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Food Control 44 (2014) 64e71

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Food Control

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Discrimination of moldy peanuts with reference to aflatoxin usingFTIR-ATR system

Hande Kaya-Celiker a, P. Kumar Mallikarjunan a,*, David Schmale III b, Maria Elisa Christie c

aBiological Systems Engineering, Virginia Tech, Blacksburg, VA, USAb Plant Pathology, Physiology & Weed Science, Virginia Tech, Blacksburg, VA, USAcOffice of International Research, Education, and Development (OIRED), Virginia Tech, Blacksburg, USA

a r t i c l e i n f o

Article history:Received 24 September 2012Received in revised form24 March 2014Accepted 25 March 2014Available online 3 April 2014

Keywords:FTIR-ATRPeanutAspergillus flavusAspergillus parasiticusPLS regressionDiscriminant analysis

* Corresponding author. Seitz Hall Room 308, 155Blacksburg, VA 24061, USA. Tel.: þ1 540 231 7937; fa

E-mail addresses: hande@vt.edu (H. Kaya-CK. Mallikarjunan), dschmale@vt.edu (D. SchmaE. Christie).

http://dx.doi.org/10.1016/j.foodcont.2014.03.0450956-7135/� 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

This study demonstrated the potential use of Fourier transform infrared spectroscopy, coupled withattenuated total reflectance unit (FTIR-ATR) for determination of aflatoxigenic and non-aflatoxigenicstrains of Aspergillus flavus and Aspergillus parasiticus invasion in peanuts. Threshold mold density onpeanut paste samples was 2.7 Log CFU/g peanut corresponding to legislative limiting aflatoxin (AF) levelof 20 ppb. Classification was performed to separate the “Acceptable” stream (AF � 20 ppb) from so called“Moldy” (20 < AF < 1200 ppb) and “Highly Moldy” (>1200 ppb). All of the samples (n ¼ 164) wereclassified correctly when discriminant analysis technique was employed. Second threshold value was setat 300 ppb aflatoxin to further sort out the samples in the “Moldy” class into mildly (which can be usedfor feed) or highly toxic (which has to be discarded). Correct separation was observed at 98.5% with onlyone misclassified sample. Growth profiles of both strains of A. flavus and A. parasiticus were interpretedwith respect to spectral changes. Even when spectral alterations for aflatoxin presence were not clearlyidentifiable, similar secondary metabolites of both aflatoxigenic species led to clusters in distance plotsand showed the potential usage of the developed method to separate safer peanuts (e.g. AF � 20 ppb) ina lot when implemented. Partial least squares (PLS) regression models were developed to predict AF levelwith maximum correlation coefficient of determination (R2C ¼ 99.98% for both AFeproducing Aspergillusspp.). The fingerprint region (1800e800 cm�1) was used for regression analysis and corresponding bandswere interpreted.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Aspergillus flavus and Aspergillus parasiticus are among the mostintensively studied of all fungi since they both introduce a greatdanger by producing the most potent mycotoxin, namely aflatoxin(AF), and due to their role as a causative agent in food spoilage(Bennett, 2010). These opportunistic pathogens of crops regularlyoccur in all-inclusive food supplies, but are especially seen in oil-rich seeds (Diener et al., 1987), like peanuts. A. flavus can befound in non-soil environment, yet A. parasiticus is more commonsoil inhabiting saprophyte (Horn, 2003), and this property domi-nates prevalence of A. parasiticus in peanuts (Diener et al., 1987).There are four major AFs and they are AFB1, AFB2, AFG1, and AFG2.

Ag Quad Lane, Virginia Tech,x: þ1 540 231 3199.eliker), kumar@vt.edu (P.le), mechristie@vt.edu (M.

Among them AFB1 is the most common AF found in nature (Klich,2007). A. flavus produces AFB1 and AFB2 in wide rangingamounts, so that some strains may appear as non-toxigenic toextremely potent ones; but A. parasiticus, which can synthesizeboth B and G AFs, is commonly found in the form of aflatoxigenicstrains (Horn, 2003).

Considering health consequences, the importance of AFcontamination is obvious. The International Agency for Research onCancer (IARC) lists naturally occurring mixtures of AFs as Group Icarcinogen (IARC, 1993). Exposure to large doses (>6000 mg) of AFwas reported to cause acute toxicity with adverse consequences,even death; whereas, exposure to small doses for extended timeperiods is carcinogenic (Wagacha & Muthomi, 2008). Many AFassociated ailments and outbreaks with large number of fatalitieshave been reported (Krishnamachari, Nagarajan, Bhat, & Tilak,1975; Lye, Ghazali, Mohan, Alwin, & Nair, 1995) since it was firstimplicated to an incidence of the turkey X disease (Spensley, 1963).And among them, the biggest aflatoxicosis outbreak (resulting 317known cases and 125 deaths) occurred in rural Kenya in 2004

