Evaluation of an artificial olfactory system for grain quality discrimination

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LWT 40 (2007) 1815–1825 www.elsevier.com/locate/lwt

Evaluation of an artificial olfactory system for grain quality discrimination S. Balasubramaniana, S. Panigrahib,, B. Kottapallic, C.E. Wolf-Halld a

Department of Agricultural & Biosystems Engineering, North Dakota State University (NDSU), USA Department of Agricultural & Biosystems Engineering, NDSU, 1221 Albrecht Blvd., PO Box: 5626, Fargo, ND 58105, USA c Department of Veterinary & Microbiological Sciences, NDSU, USA d Department of Veterinary & Microbiological Sciences, NDSU, USA

b

Received 25 December 2005; received in revised form 19 December 2006; accepted 21 December 2006

Abstract A commercially available Cyranose-320TM conducting polymer-based electronic nose system was used to analyze the headspace from stored barley samples. Three types of barley samples were analyzed, namely, clean barley, naturally Fusarium infected barley and Fusarium inoculated clean barley. The barley samples were stored at moisture contents of 13, 18, 20 and 25 g of water/100 g sample. The raw signals obtained from the electronic nose system were pre-processed by various signal-processing techniques to extract area-based features. Principal component analysis was subsequently performed on the processed signals to further reduce the dimensionalities. Classification models using linear (LDA) and quadratic discriminant analyses (QDA) were developed using the extracted features. The performance of the developed models was validated using leave-1-out cross validation and bootstrapping method. The models classified the barley samples stored into two groups based on the ergosterol content, i.e., ‘‘acceptable’’ (ergosterol content o3.0 mg/g) and ‘‘unacceptable’’ (ergosterol content X3.0 mg/g). Overall, the total maximum classification accuracy obtained was 86.8% by both LDA and QDA when leave-1-out cross-validation was used. By bootstrapping validation the maximum total classification accuracy obtained was 86.4% and 86.1% respectively, by QDA and LDA. The study proves that there is potential in using an electronic nose system for indicating mold spoilage in stored grains, and necessitates future studies in this direction. r 2007 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved. Keywords: Food safety; Ergosterol; Barley; Grain storage; Electronic nose; Intelligent sensors

1. Introduction The uses of electronic noses for evaluating the quality of food products as a means of non-destructive olfactory sensing are becoming widespread, and moreover, they could be fast and reliable. The underlying hypothesis for developing electronic nose-based sensors for food safety/ quality evaluation is that the metabolic activities of microorganisms in a food product produce metabolites in the form of gas, solid and liquids. Sensing the gaseous metabolites (chiefly, volatile organic compounds) present in the headspace of the food product could be used to determine the quality of the given food product. Electronic Corresponding author. Tel.: +701 231 7270; fax: +701 231 1008.

E-mail address: [email protected] (S. Panigrahi).

nose systems can be used to sense the headspace gas/ gaseous metabolites. Thus, a multidisciplinary research project has been developed at the North Dakota State University to evaluate the capabilities of different types of electronic nose systems for evaluating quality and safety of food products. Electronic nose systems do not give any specific information about the compounds causing the aroma nor about their identity (Siegmund & Pfannhauser, 1999). However, with the aid of appropriate mathematical techniques, like artificial neural networks (ANNs) or statistical methods, the electronic nose could recognize the aroma pattern from a particular sample and help in distinguishing it from other samples (Eklov, Johansson, Winquist, & Lundstrom, 1998; Srivastava, 2003). Conducting organic polymer sensors (CP) can be used as

0023-6438/$30.00 r 2007 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2006.12.016

