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Chemical sensing employing plant electrical signal response-classification of stimuli using curve fitting coefficients as features

Chemical sensing employing plant electrical signal response-classification of stimuli using curve fitting coefficients as features
Chemical sensing employing plant electrical signal response-classification of stimuli using curve fitting coefficients as features
In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models—Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.
2079-6374
Chatterjee, Shre Kumar
aaa84ab8-3968-42b1-a9e1-d2a2e03c7b0a
Malik, Obaid
16899d3c-005e-48f1-b7c0-8040449de79e
Gupta, Siddharth
cba92d0e-8d39-4b18-9936-0ca842da8baa
Chatterjee, Shre Kumar
aaa84ab8-3968-42b1-a9e1-d2a2e03c7b0a
Malik, Obaid
16899d3c-005e-48f1-b7c0-8040449de79e
Gupta, Siddharth
cba92d0e-8d39-4b18-9936-0ca842da8baa

Chatterjee, Shre Kumar, Malik, Obaid and Gupta, Siddharth (2018) Chemical sensing employing plant electrical signal response-classification of stimuli using curve fitting coefficients as features. Biosensors, 8 (3), [83]. (doi:10.3390/bios8030083).

Record type: Article

Abstract

In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models—Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.

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Accepted/In Press date: 6 September 2018
e-pub ahead of print date: 10 September 2018

Identifiers

Local EPrints ID: 423192
URI: http://eprints.soton.ac.uk/id/eprint/423192
ISSN: 2079-6374
PURE UUID: 8c27a38a-1dc4-45ad-9039-e912018f4b8b
ORCID for Obaid Malik: ORCID iD orcid.org/0000-0001-9014-2675

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Date deposited: 19 Sep 2018 16:30
Last modified: 15 Sep 2021 02:12

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