Abstract
The number and types of sensors that are available for most sensing situations are very large; the computational capacity is abundant and cheap. Miniaturization is a strong trend in the chemical sensor field. The stage is then set for extraction of information from data by means of computational multivariate analysis.
The two terms have different meaning.By data we mean raw output from the sensor, usually in the form of an electrical signal. In a well-behaved individual sensor, the relationship between the output signal and the concentration of a specific analyte is defined and reproducible. This is what we have learned so far from the discussion of the principles of the individual sensors in the preceding chapters.
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Abbreviations
- C :
-
Concentration
- D :
-
Desired output
- d :
-
Distance or diameter
- f(or F):
-
Frequency
- m :
-
Number of input elements
- N :
-
Number of sensors
- n :
-
Number of nodes
- \(\Re\) :
-
Response function
- \(S(\omega)\) :
-
Cross-energy spectral density
- t :
-
Retention time
- V :
-
Linear velocity
- \(W_{n}\) :
-
Weighing factors
- X :
-
Sensor response
- Y :
-
Actual output
- \(\lambda\) :
-
Wavelength
- \(\eta\) :
-
Adjustable learning rate
- \(\tau\) :
-
Delay time
- \(\gamma^2\) :
-
Coherence
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Janata, J. (2009). Multivariate Sensing. In: Principles of Chemical Sensors. Springer, Boston, MA. https://doi.org/10.1007/b136378_10
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DOI: https://doi.org/10.1007/b136378_10
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