g. Ionization effect is measured in Ionization Chamber, Proportional Counter and Geiger Muller Counter.
The scintillation effect of radiation is measured using scintillation detector and the photographic effect is measured by Autoradiography.
Gas Filled Detectors:
Ionization Chamber:
Proportional Counters:
Geiger-Muller Counter
Properties of α, β, γ radiations
Half –life of Radioelement
Sodium Iodide (I131)
Handling and Storage of Radioactive Material:
Storage of Radioactive Substances –
Precautions For Handling Radioactive Substances
Labelling of Radioactive Substances
Pharmaceutical Application Of Radioactive Substances
Limit tests, Introduction, Definition,
Limit Test For Chlorides
Limit Test For Sulphates
Limit Test For Iron
Limit Test For Lead
Limit Test For Arsenic
Neutralization curves in acid base analytical titrations, indicators.nehla313
Neutralization curves in acid base analytical titrations, indicators,
strong acid strong base
weak acid strong bse
strong acid weak base
weak acid and weak base
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
Pharmaceutical Inorganic chemistry UNIT-V Radiopharmaceutical.pptx
Isotopes Types of decay
Alpha rays, which could barely penetrate a piece of paper
Beta rays, which could penetrate 3 mm of aluminium
Gamma rays, which could penetrate several centimetres of lead
Units of Radioactivity:
Measurement of Radioactivity
The measurement of nuclear radiation and detection is an important aspect in the identification of type of radiations (, , ) and to assay the radionuclide emitting the radiation, suitable detectors are required. The radiations are identified on the basis of their properties.
e.g. Ionization effect is measured in Ionization Chamber, Proportional Counter and Geiger Muller Counter.
The scintillation effect of radiation is measured using scintillation detector and the photographic effect is measured by Autoradiography.
Gas Filled Detectors:
Ionization Chamber:
Proportional Counters:
Geiger-Muller Counter
Properties of α, β, γ radiations
Half –life of Radioelement
Sodium Iodide (I131)
Handling and Storage of Radioactive Material:
Storage of Radioactive Substances –
Precautions For Handling Radioactive Substances
Labelling of Radioactive Substances
Pharmaceutical Application Of Radioactive Substances
Limit tests, Introduction, Definition,
Limit Test For Chlorides
Limit Test For Sulphates
Limit Test For Iron
Limit Test For Lead
Limit Test For Arsenic
This content is suitable for medical technologists/technicians/lab assistants/scientists writing the SMLTSA board exam. The content is also suitable for biomedical technology students and people also interested in learning about test methodologies used in medical technology. This chapter describes test quality assurance (QA) and quality control (QC). Please note that these notes are a collection I used to study for my board exam and train others who got distinctions using these.
Disclaimer: Credit goes to those who wrote the notes and the examiners of each exam question. Please use only as a reference guide and use your prescribed textbook for the latest and most accurate notes and ranges. The material here is not referenced as it is a collection of pieces of study notes from multiple people, and thus will not be held viable for any misinterpretations. Please use at your own discretion.
The significant figures in a numerical expression are defined as all those whose values are known with certainty with one additional digit whose value is uncertain.
Systematic error means that your measurements of the same thing will vary in predictable ways: every measurement will differ from the true measurement in the same direction, and even by the same amount in some cases
Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).
Understanding of Analytical Method Validation Approach in Pharmaceutical Industry. Analytical method validation Verification is a wide chapter and a huge scope of applicability. In different types of methods, instrument, measurement approach all can effect the validation effort. However the basic fundamental will remains same, the parameters, acceptance criteria, functionality may vary depending upon the type of method, instrument etc.
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Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
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Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
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3. Determinate / constant error
• Unsuspected
• May be avoided / determined
• Corrected
• Example
• Errors in calibration
• Operation of measuring instrument
• Impurities in the reagent.
4. Biased personal error
• Reading of
meniscus.
• In weighing
• In matching colours
• In calculation
5. Determinate error can be combated by
• Use of calibrated apparatus.
• Use of blanks and controls.
• Several analaytical procedures.
• By eliminating impurities.
• By carrying out the experiment
under various condition.
6. Operational or personal error
• Individual Analyst is
responsible.
• Errors are physical in nature.
• It may occur when sound
analytical technique is not
followed.
