Hitachi F7000 Instruction Manual

							9.2    Use of AS-3000 Intelligent Autosampler 
9 - 10 
9.2    Use of AS-3000 Intelligent Autosampler 
 
Set the rack parameters, injection volume and other items with the   
AS-3000 Intelligent Autosampler.    For details, refer to the instruction 
manual attached to the AS-3000. 
 
 
 
 
 
 
 
 
 
 
 
Fig. 9-14  Cord Connection 
 
 
Parameter setting:    Set all parameters in the AS-3000. 
 
NOTE:  When using a sample table, match the number of samples set 
in the AS-3000 with that set in the sample table.    If the latter 
is less than the former, some samples cannot be measured. 
 
 
PCAS-3000 USBEXTIN
COM1OUT1 IN1 Communication cable 
(furnished with F-7000)Connecting cord   
(furnished with AS-3000) 
F-7000 
						

							9.2 
9 - 11 
 
(1)  Select the Measure command from the Spectrophotometer menu 
of the FL Solutions program or click the 
 (measurement) 
button on the toolbar.    A window as in Fig. 9-15 will open. 
 
 
 
Fig. 9-15    For Start from Creation of Calibration curve 
 
 
(2) Press the START key on the AS-3000. 
 
(3)  Measurement will start. 
 
  
						

							9.3  Analog Output 
9 - 12 
9.3  Analog Output 
 
For connection, use the analog output terminal.    Select the Analog 
Output command from the Utility menu.    (The analog output I/F (option) 
is required.) 
 
 
 
Fig. 9-16 
 
 
This is a setting of data value corresponding to analog output of 1 V. 
Analog output value (V) becomes as follows. 
 
Low :  (data value)/1000 
High :  (data  value)/10000 
 
 
  
						

							A - 1 
APPENDIX A    DETAILS OF QUANTIFICATION   
 
A.1  Foreword 
 
In the Photometry mode of the F-7000 spectrophotometer, the following 
four calibration curve types are available. 
 
  Linear working curve 
  Quadratic working curve 
  Cubic working curve 
  Multiple segment working curve 
 
These are explained in detail below. 
 
 
A.2    Linear Working Curve (1st order) 
 
A regression line is determined via the least squares method from a 
maximum of 20 data.    The calculation formula is as follows: 
 
x = A1 
 y + A0 
()
Ayx
nyx
y
nyii i i
ii11
122=−⋅
−∑ ∑ ∑
∑ ∑, AxnAy
nii01=−⋅∑∑ 
 
where,  x :  Sample concentration (input value) 
  y :  Sample data (measured value) 
  n :  Number of samples 
 
  
						

							A - 2 
A.3    Quadratic Working Curve (2nd order) 
 
A quadratic curve is determined via the least squares method from a 
maximum of 20 data.    The calculation formula is as follows: 
 
x = A2 
 y
2 + A1 
 y + A0 
()()()()
()()(){}
ASy xSyy SyxSyy
SyySy y Syy 2
22
22 22
=

 
()()Syy yy
nii=∑
∑22
 
()()()()
()()(){}
ASyxSy y Sy xSyy
SyySy y Syy 1
22 2 2
22 22
=

 
()Syx y xyx
niiii=−⋅∑ ∑
∑
 
AAAx
nx
ny
nii i012
2
= ∑∑ ∑
 ()Syy yyy
niii
232=−⋅∑ ∑
∑
 
()Sy x y xyx
ni
iii
222=−⋅∑ ∑
∑
 
()
()Sy y yy
nii
22 422=∑
∑ 
 
where,  x :  Sample concentration (input value) 
  y :  Sample data (measured value) 
  n :  Number of samples 
 
 
A.4    Cubic Working Curve (3rd order) 
 
A cubic curve is determined via the least squares method from a 
maximum of 20 data.    The calculation formula is as follows: 
 
x = A3 
 y
3 + A2 
 y2 + A1 
 y + A0 
 
where,  x :  Sample concentration (input value) 
  y :  Sample data (measured value) 
  n :  Number of samples 
 
  
						

							A - 3 
A.5    Multiple Segment Working Curve (Segmented) 
 
The calibration curve is apt to bend when measuring turbid samples or 
the like.    Use of a quantification program allows correcting the 
calibration curve by using up to 20 standards.    Figure A-1 shows an 
example of curve correction.    For the part which exceeds the 
measuring range of the standards, simply extend the line as it is.   
 