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detectors/transducers in electronic nose systems and they exhibit a change of resistance when the sensors adsorb gas (Schaller, Bosset, & Escher, 1998). This change of resistance is sensed and delivered as the detector output. Conducting polymer sensors are very sensitive to polar compounds (Schaller et al., 1998) and can be used at ambient temperatures (Annor-Frempong, Nute, Wood, Whittington, & West, 1998). The use of electronic nose systems for evaluating the quality of stored grains has been reported (Abramson, Hulasare, York, White, & Jayas, 2005; Keshri & Magan, 2000; Magan & Evans, 2000; Olsson, Borjesson, Lundstedt, & Schnurer, 2000). Damp storage conditions could cause the growth of fungi like Penicillium, Aspergillus and Fusarium species (Demyttenaere et al., 2002; Olsson, Borjesson, Lundstedt, & Schnurer, 2002). These fungi produce mycotoxins, which could be detrimental to living organisms when ingested (Jestoi, Ritieni, & Rizzo, 2004; Nielsen, 2003). Also, the growth of molds in grains reduces the nutritional quality of the grains, and subsequent decrease in germination results in economic loss (Schnurer, Olsson, & Borjesson, 1999). Aspergillus and Penicillium species, prevalent in indoor environments (Nielsen, 2003) contribute to storage problem. Fusarium species infects cereal crops under field conditions (Miedaner & Perkowski, 1996) producing a toxin called deoxynivalenol (DON). Some of the commonly adapted techniques to determine mold contamination are to estimate the number of colony forming units (CFU) and by determining the amount of ergosterol (a sterol specific to fungi) present (Schnurer et al., 1999). However, these methods are quite time consuming and laborious. Electronic nose systems could accurately and rapidly identify mold metabolic activity in stored grains by analyzing the headspace volatiles in the grains. Appropriate data analysis and pattern recognition techniques needs to be applied to construct reliable algorithms for interpreting the acquired signal or smell patterns for classification or prediction purposes. The smell patterns obtained from the electronic nose detectors can be analyzed using various statistical and neural network tools. Pattern recognition techniques like principal component analysis (PCA), partial least squares (PLS), functional discriminant analysis (FDA), cluster analysis, fuzzy logic or ANN have been used for data analysis in electronic nose applications (Haugen & Kvaal, 1998). It also becomes critical to build a reliable and robust classification model that could perform satisfactorily in real world conditions. Several techniques such as leave-k-out (leave-1-out) and bootstrap methods have been used for this purpose (Panigrahi, Doetkott, & Marsh, 1998). Panigrahi, Balasubramanian, Gu, Logue, and Marchello (2006) used linear and quadratic discriminant analysis techniques to classify spoiled beef samples from unspoiled ones based on the resistance generated by the electronic nose system. They reported highest classification accuracies of 97.4% and 98.5% for meat samples stored at 10 and 4 1C, respectively,

when quadratic discriminant analysis and bootstrapping technique was used for classification. Fusarium infections of small grains have been a persistent problem for the Upper Midwest in recent years. Barley is one of the most common grains used for malt. In recent years, barley has been affected significantly by Fusarium (McMullen, Jones, & Gallenberg, 1997). The fungal infections cause mycotoxin contaminations of the grain. Trichoethecene mycotoxins, including deoxynivalenol (DON or vomitoxin), nivalenol (NIV), T-2 toxin, HT-2 toxin and diacetoxyscirpenol (DAS) as well as the estrogenic mycotoxin, zearalenone (ZEN) have been detected in Fusarium infected barley from the Upper Midwestern United States (Schwarz, Casper, & Barr, 1995). Detecting the fungal-specific marker sterol, ergosterol, could help in determining the extent of fungal contamination in grains. Early detection of contaminated stored grains could immensely benefit the processors and growers. Thus, the objective of this study was to evaluate the performance of an electronic nose system for identifying the quality of stored barley (sound barley and barley samples inoculated with Fusarium graminearum) based on the ergosterol content. The specific objectives were to: (i) develop methods for acquiring olfactory signatures from the headspace of stored barley samples, and (ii) develop and evaluate the performances of statistical-based classification models for quality determination of barley. 2. Materials and methods 2.1. Sample preparation For this study, three types of barley samples were included: naturally Fusarium-infected barley, clean barley (no detectable DON), and Fusarium inoculated clean barley. The Fusarium inoculated clean barley was prepared by inoculating barley with spores of F. graminearum culture (106 spores/g of barley). An automatic grain moisture meter (Motomco Instruments, Paterson, NJ) was used to determine the initial moisture contents of the barley samples. For each treatment, 75 g of barley sample was weighed out, and divided into two sterile and clean mason glass jars. One of the two jars was used for headspace analysis using gas chromatography and mass spectrometer (GC–MS), and the other jar was used for electronic nose analysis. Barley samples were adjusted to four different moisture contents (13, 18, 20, and 25 g of water/g of sample) by adding sterile distilled water. For Fusarium inoculated clean barley, the barley samples were inoculated with Fusarium after adjusting the moisture content in the samples. It has to be mentioned that the naturally infected barley samples were not stored at moisture content of 13 g of water/g of sample. This is because the naturally infected barley was already at a moisture content of 18 g of water/g of sample. In order to prevent the loss of volatiles, the glass jars were sealed tightly with two layers of Paraffin film (Pechiney Plastic

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Packaging, Menasha, WI) following which, the jars were closed with lids containing pre-punched holes. The holes in the metal lids allowed easy penetration of needles through the Paraffin film for collecting headspace. The barley samples were stored in an environmental chamber (Caron Products and Services, Inc., Marietta, OH) at 25 1C and 85% humidity for 4 weeks. 2.2. Electronic nose analysis

