Biological Machines/Sensory Systems/Olfactory System/Signal Processing
|Substance||mg/L of Ari|
|Oil of peppermint||0.02|
Only substances which comes in contact with the olfactory epithelium can be excite the olfactory receptors. The right table shows some threshold for some representative substances. These values give an impression of the huge sensitivity of the olfactory receptors.
It is remarkable that humans can recognize more than 10'000 different odors but they should at least differ about the 30% before they can be distinguished. Compared to the visual system, such precision would mean a 1% change in light intensity, where as compared to hearing the direction perception may be indicated by the slight difference in the time of arrival of odoriferous molecules in the two nostrils . It is amazing how the same number of carbon atoms (normally between 3 and 20) in odors molecules can leads to different odors just by slightly change in the structural configuration.
An interesting feature of the olfactory system is how a simple sense organ that apparently lacks a high degree of complexity can mediate discrimination of more than 10'000 different odors. On the one hand this is made possible by the huge number of different odorant receptor. The gene family for the olfactory receptor is infect the largest family studied so far in mammals. On the other hand the neural net of the olfactory system’s provide with their 1800 glomeruli a large two dimensional map in the olfactory bulb that is unique to each odorant. In addition, the extracellular field potential in each glomerulus oscillates, and the granule cells appear to regulate the frequency of the oscillation. The exact function of the oscillation is unknown, but it probably also helps to focus the olfactory signal reaching the cortex .
Olfaction, as described in the research of R. Haddad et al., consists of a set of transforms from physical space of odorant molecules (olfactory physicochemical space), through a neural space of information processing (olfactory neural space), into a perceptual space of smell (olfactory perceptual space). The rules of these transforms depend on obtaining valid metrics for each of those spaces.
Olfactory perceptual space
As the perceptual space represent the “input” of the smell measurement, it’s aim is to describe the odors in the most simple possible way. Odor are infect ordered so that their reciprocal distance in space confers them similarity. This mean that odors the more two odors are near each other in this space the more are they expected to be similar. This space is thus defined by so called perceptual axes characterized by some arbitrarily chosen “unit” odors.
Olfactory neural space
As suggested by its name the neural spaces are generated from neural responses. This gives rise to an extensive database of odorant-induced activity, which can be used to formulate an olfactory space where the concept of similarity serves as a guiding principal. Using this procedure different odorant are than expected to be similar if they generate a similar neuronal response. This database can be navigated at the Glomerular Activity Response Archive .
Olfactory physicochemical space
The need of identify the molecular encryption of the biological interaction, make the physicochemical space the most complex one of the olfactory space described so far. R. Haddad suggest that one possibility is to span this space would to represent each odorant by a very large number of molecular descriptors by use either a variance metric or a distance metric. In his first description single odorants may have many physicochemical features and one expect these feature to present themselves at various probabilities within the world of molecules that have a smell. In such metric the orthogonal basis generated from the description of the odorant leads to represent each odorant by a single value. While in the second, the metric represents each odorant with a vector of 1664 values, on the basis of Euclidean distances between odorants in the 1664 physicochemical space. Whereas the first metric enabled the prediction of perceptual attributes, the second enabled the prediction of odorant-induced neuronal response patterns.
Electronic measurement of odors
Nowadays odors can be measured electronically in a huge amount of different way, some examples are: mass spectrography, gas chromatography, raman spectra and most recently electronic nose. In general they assume that different olfactory receptors have different affinities to specific molecular physicochemical properties, and that the different activation of these receptors gives rise to a spatio-temporal pattern of activity that reflects odors.
eNoses are analytic devices for mimicking the principle of biological olfaction that have as main component an array of non specific chemical sensors. Combining electronics, path recognition and modern technology, the eNoses uses gas sensors to translate the chemical signal into an electrical signal when an volatile odorant from a sample reaches the gas sensor array. Usually the pattern recognition is used to perform either the quantitative or the qualitative identification. In order to reproduce the olfactory epithelium a gas sensor array is sealed in a chamber of the eNose. A cross-sensitive chemical sensors will than act as olfactory neuron transferring the odor information from a chemical into an electric form similar to the one process which occur in the olfactory bulb where the signal is integrated and enhanced. The information is than elaborated by an artificial neuronal network, which provide coding, processing and storage. The gas sensor array transforms odor information from the sample space into a measurement space. This is a key procedure for information processing within an eNose. Gas sensors with different transduction principles and different fabrication techniques provide various ways to obtain odor information. Commercially a lot of different sensor types are available the most frequently used sensor types include metal oxide semiconductors (MOS), quartz crystal microbalances (QCM), conducting polymers (CP) and surface acoustic wave (SAW) sensors. A big influence in the choice of the sensor is made by the fast response, reversibility, repeatability and high sensitivity of the sensor. While constructing the sensor array for a eNose the sensors are selected to be cross-selective to different odors, such that their sensitivity is overlapped with the same odor, to make the most of type-limited sensors for obtaining adequate odor information. In general the amount of raw data generated from the array of sensor’s is huge, so that the information has to be transferred from a high dimensional space into a lower one. Pattern recognition are then needed to encode the signal into a so called classification space. Both are important and necessary for designing a powerful information processing algorithm and constructing an array with high quality gas sensors. Many pattern recognition methods have been introduced into eNose, including parameterized and non-parameterized multivariate statistical methods. Artificial neural network have various significant advantages: (i) Self-adaptive, (ii) capability of error tolerance and generalization suitable for treating the problems (iii) parallel processing and distributed storage.