evaluation of a radial basis function neural network for the determination of wheat quality from...

Ž .Sensors and Actuators B 69 2000 348–358www.elsevier.nlrlocatersensorb

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

Phillip Evans a, Krishna C. Persaud a,), Alexander S. McNeish b, Robert W. Sneath c,Norris Hobson c, Naresh Magan d

a ( ) ( )Department of Instrumentation and Analytical Science DIAS , UnÕersity of Manchester Institute of Science and Technology UMIST , Chemistry Tower,Faraday Building, SackÕille Street, Manchester, M60 1QD, UK

b Osmetech plc, Electra House, Electra Way, Crewe, CW1 6WZ, UKc Silsoe Research Institute, Wrest Park, Silsoe, Bedford, MK45 4HS, UK

d Cranfield Biotechnology Centre, Cranfield UniÕersity, Cranfield, Bedfordshire, MK43 0AL, UK

Received 14 October 1999; accepted 8 February 2000

Abstract

Odorous contaminants in wheat have been detected using a conducting polymer array. A radial basis function artificial neural networkŽ .RBFann was used to correlate sensor array responses with human grading of off-taints in wheat. Wheat samples moulded by artificialmeans in the laboratory were used to evaluate the network, operating in quantitative mode, and also to develop strategies for evaluatingreal samples. Commercial wheat samples were then evaluated using the RBFann as a classifier network with great success, achieving a

Ž .predictive success of 92.3% with no bad samples misclassified as good in a 40-sample population 24 good, 17 bad using a training setŽ .of 92 samples 72 good, 20 bad . q 2000 Elsevier Science S.A. All rights reserved.

Ž .Keywords: Radial basis function; Artificial neural network; Electronic nose; Wheat quality; Sensors; Conducting polymer s

1. Introduction

Cereal grains, wheat in particular, represent one of themost important crops in global terms. There is a significantrequirement to ensure the organoleptic quality of suchcrops to ensure good commercial returns and ensure safety

w xof the product 1,2 .Often, the initial screening procedure is on the basis of

an inspector’s or buyer’s olfactory perception. However,this is a subjective measurement and is unsuitable inmodern agricultural economies where large financial gainsand loses can be made as a result of changing the gradingof a crop.

The European ISO605 standard and American UnitedŽ .States Department of Agriculture USDA grain grading

procedures in place relate to the odour determination ofgrains but these are vague and do not define any standard

) Corresponding author. Tel.: q44-161-200-4912; fax: q44-161-200-4879.

Ž .E-mail address: [email protected] K.C. Persaud .

or surrogate odours against which malodorous samplesmay be compared or against which inspectors and buyers

w xcan be trained 3–5 . Attempts have been made to qualifysome of the odour descriptors used in the industry todescribe malodorous samples but a consensus has not yet

w xbeen reached, even for the most typical malodours 6 .Consequently, there are strong commercial reasons fordeveloping an odour sensing system for the determinationof grain quality at points of transfer and purchase.

Allied to and perhaps more important than commercialconsiderations are the health and safety aspects involved in‘sniffing’ grain. Concurrent with the well-documented po-tential mycotoxin dangers are the chronic respiratory dan-gers associated with exposure to small particulates and

w xpossibly toxic volatile organic compounds 2 .Fungal contamination is the major problem leading to

downgrading or rejection of grain. This is usually as aresult of poor or inadequate storage after harvesting. Grainaffected in this way is often described as being musty orsour smelling. Early detection of fungally infected graincan in some cases be remediated and the crop saved.

0925-4005r00r$ - see front matter q 2000 Elsevier Science S.A. All rights reserved.Ž .PII: S0925-4005 00 00485-8

( )P. EÕans et al.rSensors and Actuators B 69 2000 348–358 349

Numerous studies have been carried out that have inves-tigated the association between fungal infections, storageconditions and the volatiles evolved by the various species

w xof fungi 7–9 .A number of workers have already reported attempts at

measuring cereal grain quality, specifically wheat, usingw xvolatile chemical sensor array based technologies 10–14 .