H. Kaya-Celiker et al. / Food Control 44 (2014) 64e71 65

where the source was AF- contaminated homegrown maize (Lewiset al., 2005). However, in developed countries, aflatoxin incidents infood and thus, the occurrences of acute human aflatoxicosis arerare. As a result, studies have leaned towards carcinogenic potentialof aflatoxin when consumed for prolonged times in diet. Thesestudies shows a strong correlation between liver cancer and thepresence of hepatitis and aflatoxin (Williams et al., 2004). Apartfrom public health outcomes, economical losses are significant,since, the estimated mycotoxin contaminated crops amounts to25% of the world’s food crops (WHO, 1999). Besides, in the UnitedStates, $932 million of annual loss from discarding the mycotoxincontaminated crops and consequent losses for quality controlmeasures summing to additional $466 million reported annually(Dohlman, 2004). Specifically, annual cost of AF to farmers ofsoutheast U.S. peanut industry was reported to be $25 million(Lamb & Sternitzke, 2001). The total cost of AF contamination in allextent is hard to assess; yet, considering the reduction in cropyields, livestock productivity, and both domestic and internationaltrade may reveal the concerns about the AF and fungal attack oncrops (Murphy, Hendrich, Landgren, & Bryant, 2006). Correspond-ingly, concerns about the AF hazard on human health urged theauthorities inmany countries to implement an accepted level for AFwhen consumed in food or feed. Even, this value varies fromcountry to country. Food and Drug Administration (FDA) has set thelimiting value at 20 ppb for all foods and 300 ppb in feed in UnitedStates (USFDA, 2009).

There are several methods to evaluate the toxicity of fungus-infected crops (Gilbert & Anklam, 2002). Yet, the use of opticalmethods for detecting and separating crops severely contaminatedby fungi have been gaining attention by researchers since suchmethods offer a rapid and non-destructive assessment opportunity.Studies mainly focused on changes (differences or ratios) in spec-tral characteristics of fluorescence (Farsaie, McClure, & Monroe,1977; McClure & Farsaie, 1980; Tyson & Clark, 1974), trans-mittance, and reflectance in the visible and near infrared regions(Hirano, Okawara, & Narazaki, 1998; Pearson, Wicklow, Maghirang,Xie, & Dowell, 2001; Pearson, Wicklow, & Pasikatan, 2004). Visualobservations based on exhibition of bright, greenish-yellow (BGY)fluorescence under UV light (Shotwell & Hesseltine, 1981) was oneof earliest presumptive AF detection test in seed lots. This UV-fluorescence based method is not a good indicator when theinfection is not on the surface (Pasikatan & Dowell, 2001).

Many techniques have been developed for the study of moldinfestation on crops using Fourier transform infrared spectroscopy(FTIR) (Gordon, Jones, McClelland, Wicklow, & Greene, 1999;Greene et al., 1992; Kos, Lohninger, & Krska, 2002, 2003;Mirghani, Man, Jinap, Baharin, & Bakar, 2001). As an alternative tothe old transmission methods, reflectance techniques have beensuggested for food matrices. One of these techniques, attenuatedtotal reflectance (ATR), was utilized in FTIR investigations and usedby many researchers (Kos et al., 2002, 2003; Kos, Lohninger,Mizaikoff, & Krska, 2007; Mirghani et al., 2001). ATR system of-fers a short analysis time and simpler sample preparation (such as,grinding for homogeneity of samples for 3 min, in the currentstudy). Two of such studies were conducted by the same researchgroup to detect Fusarium graminearum infection on corn withrespect to deoxynivalenol (Kos et al., 2002) and ergosterol (Kos,et al., 2003) concentrations. AF level in peanuts were also exam-ined using ATR accessory (Mirghani et al., 2001) for the four majorAFs separately.

The purpose of the current study is to develop a classificationmodel, which can separate peanut samples having little or noamount (�20 ppb) of aflatoxin. In addition, moldy peanuts were tobe further separated as reasonably toxic, which may be used asanimal feed, or highly toxic, which needs to be discarded. The

potential use of FTIR-ATR system to quantify the AF level in peanutpaste samples was also intended.

2. Material and methods

2.1. Preparation of Aspergillus spp. invaded peanut paste samples

The peanut samples were acquired from local supermarkets andstored at 4 �C in re-closable plastic bags until analysis. Peanuts werekept in 1.0% NaOCl for 3 min and rinsed thoroughly with distilledwater to ensure the surface sterilization and mold-free samples.Seeds were placed in moist chambers after contaminating withspore suspensions of 4 different Aspergillus spp., which are: A. flavusNRRL 1957 (non AF producing mutant), A. flavus NRRL 3357 (AFproducer), A. parasiticus NRRL 21369 (non AF producing UV colormutant), A. parasiticus NRRL 5862 (AF producer). Aspergillus spp.were kindly obtained from the USDANational Peanut Research Lab.,Georgia, USA. Spore suspensions were prepared by blending the 7-day old colonies cultured on potato dextrose agar with distilledsterilized water. Colonies were loosened into water with a plasticinoculation loop and the suspension was collected into falcontubes, which later filtered through sterile cheesecloth. Initial sporeconcentration was 5e6 Log CFU/mL water, and then w10 mL of thesuspension was dropped onto individual peanuts. Infected peanutswere further incubated in the moist chamber until seeds werecovered with masses of fungi of interest. Highly contaminatedpeanuts were later blended with pre-sterilized clean peanuts on aweight basis to prepare samples of calibration set having varyingcell concentrations of Aspergillus spp. of interest. Paste wasmade bygrinding raw peanuts by following the procedure in which sampleswere processed, scraped from the sides of the food processor(Butterfly Emerald Mixer, Gandhimathi Appliances Ltd., TamilNadu, India) sequentially in every 0.5 min for a total of 6 times inorder to reach a homogeneous sample and particle size as statedbefore (Kos et al., 2007).

