Week 2: Spectrophotometry
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Spectrophotometry
Beer’s law
Standard curve
Dilution problems
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Quality control
Control samples
Levey-Jenning chart
Shift and trend
Out of control
Spectrophotometry
• Darkness = light absorption
• Corresponds to concentration
• A = 2 - log(%T)
Light  [Solution]  Transmission
Beer’s Law
A = abc = kc
A = absorbance
a = absorptivity
b = light path length
c = concentration
k = constant (k-value)
Absorption Measurement
• Colorimeter with filter
• Spectrophotometer
• Prism
• Diffraction grating
Spectrophotometer
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Light source: tungsten
Monochromator: diffraction grating
Cuvette
Photodetector: PMT, photocell
Readout
Colman Spectrophotometer
Spectrophotometry
1. Turn instrument on
2. Select correct wavelength 
3. Block light, set Zero (no light = infinity
absorption = 0% T)
4. Choose and clean cuvette
5. Open light, insert Blank (maximum light =
no absorption = 100% T)
6. Measure absorption of Standards, Controls
and Patient samples to 3rd decimal place
Standards
• Precisely prepared = known concentration
• Usually pure solution of single compound
• Plot absorbance vs concentration: standard
curve
Beer’s Law
If standard curve is linear, use Beer’s law
k = Astd/Cstd = Aunk/Cunk
Cunk = Aunk/k
For example…
Std 1 = 100 mg% glucose
Std 2 = 200 mg% glucose
Abs Std 1 = 0.400
Abs Std 2 = 0.800
Abs Pt 1 = 0.200
Therefore…
k Std 1 = 0.400/100 = 0.00400
k Std 2 = 0.800/200 = 0.00400
Average k = 0.00400
Then…
Since Abs Pt 1 = 0.200
And C = A/k
C of Pt 1 = 0.200 ÷ 0.00400 = 50 mg%
 Since glucose reference range is 70 - 110
mg%, she is hypoglycemic!
Units
dL = 100 mL
%(w/v) = g/dL
mg% = mg/dL
%(v/v) = mL/dL
CLIA 88: two or more standards are required
for most tests; make sure k-values match
Murphys’s Law
• If something can go wrong, it will!
• Sources of errors: random vs technical
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Sample - pre-analytical
Reagent
Method
Technique
Reporting - post-analytical
How to Ensure Accuracy?
• Repeat tests many times (how many?) and
take average
• Run another sample that was tested before
along with patient samples and make sure
its result is close to what it should be
Control Samples
• Similar in composition to patient sample
• Usually pooled from many donors
• Tested at least 30 times to calculate the
average (target value) and allowable range
of variation
Are You in Control?
• Was your control value close to the target
value?
• Predetermined mean value
• How close is close enough?
• Within ± 2 standard deviation from the mean
Formulae
Mean = Sum of all values = ∑ xi
Sample population
n
± 2 Std = ± 2
√
∑ (xi - mean)2
n-1
Calculate the QC Statistics
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4
5
Chol (mg%)
103
98
100
96
103
Mean = 500mg% ÷ 5 = 100 mg%
Calculate the QC Statistics
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2
3
4
5
Chol (mg%)
103
98
100
96
103
d
3
-2
0
-4
3
d = xi - mean
Calculate the QC Statistics
#
1
2
3
4
5
Chol (mg%)
103
98
100
96
103
d
3
-2
0
-4
3
∑ d2 = 38
d2
9
4
0
16
9
Calculate the QC Statistics
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1
2
3
4
5
Chol (mg%)
103
98
100
96
103
± 2 Std = ± 2
√
d
3
-2
0
-4
3
d2
9
4
0
16
9
∑ (xi - mean)2 = ± 2
n-1
= ± 6.2 mg%
√
38
4
That Means…
Target value = 100 mg%
Allowable range = ± 6.2 mg%
Acceptable control range: 93.8 - 106.2 mg%
If your control value was within the
above range, you are in control.
Levey-Jennings QC Chart
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Graphical record of QC
Better able to spot shift and trend
Visualize degree of random distribution
CLIA 88 requires at least two levels of
controls per test
Shift
Trend
Westgard’s Rule
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12S: warning
13S: usually random; re-run
22S: both of consecutive violation
R4S: usually random; large range
41S: indication of shift
10x: shift
If all above are OK, then accept result
Precision vs Accuracy
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Reproducibility
Close to the true value
Estimation of true value?
In clinical labs, we need both plus speed:
efficiency