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Handling Data and Figures of Merit. Data comes in different formats time Histograms Lists But…. Can contain the same information about quality. What is meant by quality?. (figures of merit) Precision, separation (selectivity), limits of detection, Linear range. My weight .

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slide1

Handling Data and Figures of Merit

Data comes in different formats

time

Histograms

Lists

But….

Can contain the same information about quality

What is meant by quality?

(figures of merit)

Precision, separation (selectivity), limits of detection,

Linear range

slide2

My weight

Plot as a function of time data was acquired:

slide3

Comments:

background is white (less ink);

Font size is larger than Excel default (use 14 or 16)

Do not use curved lines to connect data points

– that assumes you know more about the relationship of the data than you really do

slide5

Bin refers to what groups of weight to cluster. Like

A grade curve which lists number of students who got between 95 and 100 pts

95-100 would be a bin

slide6

Assume my weight is a single, random, set of similar data

Make a frequency chart (histogram) of the data

Create a “model” of my weight and determine average

Weight and how consistent my weight is

slide7

average

143.11

Inflection pt

s = 1.4 lbs

s = standard deviation

= measure of the consistency, or similarity, of weights

slide8

Characteristics of the Model Population

(Random, Normal)

Peak height, A

Peak location (mean or average), m

Peak width, W, at baseline

Peak width at half height, W1/2

Standard deviation, s, estimates the variation in an infinite population, s

Related concepts

slide9

Width is measured

At inflection point =

s

W1/2

Triangulated peak: Base width is 2s < W < 4s

slide10

Pp = peak to peak – or – largest separation of measurements

Area= 68.3%

+/- 1s

Area +/- 2s = 95.4%

Area +/- 3s = 99.74 %

Peak to peak is sometimes

Easier to “see” on the data vs time plot

slide11

(Calculated s= 1.4)

144.9

Peak to

peak

139.5

s~ pp/6 = (144.9-139.5)/6~0.9

slide12

There are some other important characteristics of a normal (random)

population

1st derivative

2nd derivative

Scale up the first derivative and second derivative to see better

slide13

Population, 0th derivative

2nd derivative

Peak is at the inflection

Of first derivative – should

Be symmetrical for normal

Population; goes to zero at

Std. dev.

1st derivative,

Peak is at the inflection

Determines the std. dev.

slide14

Asymmetry can be determined from principle component analysis

A. F. (≠Alanah Fitch) = asymmetric factor

slide15

Comparing TWO populations of measurements

Is there a difference between my “baseline” weight and school weight?

Can you “detect” a difference? Can you “quantitate” a difference?

slide16

Exact same information displayed differently, but now we divide

The data into different measurement populations

school

baseline

Model of the data as two normal populations

slide17

Standard deviation

Of the school weight

Standard deviation

Of baseline weight

Average school

weight

Average

Baseline weight

slide18

We have two models to describe the population of measurements

Of my weight.

In one we assume that all measurements fall into a single population.

In the second we assume that the measurements

Have sampled two different populations.

Which is the better model?

How to we quantify “better”?

slide19

Compare how close

The measured data

Fits the model

The red bars represent the difference

Between the two population model and

The data

The purple lines represent

The difference between

The single population

Model and the data

Which model

Has less summed

differences?

Did I gain weight?

slide20

Normally sum the square of the difference in order to account for

Both positive and negative differences.

This process (summing of the squares of the differences)

Is essentially what occurs in an ANOVA

Analysis of variance

In the bad old days you had to work out all the sums of squares.

