1. Principles of Instrumental Analysis
Chapter 1
Introduction

2. TABLE 1-1
Chemical and Physical Properties Used in Instrumental Methods
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3. FIGURE 1-1 Block diagram showing the overall process of an
instrumental measurement.
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4. TABLE 1-2 Some Examples of Instrument Components
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5. FIGURE 1-2 Data-domain map. The upper (shaded) half of the map consists
of nonelectrical domains. The bottom half is made up of electrical domains.
Note that the digital domain spans both electrical and nonelectrical domains.
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6. data-domain transformations during the measurement process.
FIGURE 1-3 A block diagram of a fluorometer showing (a) a general diagram
of the instrument, (b) a diagrammatic representation of the flow of information
through various data domains in the instrument, and (c) the rules governing the
data-domain transformations during the measurement process.
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7. FIGURE 1-3(a)
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8. FIGURE 1-3(b)
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9. FIGURE 1-3(c)
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10. FIGURE 1-4 Analog signals.
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11. FIGURE 1-4 Analog signals. (a) Instrument response from the
photometric detection system of a
flow injection analysis experiment.
A stream of reaction mixture
containing plugs of red Fe(SCN)2+
flows past a monochromatic light
source and a phototransducer,
which produces a changing voltage
as the sample concentration
changes.
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12. FIGURE 1-4 Analog signals. (b) The current response of a
photomultiplier tube when the light
from a pulsed source falls on the
photocathode of the device.
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13. FIGURE 1-5 Time-domain signals. The horizontal dashed lines represent
signal thresholds. When each signal is above the threshold, the signal is HI,
and when it is below the threshold, the signal is LO.
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14. FIGURE 1-6 Diagram illustrating three types of digital data: (a)
count serial data, (b) binary-coded serial data, and (c) parallel
binary data. In all three cases, the data represent the number
n = 5.
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15. FIGURE 1-7 Chemical sensor. The sensor consists of a molecular
recognition element and a transducer. A wide variety of recognition
elements are possible. Shown here are some fairly selective recognition
elements particularly useful with biosensors. The recognition phase converts
the information of interest into a detectable characteristic, such as another
chemical, mass, light, or heat. The transducer converts the characteristic
into an electrical signal that can be measured.
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16. curve for the determination of isooctane in a hydrocarbon
FIGURE 1-8 Calibration
curve for the determination of
isooctane in a hydrocarbon
mixture. The residual is the
difference between an
experimental data point yi
and that calculated from the
regression model, mxi + b, as
shown in the insert.
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17. FIGURE 1-9 Effect of calibration curve uncertainty
FIGURE 1-9 Effect of calibration curve uncertainty. The dashed lines show confidence
limits for concentrations determined fro the regression line. Note that uncertainties
increase at the extremities of the plot. Usually, we estimate the uncertainty in analyte
concentration only from the standard deviation of the response. Calibration curve
uncertainty can significantly increase the uncertainty in analyte concentration from sc to
s’c.
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18. FIGURE 1-10 Linear calibration plot for the method of standard additions.
The concentration of the unknown solution may be calculated from the slope
m and the intercept b, or it may be determined by extrapolation, as explained
in the text.
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19. FIGURE 1-11 Spreadsheet for standard-addition Example 1-1.
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20. spectrometric determination of sodium.
FIGURE 1-12 Spreadsheet to illustrate the internal-standard method for the flame
spectrometric determination of sodium.
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21. TABLE 1-3 Numerical Criteria for Selecting Analytical Methods
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22. Other Characteristics to Be Considered in Method Choice
TABLE 1-4
Other Characteristics to Be Considered in Method Choice
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23. TABLE 1-5 Figures of Merit for Precision of Analytical Methods
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24. FIGURE 1-13 Useful range of an analytical method. LOQ = limit
of quantitative measurement; LOL=limit of linear response.
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