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Table 1 Signal acquisition parameters for electronic nose system Operation

Time, s

Baseline correction (Laboratory air intake) Sample draw-in Laboratory air purge Sample purge Manual purge

10 120 5 30 240

Total run for one sample

405

TM

A commercially available Cyranose-320 electronic nose (Cyrano Sciences, Pasadena, CA) was used to obtain the smell patterns from the headspace of the barley samples. This electronic nose contains an array of 32 conducting polymer detectors, which were non-specific to any particular headspace gas. Each detector has a certain degree of affinity towards specific chemical or volatile compounds. When the detector is exposed to a chemical, the chemical is adsorbed by the sensing element (detector); subsequently, a change in resistance is experienced by the sensor that is proportional to the amount of chemical absorbed by the conducting polymer surface. This change in resistance over a specific time interval constituted the signal or the response of a given detector. The acquired signal is stored as an output file, which can be exported to MS Excel (Microsoft Corporation, Seattle, WA). Out of the 32 detectors, four (sensors 5, 6, 23, and 31) were sensitive to polar compounds (like water vapor) present in the headspace due to the respiration in the barley. 2.3. Electronic nose sampling protocol In total, the barley samples were stored for a period of 4 weeks. The headspace from the stored barley samples were sampled every day using the electronic nose system and a gas chromatograph for the first 7 days of the week. In the subsequent weeks, the headspace of the stored barley samples was sampled on the first day of the week. Hence, after the first week of sampling, the subsequent headspace sampling was carried out on the 8th, 15th and 22nd days of storage. Thus, the total sampling period was for 10 days. On each day there was one replication for each treatment of barley sample (three treatments in total; namely Fusarium inoculated barley, naturally Fusarium infected barley and clean barley) and for every moisture content level, giving a total of 11 barley samples for each instrument. Hence, there were a total of 110 barley samples during every experimental period. The experiment was repeated three times. For the electronic nose analysis, the headspace was analyzed for 120 s. Prior to sampling the headspace of the barley samples; the lab air was passed through Drierite (anhydrous calcium sulfate which acts like a filter to remove the moisture from the lab air) and some cotton balls. This was done to purge the sensors. Table 1 shows the data acquisition parameters followed for acquiring the detector responses to barley headspace. It has to be mentioned that

though the P-module E-nose system did have in-built data processing capabilities, they were disabled in order to collect only the raw detector responses. After headspace analysis, the samples were freeze-dried and stored at 18 1C until they were analyzed for ergosterol content. 2.4. Headspace volatiles analysis using gas chromatography Headspace volatile samples from the stored grain samples for each treatment were collected by adsorbing them on to a solid phase micro-extraction (SPME) filament. A PDMS/DVB/Carboxen SPME filament was used for extraction of the headspace volatiles. The filament was exposed for 15 min at room temperature for adsorption of volatiles. The adsorbed volatile analytes were next analyzed using a HP 5890 Gas Chromatograph (GC) (Agilent Technologies Inc., Palo Alto, CA) by directly desorbing the volatiles into the GC injector for 10 min. Separation of the volatiles by the GC (HP 5890) was carried out using a fused capillary column (30  0.32 mm  1 mm, J and W Scientific Inc., Folsom, CA). Operation parameters included: injector temperature of 180 1C; detector temperature of 180 1C, and oven temperature was raised from initial 40–180 1C at a rate of 5 1C/min. Once the oven temperature reached 180 1C, it was maintained at this temperature for 10 min until the analysis was completed. The flow rate of helium was 1.0 ml/min. Running an internal standard and determining the peak area from the GC quantified the extracted volatiles. Standard curves with different concentrations were prepared for the volatile compounds and compared with the chromatograms obtained from the headspace of the incubated barley. 2.5. Ergosterol analysis The Ergosterol content is used as an assessment for fungal biomass. Ergosterol was determined using a modification of the method described by Young (1995). Freeze-dried barley samples were ground using a coffee mill (model KSM-2, Braun, Lynnfield, MA). The ground samples were weighed (100 mg) into 15 ml culture test tubes (Pyrex, Cole-Palmer Instrument Company, Vernon Hills, IL), and the samples were treated with a solution of 2 ml of methanol (EMD, Gibbstown, NJ) and 0.5 ml of aqueous

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2.6. Electronic nose data preprocessing