Our approach has been to evaluate wheat moulded undercontrolled conditions using a variety of sampling ap-proaches and then to develop suitable sensors and proto-cols for in-linerat-line monitoring of grain at the point oftransfer. For the task at hand, there is a need to map thesensor array response to human organoleptic parametersthat determine whether a particular wheat sample is to be

w xaccepted or rejected 6 . In addition, there is a requirementfor any system developed to provide a rapid and reliableanswer to the operatorrinspector. The system must berobust and reliable with a minimum of maintenance anddata interpretation and also operate in real time.

A variety of pattern recognition techniques includingneural networks may be applied to the classification ofdifferent odours and quantitative prediction and recogni-tion of unknown gases and odours. Backpropagation, amodel of multilayer perceptron networks, is probably themost widely used neural network paradigm. One disadvan-tage of this model is the difficulty in classifying a previ-ously unknown pattern that is not classified to any of theprototypes in the training set. This paper focuses on theapplication of a rapid data interpretation system using a

Ž .radial basis function artificial neural network RBFann tow xmap grain odour to human organoleptic perception 15–17 .

RBF networks train rapidly, usually orders of magni-tude faster than backpropagation, while exhibiting none ofbackpropagation’s training pathologies such as paralysis or

w xlocal minima problems. A RBF network 15 is a two-layernetwork where the output units form a linear combination

Ž .of the basis functions computed by hidden units Fig. 1 .The basis functions in the hidden layer produce a localised

Fig. 1. Schematic network to represent the basic RBFann architecture,where x , x . . . x are input neurones with the non-linear function1 2 m

Žembedded in the hidden layer, and S is the linear combiner l – l are1 c.the hidden layer outputs, l is the weighting factor .0

response to the input and typically, uses hidden layerneurones with Gaussian response functions:

y 2

F y sexp y 1Ž . Ž .2ž /b

where b is a real constant. The outputs of the hidden unitlie between 0 and 1; the closer the input to the centre ofthe Gaussian, the larger the response of the node.

The activation level of an output unit:

O sÝW O 2Ž .j ji i

where W is the weight from hidden unit i to output unitji

j, forms a linear combination of the non-linear basis func-tions.

Finding the centres, widths, and the weights connectinghidden nodes to the output nodes does the training in aRBF network. The performance of radial basis functionclassifiers is highly dependent on the choice of centres andwidth. This has been the focus of our attention in order to

w xoptimise RBF networks for odour classification 16,17 .For a minimum number of nodes, the selected centresshould be closely representative of the training data foracceptable classification.

The subtleties and complexities in the optimisation ofw xthe RBFann are dealt with elsewhere 16,17 . This paper

focuses on how the network may be applied to rapid anduseful classification of real data from electronic nose datato human psychophysical parameters.

2. Method

2.1. Moulding of wheat samples

ŽField-harvested wheat Silsoe Research Institute, Beds.,.UK was modified to different water contents using a

moisture sorption isotherm. Known amounts of water wereadded to grain samples in 500-ml flasks and stored at 48Cfor 24 h with regular shaking to obtain an even moisturecontent. The grain was then incubated at 258C and re-moved when visible moulding was noticed in the wettestsamples. This enabled a range of samples from unmouldedto completely moulded to be obtained. When not in use,the wheat was stored in a refrigerator at 58C to arrest thegrowth of the fungi present. Wheat was incubated at 18%,

Ž .20% and 25% moisture contents mc .After moulding, the wheat samples were divided into

two batches, one supplied ‘as moulded’ and the otherair-dried to a nominally constant moisture content of 15%.

Ž .Colony counts colony forming units, CFU were mea-sured using standard techniques as follows.

Ž .Sub-samples 1 or 5 g were weighed and placed in 9 or95 ml of 0.1% water agar diluent. A serial dilution serieswas carried out and 0.1 ml spread plated onto 2% malt

Žextract agar, and 2% malt, 10% salt low water availabil-

( )P. EÕans et al.rSensors and Actuators B 69 2000 348–358350

.ity using a sterile bent spreader. The plates were incu-bated at 258C for up to 7 days and the fungal coloniescounted where they appeared at between 10 and 100 perplate. The major dominant species were identified.

