electronic nose–trufflebot Research paper Discussion of the technology or technologies you have identified in the context of competitive technologies, or even relative to technology embedded in existing products.

Discussion of the technology or technologies you have identified in the context of competitive technologies, or even relative to technology embedded in existing products. Make sure that you end with a clear discussion of the “unique technology capabilities” that you have identified, including identifying and discussing the research group from which the technology comes. Discuss concerns (ie competing patents and IP).

It is a group work homework and following is background information of what is trufflebot :

This report should communicate the research and analysis of the electronic nose technology opportunity space.

The main goal is to describe what the team learned about the trend, identify unique opportunities and capabilities of Trufflebot compared to other similar technologies, understand why it’s more accurate and in what cases and interpret the results that Jacob Rosenstein got so far. The second main goal is to identify general applications in industries. This report will also address the barriers, challenges and other problems in the use of this technology.

Keywords

Main Keywords: Electronic nose, e-nose, machine olfaction, odor classification, gas sensor, chemical sensor network, artificial olfaction, EAD (Electronic aroma detection)

Other Keywords: Volatile organic compounds, Breathomics, airway inflammation, asthma, Chronic obstructive pulmonary disease, lung cancer, medical diagnostics, pressure, tunable optical sensor, infrared sensor, Metal Oxide (MOX) gas sensors, conducting polymers

Summary (Stephanie)

Electronic-nose devices have received considerable attention in the field of sensor technology during the past years, due to the discovery of applications in commercial industries, including the agricultural, biomedical, cosmetics, environmental, food, manufacturing, military, pharmaceutical, regulatory, and various scientific research fields.

Advances have improved product attributes, uniformity, and consistency as a result of increases in quality control capabilities afforded by electronic-nose monitoring of all phases of industrial manufacturing processes.

Introduction

An electronic nose is a device to mimic the discrimination of the mammalian olfactory system for smells. They used three different metal oxide gas sensors and identified several substances by the steady-state signals of these sensors.

One of the initial hopes for work in this area was to instrumentally assess attribute descriptors such as fruity, grassy, earthy, malty, etc. reliably by the results of an electronic nose measurement. In other words, capturing the “flavor fingerprint” or “recognizing the odor”.

Even if one concentrates solely on the different sensitivity characteristics of technical sensors and biological receptors, it is not surprising that despite 25 years of research this is still not possible. The comparison between an electronic nose and a human nose is in the best case like the comparison of an eye of a bee with a human one. It is blind for a part of the visible spectrum but sensitive for other wavelengths. For this reason only in well-defined cases the correlation between human odor impressions and electronic nose data makes sense. On the other side the evaluation of non-odorant volatiles, such as the detection of explosives, becomes reachable.

The term “electronic nose” may be misleading and makes the uninformed reader believe in system capabilities comparable to those of the human nose. Attempts to avoid this term and to replace it (e.g., by “application-specific sensor system”) have not taken root up to now, and in most of the current literature the term “electronic nose” is still used.

We are researching low-cost and flexible platforms for bio-inspired machine olfaction, which aims to extend traditional electronic nose approaches by adding fluid-mechanical and spatiotemporal dimensions. The TruffleBot contains an array of chemical, pressure, and temperature sensors in a small embedded platform.

By “sniffing” vapors in a temporally-modulated sequence through four different air paths across eight sensor locations, scientists have introduced spatial and temporal information that significantly enhances classification of odors using only chemical time series to do the classification accuracy for nine odors. With the addition of pressure and temperature time series, TruffleBot’s classification accuracy can exceed 98%.