Resistance (Raw signals), ohms

sodium hydroxide (2 M). The tubes were tightly sealed with a teflon-lined screw cap and placed into 200 ml plastic bottles. The tightly sealed plastic bottles were next placed inside a microwave (Goldstar, Model MA-653 M, Huntsville, AL) which operated at 2450 MHz (650 W maximum output). These samples were heated in the microwave for 35 s at 50% of the microwave power in two steps: (i) in the first step, the samples were heated for 20 s and cooled for 7 min; (ii) in the second step, the samples were again heated for 15 s. This two-step heating approach was implemented since heating the sample directly by microwave for 35 s could cause the solvent mixture to leak out of the test tubes. Following the heating, the samples were air cooled for exactly 15 min. Subsequently, the test tubes were opened and the cooled contents were neutralized with 1 M aqueous hydrochloric acid. The neutralized extracts were next extracted with pentane (3  ca. 2 ml) within the test tubes. The pentane extracts were transferred into another culture tube and evaporated to dryness by heating under nitrogen. The residues were dissolved in 1000 mL with isopropanol in sealed vials. The samples were analyzed using high performance liquid chromatography (Waters, Model 2695, Milford, MA). The column used for achieving separation was a cyano-based reverse-phase column (CN 30505-1546WT, 5 mm particle size, 30 nm pore size, 150  4.6 mm, YMC Inc., Wilmington, NC). The mobile phase comprised of hexane (EMD, Gibbstown, NJ) (90%) and isopropanol (EMD, Gibbstown, NJ) (10%) and the flow rate used was 1.2 ml/min. The temperature of the column was held at 25 1C and the ergosterol in the samples was detected at 282 nm and verified by the spectra generated by the photodiode array detector (Model 996, Waters, Milford, MA). Ergosterol standards were dissolved in methanol and were used to generate a standard curve based on peak areas.

The acquired signals from the P-module E-nose system (Fig. 1) in the form of change in resistance over time were first pre-processed using binomial smoothing, averaging and normalization techniques to reduce noise (Panigrahi et al., 2006). After binomial smoothing was carried out, the averaging technique further smoothed the data using a 21 data reduction technique. These processing operations were carried out using appropriate functionalities available in GRAMS/32 software (Thermo Galactic, NH). The smoothed sensor data were then normalized using the following equation: Rnm ¼ ðRi  Rmin Þ=Rmin ,

(1)

where, Rnm is the normalized response of a detector, m, at a given instant, i, Ri is detector response at a given instant, i, Rmin is minimum detector response obtained between time intervals of 0 and 30 s, where, zero seconds represents the start of purging with ambient air. The area under the curve was determined using the following relationship: As ¼ DT

i¼130 X

Rnmi ,

(2)

i¼T min

where As is the area under the curve for the unscaled signal of a detector, m; DT is change in time interval between two successive signals, (i1) and i; Rnmi is normalized response of a detector, m, at an instant, i; Tmin is time in s, at which the minimum detector response (Rmin) occurs between a time interval of 0 and 30 s. Fig. 2 shows the data pre-processing steps undertaken towards noise removal and extraction of the relevant features. For each data set, 32 areas (features) corresponding

3.388 3.386 3.384 3.382 3.38 3.378 3.376 Rmin

3.374 3.372

Tmin 20 10

0

40

60

80

100

120 130

Time, seconds A

B

A = 10 s (sensor purge for baseline correction) B = 120 s (headspace sample collection from meat package) Rmin = Minimum Sensor Response Fig. 1. A typical raw signal/response of a sensor from the P-module E-nose system (Cyranose-320TM).

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Barley samples

Electronic nose

1819

Reclassify ergosterol data as either 1 or 2 based on the classification criteri a. For ergosterol analysis '1' = acceptable (erg < 3.0 μg/g) '2' = unacceptable (erg ≥ 3.0 μg/g)

Raw sensor signals

Pre-Processing (Grams-32 s/w) 1. Binomial Smoothing (points=2) 2. Average (2→1)

Run principal component analysis (PCA) for the 28 or 32 detector area features

Produce principal components input file for classification analysis For each detector find Rmin (minimum resistance value) between 0-30 s and record the time (Tmin) where Rmin occurs Discriminant Analysis

Find (Ri-Rmin)/Rmin for each detector (normalization process); where Ri is the detector response at a given instant 'i' Linear

Find area under the curve (between Tmin and 130 s) for all pre-processed curves and re-group them by sample number

Pre-processed file ready for data analysis

Dataset containing all 32-area features

Remove detector (s5, s6, s23, and s31) areas to prevent these high variation detector areas from dominating the analysis resulting in a dataset containing 28 area features

Leave-1-Out Cross Validation

1.LDA with Leave-1-Out cross validation 2. QDA with Leave-1-Out cross validation

Quadratic

Bootstrap

1.LDA with Bootstrap 2.QDA with Bootstrap

Calculate and compare classification accuracies of the 4 models

Fig. 2. Data collection, processing, and statistical analysis methods used for developing the classification models.

