07 calibration

7/28/2019 07 Calibration

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l i n e a

r r a n g e

analyte amount

r e s p o n s e

LOD

LOQLOL

LODlimit of detection

LOQlimit of quantitation

LOLlimit of linearity

LODlimit of detection

LOQlimit of quantitation

LOLlimit of linearity

Most methods have a fixed rangewhere the relationship betweenresponse and analyte amount is valid.

R =b 1X +b 0+e

R =response

b 1 =slopeparameter

X =Standard value

b 0 =intercept parameter

e =residualerror

s b 0

2=

N X i -X ^ h2

!s Y

2

X 2

!

s b 12=

X i -X ^ h2

!

s Y 2

s e 2=

N -2^ h

Y i -Y ^ h2

! -b 12

X i -X ^ h2

!



Use one

sided

t value

variation of

response

variation of

response

resulting variationof analysis

resulting variation

of analysis

variation

of

standard

variation

of

standard



-2000

0

2000

4000

6000

8000

10000

12000

0 20 40 60 80 100 120



0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 2 4 6 8 10 12 14 16 18 20

Concentration

N o i s e

+ / - 2 0 0 0

0

20

40

60

80

100

120

140

160

180

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Concentration

N o i s e

+ / - 1 0 0

This residu

plot indicat

a reasonab

fit of the da

to the mode

This residua

plot indicate

a reasonab

fit of the dat

to the mode



-2000

0

2000

4000

6000

8000

10000

12000

0 20 40 60 80 100 120

N o i s e

+ / - 2 0 0 0

Concentration

Regression of Noise +/- 2000 by Concentration(R !=0.965)

-2000

0

2000

4000

6000

8000

10000

12000

0 20 40 60 80 100 120

N o i s e

+ / - 1 0 0

Concentration

Regression of Noise +/- 100 by Concentration(R !=1.000)



Regression of MPG by Weight, tons

5

10

15

20

25

30

35

40

1.5 2 2.5 3 3.5 4 4.5

Weight, tons

M

P G



It’s clear that there

are two different

types of samples s

simple calibration

model won’t work

This was a candida

for ANCOVA,



Octane # / Standardized residuals

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

83 85 87 89 91 93

Octane #

S t a n d a r d i z e d

r e s i d u a l s

ANCOVA can then

be used to build a

model that includes

the ‘season’ factor asa way of merging

what are clearly two

different (but related)

models.

R 0=k C 0=k V 0

n 0c m

R T =R 0+R S =k V 0+V S

n 0+n S c m

Q =R T V 0+V S ^ h =k n 0+k n S

07 calibration

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