 
 
Fig. A-1    Correction of Curve Bending 
 
 
Data
Conc  
						

							A - 4 
＜Notes on Preparation of Multiple Segment Working Curve> 
(1)  A correct calibration curve can be created only when the 
measured value increases or decreases monotonically versus the 
concentration value.    Especially when the inclination is negative-
going, be sure to measure a blank having a data value larger than 
the other standards.    A curve not showing a monotonic increase 
will appear as in Fig. A-2.    And when the data value of the blank 
is small, regardless of a negative-going inclination, the curve will 
appear as in Fig. A-3. 
 
 
 
Fig. A-2  Example of Curve without Monotonic Increase   
(unsuitable curve) 
 
 
 
 
Fig. A-3  Example of Negative-going Curve where Measured   
Value of Blank is Small (unsuitable curve) 
 
 
Data
Conc 
Data
Conc  
						

							A - 5 
(2)  Remeasurement of Standards 
 
The standards can be remeasured after once preparing a 
calibration curve.    The curve prepared in such a case will appear 
as in Fig. A-4. 
 
 
 
Fig. A-4    Calibration Curve when Standards are Remeasured 
 
 
 
Data
Conc
Redrawn 
calibration curve
Remeasured STD1
Initially measured STD1 
						

							A - 6 
APPENDIX B  DETAILS OF RATE ANALYSIS 
FUNCTION 
 
B.1  Foreword 
 
Rate analysis is used in the analysis of enzyme reactions.    It is utilized 
for clinical and biochemical tests by reagent manufacturers, hospitals 
and so on.    A computer is used to calculate the concentration from the 
variation in data per unit time, and the result is displayed and printed out. 
 
 
B.2  Calculation Method 
 
A timing chart for rate analysis is shown in Fig. B-1.    Data is acquired 
when the initial delay time has elapsed after pressing the Measure 
button.    A regression line is determined from this data via the least 
squares method, and the gradient and activity value are calculated.   
The calculation formula is as follows. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. B-1 
 
 
Data 
Td Tc
Tt
Tm
A0
A1
A5
A4
A3
A2
A6
A7
Time
Start of 
measurement Data: 
A0, A1, A2, A3, 
A4... 
Td  :  Initial delay time
Tm :  Measurement time 
Tc :  Sampling interval 
Tt :  Calculation time  
						

							A - 7 
Prepare a regression line via the least squares method from the 
measured data, and obtain a determination coefficient. 
 
y = ax + b 
where, 
 
()
axyxy
n
xx
niiii
ii=

∑ ∑
∑
∑
∑
22
  
()byax
nii=−∗∑
∑ 
 
x
i :  Time (s) of each data 
y
i :  Value of each data 
n :  Number of samples 
 
The determination coefficient CD becomes as follows: 
 
()
() ()
CDnxy x y
nx x ny yii i i
ii ii=−
− ⎛
⎝ ⎜⎞
⎠ ⎟− ⎛
⎝ ⎜⎞
⎠ ⎟∑ ∑ ∑
∑ ∑∑∑
2
22
22
 
 
  Gradient (variation per minute) 
min) (/ a 60
Tka
Di= = 
 
 Activity 
C
i = k 
 Di 
 
  R (relative coefficient) 
() ()⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑∑−
⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑∑−⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑∑∑2
i i2
2
i i22
i i
i iy y n x x ny x y
R
  x n
CD－
＝ ＝
 
 
  R2 (determination coefficient) 
()
⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑
⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑ −
⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑
⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑ −⎟ ⎟
⎠ ⎞
⎜ ⎜
⎝ ⎛
∑∑∑−
= =
2
i 2
i 2
i 2
i2
i i i i 2
y y n x x ny x y x n
CD R
 
 
 
 
NOTE:  If the range for rate calculation does not coincide with the 
actual measured data range, then use only the measured data 
within the range for the calculation.