Ž .Commercial wheat samples UK grown were obtainedfrom a commercial grain trader via Silsoe Research Insti-tute. The samples were provided with some analytical datasuch as moisture content, protein content, density, Hagburgfalling number and screening measurements and were usedas received. Reject grains typically only contained datapertaining to their moisture content and density.

2.2. Odour measurement basis

The basis of the odour sensing system has previouslyw xbeen described elsewhere in Ref. 18 . The odour emitted

by the wheat samples was actively sampled by passingpre-conditioned air through the sample and then over a

Ž .sensor array see later . The array consisted of 32 conduct-ing polymer elements. Each element possessing a broadspecificity with overlap between the responses of all 32elements. The sensor array response was recorded eachsecond and data transmitted to an IBM compatible PC.

ŽRaw data was processed using Osmetech software v3.1,.Osmetech, Crewe, UK to produce normalised patterns for

input to the RBFann.

2.3. Radial basis function artificial neural network

We have developed a RBFann adapted for rapid odourclassification. This was evaluated for its ability to discrimi-nate between mouldy and good wheat samples. It wasinitially trained using data from wheat samples producedby moulding under controlled conditions. Later, sampleswere obtained from a commercial supplier to provide amore representative population of wheat encountered un-der normal conditions and the correlation between the

inspectors evaluation and the sensor array output evalu-ated.

2.4. Sensing apparatus

2.4.1. Manual sampler for eÕaluating laboratory mouldedwheat

Ž .An Osmetech formerly Aromascan 32-sensor arraymounted inside an A8S sample station for extra tempera-

Ž .ture control was used Osmetech, Crewe, UK . WheatŽ .samples 50 g were weighed into screw neck Pyrex tubes

Ž .Fisher Scientific, Loughborough, UK fitted with phenolicplastic lids when not in use. A sparging system was usedto transfer wheat odour form the sample tube to the sensorarray. The sensor array was set to sample reference air for1 min, then to sample wheat odour for a period of 2 min.This was followed by a wash cycle of 100% RH air at308C for 1 min and finally, a return to sampling reference

Ž y1 .air for 1 min all at 160 ml min . The reference air wasmaintained at a humidity of 30% RH at 308C and wasswitched to the sample tube during the wheat odour mea-surement period. The A8S was held at 308C while thearray itself was held at 358C throughout. In total, includingthe 5-min sample equilibration time at 308C, each vial took10 min to be processed. From the raw data, odour patterndatabases were constructed using averaged time slices overthe last 20 s of the odour exposure or using three 5-s slicestaken at the end of the exposure to the sample, as appropri-

Ž .ate. Principal components analysis PCA maps were cal-culated for comparison with the RBFann output.

2.4.2. Autosampler arrangement for eÕaluating commer-cial wheat

An Osmetech 32-sensor array coupled with autosamplerfacility was used to obtain the data presented for the

Ž .commercial wheat samples Osmetech, Crewe, UK . Sam-ples were 10 g aliquots weighed into standard sample vialsŽ .22 ml, Osmetech, Crewe, UK and crimp sealed with

Fig. 2. Sammon map of wheat moulded under controlled conditions when mapped along with standards.


Table 1Colony counts for mould on the surface of the wheat moulded undercontrolled conditions

CFUs

Ž .Moisture content % Log counts10

First batch12.5 6.81915 5.49518 6.37520 7.39725 6.765

Second batch6.8195.495

14.2 6.86915.7 7.43718.2 7.284

aluminium hole caps lined with polytetrafluroethyleneŽ .PTFE rsilicone septa. Prior to each set of samples, two

Ž .distilled water vials 5 ml each were run to ensure thatcontamination from any previous samples used were notcarried over during the measurement. A distilled water

Ž .sample 5 ml was also run at the end of each batch. Thesensor array was set to sample for a period of 3 min, at aflow rate of 60 ml miny1, followed by a wash cycle ofdistilled water for 4 min at 160 ml miny1. The referenceair was maintained at a humidity of 30% RH at 308C whilethe wash was again 100% RH air at 308C. The platen wasmaintained at 308C, the sample loop at 408C and thetransfer line was at 508C throughout the experiment. Thesensor array and data processing arrangements were asdescribed in Section 2.4.1.

