Computers and Electronics in Agriculture 77 (2011) 74–80

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Computers and Electronics in Agriculture

journal homepage: www.elsevier .com/locate /compag

The potential of visible-near infrared hyperspectral imaging to discriminatebetween casing soil, enzymatic browning and undamaged tissue on mushroom(Agaricus bisporus) surfaces

Masoud Taghizadeh, Aoife A. Gowen ⇑, Colm P. O’DonnellBiosystems Engineering, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland

a r t i c l e i n f o

Article history:Received 11 March 2010Received in revised form 13 January 2011Accepted 25 March 2011

Keywords:Visible-near infrared hyperspectral imagingDiscriminateCasing soilEnzymatic browningMushroom surfaces

0168-1699/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.compag.2011.03.010

⇑ Corresponding author. Tel.: +353 1 7167413; fax:E-mail addresses: masoud.taghiza@ucd.ie (M. Tag

(A.A. Gowen), colm.odonnell@ucd.ie (C.P. O’Donnell).

a b s t r a c t

The potential of hyperspectral imaging (HSI) in the visible-near infrared (445–945 nm) wavelength rangeto discriminate between casing soil, enzymatic browning and undamaged tissue on mushroom (Agaricusbisporus) surfaces was investigated. A calibration set of 108 damage free mushrooms, grown under con-trolled conditions in a research station, were first tested as undamaged class (U) and then were dividedinto 2 groups of 54 samples. The first group was smeared with casing soil and designated as casing soilclass (C) and the second group was subjected to vibrational damage resulting in enzymatic browning anddesignated as damaged class (D). Partial least squares discriminant analysis (PLS-DA) models were devel-oped to classify mushroom tissue as one of the three classes investigated (U, C and D) using pixel spectrafrom each class. Prediction maps were obtained by applying the developed models to the hyperspectralimages of candidate mushrooms. Percentages of pixels classified into each class were also calculated forthe mushrooms studied in the calibration set. Results obtained showed that the developed models per-formed satisfactorily to discriminate between the 3 classes studied. Comparison of red–green–blue (RGB)and hyperspectral image analysis showed that HSI was better able to identify the regions containing cas-ing soil. Model validation was performed using 3 different test sets of mushrooms obtained from a com-mercial producer. It was found that the developed PLS-DA models were satisfactorily capable ofidentifying undamaged regions, casing soil and enzymatic damaged areas on mushrooms from the vali-dation sets.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

In the commercial production of Agaricus bisporus (J.E. Lange)Imbach mushrooms, a nutritional composted substrate colonisedwith mycelium is covered with a casing soil layer to initiate thedevelopment of sporophore (fruit body) production (Flegg andWood, 1985). Agaricus bisporus mushrooms are valued for theirwhite appearance; however, harvested mushrooms commonlycontain casing soil particles which adhere to the surface, givingthe produce an unpleasant appearance. The European Unionprovides guidelines for classification of cultivated mushroomsaccording to their appearance. Mushrooms classified as ‘‘Extraclass’’ have superior quality; only very slight superficial defectsare permitted and they should be practically free of casing mate-rial. Mushrooms in ‘‘Class I’’ have good quality and slight defectsin shape and/or colouring, slight superficial bruising and onlyslight traces of casing materials are permitted. Mushrooms classi-

ll rights reserved.

+353 1 7167415.hizadeh), aoife.gowen@ucd.ie

fied as ‘‘Class II’’ may have defects in shape or colouring, slightbruising or damage to the stalk, hollow stalks, slight internal mois-ture of stalks, and traces of casing material (Freshfel Europe, 2004).

Due to the high tyrosinase and phenolic content of mushrooms,they are very susceptible to enzymatic browning (Brennan et al.,2000) which is the major cause of quality losses that accountsfor the reduction in the market value (Mohapatra et al., 2008).The existence of damaged areas on mushroom surface tissuecaused by browning is the most common and challenging qualitydefect encountered in the mushroom industry. Residual casing soilparticles also lower mushroom quality. Such residues can bepotentially misclassified as enzymatic browning when monitoredvisually. Both defects result in similar visual effects, i.e. the pres-ence of brown regions on mushroom surfaces; however, they tendto result from different sources. Enzymatic browning indicatesproblems in distribution of mushroom products (e.g. excessivevibration during transportation), while occurrence of casing soilon mushroom surfaces suggests problems at the earlier stages ofgrowing and/or picking.