7. Example of personal error
• Mechanical loss of material in various steps of an
analysis.
• Underwashing or overwashing of precipitate.
• Ignition of precipitates at incorrect temperature.
• Insufficient cooling of crucibles before weighing.
• Allowing hygroscopic materials to absorb moisture
before or during weighing.
• Use of reagents containing impurities.
• Person unable to judge changes sharply in visual
titrations which may result in slight overstepping of the
endpoint.
9. Reagent error
• The attack of reagents upon glassware result
in introduction of foreign materials.
• Volatilization of platinum at very high
temperatures.
10. Errors due to methodology
• Originate from incorrect sampling and
incompleteness of a reaction.
• In gravimetric analysis arise due to solubility
of precipitate, co-precipitation and post
precipitation.
• Volatilisation and decomposition on ignition.
• Precipitation of substance other than
intended one.
11. Errors due to methodology
• In titrimetric analysis , error may due to
occurrence of induced and side reaction.
• Reaction of substances other than the
constituent being determined.
• A difference in the observed and the
stoichiometric end point of a reaction.
12. Additive and proportional error
• Proportional error arise by the impurities
present in a standard substance.
• It leads to an incorrect value in the
normality of the standard solution.
• Loss in weight of crucible in which
precipitate is ignited and errors of
weights - additive error.
13. Indeterminate / Accidental error
• Successive measurements made by the same
observer under identical conditions produce
slight variations – indeterminate error
• It ‘s elimination by the analyst is impossible.
• Errors occur by accident or chance.
• It cannot be allowed for correction because of
the natural fluctuations that occur in all
measurements.
14. pseudo accidental or variable
determinate errors.
• Errors that arise from random fluctuation in
temperature or other external factors belong
to
the determinate error are often called as
pseudo
accidental or variable determinate errors.
• These errors may be reduced by controlling
conditions through the use of constant
temperature baths and ovens.
• The employment of buffers, maintenance of
constant humidity and pressure, reading
fraction
of units on graduates, balances and apparatus
16. Calibration of apparatus
• All apparatus like weights, flasks, burettes and
pipettes should be calibrated.
• The appropriate corrections applied to the
original measurements.
• In some case errors cannot be eliminated.
• Apply a correction for that effect.
• An impurity in a weighed precipitate may be
determined and its weight deducted
17. Running a blank determination
• It is carried out as a separate determination,
the sample being omitted, under exactly, the
same experimental conditions as employed in
the actual analysis of the sample.
• The object is to find out the effect of the
impurities introduced through the reagents
and vessels.
18. Running a control determination
• Determination carried out as nearly as
possible identical experimental conditions
upon a quantity of a standard substance
which contains the same weight of the
constituent.
19. Use of independent
methods of analysis
analysis• Determination HCl both bytitration with
solution of a strong base and by
precipitation and weighed as AgCl.
• If the results obtained by the two radically
different methods are concordant . It is highly
probable that the values are correct within
small limits of error.
20. Running parallel determination
• It serve as a check on the result of a single
determination and indicate only the precision
of the analysis.
• The values obtained should not less than
three parts per thousand.
• If larger variation is there then it must be
repeated until satisfactory concordance is
obtained.
• Duplicate / triplicate determination is suffice.
21. Standard addition
• A known amount of the constituent being is
added to the sample, which is then analysed
for the total amount of constituent present.
• The difference between the analytical results
for samples with and without added
constituent gives the recovery of the amount
of added constituent.
• If the recovery is satisfactory our confidence in
the accuracy of the procedure is enhanced.
22. • Even under constant experimental conditions
(same operator, same tools, and same
laboratory, short time intervals between the
measurements), repeated measurements of
series of identical samples always lead to
results which differ among themselves and
from the true value of the sample. Therefore,
quantitative measurements cannot be
reproduced with absolute reliability.
23. Random Errors.
• Random errors are the components of
measurement errors that vary in an
unpredictable manner in replicated
measurements.
• measuring techniques (e.g. noise),
• sample properties (e.g. In homogeneities),
• and chemical effects (e.g. equilibrium).
• Even under carefully controlled conditions
random errors cannot, in principle, be avoided,
they can only be minimized and evaluated with
statistical methods.