2.4.3. EÕaluation of RBFann using measurements of artifi-cially moulded wheat

The RBFann can be used in quantitative or qualitativemode. The network was used in quantitative mode andtrained to predict values from the sensor outputs for three

Ž .possible classification scenarios. These were: i angle ofŽ .clusters from the 0,0 origin of a Sammon map see Fig. 2 ;

Ž . Ž .ii log of CFU on the wheat samples; and iii the10

moisture content of the wheat when moulded.The data used for the trial of the RBFann was based

upon 20 wheat sample runs using the manual samplingA8S system. The samples were four aliquots each of12.5%, 18%, 20% and 25% mc laboratory moulded wheat.The data were taken as three 5-s slices at the end ofsampling period rather than averaged over the period as forthe autosampled wheat data, thereby creating three sets ofdata per run and a total of 60 data sets for the evaluation intotal. The data order was randomised and the first 15samples taken as the training set in the first instance andthe remaining 45 samples used as the unknowns. Afternetwork evaluation, the data was re-sorted into order to aidclarity when presenting the results.

3. Results

3.1. Artificially moulded wheat

At 12% and 15% mc the fungi were almost entirelyfield fungi with a very small population of Penicilliumspp. present, whereas at 18% and 20% mc the predominantfungi present were Penicillium spp. At 25% mc, there was

ŽFig. 3. Distribution of fungal species in wheat moulded under controlled conditions NB no accurate identifications were carried out at 12.5% but these.were almost entirely field fungi with only a trace of Penicillium spp. present .


Fig. 4. Comparison of RBFann training errors for each of the four classes and three classification scenarios used for wheat moulded under controlledŽ .conditions open bars are for 15 data points in the training set and the hatched bars are for 45 data points in the training set .

Ž .a broader distribution of fungi see Table 1 and Fig. 3with Acremonium sp. dominating.

3.2. EÕaluation of RBFann using measurements of artifi-cially moulded wheat

Artificially moulded wheat was used to evaluate theperformance of the network operating in quantitative mode.The data presented in Fig. 4 is for the error in prediction ofthe learning set vs. the actual values for the three dataclasses evaluated, while the data presented in Fig. 5 is thecorresponding error output for the unknowns presented tothe network after the initial training phase. Data is pre-

sented for two training and unknown scenarios. The openbars represent a training set of 15 data points and anunknown set of 45 data points whereas the grey barsrepresent the reversed case of 45 training data points and15 unknown data points. Note that the data was evaluatedin a random order and resorted after processing for clarity.

The data in Fig. 6a–b represents the success of theRBFann in predicting the differences between wheat sam-ples moulded under controlled conditions using naturallyoccurring fungi. The dotted lines represent the error inprediction associated with each class while the grey barsare the predicted values based upon the sensor valuesproduced on exposure. The data presented in Fig. 6a are

Fig. 5. Comparison of RBFann predictive errors for each of the four classes and three classification scenarios used for wheat moulded under controlledŽ .conditions open bars are for 15 data points in the training set and the hatched bars are for 45 data points in the training set .


Ž . Ž .Fig. 6. a Prediction of wheat moisture content using RBFann artificially moulded wheat, based upon a training set of 15 samples . The black barŽ .represents the actual value while the hatched bars represent the predicted values from the RBFann. b Prediction of wheat moisture content using RBFann

Ž .artificially moulded wheat, based upon a training set of 45 samples . The black bar represents the actual value while hatched bars represent the predictedvalues from the RBFann.

for a training set of 15 random samples and an unknownset of 45 random samples while Fig. 6b is the reversed

Ž .scenario 45 training samples and 15 unknown samplesand the nominal value that the network fitted the data to isillustrated by the first, dark bar for each class. The wheatdata used was that described in Figs. 2 and 3 and Table 1and was uncorrected moisture content only. The differenti-ation between moulded and non-moulded wheat samples is

clearly defined in this case. However, the differentiationbetween sub-classes of mouldy is less clear.

