Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data

Phillip Evans; Krishna C. Persaud; Alexander S. McNeish; Robert W. Sneath; Norris Hobson; Naresh Magan

doi:10.1016/S0925-4005(00)00485-8

Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data

Phillip Evans, Krishna C. Persaud, Alexander S. McNeish, Robert W. Sneath, Norris Hobson, Naresh Magan

Research output: Contribution to journal › Article › peer-review

Abstract

Odorous contaminants in wheat have been detected using a conducting polymer array. A radial basis function artificial neural network (RBFann) was used to correlate sensor array responses with human grading of off-taints in wheat. Wheat samples moulded by artificial means in the laboratory were used to evaluate the network, operating in quantitative mode, and also to develop strategies for evaluating real samples. Commercial wheat samples were then evaluated using the RBFann as a classifier network with great success, achieving a predictive success of 92.3% with no bad samples misclassified as good in a 40-sample population (24 good, 17 bad) using a training set of 92 samples (72 good, 20 bad).

Original language	English
Pages (from-to)	348-358
Number of pages	10
Journal	Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers
Volume	69
Issue number	3
DOIs	https://doi.org/10.1016/S0925-4005(00)00485-8
Publication status	Published - 25 Oct 2000

Access to Document

10.1016/S0925-4005(00)00485-8

Cite this

Evans, P., Persaud, K. C., McNeish, A. S., Sneath, R. W., Hobson, N., & Magan, N. (2000). Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data. Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers, 69(3), 348-358. https://doi.org/10.1016/S0925-4005(00)00485-8

@article{538c1dbc3f094b2b84a080747b3e4da9,

title = "Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data",

abstract = "Odorous contaminants in wheat have been detected using a conducting polymer array. A radial basis function artificial neural network (RBFann) was used to correlate sensor array responses with human grading of off-taints in wheat. Wheat samples moulded by artificial means in the laboratory were used to evaluate the network, operating in quantitative mode, and also to develop strategies for evaluating real samples. Commercial wheat samples were then evaluated using the RBFann as a classifier network with great success, achieving a predictive success of 92.3% with no bad samples misclassified as good in a 40-sample population (24 good, 17 bad) using a training set of 92 samples (72 good, 20 bad).",

author = "Phillip Evans and Persaud, {Krishna C.} and McNeish, {Alexander S.} and Sneath, {Robert W.} and Norris Hobson and Naresh Magan",

note = "Times Cited: 28 6th International Symposium on Electronic Noses (ISOEN 99) SEP 20-22, 1999 TUBINGEN, GERMANY",

year = "2000",

month = oct,

day = "25",

doi = "10.1016/S0925-4005(00)00485-8",

language = "English",

volume = "69",

pages = "348--358",

journal = "Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers",

issn = "0925-4005",

publisher = "Elsevier BV",

number = "3",

}

Evans, P, Persaud, KC, McNeish, AS, Sneath, RW, Hobson, N & Magan, N 2000, 'Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data', Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers, vol. 69, no. 3, pp. 348-358. https://doi.org/10.1016/S0925-4005(00)00485-8

Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data. / Evans, Phillip; Persaud, Krishna C.; McNeish, Alexander S. et al.
In: Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers, Vol. 69, No. 3, 25.10.2000, p. 348-358.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data

AU - Evans, Phillip

AU - Persaud, Krishna C.

AU - McNeish, Alexander S.

AU - Sneath, Robert W.

AU - Hobson, Norris

AU - Magan, Naresh

N1 - Times Cited: 28 6th International Symposium on Electronic Noses (ISOEN 99) SEP 20-22, 1999 TUBINGEN, GERMANY

PY - 2000/10/25

Y1 - 2000/10/25

N2 - Odorous contaminants in wheat have been detected using a conducting polymer array. A radial basis function artificial neural network (RBFann) was used to correlate sensor array responses with human grading of off-taints in wheat. Wheat samples moulded by artificial means in the laboratory were used to evaluate the network, operating in quantitative mode, and also to develop strategies for evaluating real samples. Commercial wheat samples were then evaluated using the RBFann as a classifier network with great success, achieving a predictive success of 92.3% with no bad samples misclassified as good in a 40-sample population (24 good, 17 bad) using a training set of 92 samples (72 good, 20 bad).

AB - Odorous contaminants in wheat have been detected using a conducting polymer array. A radial basis function artificial neural network (RBFann) was used to correlate sensor array responses with human grading of off-taints in wheat. Wheat samples moulded by artificial means in the laboratory were used to evaluate the network, operating in quantitative mode, and also to develop strategies for evaluating real samples. Commercial wheat samples were then evaluated using the RBFann as a classifier network with great success, achieving a predictive success of 92.3% with no bad samples misclassified as good in a 40-sample population (24 good, 17 bad) using a training set of 92 samples (72 good, 20 bad).

U2 - 10.1016/S0925-4005(00)00485-8

DO - 10.1016/S0925-4005(00)00485-8

M3 - Article

SN - 0925-4005

VL - 69

SP - 348

EP - 358

JO - Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers

JF - Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers

IS - 3

ER -

Evans P, Persaud KC, McNeish AS, Sneath RW, Hobson N, Magan N. Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data. Sensors and Actuators B: Chemical: international journal devoted to research and development of physical and chemical transducers. 2000 Oct 25;69(3):348-358. doi: 10.1016/S0925-4005(00)00485-8

Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data

Abstract

Access to Document

Fingerprint

Cite this