analytical concepts

Question 1

analyte

Answer 1

chemical substance being measured

Question 2

assay

Answer 2

process of determining amount of analyte in sample

Question 3

qualitative analysis

Answer 3

identification of elements/compounds/etc in sample

Question 4

quantitative analysis

Answer 4

determination of quantity of analyte in sample

Question 5

signal

Answer 5

measured quantity which correlates to the amount of sample. Ex: absorbance, acid-base indicators

Question 6

visual detection of signal (examples, pros and cons)

Answer 6

Ex: colour change, formation/disappearance of solid, other volumetric analysis

Pros: simple, low-cost, no maintenance

Cons: subjective leading to poor accuracy and precision, not sensitive, large sample volume required, time-consuming

Question 7

Electrical detection of signal (examples, pros, cons)

Answer 7

Ex: voltage, current, transducer (converts light/heat/pressure to electrical output)

Pros: objective, highly sensitive, fast and automated, small sample volume

Cons: costly, maintenance and repairs (eg. calibration)

Question 8

Noise

Answer 8

Variation in measured quantity. Aka standard deviation, denoted σ(bkg)

Question 9

Background

Answer 9

Approximate constant base-level signal. Denoted µ(bkg)

Question 10

S/N. How to improve it?

Answer 10

signal-to-noise ratio. Indicates validity of signal as being actually caused by analyte.

Proportional to sqrt(n) (n = number of measurements). Can be improved by signal averaging.

Valid S/N is >3

Question 11

Detection limit

Answer 11

Amount of analyte corresponding to
S >= µ(bkg) + 3σ(bkg).
Setting µ(bkg) = 0 gives S/N>3

Question 12

Matrix

Answer 12

All sample components apart from analyte

Question 13

Blank

Answer 13

Man-made “sample matrix”

Question 14

Positive control

Answer 14

Sample containing known amount of analyte (helps prevent false negative results)

Question 15

False negative

Answer 15

Assay indicates no analyte when it is actually present

Question 16

Negative control

Answer 16

Sample containing no analyte (helps prevent false positive results)

Question 17

False positive

Answer 17

Assay indicates analyte presence when it is actually not present

Question 18

Interference

Answer 18

Chemical in matrix which causes systematic error.

Question 19

How does interference affect measurements (4 ways)?

Answer 19

• Acts on analyte or reagent
• Source of large background signal
• Cause negative or positive bias
• Cause absolute or relative errors (affects accuracy)

Question 20

Selectivity

Answer 20

The extent to which other substances interfere with analyte determination

Question 21

Masking agent

Answer 21

Prevents components in matrix from interfering

Question 22

Accuracy

Answer 22

Closeness of expected value to true value

Question 23

Absolute error (formula+example)

Answer 23

E = xi - µ
Ex: signal always 10% above true value

Question 24

Relative error (formula+example)

Answer 24

E = (xi - µ)/µ
- Greater effect when signal is small
Ex. Always measures 2 units below true value

Question 25

Precision

Answer 25

Agreement among results. Can be expressed using standard deviation (s)

Question 26

Replication

Answer 26

Expected to give the same result in the absence of error.
Samples are…
- From the same source
- Run using the same method
- Under the same conditions

Question 27

Random error

Answer 27

AKA indeterminate error.
Introduces uncertainty/stdev. Symmetric about µ.
Can be treated with statistics.
* Problem in precision!!

Question 28

Systematic error

Answer 28

AKA determinate error.
Can be absolute or relative.
Skewed results, xi always either higher or lower than µ.
* Problem in accuracy!!

Question 29

Types of systematic error (3)

Answer 29

Instrument error
- Calibration can minimize it

Method error
- Chemistry doesn’t behave as expected, something overlooked
- Difficult to ID

Personal error
- Incorrect data recording
- Deviation from established method

Question 30

Confidence interval

Answer 30

Likelihood of sample mean being accurate to true mean. Computed with t statistic

Question 31

Case 1 t-test

Answer 31

Compare sample mean to known value (from a reference standard).
t(exp)>t(table) means significant difference.

Question 32

Case 2 t-test

Answer 32

Compare results from replicate analyses of same sample.
t(exp)>t(table) means significant difference.

F-test should be done first to verify that the precision/variance of the trials is the same.

Question 33

F-test

Answer 33

Compares precision of two methods.
F(exp)>F(table) means the difference in precision is significant

Question 34

Case 3 t-test

Answer 34

Compare means of paired data
1. Two methods used to measure different samples from same source
2. Measurements before and after drug treatment

Question 35

G-test

Answer 35

First test done in statistical analysis, rules out outliers.
Find G(exp) of a sus point.
If G(exp)>G(crit), point is rejected.

Question 36

Least squares analysis

Answer 36

Used to fit linear regression line. Assumes only error in y data.

Question 37

Assumptions for fitting linear calibration curves via least squares analysis (4)

Answer 37

1. Relationship between signal and quantity is linear
2. Residuals are the result of random error affecting y
3. Error affecting y is normally distributed
4. Errors in y are independent of the value of x

Question 38

Sensitivity

Answer 38

Slope of calibration curve. Signal per unit analyte

Question 39

Dynamic range

Answer 39

Concentration over which calibration curve is useful (no extrpolation)

Question 40

Selectivity

Answer 40

Given two compounds (1 and 2), the selectivity of a method of analysis is =m1/m2

Question 41

Standard addition

Answer 41

Add known, increasing concentrations of analyte to sample. Plot line of best fit, x-intercept gives analyte in original sample

Question 42

Limitations of standard addition

Answer 42

• Precise results only when amount of standard added is comparable to analyte quantity
• Time consuming, need multiple samples
• Dilution error

Question 43

Internal standard

Answer 43

• Measure signals for both analyte and substance behaving similarly to analyte (known values)
• Find F value
  Ax/[X] = F(Ay/[Y])
• Measure unknown sample signal (Ax)
• Spike with known standard (Y), measure signal again (Ay)
• Solve for [X] using F value

Can make Ax/Ay vs [X]/[Y] plot to average veriability