3.3. EÕaluation of RBFann using measurements of com-mercial wheat samples

Commercial wheat samples were run on the autosam-pling system. The data presented in Table 2 illustrates the

Table 2Physical data for commercial wheat samples

Sample profiles Good wheat Bad wheat

Average Maximum Minimum Average Maximum Minimum

Ž .Moisture content % 14.4 15.6 13.2 16.6 18.9 15.4Ž .Density kgrHL 76.3 81.3 71.3 71.1 72.9 65.5


Fig. 7. RBFann output when exposed to commercial wheat samples when operated in classifier mode.

link between increasing moisture content and loss in wheatquality and the concurrent drop in wheat density with lossof quality. The trends in these values for good and badwheat clearly corroborate the predictions of the neural

Ž .network relating to wheat quality see Fig. 7 . The tablewas derived from the data supplied with the commercialwheat samples.

The data presented in Figs. 7 and 8 was collected over a5-day period, using the autosampler configuration, withruns of 40 good and 8 bad samples on day 1, 32 good and12 bad on day 2 and 24 good and 17 bad on day 5. Thefirst two data sets were then used to train the RBFannwhile the third set was used as the unknown. One-third ofthe samples tested was classified as being bad by thewheat supplier. Samples were run in a random order andwere 4=10 g aliquots from 34 commercial wheat samplesŽtwo data collections were corrupt and were not used in the

Fig. 8. PCA map of the same commercial wheat data presented in Fig. 7.

.final analysis . The zone of uncertainty referred to in Fig.8 is an arbitrary value rather than a calculated one intendedto illustrate the area of overlap between the clusters ofwheat defined as good or bad. In this area, some samplescannot be classified as belonging clearly to one class or theother.

Fig. 7 depicts the predictive output of the RBFannoperating as a classifier network based upon good wheathaving an arbitrary value of 1 and the bad wheat having anarbitrary value of 2. Three good samples were misclassi-fied by the network, while no predictive errors arose forthe bad wheat samples, representing a classification errorof 7.3% overall for 48 unknown samples. The arbitrarydefinition of wheat quality, in terms of human perception,has the consequence that a degree of uncertainty is inher-ent in any description derived or used.

4. Discussion

This paper describes one aspect of an ongoing project toproduce an electronic nose based technology that may beapplied to the organoleptic determination of wheat quality.To produce such an instrument, a broad array of factorsneeds to be considered. These include sampling protocols,obtention of representative samples, data acquisition,processing and classification, reliability and relevance toconsumer demand. The data acquisition, processing andinterpretation aspects are dealt with here along with thesampling protocols.

Any grain odour measurement system must be robustand simple to use without the need for extensive operatortraining and expertise to gain useful results. To meet thesedemands, a simple and robust data processing and classifi-cation system is required. The RBFann approach providesa solution to this challenge. Other neural network architec-


tures require long training times and can sometimes reachfalse minima in the training cycle. The use of the RBFanncircumvents this and provides for very rapid training androbust predictive power.

4.1. Artificially moulded wheat

Table 1 and Fig. 3 demonstrate that differing moisturecontents favour certain fungal species. This is clearlydemonstrated by the change of dominant species from thewhite yeasts at 15% mc to the Penicillium spp. at 18% and20% mc and then to Acremonium sp. dominating at 25%.Consequently, it may be expected that different volatileswill be produced according to the original moisture contentof the wheat. The proliferation of CFU with increasingmoisture content is also significant. A concurrent increasein volatile production would also be expected as a conse-quence of the sheer number of fungi active on the surfaceof the grains.

Two profiles are shown in Fig. 3 since half of eachmoisture content batch was later air-dried to try to producewheat of variable quality at similar moisture content. Un-fortunately, this was not possible, primarily as a result ofthe metabolic processes of the fungi driving the moisturecontent upwards.

4.2. EÕaluation of RBFann using measurements of artifi-cially moulded wheat

4.2.1. Small training set, large unknown setDuring the network training stage, the following pro-

Žfiles in the predictive errors as an indication of the overall. Ž .accuracy were observed see Figs. 4 and 5 and Table 3 :

Žthe lowest errors and by association the highest correla-.tion were found when the data were related to the cluster

Ž .position relative to the Sammon map origin Fig. 2 ; boththe CFU count and the moisture content data also corre-lated strongly.