to the 32 detectors in the sensor array of the electronic nose system and 28 areas (features) corresponding to 28 detectors (excluding the four polar sensitive detectors) were extracted. The responses from the four polar-sensitive detectors were excluded while developing the classification model in order to eliminate/minimize the effect of humidity on the detector responses. Hence, the classification models were developed for each data set using the responses from all the 32 detectors and the responses from the 28 detectors. This could help in understanding the effect of water vapor present in the barley samples on the overall

performance of the electronic nose system. Principal Component Analysis (PCA) was performed on these extracted features so that the extracted principal components accounted for equal to or greater than 99% of the total variation in the original data set. 2.7. Classification model development Two types of discriminant analysis models (linear and quadratic) were used to classify the stored barley samples into two groups based on the ergosterol contents. Barley

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samples with ergosterol values X3.0 mg/g were classified as ‘‘unacceptable’’, and samples with ergosterol contento3.0 mg/g were classified as ‘‘acceptable’’. This classification scheme was based on the information found in prior literature (Muller & Boley, 1993). In their report, grain with ergosterol concentration of 2–3 mg/g was considered to be of good quality. Procedure ‘‘PROC DISCRIM’’ available in SAS (version 8.2; Statistical Analysis Software, SAS Institute, Cary, NC) was used for developing linear discriminant and quadratic discriminant analysis-based models. PROC DISCRIM calculates the generalized squared distance. Each observation (response variable) is then assigned a probability of belonging to a given group based on the generalized squared distance from the group mean. The response variable can thus be grouped into one of the two groups. If the classification is based on the pooled covariance matrix, the resulting discriminant function is linear (Rao, 1973). If the classification criterion is based on the individual within-group covariance matrices, it results in a quadratic discriminant function. 2.8. Model validation techniques To increase the robustness of our classification models, we used two different validation techniques. They are: (1) leave-1-out and (2) bootstrap analysis. 2.8.1. Leave-1-out cross-validation In the leave-1-out method, for a given data set with n observations, one observation is randomly removed. The model is developed using the rest of the observations (n1) and is called the ‘‘training or calibration data set’’. The single observation is now used as a validation/test data set. This process of data separation is continued to create n training and testing data sets. For each pair of training and testing data set, the model is developed and validated. The performances of the model (classification accuracies) on n number of validation (test) data sets were determined, and the average classification accuracy was noted. The total classification accuracy of the model is expressed as the total number of correctly classified samples (both ‘‘contaminated’’ and ‘‘uncontaminated’’) divided by the total number of samples. This total classification accuracy is expressed as a percentage. 2.8.2. Bootstrapping Bootstrap analysis is an emerging and relatively novel concept to validate a given model in a rigorous manner. Unlike other validation techniques like leave-1-out method and the train-test validation method, it is possible to estimate the unknown error using bootstrapping (Schaffner, 1994). Hence, a robust classification model could be developed where the uncertainties could be accounted for in the model. This is because in bootstrapping the models are validated by creating a number of test data sets

with replacement from the original data set (Efron & Tibshirani, 1993). In our case, we created 1000 data sets (each with sample size equal to the original data set size) from the original data set. These 1000 data sets were next used to validate the classification models developed, and the overall classification accuracy was determined. Also, the unknown error was estimated, and this error was subtracted from the overall classification accuracy to obtain the ‘‘true’’ overall classification accuracy. The bootstrap procedure was carried out as mentioned in our previous published paper (Panigrahi et al., 2006). For this study, a total of three data sets from three experiments were used for the development and evaluation of the classification models for ergosterol analysis. A fourth data set was formed by combining all the observations from the three experiments. The total number of barley samples in the three experiments was 91, 106 and 108. The combined data set had a total number of 305 samples. 3. Results and discussions 3.1. Headspace volatiles analysis using gas chromatograph (GC) The GC analysis revealed the presence of noted fungal volatile compounds such as 2-methyl-1-propanol, 3-methyl1-butanol, 1-octen-3-ol, and 2-methoxy-4-ethyl-phenol. Production of volatile compounds 2-methyl-1-propanol, 3-methyl-1-butanol, and 1-octen-3-ol has been previously reported in various studies involving the presence of molds in stored grain (Borjesson, Stollman, & Schnurer, 1990, 1992; Sinha, Tuma, Abramson, & Muir, 1988; Tuma, Sinha, Muir, & Abramson, 1989) in general, and stored barley in particular (Abramson, Sinha, & Mills, 1983; Jelen, LatusZietkiewicz, Wasowicz, & Kaminski, 1997). The presence of these volatiles could be an indicator of the presence of molds in the stored grain. In addition, a wide variety of compounds belonging to various functional groups including alcohols, aldehydes, esters, ketones, acetates, and furans were also identified from the headspace of the barley samples. 3.2. Ergosterol analysis The ergosterol content in the sound barley samples ranged from 0.045 to 20.093 mg/g over the entire storage period for the barley samples at different moisture contents. For the naturally occurring Fusarium samples, the ergosterol contents ranged from 0.016 to 20.691 mg/g. The Fusarium inoculated barley samples had an ergosterol content ranging from 0.043 to 18.857 mg/g. The production of ergosterol increased with increase in storage time at all moisture contents for the barley samples (Fig. 3). This observation is similar to the findings reported by Abramson et al. (2005) for wheat.