2.2. FTIR-ATR spectra

The infrared spectra of contaminated peanut paste sampleswere recorded on FTIR spectrometer (Nicolet 6700, Thermo FisherScientific Inc., Madison, WI, USA), which has DTGS KBr detector.Sample compartment was equipped with Smart iTR diamondattenuated total reflectance (ATR) accessory (Thermo Fisher Sci-entific Inc., Madison, WI, USA) consisting of laminated diamond ascrystal material, mounted in stainless-steel plate. The diamondcrystal has a reflective index of 2.4 at 1000 cm�1 and an angle ofincidence of 45�. Spectra were collected with the Nicolet OmnicSoftware (Thermo Scientific). Each spectrum was recorded at aresolution of 4 cm�1 in the range of 4000e625 cm�1 with 64 scansaveraged for each spectrum. Background measurements weremade against air. The crystal was covered during each measure-ment and cleaned by 70% methanol and wiped with clean papertowel until the crystal is fully dry. Three repetitive measurementswere collected for each sample prepared.When same samples wereanalyzed the following day, the last sample of previous day was runagain to include the blocking effect in to the model.

2.3. ELISA test

AF content characterization was performed by the ELISA test(AgraQuant Total AF Test Kit, Romer Labs, Singapore). Briefly, 20 gground portion of each sample was mixed with 100 ml 70% meth-anol extraction solvent for final extraction solvent ratio of 1:5 (w/v)in sealed vials. After shaking for 2 min, mixture was filtered and thefiltrate was directly tested with ELISA kit as the manufacturer

Table 1Functional groups and vibration modes assigned for Aspergillus growth, aflatoxinincidence and peanut matrix in fingerprint region of 1800e800 cm�1 (Billes et al.,2006; Guillen & Cabo, 1997; Heathcote & Hibbert, 1978; Naumann, 2000).

Frequency range Mode of vibration

1800e1700 cm�1 C]O stretching of carbonyl groups (ketone, esters andacids)

1690e1485 cm�1 C]O, CeN stretching of amide INeH, CeO bending and CeC, CeN stretching of amide IIC]C (ring) stretching, CeC, CeO bending of AF

1485e1425 cm�1 CeC (ring) stretching and CeH (ring) bending of AFStretching of eC-H of the CH2 and CH3 aliphatic groupsof fatty acids

1410e1330 cm�1 Symmetric eC-H bending of methyl group1200e1000 cm�1 eC-O, eCH2e stretching, bending

CeH bending, (out of plane) & CeO, C]C (ring) stretchingof AFRing vibrations of carbohydrates

H. Kaya-Celiker et al. / Food Control 44 (2014) 64e7166

described. The extracted sample and enzyme-conjugated aflatoxinwas mixed and added to anti-body coatedmicrowell. AF in samplescompetes for binding sites with enzyme-conjugated AF and in thepresence of the substrate develops a blue color. The color is inverselyproportional to theAF concentration.Addinga stopsolutionchangesthe blue to a yellow color. This can be read using a dual wavelength(450 nm and 630 nm) microwell reader (Model: EL312e, Bio-TekInstruments Inc., Winooski, VT, USA).

2.4. Multivariate data analysis

Spectra of invaded peanut paste samples were analyzed usingTurboQuant IR calibration and prediction software package(Thermo Fisher, Madison, WI, USA). To reduce the dependency onmagnitude, subtracting the average spectrum from each calibrationspectrum normalized the spectra. Mean centered data was furtherfiltered to smooth the peaks that resemble random noise withSavitzkyeGolay’s smoothing filter by setting the data points to 7and polynomial order to 3rd order (cubic) polynomial for averagesmoothing of the first derivative of the data. Blends of moldy andclean peanut paste samples were prepared as mold concentrationvalues varied between 2.5 and 6.0 Log CFU/g peanut.

2.5. Region selection

Both Aspergillus spp. and peanut have very complex molecularstructure and spectraofmixturewill containhundredsof vibrationalmodes. Thus, spectral region selection is a crucial step to derive themost relevant data. TheOeH stretchingofwater (3600e3200 cm�1)and the absorptions from the diamond crystal (2340e1800 cm�1)were excluded. Wave numbers between 3050 and 2750 cm�1 werealso excluded from all data analysis (Kos et al., 2002). The bestwavelength region giving the most correlated variability for afla-toxin andmoldpresence inpeanutpaste sampleswas selectedas thefingerprint region lying between 1800 and 800 cm�1.

2.6. Discriminant analysis

Samples having varying levels of Aspergillus spp. under thisstudy were grouped into 3 batches having mold concentrations of:Log CFU/g � 2.7, Log CFU/g ¼ 3.5e4.5, and Log CFU/g � 5.0. In thefirst group ([mold] ¼ Log CFU/g � 2.7), the AF amount remainedunder 20 ppb. Second group ([mold]¼ Log CFU/g¼ 3.5e4.5) had AFranging between 60 and 1200 ppb, while third group ([mold]¼ LogCFU/g � 5.0) had AF more than 1800 ppb. Pre-processed (mean-centered, filtered by smoothing the 1st derivative) spectral data ofeach groupwere analyzed with the discriminant analysis techniqueusing TurboQuant IR calibration and prediction software. Duringcalibration, the software computes a mean spectrum and thengenerates a distribution model by estimating the variance at eachfrequency in the analysis range. Results were presented as distanceplots in Mahalanobis distance units. The validation of developedtechnique was represented by performance index using the algo-rithm of % difference defined as:

ðjactual� calculatedj=expected rangeÞ � 100

Larger PI indicates better performance (TQ Analyst, 2007). Thenumber of principle components giving the largest PI was selectedfor discrimination.

