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Signal processing for chemical sensors

Martin Holmberg

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Applied Physics, Department of Physics and Measurement Technology (IFM)

Linköping University

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Human olfactory system a large number of olfactory cells (10 million) but only a small number of

sensitivity classes (30) a mixture of volatile compounds results in a signal pattern to the brain via the

mitral cells (10 thousand) the brain interprets the signal pattern as a specific odour

odour

nasal chamber

olfactory receptor cells

epithelium

mitral cells

olfactory bulb

brain

perception

odour moleclues bind to olfactory proteinsproduces an electrical response

neurons

olfactory cortex

signal processing:data reduction

signal processing:pattern recognition

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Electronic nose concept sensitivity to many compounds using a limited number of chemical

sensors with different and partly overlapping selectivity profiles analyse the sensor signal pattern rather than the individual signals 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 sensorspre-processing

computer

informationpatternrecognition

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adsorption/desorption or chemical reactions

on surfaces or in (thin) films

causePHYSICAL CHANGES

massconductivity

polarisation

temperature optical properties

which depend onMOLECULAR PROPERTIES

sizereactivity

affinity

polarity polarisability

which can be used in several types ofDEVICES

calorimetricfibre optic

acoustic

resistive field effect

Solid-state gas sensor principles

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Electronic nose gas flow diagram

Air

Sample

ValveField-effect sensors

Metal oxide sensorsFigaro

Metal oxide sensorsFiS

IR CO2 sensor

Massflow controller

Drain

Pump

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Signal processing: feature extraction

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baseline

off integralon integral

on derivative

off derivative

response

offsample on

MOSFET 3 MOSFET 7 Figaro 3

sens

or s

igna

l

time [s]

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Background; The Tongue

Based on voltammetry Currents are measured as a

function of applied potential on electrode surfaces

The currents are caused by: Redox reactions at the surface Ionic movement in the sample

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Cu

rre

nt

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ten

tial [

V]

Au Ir Rh Pt

The currents are sampled once every [ms] 56.000 variables

Background; Voltammetry

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Information overflow An electronic nose or tongue generates large data series

(one measurement can consist of up to 56.000 variabler Linköping’s telephone book)

Impossible to get a good grip of these numbers We need to decrease the information content A lot of redundant information means that compression

is possible

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Other possible problems Noise Drift Low sensitivity Sampling problems Measurements system …

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What is drift?

Definition:

“a gradual change in any quantitative characteristic that is supposed to remain constant”

(Webster’s Seventh New Collegiate Dictionary)

For chemical sensors:Measurements made under identical chemical conditions give different sensor responses at different times.

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Causes of drift

Reactions on the sensor surface (poisoning) changes in the physical properties of the

sensing material (e.g. the size of the metal islands on a MOSFET)

adsorption of species on the sensor surface layer formation of reaction products

Variations in the gas (composition, pressure, temperature,…) Remaining gas in the measurement system

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Example of drift

Measurements made during 60 days on nine different mixtures of four gases

Gradual change + jumps Drift + noise Drift in different directions

for the different sensors

Below are shown the responses of three sensors as a function of time

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resp

onse

(mV)

time (days)

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Comparison noise and drift

Drift low frequency caused by changes in

the measurement system similar for similar

sensors

Noise high frequency caused by randomness

in the measurements individual for each

sensor

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What do we do? Pre-processing to compensate for some of the

problems mentioned previously Pattern recognition models to give the desired

information If possible, give feedback regarding the

measurements

odour

sensor chamber(s)

chemical sensorspre-processing

computer

informationpatternrecognition

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Geometrical interpretation

?56.000 variables1,2,3-variables

Easy to visualise Abstract

Reduce the number of dimensionsOften made by choosing directions with a lot of variation

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Principal Component Analysis

Can be used for data compression, feature extraction, or visualisation

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Examples of pre-processing

1st example uses PCA to find a direction where irrelevant information is dominant. This direction is then removed from the data set.

2nd example shows how wavelets are used to compress data, and how the choice of wavelets depends on the application.

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Component correction

Uses a reference gas Calculates the direction of drift by PCA for the reference

gas Removes this direction(s) from all other measurements

Example: Artursson et al. J. of Chemometr. 14, 5/6 (2000) 711-724

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Component correction

1st (or several) component in a PCA analysis of the reference gas will describe the drift direction, p

Project the samples, X, on the first loading, p from the PCA on the reference gas,

t=Xp Subtract the bilinear expression, tpT, from X

Xcorrected=X- tpT

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Component correction

Also used for quantification

before

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2 (2

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PC 1 (63%)

component correction

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PC 1 (71%)

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Wavelets

Data is described using wavelet base functions with different scales

The number of wavelets = the number of original variables

A 2 D 2

L P F H P F

D 1A 1

L P F H P F

A 2 D 2

w a v e l e t c o e f f i c i e n t s

S c a l e 1

S c a l e 2

D 1. . . . . . . A 2 D 2

L P FL P F H P F

D 1A 1 D 1A 1

L P F H P FL P FL P F H P FH P F

A 2 D 2A 2 D 2

w a v e l e t c o e f f i c i e n t s

S c a l e 1

S c a l e 2

D 1. . . . . . .

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Wavelet selection By selecting only the most relevant wavelets, a data

reduction is obtained Different criteria for selection in different applications

(e.g. variance or discrimination) Here approximately 100 wavelets are chosen from the

original ca. 1800-14000 variables

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0.00E+000

1.00E-011

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vari

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variables

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crim

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Variables

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Reconstruction

The original signal can be reconstructed from the compressed data

Different results from different wavelet selection criteria

Variance selected Discrimination selected

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-0.000002

0.000000

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0.000004

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RhodiumPlatina

resp

on

se

variables

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Pattern recognition 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 sensorspre-processing

computer

informationpatternrecognition

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Data models Classification or quantification? Statistical tools for class membership

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Scores vs. scores

Scores for PC# 1 95.6 %

Sco

res

for

PC

# 2

4.1

%

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Data models Quantification of gases or liquids:

Regression models (e.g. PLS) for linear relationships

Artificial neural networks (ANN) for non-linear black-box modeling

Important always to validate data, especially when many parameters are used in the models!!!