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a Ergosterol, ug/g

20 13

15

18

20

25

10 5 0 0

5

10 15 Days of Storage

20

25

b Ergosterol, ug/g

20 13

18

20

25

15 10 5 0 0

5

10

15

20

25

Days of Storage

c Ergosterol, ug/g

20 18

20

25

15 10 5 0 0

5

10

15

20

25

Days of Storage Fig. 3. Effect of moisture and storage time on ergosterol production in (A) clean barley, (B) Fusarium-inoculated clean barley, and (C) Naturally Fusarium-infected barley.

3.3. Correlation between ergosterol and volatiles emitted For most of the moisture contents, the production of volatiles, 2-methyl-1-propanol and 3-methyl-butanol (to some extent 1-octen-3-ol) were significantly (Po0.05) and positively correlated (0.62–0.92) with ergosterol production in all the three types of barley samples. In most of the cases (volatiles), the correlation coefficient was higher at the highest moisture content. Microorganisms (fungi) have more access to the substrate at higher moisture (Frazier & Westhoff, 1997). Abramson et al. (1983) attributes the higher production of volatile compounds to the high moisture content in the grains. Omelianski (1923) reported that the odors could be unique to the related individual species producing them. 2-methyl-1-propanol, 3-methyl-butanol, and 1-octen-3-ol compounds are predominantly associated with Penicillium and Aspergillus species (Borjesson et al., 1990, 1992; Sinha et al., 1988). 2-methoxy-4-ethyl-phenol was only detected in the Fusarium inoculated barley samples and, detected

only at moisture contents of 20 and 25 g of water/g of sample, respectively. Jelen et al. (1997) reported that 2-methoxy-4-ethyl-phenol was specific to F. graminearum; hence, it may not be surprising that it was detected in the Fusarium inoculated barley samples, as these samples were prepared by artificially inoculating barley with F. graminearum culture. Larsen (1997) reported that volatile metabolites could be used as chemical markers to investigate the fungal infestation of seeds. The positive correlation obtained between the ergosterol and volatile metabolites in the present study further strengthens the justification of using ergosterol content for indicating fungal mold growth in stored grains. 3.4. Principal component analysis plots Figs. 4 and 5 show sample plots of the first two principal components, PC1 and PC2. Though they provide some indication of separation of good and bad barley samples,

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4.00 Good

Bad

3.00

Scores, PC2

2.00 1.00 0.00 -1.00 -2.00 -3.00 -4.00 -3.50

-3.00

-2.50

-2.00

-1.50

-1.00 -0.50 Scores, PC1

0.00

0.50

1.00

1.50

2.00

Fig. 4. Typical plot of first two principal component scores (PC1, PC2) from all 32 sensor readings obtained for barley samples during experiment 1 (good ¼ ergosterol p3 mg/g, bad ¼ ergosterol X3 mg/g).

4.00 Good

Bad

3.00

Score, PC2

2.00 1.00 0.00 -1.00 -2.00 -3.00 -4.00 -3.50

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

Score, PC1 Fig. 5. Typical plot of first two principal component scores (PC1, PC2) from 28 sensor readings obtained for barley samples during experiment 1 (good ¼ ergosterol p3 mg/g, bad ¼ ergosterol X3 mg/g).

it is evident that these two components alone are not sufficient to provide better or complete separation. We found that, for both the cases (data from 32 or 28 detectors), more than two principal components accounted for our desired 99% of total variation. Thus, we postulated that simultaneous consideration of multiple (42) principal components could provide better classification. Hence, use of multivariate statistical data analysis techniques such as discriminant analysis was further justified to discriminate between the desired two classes.

