Unit 1 Flashcards
assay
- process of determining amount of analyte in sample
analyte
- chemical substance being measured
signal
- observable change in some property
Advantages of visual detection
- low cost and maintenance
Disadvantages of visual detection
- subjectivity affects accuracy/precision
- may not be very sensitive
- may require large sample volumes
- often time-consuming
voltage
- electrical potential energy between two points
current
- rate of flow of charge past a point in a circuit (usually electrons moving)
transducer
- device that converts input stimulus into electrical output
Advantages of electrical detection
- objective
- often very sensitive
- often faster
- can analyze smaller sample sizes
Disadvantages of electrical detection
- high cost/maintenance
- calibration required
analog signal
- “real world”
- takes on any value
- transducer input signal
digital signal
- computer world
- recorded as bits
- discrete values
signal
-a measured quantity that is correlated to the amount of analyte
noise
- unwanted variation in a measured quantity
- often takes the form of random fluctuations in a measured signal
signal-to-noise ratio (S/N)
- the magnitude of the signal divided by the magnitude of the noise
- similar term: signal-to-background ratio
detection limit
-the amount of analyte that corresponds to a signal just greater than the mean of the background plus three standard deviations of its noise
background
-an approximately constant signal, measured in the absence of analyte
How can you increase S/N ratio?
- multiple scans
- signal averaging
Determine how many more scans are required to achieve a S/N ratio of 2?4?9?
N=4,16,81
Blank
- a measured sample that lacks the analyte
- contains solvent, reagents, etc. used in the analysis
sample matrix
- all the components of a sample except the analyte
- blank tries to approximate the sample matrix
positive control
- a standard sample that contains a known quantity of the analyte of interest
- prevents false negative results
negative control
- a standard sample that does not contain any analyte
- prevents false positive results
interference
a specific chemical substance in a sample matrix that causes a systematic error in a measured quantity
Which of the following combinations best matches the definition of analyte, sample matrix and blank? Pick all that apply.
(a) Glucose; Blood; Glucose
(b) Glucose; Blood; Synthetic blood
(c) Glucose; Blood; Saliva
(d) Glucose; Sucralose; Blood
(e) None of the above
(f) Lead and Mercury; Blood; Distilled water
(g) Saliva; Lead and Mercury; Saliva
(h) Steroid drugs; Urine; Synthetic Urine
(b) glucose; blood; synthetic blood
(h) Steroid drugs; Urine; Synthetic Urine
In a single-molecule fluorescence detection experiment, the average background was measured to be 330 cps ± 50 cps (±1 std. dev.). What must be the minimum magnitude of a signal burst to record detection of a single molecule?
(a) 180 cps
(b) 280 cps
(c) 330 cps
(d) 380 cps
(e) 480 cps
e) 480cps
How can interferences affect results?
- act on the analyte (or a measured form thereof)
- act on a reagent used in the detection method
- be the source of a large background signal
- cause negative/positive bias
- cause absolute/proportional errors
selectivity
- the extent to which other substances interfere with the determination of an analyte
- typically via reactivity/molecular interactions
good selectivity
analysis method has minimal interferences
poor selectivity
analysis method prone to certain interferences
masking agent
a reagent that prevents one or more components in a sample matrix from interfering with an analyte
What is the analytical signal, and possible interferences that would be found in colourimetric analyses via complexation?
- analytical signal: colour change upon complexation between analyte and reagent
- interference with analyte: matrix component complexing with analyte
- interference with reagent: matrix component complexing with reagent
- background interference: matrix component that adsorbs same wavelength of light as analytical complex
Accuracy
-closeness of an experimental value, xi (or mean value of a set of measurements, x̅) to the true value, μ
Precision
- agreement among results, s
- reproducibility between replicate measurements
Absolute error
-the difference between the measured and true value
-value will be positive or negative
E=xi-μ
Relative error
-error in measurement
-expressed as a %
E=(xi-μ)/μ
Replicates
- samples from the same source, run using the same method, under the same conditions
- expected to give the same result in the absence of error
Random/Indeterminate errors
- introduce uncertainty
- symmetric about the true value(μ)
- treat with statistics
Systematic/Determinate errors
- introduce bias
- measured value (xi) always higher/lower than true value (μ)
- can be proportional or constant
Proportional error
-effect is independent of the magnitude of the measurement
Constant error
-effect dependent on measurement
Given the data, determine if the errors are constant or proportional:
Measured True Relative Error
- 004V 0.004V error too small
- 022V 0.020V 10.0%
- 389V 0.354V 10.0%
- 200V 2.000V 10.0%
Proportional error
Given the data, determine if the errors are constant or proportional:
Measured True Relative Error
- 002V 0.004V 50.0%
- 018V 0.020V 10.0%
- 352V 0.354V 0.6%
- 998V 2.000V 0.1%
Constant error
What are 3 types of systemic errors?
