ELECTRONIC TONGUE / ELECTRONIC NOSE (ETongue, ENose) are systems for automatic analysis and recognition (classification) of liquids or gases, including arrays of non-specific sensors, data collectors and data analysis tools. Electronic tongues are used for liquid samples analysis, whereas electronic noses - for gases. The result of Etongue/Enose can be the identification of the sample, an estimation of its concentration or its characteristic properties.
This new technology has many advantages. Problems associated with human senses, like individual variability, impossibility of on-line monitoring, subjectivity, adaptation, infections, harmful exposure to hazardous compounds, mental state, are no concern of it.
Synonyms of an electronic tongue: artificial tongue, taste sensor
Synonyms of an electronic nose: artificial nose, olfactory system
APPLICATIONS OF E-TONGUES/E-NOSES:
Foodstuffs Industry
- food quality control during processing and storage (water, wine, coffee, milk, juice...)
- optimalization of bioreactors
- control of ageing process of cheese, whiskey
- automatic control of taste
Medicine
- non-invasive diagnostics (patient's breath, analysis of urine, sweat, skin odour)
- clinical monitoring in vivo
- identification of unpleasant odour of pharmaceuticals
Safety
- searching for chemical/biological weapon
- searching for drugs, explosives
- friend-or-foe identification
Environmental pollution monitoring
- monitoring of agricultural and industrial pollution of air and water
- identification of toxic substances
- leak detection
Quality control of air in buildings, closed accommodation (i.e. space station, control of ventilation systems)
Chemical Industry
- products purity
- in the future - detection of functional groups, chiral distinction
Legal protection of inventions - digital "fingerprints" of taste and odours
SENSING METHODS APPLIED
| ETongue | ENose |
| Potentiometric sensors Measurements of conductivity Voltamperommetry Optical sensors Biosensors | Conductivity sensors:
|
PATTERN RECOGNITION
The electronic tongue or nose system performance is dependent on the quality of functioning of its pattern recognition block. Various techniques and methods can be used separately or together to perform the recognition of the samples. After measurement procedure the signals are transformed by a preprocessing block. The results obtained are inputs for Principal Components Analysis, Cluster Analysis or Artificial Neural Network.
Measurement
Sensors arrays' outputs are arranged in data matrix (Fig. 1).
Fig. 1. Data matrix
Each sample is characterized by unique and typical set of data, forming "fingerprint" of an analyte in m-dimensional pattern space.
Preprocessing
Preprocessing is the phase in which linear transformation on the data matrix is performed (without changing the dimensionality of the problem) in order to enhance qualitative information. Typical techniques include manipulation of sensor baseline, normalization, standarization and scaling of response for all the sensors in an array.
Principal Component and Cluster Analysis
A multi-sensor system produces data of high dimensionality - hard to handle and visualize. Principal Component Analysis (PCA) and Cluster Analysis (CA) are multivariate pattern analysis techniques reducing dimensionality of the problem and reducing high degree of redundancy.
PCA is a linear feature-extraction technique finding most influential, new directions in the pattern space, explaining as much of the variance in the data set as possible. This new directions - called principal components - are the base for a new data matrix. Usually 2 or 3 of them are sufficient to transfer more than 90% of the variation of the samples.
The base principle of Cluster Analysis is the assumption of close position of similar samples in multidimensional pattern space. Similarity between each 2 samples is calculated as a function of the distance between them - usually in Euclidean sense - and displayed on a dendrogram (Fig. 2).
Fig. 2. Cluster Analysis: a), b) different types of dendrograms
Artificial Neural Networks (ANN)
Neural Networks are information processing structures imitating behavior of human brain. Their main advantages, such as: adaptive structure, complex interaction between input and output data, ability to generalize, parallel data processing and handling incomplete or high noise level data make them useful pattern recognition tools. There are many possible architectures and algorithms available in the literature, but the most common in measurement applications is feed-forward network (multilayer perceptron MLP) and back-propagation learning algorithm.
The base units of artificial neural networks are neurons and synapses. Neurons are organized in layers and connected by synapses. Their task is to sum up their inputs and non-linear transfer of the result, which is then transmitted via synapsis with modification by means of the synapsis weights - this signal, in turn, is the input for the next layer of the network (Fig. 3).
Fig. 3. Neural Networks: a) single neuron, b) feed-forward network
The use of ANN involves 3 phases:
- The learning phase - after establishing number of neurons, layers, type of architecture, transfer function and algorithm, network is forced to provide desired outputs corresponding to a determined input. It is made by adjusting the synapses' weights in order to minimize the difference between desired and current output.
- The validation phase - verification of the generalization capability of network by means of data different (but with similar characteristics) from data used in the learning phase.
- The production phase - in which the network is capable of providing outputs corresponding to any input.
REFERENCES:
- Craven M. A., Gardner J. W., Electronic noses - development and future prospects, Trends in Analytical Chemistry, vol. 15 (1996), 486
- D'Amico A., Di Natale C., Paolesse R., Portraits of gasses and liquids by arrays of nonspecific chemical sensors: trends and perspectives, Sensors and Actuators B, 68 (2000), 324
- Nagle H. T., Schiffman S. S., Gutierrez-Osuna R., The how and why of electronic noses, IEEE Spectrum, September 1998, 22
- Di Natale C., Davide F., D'Amico A., Pattern recognition in gas sensing: well-stated techniques and advances, Sensors and Actuators B, 23 (1995), 111
- Gardner J. W., Detection of vapours and odours from a multisensor array using pattern recognition Part I. Principal Component and Cluster Analysis, Sensors and Actuators B, 4 (1991), 109
- Gardner J. W., Hines E. L., Tang H. C., Detection of vapours and odours from a multisensor array using pattern recognition Part II. Artificial Neural Networks, Sensors and Actuators B, 9 (1992), 9
- Toko K., Taste sensors with global selectivity, Materials Science and Engineering, C4 (1996), 69
- Vlasov Y., Legin A., Non-selective chemical sensors in analytical chemistry: from "electronic nose" to "electronic tongue", Journal of Analytical Chemistry, 361 (1998), 255
- Krantz-Ruckler C., Stenberg M., Winquist F., Lundstrom I., Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review, Analytica Chimica Acta, 426 (2001), 217
- Winquist F., Holmin S., Krantz-Ruckler C., Wide P., Lundstrom I., A hybrid electronic tongue, Analytica Chimica Acta, 406 (2000), 147
LINKS:
Commercially available e-noses/e-tongues:
- http://www.alpha-mos.com/
- http://www.detect-measure.com/neo.htm
- http://www.osmetech.plc.uk/
- http://www.appliedsensor.com/
- http://www.airsense.com
- http://cyranosciences.com/
- http://estcal.com/
- http://www.hkr-sensor.de/
- http://www.lennartz-electronic.de/
Chemometrics:
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