Pattern Recognition for Microcantilever Arrays

  • Recently we are witnessing noticeable success in the development of a new class of chemical and biological sensors – microfabricated cantilever sensor arrays actuated at their resonance frequencies and functionalized by polymer coatings. The major advantages of such miniature sensors are their small size, fast response, remarkably high sensitivity, and the endless possibilities of reaching high selectivity via customized combination of polymer coatings. These devices are inexpensive, portable, and have the ability to operate in various environments, such as vacuum, air and liquids. The areas of applications of microfabricated cantilever sensor arrays are almost countless, including a variety of scientific research in physics, chemistry, biochemistry, biology, and genetics, food and beverage industry, perfume industry, pharmacology, medicine, environmental monitoring, and most recently, related to the national security due to a high risk of terrorist attacks. However, despite the remarkable achievements in fabrication of microcantilever sensor arrays, creating an accurate and reliable pattern recognition algorithm as a part of the sensory system is still an essential and not yet completely solved problem. Most pattern analysis algorithms that have been used with the cantilever sensor arrays today are highly customized, ad hoc algorithms. They often lack generality and cannot be easily carried from one set of experimental data to another.
  • The main goal of this project is to develop pattern recognition algorithms that can be effectively used as a reliable detection system with the specific sensory data obtained during the experiments with a microfabricated cantilever sensor array and further feature extracting procedure. Five different pattern recognition algorithms have been created for the current research. All of those algorithms and the open source implementation of the sixth algorithm (multiclass SVMs) were used for testing on benchmark data sets and collected sensory data. It has been shown that the kernel-based algorithms have the greatest potential to be used with the microfabricated cantilever sensor array in the detection systems. Four out of six pattern recognition algorithms have produced high accuracy classification results upon processing the cantilever sensor array data.
  • This work is supported by the National Science Foundation EPSCoR Ring True III award EPS0447416.