Mapping the data to CFU counts is patently flawedsince the counts for 12.5% and 25% mc wheat were

Žsimilar even though the mould species were totally differ-. Ž .ent Table 1 . These wheat samples lie at opposite ends of

the mouldiness scale and as such, should be well sepa-rated. Based upon this data, while displaying a strongcorrelation, the output was not considered as useful fordetermining the fitness of the wheat samples.

ŽThe data based upon Sammon map data originally.including standard based data was strongly correlated

with errors of less than 5% in the training matrix. Usingthis model, the good wheat was very well separated fromthe mouldy wheat with no confusion or overlap betweenthe two broad categories. The three sub-classes withinmouldy were also well separated. There was also a verystrong correlation in the data between moisture content andthe array output suggesting that humidity was the predomi-nant force in operation when discriminating between theclasses. However, from Tables 1 and 2, moisture content,and by inference humidity, is clearly a significant factor inthe classification. The bad commercial wheat samples areclearly ‘wetter’ than the good ones. This is not unexpectedsince fungal or microbial activity leading to loss in qualitywould be associated with increased volatile production.Allied to this, poor storage conditions inevitably lead towetter wheat which would encourage spoilage fungi andbacteria to proliferate.

Observation of the correlation between the sensor out-put and the various classifiers used suggests that all threepredictive models were successful although using CFUcount is not a useful approach.

When applied to the unknown data set, the networkmade reasonable predictions for the data although theerrors observed were much larger than those in the training

Ž .set Table 3 . One data set in particular presented thenetwork with consistent problems. The 18% mc mouldywheat was consistently associated with the 20% mc mouldy

Ž .wheat Fig. 6a . The training data may have been lesstypical of the set and hence, larger deviations from theexpected values would result in the network fitting data toan incorrect class. However, it is worth considering thatboth sets of wheat had high Penicillium spp. counts and assuch, may have generated very similar volatile profilesŽnot withstanding the high bias toward moisture content

.observed .Data based upon CFU counts showed relatively low

errors but this data is flawed since it could not accuratelydistinguish between 25% mc mouldy wheat and 12.5% mcgood wheat.

The Sammon mapped data agreed well with the actualvalues with very clear demarcation between good and badwheat with no regions of uncertainty when plotted. Forsub-classification of bad grain into sets based on moisturecontent, only the 18% mc wheat was misclassified. Simi-

Table 3Ž .Average error RMS in prediction for each attempted fit by the RBFann for wheat moulded under controlled conditions

Ž .Attempted fit Average error RMS in prediction from true value

Training set Unknown set Training set Unknown setŽ . Ž . Ž . Ž .15 points 45 points 45 points 15 points

Measured distance 0.007 0.014 0.002 0.008CFU count 0.005 0.078 0.005 0.015Wheat moisture content 0.021 0.089 0.009 0.002


larly, network output based on moisture content of thewheat also correlated very well with the actual valuealthough this set produced the largest errors when mappingthe data.

4.2.2. Large training set, small unknown setAfter the first trial, the training and unknown sets were

swapped to estimate the effect of using a much largertraining set and trying to fit a smaller set of unknown data

Ž .points to the output Figs. 4 and 5 and Table 3 . The mostnoticeable outcome in using a larger training data set wasthe reduction in the overall error. A much better fit ofpredicted to actual data was observed when comparing thesmaller training data set with the larger one. Even so, thepredicted output for 18% mc mouldy wheat still displayeda high discrepancy from the actual value. Generally, theeffect of having a much larger training set was a tighter

Ž .prediction of all four classes Fig. 4 .When applied to the smaller unknown set, the error in

Ž .prediction was much smaller as expected Figs. 5 and 6b .This was most noticeable for the 18% mc mouldy wheatset but was much reduced for all of the predictive data.Overall, the predictive relevancy of the data remained thesame with correlation to moisture content and CFU countimproved while that of the Sammon map based improvedslightly but was still superior to the other two scenarios.

