S-SENCE Signal processing for chemical sensors Martin Holmberg S-SENCE Applied Physics, Department of Physics and Measurement Technology (IFM) Linköping University

S-SENCE Human olfactory system n a large number of olfactory cells (  10 million) but only a small number of sensitivity classes (  30) n a mixture of volatile compounds results in a signal pattern to the brain via the mitral cells (  10 thousand) n the brain interprets the signal pattern as a specific odour

S-SENCE Electronic nose concept n sensitivity to many compounds using a limited number of chemical sensors with different and partly overlapping selectivity profiles n analyse the sensor signal pattern rather than the individual signals n gives a description of the total measurement state, i.e. the sum of all components, which can be more important than a detailed knowledge of each of the individual components odour sensor chamber(s) chemical sensors pre- processing computer information pattern recognition

S-SENCE Solid-state gas sensor principles

S-SENCE Electronic nose gas flow diagram

S-SENCE Signal processing: feature extraction

S-SENCE Background; The Tongue n Based on voltammetry n Currents are measured as a function of applied potential on electrode surfaces n The currents are caused by: n Redox reactions at the surface n Ionic movement in the sample

S-SENCE The currents are sampled once every [ms]  variables Background; Voltammetry

S-SENCE Information overflow n An electronic nose or tongue generates large data series (one measurement can consist of up to variabler  Linköping’s telephone book) n Impossible to get a good grip of these numbers n We need to decrease the information content n A lot of redundant information means that compression is possible

S-SENCE Other possible problems n Noise n Drift n Low sensitivity n Sampling problems n Measurements system n …

S-SENCE What is drift? n Definition: “a gradual change in any quantitative characteristic that is supposed to remain constant” (Webster’s Seventh New Collegiate Dictionary) n For chemical sensors: Measurements made under identical chemical conditions give different sensor responses at different times.

S-SENCE Causes of drift n Reactions on the sensor surface (poisoning) n changes in the physical properties of the sensing material (e.g. the size of the metal islands on a MOSFET) n adsorption of species on the sensor surface n layer formation of reaction products n Variations in the gas (composition, pressure, temperature,…) n Remaining gas in the measurement system

S-SENCE Example of drift n Measurements made during 60 days on nine different mixtures of four gases n Gradual change + jumps n Drift + noise n Drift in different directions for the different sensors n Below are shown the responses of three sensors as a function of time

S-SENCE Comparison noise and drift n Drift n low frequency n caused by changes in the measurement system n similar for similar sensors n Noise n high frequency n caused by randomness in the measurements n individual for each sensor

S-SENCE What do we do? n Pre-processing to compensate for some of the problems mentioned previously n Pattern recognition models to give the desired information n If possible, give feedback regarding the measurements odour sensor chamber(s) chemical sensors pre- processing computer information pattern recognition

S-SENCE Geometrical interpretation ? variables1,2,3-variables Easy to visualiseAbstract Reduce the number of dimensions Often made by choosing directions with a lot of variation

S-SENCE Principal Component Analysis Can be used for data compression, feature extraction, or visualisation

S-SENCE Examples of pre-processing n 1 st example uses PCA to find a direction where irrelevant information is dominant. This direction is then removed from the data set. n 2 nd example shows how wavelets are used to compress data, and how the choice of wavelets depends on the application.

S-SENCE Component correction n Uses a reference gas n Calculates the direction of drift by PCA for the reference gas n Removes this direction(s) from all other measurements n Example: Artursson et al. J. of Chemometr. 14, 5/6 (2000)

S-SENCE Component correction n 1 st (or several) component in a PCA analysis of the reference gas will describe the drift direction, p n Project the samples, X, on the first loading, p from the PCA on the reference gas, n t=Xp n Subtract the bilinear expression, tp T, from X n X corrected =X- tp T

S-SENCE Component correction n Also used for quantification beforecomponent correction

S-SENCE Wavelets n Data is described using wavelet base functions with different scales n The number of wavelets = the number of original variables

S-SENCE Wavelet selection n By selecting only the most relevant wavelets, a data reduction is obtained n Different criteria for selection in different applications (e.g. variance or discrimination) n Here approximately 100 wavelets are chosen from the original ca variables

S-SENCE Reconstruction n The original signal can be reconstructed from the compressed data n Different results from different wavelet selection criteria Variance selectedDiscrimination selected

S-SENCE Pattern recognition n After reducing initial obstacles with different pre- processing techniques, a model that gives the user something he/she can understand must be made odour sensor chamber(s) chemical sensors pre- processing computer information pattern recognition

S-SENCE Data models n Classification or quantification? n Statistical tools for class membership

S-SENCE Data models n Quantification of gases or liquids: n Regression models (e.g. PLS) for linear relationships n Artificial neural networks (ANN) for non-linear black-box modeling n Important always to validate data, especially when many parameters are used in the models!!!