- Instrument errors
- Method errors
- Personal errors
Instrument errors
- minimize with careful, regular calibration
- voltage fluctuations/drift
- can be corrected
Method errors
- chemistry doesn’t behave as expected
- Difficult to identify and correct
- incomplete reactions
- interference from non-analytes
- false positive/negative results
Personal errors
- poor lab technique
- incorrect recording of data
- deviations from an established method
Which of the following would not lead to a systematic error?
(a) An interfering species in the sample matrix that made some of the analyte unavailable
(b) An interfering species in the sample matrix generates a high background signal
(c) Fluctuating intensity of the lamp during absorbance measurement
(d) Decomposition of the sample during your sample preparation
(e) More than one of the above
(c) Fluctuating intensity of the lamp during absorbance measurement
Binomial distribution
-discrete probability distribution (50/50)
Gaussian distribution
-known as normal distribution
-used to describe data clustered about a mean value,μ
-continuous and symmetric distribution
-“bell curve” shape
-peak position determined by the mean
-width and height determined by the standard deviation, s
-total area under the curve is 1
-probability of measuring z is the area of that range under the curve
z=(x-x̅)/s
At least how many measurements must be made until the sample standard deviation approaches the population value?
N > 20
Population
all possible measurements of interest
Sample
a limited number of measurements that are representative of the population
How are replicate measurements evaluated?
Replicate measurements are evaluated using the mean and standard deviation
Deviation
Difference between a measured value and the mean value of all measurements
Standard deviation and Relative standard deviation(RSD)
a measure of the uncertainty and precision associated with a measurement
Degree of Freedom
number of independent measurements
Purpose of the t-statistic
- permits use of sample data to test hypotheses about unknown population means without knowledge of the population standard deviation
- accounts for limited sample size to better reflect population values
- as N increases, t value decreases
- as confidence interval increases, t value increases
What is the purpose of significance testing?
-to determine whether the difference between two or more values is too large to be explained by indeterminate error
Null hypothesis
- used to test experimental results
- postulates that two observed quantities are the same
- assume two results are the same, then apply a test to see if the null hypothesis can be statistically rejected
case 1 t-test
- statistical test that compares the average experimental data of a sample to actual value
- t exp < t table =no significant difference
- t exp > t table =significant difference
case 2 t-test
-statistical test that compares the means of two different analyses of replicate measurements
F-test
-statistical test that compares the precision (standard deviation) of two sets of measurements
G-test
-statistical test used to exclude an outlier from a data set
Type 1 error
rejection of the null hypothesis when it is actually true
Type 2 error
acceptance of the null hypothesis when it is actually false
case 3 t-test
statistical test that compares the methods of single measurements on several different samples
Absolute calibration method
-based on evaluation with use of fundamental physical constants and/or universal quantities only
Empirical calibration method
-result of empirical analysis of unknown sample is only as good as the calibration
Calibration curve
- plot of measured signal vs known quantity
- used to interpolate unknown x values from measured y values
Sensitivity
the slope (m) of the calibration curve
Dynamic range
concentration range over which the calibration curve is analytically useful
Least Squares Method of Analysis
- determines line/curve of best fit to experimental data
- minimizes the residuals between the data points and the line of best fit
- assumes error only in y data
Selectivity of calibration curves
-for linear calibration plots, selectivity for method for compound 1 vs compound 2 is reflected by the ratio of their slopes m1/m2
Matrix Effect
-combined effect of all non-analyte components in a sample on the quantitative measurement of the analyte
What are 3 examples of matrix effects?
- Some component in the sample generates a signal similar to the analyte.
- Some component in the sample has a chemical interaction with the analyte.
- Some component in the sample is co-isolated with the analyte.
Standard Addition Concept
- means of accounting for matrix effects
- add increasing amounts of a standard analyte to aliquots of the original sample
- result: amount of analyte to be “removed” from the sample to get signal=0
Limitations of Standard Addition
- most precise results obtained when the amount of standard added is comparable in magnitude to the original quantity of analyte
- time consuming
- opportunity for dilution error
- added standard should not overwhelm any interferences
- cannot account for shifts in baseline
- dilution of a sample decreases the ability to detect low concentrations
- may require large quantities of sample
- common variation is to add an increasing amount of standard in increments to the same volume of sample
Internal Standard
- intentionally added substance of known quantity that is not expected to be found in the sample, but is expected to behave similarly
- accounts for losses during sample processing or fluctuations in instrument signals
- reference the analyte signal to the internal standard signal
Sampling
- process by which a sample population is reduced to a size suitable for laboratory analysis
- composition of sample must be representative of the population
Sampling Process
- Identify population
- Develop sampling strategy
- Collect and preserve a gross sample(s)
- Reduce the gross sample(s) to a lab sample
- Replicat analyses of the sample(s)
What are some challenges with real samples?
- defining the problem (how will the experiment be conducted? i.e., when where how many samples to take? what size sample? is accuracy and precision required? what is the expected concentration range of analyte?)
- storing and preserving sample prior to analysis (i.e., protection from light, temperature, and pH changes)
- homogenization (i.e., may require crushing, grinding, drying, digesting, decomposing, etc.)
- overwhelming matrix effects (i.e., requires separation of analyte from interferences see unit 5)
- analyte too dilute for reliable analysis