The principal purpose of the second set was to illustratethe value of a larger training set when making predictions.This was clearly illustrated although the ability to discrimi-nate between good and bad wheat was not particularlyenhanced in this example. The separation between classesof bad wheat was however significantly improved suggest-ing that as the prediction becomes more complex, a morecomprehensive training set is required to be more confi-dent in the predicted classification of unknown data setsŽ .Fig. 6a–b .

4.2.3. Summary of artificially moulded wheat obserÕationsThe strongest correlation found between sensor re-

sponse and network output was when the data was corre-lated to cluster position on the Sammon map. In both

Ž .training sets reported Fig. 4 , the errors were very lowwith correspondingly good success in predicting unknown

Ž .data Fig. 5 .Data mapped to CFU count was previously identified as

being irrelevant to the desired outcome in defining thewheat as good or bad. In this way, the network cannotdetermine the basis upon which any observed discrimina-tion exists. Caution therefore must be exercised in makingjudgements on the value of data as a result of correlationsdetermined using the network.

The necessity of using an adequate training set isclearly illustrated by the data presented. The prediction

Ž .based upon the smaller training set Fig. 6a is clearbetween good and bad wheat but is unable to differentiatebetween wheats moulded at different moisture contents.

The predictions based upon the larger training data setŽ .Fig. 6b are much better defined with respect to themoisture content of moulding but the classifications arenot entirely unambiguous between 18% and 20% mc wheatsamples. The ambiguity between the 18% and 20% mcwheat samples may be related to the high penicillia counts

Ž .observed in both samples see Fig. 3 . While total separa-tion may be expected if the differentiation were purely onmoisture content parameters, the similarity in predicted

Ž .output and the closeness of the mapping by PCA Fig. 2suggest that fungal volatiles are also significant to themeasurement. Hence, the two samples would be expectedto map similarly even though a higher CFU count was

Ž .observed for the wheat moulded at 20% mc Table 1 .No weighting or biasing adjustments were made to

improve the predictive power of the network. Ignoring thebasis of discrimination, it can be seen that this particularnetwork is very effective at quantitative prediction of data,especially when a suitable training set is used during theinitial training.

4.3. EÕaluation of RBFann using measurements of com-mercial wheat

Commercial wheat samples were obtained via SilsoeResearch Institute from a commercial grain trader. Thesamples were previously stored at ambient temperature insealed plastic bags in the trader’s retained sample store.The grains were screened at Silsoe before testing andclassified as being good or bad based upon odour percep-tion. Most of the rejected wheat samples sorted in this waywere also marked as ‘DNQ’ and had a reduced level ofanalytical information on the bags.

The samples were run in a randomised order over 3days in an attempt to eliminate any memory effects orsensor fatigue that may have arisen from exposing thearray to correlated wheat runs.

The RBFann can clearly be seen to be effective atdifferentiating between samples where PCA mapping pro-

Ž .duces ambiguous results Figs. 7 and 8 . Two of the threeRBFann misclassified samples lie in the zone of uncer-

Ž .tainty the other being the isolated sample . The isolatedgood data point is the first exposure to wheat and is asystem artefact. The predictive accuracy of the networkmore than favourably compares with previous studies where

w x83% approximated to a human success rate 10 .The PCA mapped data for the first 2 days of the trial

was similar to that presented in Fig. 8 for the last day.Without prior knowledge of the wheat, the data would beimpossible to distinguish as two separate clusters by PCAŽ .or Sammon mapping . It is clear from this data that goodand bad grains, as defined in this study by olfactoryperception, are not discrete groupings. It is probable thatdifferent people would draw the distinction between theclasses at different points to those used here depending ontheir perception of the malodour. However, based on the


data, it seems likely that the ‘zone of uncertainty’ wouldremain fairly consistent appearing as an interface betweentwo nominal good and bad clusters. Although the arraydrifted very slightly as a result of systematic variation, theRBFann was able to make its prediction without resort tocompensation for this effect. However, further work isunderway to evaluate how this drift may be accounted forin a bid to further improve the accuracy and reliability ofthe prediction.