2.7. PLS regression

The PLS regressionmodels were developed to transform the rawdata into a new set of data by extracting a set of latent variables,

which have the optimal spectral and concentration information.The number of factors was decided automatically when the pre-dicted residual error sum of squares (PRESS) values reached tominimum or leveled off. Contaminated peanut paste sampleshaving AF amount up to 1200 ppb were used for PLS model cali-bration. The performance of developed PLS models were evaluatedthrough correlation coefficient of determination (R2), root mean-squared error of calibration (RMSEC), and root mean-squared er-ror of prediction (RMSEP). The Chauvenet test was applied toeliminate outliers, if any present.

3. Results & discussion

3.1. Interpretation of IR spectrum

Interpretation of IR spectrum is inclusive; as chemical com-plexes vary so does the spectra. In other words, the IR spectrumcombines the entire information about the molecular structures ofstudied complexes; and peanuts, AFs and their source fungi are allhave complex structures and represent highly unsaturated mole-cules which may lead to observe characteristic bands at the sameregions (Table 1). The strong peaks belonging to coumarin moiety(C]O stretch of ketone) at 1770 cm�1 (1725 for G AFs) would beused for examining the AFs in peanuts (Billes, Moricz, Tyihak, &Mikosch, 2006; Heathcote & Hibbert, 1978). However, thecarbonyl stretching of esters of triglycerides in peanut matrixdominates in this region; and as fungal growth proceeds, the lipidhydrolysis takes place, which later dominates in the same region.As the fungal invasion progresses, the lipid breakdown product offree fatty acid content increases by the activity of lipase which canbe observed as an increased peak height at 1710 cm�1 (Ismail,Vandevoort, Emo, & Sedman, 1993). Previously, increased freefatty acid content through fungal growth has been suggested as anindicator of grain deterioration (Bothast, 1978) and used for sortingthe internally moldy peanuts out by near infrared (NIR) spectra(Hirano et al., 1998). Consequently, enzymatic degradation of tri-glycerides caused a decline at fatty acid ester linkage (eC]O)centering at 1743 cm�1. Similarly, another fat associated band of CeO stretching at 1160 cm�1 showed a steady decreasewith respect tofungal deterioration of peanuts. The eC-H of the methylene andmethyl groups of the fatty acids, centering at 1464 and 1378 cm�1,also showed a slight decrease (Guillen & Cabo, 1997). The sameregion includes skeletal vibrations of CeC ring stretching, as well aseC-H bending within the ring structure and methyl group of AF(Billes et al., 2006; Heathcote & Hibbert, 1978) but because of thehigh-heterogeneity in peanut compounds and the relatively smallamount of AF standards in peanut samples, no significant change in

H. Kaya-Celiker et al. / Food Control 44 (2014) 64e71 67

spectra was observed in the specified regions. Sugars provide car-bon source for both A. flavus and A. parasiticus, and drop in sugarconcentration through metabolism of Aspergillus were reported,either before or simultaneously with triglyceride hydrolysis(Mellon, Cotty, & Dowd, 2000; Mellon, Dowd, & Cotty, 2002). Sugarmetabolism can be observed as slight changes in the region be-tween 1200 and 900 cm�1, which is referred as ring vibrations ofoligo and polysaccharides (Naumann, 2000). The protein structureof peanut samples before and after Aspergillus invasion followed aconsistent trend (at 1652 cm�1 and 1538 cm�1): first a drop in peakheight was observed, and then steadily raised to its initial value.This results agrees with literature and indicates fungal hydrolysis ofhost protein first, followed by synthesis of fungal proteins frombuilding blocks during advanced stages of deterioration (Bothast,1978).

Fig. 2. Total aflatoxin amount interpolated against cell count: (6): A. parasiticusNRRL5862; (B): A. flavus NRRL 3357.

3.2. Discriminant analysis

Four different Aspergillus species, two AF producers (A. flavusNRRL 3357 and A. parasiticus NRRL 5862) and two non-AF pro-ducers (A. flavus NRRL 1957 and A. parasiticus NRRL 21369), weregrown on peanut seeds. Seeds covered with fungi of interest wereblended with clean, pre-sterilized peanuts in order to obtainsamples having varying levels of mold concentration to train themodel. For each species, 23 different samples were prepared andpre-processed spectral data (mean-centering, smoothing the 1stderivative) were classified using discriminant analysis. PLSregression was employed for developing a calibration model withrespect to the AF levels. Fig. 1 shows the average of 3 repetitivereadings of clean and contaminated peanut samples. AF producers,A. flavus and A. parasiticus, invaded samples were taken as areference to decide on the threshold cell concentration value hav-ing 20 ppb AF or lower.

