Towards a Real-Time Transduction and Classification of Chemoresistive Sensor Array Signals

Abstract

Recently, a growing interest in artificial implementations of biological systems has been arising. In particular, several research groups have been working in mimicking the mammalian olfactory system with the so-called electronic noses (e-noses). The e-noses, which are based on a sensor array, a fluid-dynamic system, and a data processing unit, are systems devoted to detecting and analyzing volatiles, where a deep knowledge of the target application is needed. In order to achieve effective results the sampling system, the measurement protocols, the sensor array, and the pattern recognition techniques have to be carefully designed. The increasing complexity of such design poses issues in sensory feature extraction and fusion, drift compensation, and data processing, especially when high efficiency is required for real-time applications. The interconnection and cooperation of several modules devoted to processing different tasks, such as control, data acquisition, data filtering interfaces, feature selection, and pattern analysis, are already mandatory. Moreover, heterogeneous techniques used to implement such tasks may introduce module interconnection and cooperation issues.In this paper, we address the development of a dedicated instrument able to perform real-time transduction, fusion, and processing of chemoresistive sensor array signals. In particular, this instrument realizes a dynamic and efficient management of data processing techniques and automatically controls the measurement protocols and the sampling system. An array of conducting poly (alkoxy-bithiophenes) sensors, the fluid-dynamic system, the electronic section, the framework's base architecture, and the implementation of dedicated application processes are described. The classification task is based on a self-organizing map where models for artificial neurons and connections were derived from the base structures available in the framework core. According to the target application, this instrument is portable and easily tailored, calibrated, and trained. Classification of olive oil headspaces supports its utility in supplying high-efficiency routine for volatile organic compounds detection and analysis.

Assessment of bioinspired models for pattern recognition in biomimetic systems

“…As can be noticed, the MLP shows the worst performances, while the CANN is proved to exhibit the best performance. As expected [31], KSOM is able to classify ExVg, Vg and Df classes with accuracy rates respectively of 93.9%, 82.5% and 96.8%, while the FKSOM has 95.0%, 83.8% and 93.7%. The CANN performances are respectively 97.8%, 96.1% and 99.7%, showing a mean performance increase of 7.0% in comparison with the KSOM and of 7.7% in comparison with the FKSOM.…”

“…Hardware architecture. The hardware architecture consists of an electronic section able to perform fast and accurate measurements with a conductive polymer sensor array and to perform a dynamic and automatic control of the sampling process [31]. The electronic section consists of a central processing unit, an analog interface and a digital interface.…”

“…Such architectures require high-efficiency interconnection and cooperation of several heterogeneous modules, i.e. control, data acquisition, data filtering, feature selection and pattern analysis [31].…”

Food Analysis Using Artificial Senses

“…Among this group of products, olive oil is most frequently analyzed because attempts to adulterate more expensive olive oil with its cheaper alternative are very common . Therefore, the electronic tongue and nose were both used to discriminate among olive oils on the basis of oil geographic origin − and type and quality. ,− In the case of olive oil, the conducted studies were also aimed at determining rancidity and the relationship between storage time and oil quality . Also, other plant oils were analyzed via an electronic tongue , and an electronic nose, , whereas soy sauces, Chinese vinegars, and selected spices were analyzed by using an e-nose.…”

Towards a Chemiresistive Sensor-Integrated Electronic Nose: A Review

“…In other similar architecture, a sensor driver controlled by an external CPU adjusted the driving current of each sensor, and an interface subsequently readout the double-ended voltage of the sensor. This device also exhibited a wide sensor resistance range (from 500Ω to 1 MΩ) [ 161 ]. Other researchers designed a baseline cancellation circuit to read the changed resistor message efficiently.…”