5. Conclusions

The RBFann provides a simple and robust method forcorrelating chemical vapour sensor array data with humanpsychophysical parameters. The system can be operated ina qualitative or quantitative manner. In this example, thedata used was clearly classified with few errors.

The size of the training set was found to be an impor-tant factor in determining the accuracy of any predictionsmade using the network. Reversing the training and un-known sets in the example clearly improved the predictivepower of the network to this particular problem.

When employed as a classifying network, the RBFannwas very successful in predicting the quality of wheat on asimplistic good or bad scale. Based upon a commerciallysupplied data set, the predictive success was greater than92% with no samples that were described as being bad,misclassified by the network.

Acknowledgements

The authors wish to acknowledge the Ministry of Agri-Žculture Fisheries and Food for funding this project project

.number MAFF CTD 9701 and the contribution of Osme-tech in terms of loans of equipment and supply of exper-tise.

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Biographies

Ž .Dr. Phillip EÕans received his BSc Hons. in Applied Chemistry fromKingston University in 1992 and his PhD in the ‘investigation of colloidalconducting polymer films for gas sensing applications’ from the Univer-

Ž .sity of the West of England Bristol , in 1998. At UWE, he worked onnumerous sensing related projects including the development of systemsfor spoilage detection in potatoes and Serrano hams. He is currentlyemployed as a Research Associate in DIAS, UMIST investigating ‘TheDetection of Contaminants in Grain and Infestations in Bulk and In-tran-sit Grain by Sensors and Physical Methods.’

Ž .Dr. Krishna C. Persaud, BSc Hons. Biochemistry 1976 University ofNewcastle-upon-Tyne, UK; MSc Molecular Enzymology 1977, Univer-sity of Warwick, UK; PhD Olfactory Biochemistry 1980 University ofWarwick, UK. He has research interests in the area of olfaction fromphysiology to chemistry and has been involved in the development of gassensor arrays for sensing odours based on conducting polymers. He hasworked in olfactory research in Italy and the USA, and was appointed asLecturer, Department of Instrumentation and Analytical Science, Univer-sity of Manchester Institute of Science and Technology, UK in 1988, andis currently a Senior Lecturer in the Department.


Ž .Dr. Alexander S. McNeish MRSC C Chem. is currently the applicationsteam leader at Osmetech. He graduated from UMIST, Manchester with a

Ž .BSc Hons. in Chemistry specialising in Analytical Chemistry, in 1985.After industrial posts at Guinness and Fisons Instruments, which includedorganoleptic and chromatographic characterisation of samples, he com-pleted his PhD at the University of Liverpool investigating ‘chlorinatedmaterials present in the biota of the Mersey Estuary.’ Prior to hisemployment at Osmetech, he was a Research Associate at LancasterUniversity in the School of Environmental and Biological Sciences.

Ž .Robert W. Sneath I. Eng. M.I. Agr. Eng. , is currently a Senior ResearchOfficer at Silsoe Research Institute, Bedfordshire. He graduated fromSouth Bank University, London in 1972 with a Grad. Dip. in Power Eng.and a HND in Mech. Eng. His current interests lie in odour and emissioncontrol from aerobic waste treatment, methane and nitrous oxide emis-sions from livestock buildings, olfactometry and electronic nose research.

Ž . Ž .Norris Hobson received his BSc Mech. Hons. from the University ofBath in 1973 and is currently employed at Silsoe Research Institute. Hismain activities are in the harvesting and processing of cereals, investiga-tions of industrial and alternative crops and development of the wheatspecific electronic nose system.

Prof. Naresh Magan is currently Professor of Applied Mycology andAcademic Director at the Institute of BioScience and Technology, Cran-

Ž .field University. He received his BSc Hons. in Botany in 1976 and MScin Plant Pathology in 1977, both from Exeter University. He received hisPhD in Agricultural Botany from Reading University in 1982. He hasextensive biotechnology experience and has much expertise in the field ofspoilage fungi, mycotoxins and grain quality.

evaluation of a radial basis function neural network for the determination of wheat quality from...

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