Generally, A. flavus is the foremost seen species and commonlycontaminates corn, peanuts, cottonseed, and tree nuts with AFs;however, A. parasiticus is more prevailing in peanuts and contrib-utes to varying levels of AF (Diener et al., 1987). When AF

A. flavus NRRL 3 357 Invaded Peanut Spectrum

-0.0

0.1

0.2

Abs

A. parasiticus NRRL 5862 Invaded Peanut Spectru

0.1

0.2

Abs

A.flavus NRRL19 57 Invaded Peanut Spectrum

-0.0

0.1

0.2

Abs

A.parasiticus NRRL 21369 Invaded Peanut Spectru

-0.0

0.1

0.2

Abs

Clean Peanut Spectrum

0.1

0.2

Abs

2500 3000 3500 4000

Wa venu

Fig. 1. Averaged spectrum of 3 repetitive measurements of pe

concentrations are compared against the density (CFU/g) of toxi-genic A. flavus and A. parasiticus in peanuts, the AF level remainedunder 20 ppb when A. flavus cell concentration was lower than1000 CFU/g while this valuewas around 650 CFU/g for A. parasiticus(Fig. 2). Because of the rare occurrence of non-aflatoxigenicA. parasiticus, a presence of A. parasiticus may indicate AFcontamination in peanuts (Horn, 2003). The toxigenic strains ofA. flavus and A. parasiticus differed widely with respect to theirability to produce AF for different kind of substrates and incubationconditions, and in our case, A. parasiticus NRRL 5862 producedhigher amounts of total AF (B and GAFs). Besides, it was shown thatA. parasiticus grows faster when incubated at room temperature

m

m

1000 1500 2000

mbers (cm-1)

anuts invaded with Aspergillus species and clean peanut.

Fig. 4. Distance plot of classification model of invaded peanut samples having 3.5e4.5Log CFU/g mold concentration (in Mahalanobis distance unit). Class names are: (6):Moldy (AF free); (B): Toxic (AF level is between 20 and 300 ppb); (>): Highly Toxic(AF level is between 300 and 1200 ppb); (C): misclassified sample. Black data pointsare representing samples used for calibration, red data point are representing samplesused for validation. (For interpretation of the references to color in this figure legend,the reader is referred to the web version of this article.)

H. Kaya-Celiker et al. / Food Control 44 (2014) 64e7168

(Horn, 2005), which might explain the higher level of colonizationon peanuts in this current study. To be on the safe side, the limitingvalue was set to the Log CFU of 2.7 (650 CFU/g) and in classificationanalysis, this group was named as “Acceptable” stream (�20 ppb).The organoleptic quality changes started to be visually observablewhen Log CFU value reached 3.5e4. Thus, a second group havingmold concentration between Log CFU/g ¼ 3.5e4.5 was created andclassified as “Moldy”. Finally, a group of samples where the fungalinvasion was obvious ([mold] ¼ Log CFU/g � 5.0) was decided andclassified as “Highly Moldy”. The information provided by distanceplot (Fig. 3) show the data points with similar mold density clus-tered together; even when samples were originally infected withdifferent Aspergillus spp. In other words, no matter which Asper-gillus species (AF producer or not) invaded the peanut kernels,clustering happened in regard to amount of the mold. In thisanalysis, 8 principle components were used and 98.1% variabilitywas described. The PI of the Discriminant analysis was 91.3%, whichis an indication of how well the algorithm can classify the valida-tion samples given (TQ Analyst, 2007). This result proves that FTIRsystem coupled with ATR accessorize can be utilized to discrimi-nate samples with AF levels of 20 ppb and lower as a safe stream.

Within the second group ([mold] ¼ Log CFU/g ¼ 3.5e4.5), someof the samples were invaded with non-aflatoxigenic strains ofAspergillus spp. (A. flavusNRRL 1957 and A. parasiticusNRRL 21369);and there were some, which have AF level of 300 ppb or lower. Inthe US, this limiting value for crops (<300 ppb) can be utilized asfeed (USFDA, 2009). Thus, this second group (n ¼ 69) was furtherclassified into 3 streams: “Moldy”, which has only non-aflatoxigenic strains; “Toxic”, which has AF contaminated peanutsbut lower than 300 ppb; and “Highly Toxic”, which has samples ofan AF amount higher than 300 ppb. Fig. 4 illustrates the resultingdiscriminant analysis as distance values, and evidences that AF inpeanut paste samples dominates with respect to spectral

Fig. 3. Distance plot of classification model of moldy and clean peanut samples inMahalanobis distance unit. Classification was performed with respect to cell count:(>): Acceptable (mold count lower than 2.7 Log CFU/g peanut); (6): Moldy (moldcount between 3.4 and 4.5 Log CFU/g peanut); (B): Highly Moldy (mold count morethan 5.0 Log CFU/g peanut). Black data points are representing samples used forcalibration, red data point are representing samples used for validation. (For inter-pretation of the references to color in this figure legend, the reader is referred to theweb version of this article.)

measurements and leads to segregation of samples even at lowlevels between 20 and 300 ppb. The distance between data pointsof samples with AF more than 300 ppb and the other data points inthe cluster is greater than the other two classes. In this classificationmethod, 8 principle components were used and 96.6% variabilitywas described. 50 out of 69 sample readings were used for cali-bration, and the PI value was 90.3%, signifying the reliable catego-rization of classes. Only one sample was misclassified as highlytoxic while it was moldy; and 98.6% correct classification wasreached.

Additionally, the last group of highly moldy samples([mold] ¼ Log CFU/g � 5.0) was loaded into discriminant analysistool for further division into “Moldy” and “Highly Toxic” streams. Itis generally expected to see dominancy of fungal growth at such anadvanced stage of deterioration and no discrimination betweentoxic and non-toxic samples. However, according to Fig. 5, thesecondary metabolites of AF increased the similarity within thespectral data and, again, resulted in the gathering of samples con-taining high levels of AF together and a high degree of separationfrom the non-toxic samples. Similar secondary metabolites pro-duced by strains even from different taxon were clustered together(Fischer, Braun, Thissen, & Dott, 2006). Likewise, 99.2% of the totalspectral variation was described by using 8 principle componentsto calibrate the active discriminant analysis method. 17 out of 51readouts were used for validation and the PI value was 95.0%.