3.5. Electronic nose analysis 3.5.1. Classification of barley samples based on ergosterol content using discriminant analysis and leave-1-out validation Table 2 shows the classification accuracies obtained by LDA and QDA with leave-1-out cross validation. The highest total classification accuracy obtained by LDA with leave-1-out cross validation was 86.8%. QDA with the same cross validation technique also yielded the same maximum total classification accuracy (86.8%). However,

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Table 2 Classification accuracies obtained by leave-1-out cross validation for barley samples (based on ergosterol content) No. of sensors

Data set

No. of samples

Total

28 32 28 32 28 32 28 32

Experiment Experiment Experiment Experiment Experiment Experiment Combined Combined a

1 1 2 2 3 3

91 91 106 106 108 108 305 305

Acceptablea

68 68 74 74 75 75 217 217

Unacceptableb

23 23 32 32 33 33 88 88

By linear discriminant analysis (LDA)

By quadratic discriminant analysis (QDA)

Cross validation accuracy (%)

Cross validation accuracy (%)

Acceptable

Unacceptable

Total

Acceptable

Unacceptable

Total

88.2 82.3 87.8 87.8 86.7 86.7 81.1 82.5

82.6 82.6 75.0 75.0 66.7 69.7 61.4 70.4

86.8 82.4 84.0 84.0 80.6 81.5 75.4 79.0

83.8 91.2 87.8 89.2 82.7 88.0 82.5 80.2

73.9 73.9 75.0 75.0 57.6 63.6 51.1 63.6

81.3 86.8 84.0 84.9 75.0 80.6 73.4 75.4

Acceptable (good barley) ¼ o3.0 mg/g. Unacceptable (bad barley) ¼ X3.0 mg/g.

b

in the case of QDA the features from all 32 detectors was used to obtain the highest classification accuracy, while in the case of LDA the maximum classification accuracy was obtained when the features (area under the curve) was used from 28 detectors. The total classification accuracies were above 75% and 73% respectively, by LDA and QDA in all cases. In all cases (data sets), using features from 32 sensors instead of 28 sensors marginally improved (about 1% to 7% increase) the total classification accuracies obtained when QDA and leave-1-out method was used. This increase in the total classification accuracy observed indicate that the moisture content in the stored grain samples could have a slight impact on the classification models developed. The four extra sensors used (apart from the 28 other sensors) in the classification models were more sensitive to polar solvents. Olsson et al. (2002) used a different kind of commercially available electronic nose system to predict and classify the Ochratoxin A content present in barley grains. The electronic nose system used by Olsson et al. (2002) consisted of 10 metal oxide semiconductor field effect transistor (MOSFET) sensors, 6 tin-oxide based Taguchi sensors and 1 carbon-di-oxide (CO2) monitor. The responses of the sensors to the headspace volatiles were analyzed by PLS method. Olsson et al. (2002) reported a classification accuracy of 81.1% for naturally infected barley samples. Our experiments involved three types of barley samples, namely clean barley, naturally infected barley and inoculated barley. Hence, a larger variation was involved in our experiments. Moreover, the different moisture content levels of the grain samples added to the complexity of the model. The results so far obtained by our experiments indicates the potential of the applicability of electronic noses for indicating mold growth in grain samples without any prior knowledge of the moisture content level and storage period level. A closer look at the within-group classification accuracies showed that the maximum classification accuracy

obtained by LDA and leave-1-out validation was 88.2% and 82.6%, respectively for classifying the ‘‘acceptable’’ and ‘‘unacceptable’’ barley samples. While using QDA and leave-1-out cross-validation the maximum classification accuracy for classifying the ‘‘acceptable’’ and ‘‘unacceptable’’ samples were 91.2% and 75%, respectively. The reduced classification accuracy observed in classifying the ‘‘unacceptable’’ barley samples could be attributed towards the lower number of samples, which fit the criteria of ‘‘unacceptable’’ as compared with the number of ‘‘acceptable’’ samples present in the data set. 3.5.2. Classification of barley samples based on ergosterol content using discriminant analysis and bootstrapping The classification accuracies obtained by the discriminant classification models (validated by bootstrapping) are presented in Table 3. The total maximum classification accuracy obtained by LDA and QDA (bootstrapping validation) were 86.1% and 86.4%, respectively. The total classification accuracies were above 70% in all data sets when both LDA and QDA with bootstrapping were used. Combining the data sets reduced the total classification accuracies obtained. The total accuracy obtained for LDA and leave-1-out method was 79%. Similarly, bootstrap with LDA provided a total classification accuracy of 77.6%. In both the validation methods, the combined data sets provided the lowest classification accuracies as compared to the individual data set. This might be due to variations among samples inherent in biological samples such as barley. These results were similar to those obtained from discriminant analysis with leave-1-out cross-validation. Using 32 features (instead of 28 features) did not always provide the highest classification accuracies with either QDA or LDA. Olsson et al. (2000) reported a classification accuracy of 92.5% while using PLS regression analysis to analyze the data from an electronic nose in response to the headspace volatiles from wheat. Olsson et al. (2000) used an electronic