In the good new days you can ask Excel program to do it for you.

slide21

Test: is F<Fcritical? If true = hypothesis true, single population

if false = hypothesis false, can not be explained

by a single population at the

5% certainty level

slide22

In an Analysis of Variance you test the hypothesis that the sample is

  • Best described as a single population.
  • Create the expected frequency (Gaussian from normal error curve)
  • Measure the deviation between the histogram point and the expected frequency
  • Square to remove signs
  • SS = sum squares
  • Compare to expected SS which scales with population size
  • If larger than expected then can not explain deviations assuming a single population
slide23

The square differences

For an assumption of

A single population

Is larger than for

The assumption of

Two individual

populations

slide24

There are other measurements which describe the two populations

Resolution of two peaks

Mean or average

Baseline width

slide25

xa

xb

In this example

Peaks are baseline resolved when R > 1

slide26

xa

xb

In this example

Peaks are just baseline

resolved when R = 1

slide27

xa

xb

In this example

Peaks are not baseline resolved

when R < 1

slide28

2008 Data

What is the R for this data?

slide29

Visually better resolved

Visually less resolved

Anonymous 2009 student analysis of Needleman data

slide30

Visually better resolved

Visually less resolved

Anonymous 2009 student analysis of Needleman data

slide31

Other measures of the quality of separation of the Peaks

  • Limit of detection
  • Limit of quantification
  • Signal to noise (S/N)
slide32

X blank

X limit of detection

99.74%

Of the observations

Of the blank will lie below the mean of the

First detectable signal (LOD)

slide35

Other measures of the quality of separation of the Peaks

  • Limit of detection
  • Limit of quantification
  • Signal to noise (S/N)
slide36

Your book suggests 10

Limit of quantification requires absolute

Certainty that no blank is part of the measurement

slide38

Other measures of the quality of separation of the Peaks

  • Limit of detection
  • Limit of quantification
  • Signal to noise (S/N)

Signal = xsample - xblank

Noise = N = standard deviation, s

slide39

(This assumes pp school ~ pp baseline)

Estimate the S/N of this data

slide40

Can you “tell” where the switch between

Red and white potatoes begins?

What is the signal (length of white)?

What is the background (length of red)?

What is the S/N ?

slide42

Error curve

Peak height grows with # of measurements.

+ - 1 s always has same proportion of total number of measurements

However, the actual value of s decreases as population grows

slide46

Calibration Curve

A calibration curve is based on a selected measurement as linear

In response to the concentration of the analyte.

Or… a prediction of measurement due to some change

Can we predict my weight change if I had spent a longer time on

Vacation?

slide48

5 days

The calibration curve contains information about the sampling

Of the population

slide50

This is just a trendline

From “format” data

Using the analysis

Data pack

Get an error

Associated with

The intercept

slide51

In the best of all worlds you should have a series of blanks

That determine you’re the “noise” associated with the background

Sometimes you forget, so to fall back and punt, estimate

The standard deviation of the “blank” from the linear regression

But remember, in doing this you are acknowledging

A failure to plan ahead in your analysis

slide52

Sensitivity (slope)

Signal LOD

Extrapolation of the associated error

Can be obtained from the Linear

Regression data

!!Note!!

Signal LOD ≠ Conc LOD

We want Conc. LOD

The concentration LOD depends on BOTH

Stdev of blank and sensitivity

slide53

Selectivity

Difference in slope is one measure selectivity

Pb2+

H+

In a perfect method the sensing device would have zero

Slope for the interfering species

slide54

Limit of linearity

5% deviation

slide55

Summary: Figures of Merit Thus far

R = resolution

S/N

LOD = both signal and concentration

LOQ

LOL

Sensitivity (calibration curve slope)

Selectivity (essentially difference in slopes)

Can be expressed in terms of signal, but better

Expression is in terms of concentration

Tests: Anova

Why is the limit of detection important?

Why has the limit of detection changed so much in the

Last 20 years?

slide60

Which of these two data sets would be likely

To have better numerical value for the

Ability to distinguish between two different

Populations?

Needleman’s data

slide61

Height for normalized

Bell curve <1

2008 Data

Which population is more variable?

How can you tell?

slide62

Increasing the sample size decreases the std dev and increases separation

Of the populations, notice that the means also change, will do so until

We have a reasonable sample of the population