3.3. PLS regression

Mean-centered and filtered via smoothing the 1st derivative ofthe spectral data were loaded into the PLS tool. Models weredeveloped for AF amount in contaminated peanuts in order toobtain a quantitative analysis method and to demonstrate theactive portion of the spectrum with respect to calibration andvariation spectra (Fig. 6). Variations were usually observed in

Table 2Calibration and prediction regression statistics for A. flavus 3357 and A. parasiticuswith respect to aflatoxin level. Partial Least Squares were calculatedwith the SIMPLSalgorithm for training/prediction sets after preprocessing (mean-centering,smoothing the 1st derivative) the spectral data.

A. flavus NRRL 3357 A. parasiticus NRRL 5862

# Of factors 8 8Slope 1.0014 0.9999Intercept 0.1716 0.0493R2C (%) 99.98 99.98RMSEC (ppb) 4.52 4.63R2P (%) 99.98 99.92RMSEP (ppb) 6.13 10.5

Fig. 5. Distance plot of classification model of moldy peanut samples having morethan 5.0 Log CFU/g mold concentration (in Mahalanobis distance unit). Class namesare: (6): Moldy (AF free); (B): Highly Toxic (AF level is 1200e9000 ppb). Black datapoints are representing samples used for calibration, red data point are representingsamples used for validation. (For interpretation of the references to color in this figurelegend, the reader is referred to the web version of this article.)

H. Kaya-Celiker et al. / Food Control 44 (2014) 64e71 69

protein related bands (1652 and 1538 cm�1), while fat associatedpeaks (1743, 1464 and 1378 cm�1) gave maxima at correlationspectra for both A. flavus NRRL 3357 and A. parasiticus NRRL 5862,as expected (Table 1). This could be due to protein bands (amide Iand amide II) showing a non-linear behavior; whereas fat hydro-lysis showing linear correlation with fungal density. AF amount inselected samples were ranging between 0 and 1200 ppb. Toconstruct PLS as a linear predictive model, 38e34 data points fortraining set and 9e7 data points for prediction set were used for thecomponent amounts based on the spectrum data of A. flavus NRRL3357 and A. parasiticus NRRL 5862, respectively. Each spectrumwascomprised of measurements at 7002 different frequencies. The PLSfactors were computed as linear combinations of the absorbancevalues, and the responses were predicted linearly based on theseextracted factors. The resulting statistics were summarized in

A. flavus NRRL 3 357 Correlation Spectrum

0.2

0.4

0.6

0.8

A. flavus NRRL 3 357 Variance Spectrum

0.002

0.004

0.006

0.008

Abs

A. parasiticus NRRL 5862 Correlation Spectrum

0.2

0.4

0.6

0.8

A.parasiticus NRRL 5862 Va ria nce Spectrum

0.002

0.004

0.006

Abs

2500 3000 3500 4000

Wa venu

Fig. 6. Correlation and variance spectrum of A. fla

Table 2 and actual versus FTIR predicted AF graphs were illustratedin Figs. 7 and 8.

The R2 values of training and prediction sets for both Aspergillusspecies were 99.98% and RMSEC values were 4.52 and 4.63 ppb forA. flavus NRRL 3357 and A. parasiticus NRRL 5862, respectively.Relatively higher RMSEP values, which are indicative of predictivepower of the models, for both species may restrict the usage ofFTIR-ATR system as an analytic tool. But, knowing that, A. flavus andA. parasiticus have a tendency to aggregate in some regions of thefield rather than even distribution (Horn, 2003), and that AFcontamination in one kernel may reach up to 1,000,000 ppb(Pasikatan & Dowell, 2001) make the prospect of FTIR usage as avaluable tool when utilized for a specific purpose. Findings of thecurrent study can also be used for other peanut products, since themajor alterations in chemical structure of the peanut asmold growswill be the same. For example, such a designed sorter may avoidruining the whole lot just because of a few highly contaminatedkernels. Also, detecting methods to track and decontaminate thesebio-agents before the fungal contamination spreads-out to thewhole lot is very important. In the % distance diagrams (Fig. 5.7 and5.8), the predictive set data points were dispersed more widely,designating an over-fit of calibration models for both organismsunder study. Relative distance values were calculated as:

ððcalculated� actualÞ=actualÞ � 100:

1000 1500 2000

mbers (cm-1)

vus NRRL 3357 and A. parasiticus NRRL 5862.

Fig. 7. (a): Actual versus predicted aflatoxin amount in A. flavus NRRL 3357 invaded peanut paste samples; (b) Difference between the calculated and the actual AF amount relativeto the actual aflatoxin amount versus actual aflatoxin amount. (B) training set data points; (,) prediction set data points.

Fig. 8. (a): Actual versus predicted aflatoxin amount in A. parasiticus NRRL 5862 invaded peanut paste samples; (b) Difference between the calculated and the actual AF amountrelative to the actual aflatoxin amount versus actual aflatoxin amount. (B) training set data points; (,) prediction set data points.