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Table 3 Classification accuracies (based on refined estimate of true total error) obtained by bootstrap based validation for barley samples (based on ergosterol content) No. of sensors

Data set

No. of samples

Total

28 32 28 32 28 32 28 32

Experiment Experiment Experiment Experiment Experiment Experiment Combined Combined

1 1 2 2 3 3

91 91 106 106 108 108 305 305

Acceptablea

68 68 74 74 75 75 217 217

Unacceptableb

23 23 32 32 33 33 88 88

By linear discriminant analysis (LDA)

By quadratic discriminant analysis (QDA)

Cross validation accuracy (%)c

Cross validation accuracy (%)

Acceptable

Unacceptable

Total

Acceptable

Unacceptable

Total

87.0 87.8 86.5 87.8 88.0 85.3 82.3 82.4

83.9 83.4 72.9 72.8 64.4 68.0 61.1 72.6

85.6 86.1 79.8 80.4 76.3 76.7 71.8 77.6

84.6 90.6 88.9 89.0 82.1 87.1 82.3 80.6

76.9 79.5 74.1 73.3 57.6 59.3 58.1 62.5

81.7 86.4 81.7 81.6 70.0 73.6 70.7 72.0

The average of the total accuracies is reported. a Acceptable (good barley) ¼ o3.0 mg/g. b Unacceptable (bad barley) ¼ X3.0 mg/g. c Bootstrap procedure had 1000 training sets of samples size equal to the number of samples in the original dataset, and the original data set was tested against these bootstrap trained models.

nose system comprising of 10 MOSFET sensors, 6 tinoxide-based sensors and 1 CO2 monitor. They analyzed naturally infected wheat samples alone and had a smallersized data set (40 samples). Our study focused on a much broader application where we also included Fusarium inoculated barley samples with the naturally infected barley samples. This included a greater variation in our data sets and could be one of the reasons for the lower classification accuracy observed in our experiments. Borjesson, Eklov, Jonsson, Sundgren, and Schnurer (1996) used an electronic nose system consisting of 10 MOSFET sensors, 6 tin-oxide sensors and an infrared detector monitoring CO2 to classify wheat, barley and oat samples. They obtained a classification accuracy of approximately 90% when the samples were classified into two groups namely, good or bad based on the evaluation of two-grain inspectors. Borjesson et al. (1996) used back propogation neural networks (BPNN) to build the classification models. The use of non-parametric data analysis tools like ANNs could result in better classification models since these techniques are self-learning. We postulate that our classification results would improve if non-parametric classification tools like ANNs were employed to our data sets. Our future work will focus on the use of ANNs for building the classification models. When the within-group classification accuracies were analyzed, the maximum classification accuracies obtained by LDA with bootstrapping validation were 88% and 83.9%, respectively, for classifying the ‘‘acceptable’’ and ‘‘unacceptable’’ barley samples. For the QDA model with bootstrapping validation the maximum classification accuracy in classifying the ‘‘acceptable’’ and ‘‘unacceptable’’ samples were 90.6% and 79.5%, respectively. Variation in the number of ‘‘acceptable’’ and ‘‘unacceptable’’ barley samples in the data sets could be attributed towards the

variation in the classification accuracies observed between the two groups. 4. Summary and conclusions The maximum total classification accuracy observed for the barley samples by leave-1-out cross validation was 86.8%. This was obtained by both linear discriminant and quadratic discriminant analyses. Using bootstrapping, the maximum classification accuracy obtained was 86.4% with QDA. Combining the data sets reduced the total classification accuracy obtained. Thirty-two area-based features (instead of 28 area-based features) at times improved the total classification accuracy obtained. Maximum classification accuracies of 91.2% and 75%, respectively, were obtained for classifying the ‘‘acceptable’’ and ‘‘unacceptable’’ barley samples based on the ergosterol content (leave-1-out and QDA). In several cases, the ‘‘acceptable’’ barley samples were classified with an accuracy of above 80%. Thus, this study proves the proof-of-the concept of using electronic nose system for mold spoilage indication in stored barley samples of different moisture contents. Future work involves the use of non-parametric data analysis techniques like ANNs and genetic algorithms to build the classification models, and the use of additional features to be included in the model. PLS-based model can also be developed to evaluate the effect of water content in headspace gad on the responses of the detectors of the electronic nose. Acknowledgments The authors would like to express their profound gratitude to the United States Department of Agriculture-Cooperative

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