24. Systematic Errors.
• the closeness of agreement between the
expectation of a test result or measurement
result and a true value.
• According to their character and magnitude it
is classified as random and systematic.
26. Definition
• The concordance between the data and the
true value.
• It is an agreement between the data and true
value.
• If true value is not known the mean calculated
from results obtained from several different
analytical methods which are very precise and
in close agreement with one another may be
considered the true value in practical sense.
27. • The difference between the mean and the
true value is known as absolute error.
• The relative error is found by dividing the
absolute error by the true value.
• Relative error is usually reported on a
percentage basis by multiplying the relative
error by 100.
• If reported in parts by multiplying the relative
error by 1000
28.
29. Absolute method
• The substance must be of known purity.
• The test of the accuracy of the method under
consideration is carried out by taking varying
amounts of the constituent and proceedings
according to specified instruction.
• The amount of the constituent must be varied,
because the determinate errors in the
procedure may be a function of the amount
used.
30. Method I
• It is a measure of accuracy in the absence of
foreign substance
• The difference between the mean of an
adequate number of results and the amount
of the constituent actually present.
• It is usually expressed as parts per thousand.
31. Method II
• The constituent in the presence of other
substance.
• It require testing the influence of a large
number of elements each in varying the
amounts.
• Separation is required before determination.
• The accuracy of the method is largely
controlled by separation.
32. Comparative method
• In analysis it is impossible to prepare solid
synthetic samples of desired composition.
• Necessary to sort a standard sample is in
question.
• It is determined by one or more accurate
method of analysis.
• It involves secondary standard not satisfactory
from theoretical standpoint.
33. • It is useful in applied analysis.
• If fundamentally different methods of analysis
for a given constituent [ gravimetric,
titrimetric and spectrometric]. The agreement
between at least two methods of essentially
different character can usually be accepted as
indicating the absence of an appreciable
determinate error .
35. Precision
• The concordance of a series of measurements
of the same quantity.
• The mean deviation or the relative mean
deviation is a measure of precision.
• It is a measure of reproducibility of data
within a series of result.
• Results within a series which agree closely
with one another are said to be precise.
36. • Precise results are not necessarily accurate for
a determined error may be responsible for the
inaccuracy of each result in a series of
measurement.
• It is usually reported as the average deviation,
standard deviation or range.
• Precision is a measure of the agreement
among the values in a group of data.
37. • Accuracy is the agreement between the data
the true value.
• In quantitative analysis the precision of
measurements rarely exceeds 1 to 2 parts per
thousand.
• Accuracy expresses the correctness of a
measurement
• Precision the reproducibility of a
measurement.
38. • Precision always accompany accuracy, but a
high degree of precision does not imply
accuracy.
• Example
• A substance contain 49.06 + 0.02 of
constituent.
• Analyst I = 49.01
• Analyst II = 49.42
39. • The analyst I is accurate and precise but
analyst II is precise but less accurate than
Analyst I .
• It is established during the development stage
• It wont include
• Day – to – day fluctuation, lab to lab variation,
small modification in technique varying skills
of analysis, undetected, operational or
instrumental factors.
40.
41.
42.
43.
44.
45. Quality Assurance vs. Quality
Control
Quality Assurance Quality Control
A series of
analytical
measurements used
to assess the
quality of the
analytical data
(The “tools”)
An overall
management plan to
guarantee the
integrity of data
(The “system”)
46. True Value vs. Measured Value
True Value Measured Value
The known, The result of an
accepted value of individual’s
a quantifiable measurement of
property a quantifiable
property
47. Accuracy vs. Precision
Accuracy Precision
How well a How well a series of
measurement agrees measurements agree
with an accepted
value
with each other
48. Systematic vs.Random Errors
Random ErrorsSystematic Error
Unavoidable errors
that are always
present in any
measurement.
Impossible to
eliminate
Avoidable error due
to controllable
variables in a
measurement.
49. Internal Standards
• A compound chemically similar to the
analyte
• Not expected to be present in the
sample
• Cannot interfere in the analysis
• Added to the calibration standards
and to the samples in identical
amounts.
50. Internal Standards
• Refines the calibration process
• Analytical signals for calibration
standards are compared to those for
internal standards
• Eliminates differences in random and
systematic errors between samples
and standards