H. Kaya-Celiker et al. / Food Control 44 (2014) 64e7170

4. Conclusion

Results of the current study demonstrate the potential usage ofFTIR-ATR system as a rapid non-destructive qualification methodfor detection of fungal infected peanuts with respect to molddensity. The defined system can separate contaminated peanutseven when the organoleptic alterations as a consequence of fungalinvasions were not visually observable; plus, at such a low level ofinvasion the AF amount remains under legislative limit of 20 ppb. Inthis study, the parameter of measurement was mainly the chemicalfootprints of fungal invasion, not the mold of interest, especially atlow level of growth. Thus, evenwhen the mold is not present in thesamples anymore, chemical deteriorations can be determined byconsidering the hydrolysis of lipid content of peanut resulting inchanges in FTIR spectra. Protein deterioration of substrate peanutfollowed by synthesis of fungal protein was also observable inspectral meaning. In addition, the automated differentiation ofmoldy peanut kernels individually as non-toxic and toxic streams,according to the level of AF, is possible and can be achieved by usingFTIR with ATR in mid-infrared region. This may help to reduce theeconomic losses, as the only moldy but not toxic stream may beutilized as animal feed.

Acknowledgments

Authors wish to express their gratitude to Project VT-134, Pea-nut Collaborative Research Support Program funded by USAID forproviding the financial support to the project. Authors also wouldlike to extend their gratitude to Drs. Justin Barone, Archileo Kaayaand Charity Mutegi for their participation in the project.

References

Bennett, J. W. (2010). An overview of the genus Aspergillus. In M. Machida, &K. G. Wymondham (Eds.), Aspergillus: Molecular biology and genomics (pp. 1e17). Caister Academic.

Billes, F., Moricz, A. M., Tyihak, E., & Mikosch, H. (2006). Simulated vibrationalspectra of aflatoxins and their demethylated products and the estimation of theenergies of the demethylation reactions. Spectrochimica Acta Part A-Molecularand Biomolecular Spectroscopy, 64(3), 600e622.

Bothast, R. J. (1978). Fungal deterioration and related phenomena in cereals, le-gumes and oilseeds. In H. O. Hultin, & M. Milner (Eds.), Post-harvest biologyand biotechnology (pp. 210e243). Westport, CT, USA: Food and Nutrition Press,Inc.

Diener, U. L., Cole, R. J., Sanders, T. H., Payne, G. A., Lee, L. S., & Klich, M. A. (1987).Epidemiology of aflatoxin formation by Aspergillus-flavus. Annual Review ofPhytopathology, 25, 249e270.

H. Kaya-Celiker et al. / Food Control 44 (2014) 64e71 71

Dohlman, E. (2004). Mycotoxin regulations: implications for international agricul-tural trade. In Vol. Agriculture Information Bulletin No: AIB789e6. United StatesDepartment of Agriculture.

Farsaie, A., McClure, W. F., & Monroe, R. J. (1977). Development of indices for sortingIranian pistachio nuts according to fluorescence. Journal of Food Science, 43(5),1550e1552.

Fischer, G., Braun, S., Thissen, R., & Dott, W. (2006). FT-IR spectroscopy as a tool forrapid identification and intra-species characterization of airborne filamentousfungi. Journal of Microbiological Methods, 64(1), 63e77.

Gilbert, J., & Anklam, E. (2002). Validation of analytical methods for determiningmycotoxins in foodstuffs. Trac-Trends in Analytical Chemistry, 21(6e7), 468e486.

Gordon, S. H., Jones, R. W., McClelland, J. F., Wicklow, D. T., & Greene, R. V. (1999).Transient infrared spectroscopy for detection of toxigenic fungus in corn: po-tential for on-line evaluation. Journal of Agricultural and Food Chemistry, 47(12),5267e5272.

Greene, R. V., Gordon, S. H., Jackson, M. A., Bennett, G. A., McClelland, J. F., &Jones, R. W. (1992). Detection of fungal contamination in corn e potential ofFTIR-PAS and FTIR-DRS. Journal of Agricultural and Food Chemistry, 40(7), 1144e1149.

Guillen, M. D., & Cabo, N. (1997). Infrared spectroscopy in the study of edible oilsand fats. Journal of the Science of Food and Agriculture, 75(1), 1e11.

Heathcote, J. G., & Hibbert, J. R. (1978). Aflatoxins: Chemical and biological aspects.Amsterdam, The Netherlands: Elsevier Scientific.

Hirano, S., Okawara, N., & Narazaki, S. (1998). Near infra red detection of internallymoldy nuts. Bioscience Biotechnology and Biochemistry, 62(1), 102e107.

Horn, B. W. (2003). Ecology and population biology of aflatoxigenic fungi in soil.Journal of Toxicology Toxin Reviews, 22(2e3), 351e379.

Horn, B. W. (2005). Colonization of wounded peanut seeds by soil fungi: selectivityfor species from Aspergillus section Flavi. Mycologia, 97(1), 202e217.

IARC. (1993). Aflatoxins: naturally occurring aflatoxins (group1) Aflatoxin M1 (group2B). In International Agency for Research on Cancer (Vol. 56); (p. 245).

Ismail, A. A., Vandevoort, F. R., Emo, G., & Sedman, J. (1993). Rapid quantitative-determination of free fatty-acids in fats and oils by Fourier-transforminfrared-spectroscopy. Journal of the American Oil Chemists Society, 70(4),335e341.

Klich, M. A. (2007). Aspergillus flavus: the major producer of aflatoxin. MolecularPlant Pathology, 8(6), 713e722.

