Hybrid Arrays for Chemical Sensing

  • Kirsten E. Kramer
  • Susan L. Rose-Pehrsson
  • Kevin J. Johnson
  • Christian P. Minor
Part of the Integrated Analytical Systems book series (ANASYS)


In recent years, multisensory approaches to environment monitoring for chemical detection as well as other forms of situational awareness have become increasingly popular. A hybrid sensor is a multimodal system that incorporates several sensing elements and thus produces data that are multivariate in nature and may be significantly increased in complexity compared to data provided by single-sensor systems. Though a hybrid sensor is itself an array, hybrid sensors are often organized into more complex sensing systems through an assortment of network topologies. Part of the reason for the shift to hybrid sensors is due to advancements in sensor technology and computational power available for processing larger amounts of data. There is also ample evidence to support the claim that a multivariate analytical approach is generally superior to univariate measurements because it provides additional redundant and complementary information (Hall, D. L.; Linas, J., Eds., Handbook of Multisensor Data Fusion, CRC, Boca Raton, FL, 2001). However, the benefits of a multisensory approach are not automatically achieved. Interpretation of data from hybrid arrays of sensors requires the analyst to develop an application-specific methodology to optimally fuse the disparate sources of data generated by the hybrid array into useful information characterizing the sample or environment being observed. Consequently, multivariate data analysis techniques such as those employed in the field of chemometrics have become more important in analyzing sensor array data. Depending on the nature of the acquired data, a number of chemometric algorithms may prove useful in the analysis and interpretation of data from hybrid sensor arrays. It is important to note, however, that the challenges posed by the analysis of hybrid sensor array data are not unique to the field of chemical sensing. Applications in electrical and process engineering, remote sensing, medicine, and of course, artificial intelligence and robotics, all share the same essential data fusion challenges. The design of a hybrid sensor array should draw on this extended body of knowledge. In this chapter, various techniques for data preprocessing, feature extraction, feature selection, and modeling of sensor data will be introduced and illustrated with data fusion approaches that have been implemented in applications involving data from hybrid arrays. The example systems discussed in this chapter involve the development of prototype sensor networks for damage control event detection aboard US Navy vessels and the development of analysis algorithms to combine multiple sensing techniques for enhanced remote detection of unexploded ordnance (UXO) in both ground surveys and wide area assessments.


Data Fusion Sensor Array Probabilistic Neural Network Electronic Nose Hybrid Array 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The Office of Naval Research provided funding for the Early Warning Fire Detector and the Volume Sensor. Funding for the UXO research was provided by the Strategic Environmental Research and Development Program (SERDP). Dr. Kirsten Kramer is a Post-Doctoral Fellow with the National Research Council.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Kirsten E. Kramer
    • 1
  • Susan L. Rose-Pehrsson
    • 2
  • Kevin J. Johnson
    • 2
  • Christian P. Minor
    • 3
  1. 1.Cognis CorporationCincinnati Innovation Concept CenterCincinnatiUSA
  2. 2.Chemistry DivisionNaval Research LaboratoryWashingtonUSA
  3. 3.Nova Research, IncAlexandriaUSA

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