Kos, G., Lohninger, H., & Krska, R. (2002). Fourier transform mid-infrared spec-troscopy with attenuated total reflection (FT-IR/ATR) as a tool for the detectionof Fusarium fungi on maize. Vibrational Spectroscopy, 29(1e2), 115e119.

Kos, G., Lohninger, H., & Krska, R. (2003). Development of a method for the deter-mination of Fusarium fungi on corn using mid-infrared spectroscopy withattenuated total reflection and chemometrics. Analytical Chemistry, 75(5), 1211e1217.

Kos, G., Lohninger, H., Mizaikoff, B., & Krska, R. (2007). Optimisation of a samplepreparation procedure for the screening of fungal infection and assessment ofdeoxynivalenol content in maize using mid-infrared attenuated total reflectionspectroscopy. Food Additives and Contaminants, 24(7), 721e729.

Krishnamachari, K., Nagarajan, V., Bhat, R. V., & Tilak, T. B. G. (1975). Hepatitis due toaflatoxicosis e outbreak in western India. Lancet, 1(7915), 1061e1063.

Lamb, M. C., & Sternitzke, D. A. (2001). Cost of aflatoxin to the farmer, buying point,and sheller segments of the southeast United States peanut industry. PeanutScience, 28, 59e63.

Lewis, L., Onsongo, M., Njapau, H., Schurz-Rogers, H., Luber, G., Kieszak, S., et al.(2005). Aflatoxin contamination of commercial maize products during anoutbreak of acute aflatoxicosis in eastern and central Kenya. EnvironmentalHealth Perspectives, 113(12), 1763e1767.

Lye, M. S., Ghazali, A. A., Mohan, J., Alwin, N., & Nair, R. C. (1995). An outbreak ofacute hepatic-encephalopathy due to severe aflatoxicosis in Malaysia. AmericanJournal of Tropical Medicine and Hygiene, 53(1), 68e72.

McClure, W. F., & Farsaie, A. (1980). Dual-wavelength fiber optic photometermeasures fluorescence of aflatoxin contaminated pistachio nuts. Transactions ofthe American Society of Agricultural Engineers, 204e207.

Mellon, J. E., Cotty, P. J., & Dowd, M. K. (2000). Influence of lipids with and withoutother cottonseed reserve materials on aflatoxin B-1 production by Aspergillusflavus. Journal of Agricultural and Food Chemistry, 48(8), 3611e3615.

Mellon, J. E., Dowd, M. K., & Cotty, P. J. (2002). Time course study of substrate uti-lization by Aspergillus flavus in medium simulating corn (Zea mays) kernels.Journal of Agricultural and Food Chemistry, 50(3), 648e652.

Mirghani, M. E. S., Man, Y. B. C., Jinap, S., Baharin, B. S., & Bakar, J. (2001). A newmethod for determining aflatoxins in groundnut and groundnut cake usingFourier transform infrared spectroscopy with attenuated total reflectance.Journal of the American Oil Chemists Society, 78(10), 985e992.

Murphy, P. A., Hendrich, S., Landgren, C., & Bryant, C. M. (2006). Food mycotoxins:an update. Journal of Food Science, 71(5), R51eR65.

Naumann, D. (2000). Infrared spectroscopy in microbiology. In R. A. Meyers (Ed.),Encyclopedia of analytical chemistry: Applications, theory, and instrumentation.Chichester, New York: Wiley.

Pasikatan, M. C., & Dowell, F. E. (2001). Sorting systems based on optical methodsfor detecting and removing seeds infested internally by insects or fungi: a re-view. Applied Spectroscopy Reviews, 36(4), 399e416.

Pearson, T. C., Wicklow, D. T., Maghirang, E. B., Xie, F., & Dowell, F. E. (2001).Detecting aflatoxin in single corn kernels by transmittance and reflectionspectroscopy. Transactions of the American Society of Agricultural Engineers,44(5), 1247e1254.

Pearson, T. C., Wicklow, D. T., & Pasikatan, M. C. (2004). Reduction of aflatoxin andfumonisin contamination in yellow corn by high-speed dual-wavelength sort-ing. Cereal Chemistry, 81(4), 490e498.

Shotwell, O. L., & Hesseltine, C. W. (1981). Use of bright greenish yellow fluorescenceas a presumptive test for aflatoxin in corn. Cereal Chemistry, 58(2), 124e127.

Spensley, P. C. (1963). Aflatoxin, the active principle in turkey ’X’ disease. Endeavour,22, 75e79.

Tyson, T. W., & Clark, R. L. (1974). An investigation of the fluorescent properties ofaflatoxin infected pecans. Transactions of the American Society of AgriculturalEngineers, 17(5), 942e948.

Analyst, T. Q. (2007). User guide. Thermo fisher scientific. WI, USA: Madison,.USFDA. (2009). Action levels for aflatoxins in animal feeds. In Vol. Sec. 683.100 (CPG

7126.33).Wagacha, J. M., & Muthomi, J. W. (2008). Mycotoxin problem in Africa: current

status, implications to food safety and health and possible management stra-tegies. International Journal of Food Microbiology, 124(1), 1e12.

WHO. (1999). Basic food safety for health workers. World Health Organization.Williams, J. H., Phillips, T. D., Jolly, P. E., Stiles, J. K., Jolly, C. M., & Aggarwal, D. (2004).

Human aflatoxicosis in developing countries: a review of toxicology, exposure,potential health consequences, and interventions. American Journal of ClinicalNutrition, 80(5), 1106e1122.