RGB image analysis has been used for food quality characterisa-tion and defect detection for different agri-food products such as

Fig. 1. RGB images of 3 different validation sets used for model evaluation. (a) Set 1,(b) set 2, (c) set 3 (undamaged), (d) set 3 (enzymatic damaged).

M. Taghizadeh et al. / Computers and Electronics in Agriculture 77 (2011) 74–80 75

grains (Venora et al., 2009) and fruits (Lana et al., 2006). More re-cently, HSI has emerged as a powerful technique in quality andsafety evaluation of a variety of agricultural food products suchas fruits, vegetables, meat, poultry and grains (Gowen et al.,2007; Naganathan et al., 2008; Firtha, 2009). HSI offers manyadvantages such as simple and easy to use instrumentation, non-contact and non-destructive sample evaluation, estimation of bothconcentration and distribution of sample constituents and simulta-neous identification of several components on a sample (Gowenet al., 2008; Qin et al., 2009). Recently some research studies havebeen reported on evaluating different quality aspects of mush-rooms using HSI, such as freeze damage (Gowen et al., 2009), pre-diction of moisture content (Taghizadeh et al., 2009), effect ofdifferent packaging materials on mushrooms shelf life (Taghizadehet al., 2010). However, according to the literature, no research hasbeen carried out to investigate the potential of HSI to identify cas-ing soil particles on mushroom surface and distinguish them fromenzymatic browning. HSI offers the possibility to distinguish be-tween both types of quality defects based on spectral properties.This would improve the ability of mushroom producers to tracethe source of quality issues in the production chain. The objectiveof this study is to investigate the potential of hyperspectral imag-ing to discriminate between casing soil, enzymatic browning andundamaged tissue on mushroom surfaces.

2. Materials and methods

2.1. Sample preparation

2.1.1. Calibration setAgaricus bisporus mushrooms were grown in plastic bags and

tunnels in Kinsealy Teagasc Research Centre (Malahide, Co. Dublin,Ireland). Spawn running and casing took place throughout the6 weeks prior to mushroom cropping. A total of 108 damage freemushrooms, each with a diameter of 3–5 cm, were harvested inOctober 2009. The damage free samples were first tested asundamaged class (U). They were divided into 2 equal groups; 54mushrooms were distributed in 6 different plastic trays and weresmeared with casing soil to make the casing soil contamination(C). This was done by manual application of casing soil to themushroom surface using a spatula. The second group of mush-rooms were subjected to damage by physical vibration using amechanical shaker (Promax 2020, Heidolph Instruments, Schwa-bach, Germany) for 60 s at 400 rpm to induce enzymatic browning(D). The damaged samples were then distributed into 6 trays forscanning.

2.1.2. Validation setsFor model validation, mushrooms were obtained from a com-

mercial producer (Monaghan mushrooms, Monaghan, Ireland).Three different validation sets were constructed as described be-low. Sets 1 and 2 were harvested in November 2009; set 3 was har-vested in October 2009.

Set 1: A number of undamaged (‘‘U’’), casing soil (‘‘C’’), andvibration damaged (‘‘D’’) mushrooms were mixed together in 5trays (approximate weight for each full tray = 150 g). The casingsoil contaminated mushrooms were artificially smeared as de-scribed above. The vibration damaged samples were vibrated for600 s at 400 rpm resulting in enzymatic browning (Fig. 1a).

Set 2: A group of low grade mushrooms with obvious traces ofcasing soil representing class II mushrooms were put in 5 trays(approximate weight for each full tray = 400 g). The casing soil con-tamination in this case was naturally occurring (Fig. 1b).

Set 3: Two groups of mushrooms, one representing undamagedmushrooms and the other representing mushrooms exposed to

natural enzymatic browning (due to 14 days storage) were selectedand 5 trays of each group (approximate weight for each fulltray = 250 g) were used for model evaluation (Fig. 1c and d).

2.2. Hyperspectral imaging system

A hyperspectral imaging system (DV optics, Padua, Italy) oper-ating in the Vis-NIR wavelength range (400–1000 nm) was em-ployed in this study. The main components of this system are:objective lens, spectrograph, detector, acquisition system, movingtable, a light source transmitted through fibre optic line and acylindrical light diffuser (schematic of hyperspectral imaging sys-tem used can be found in Gowen et al., 2008). Hyperspectralimages were obtained in the aforementioned wavelength rangewith a spectral resolution of 5 nm. The effective resolution of thecharge-coupled-device (CCD) detector was 580 � 580 pixels by12 bits. Hyperspectral images of mushrooms for calibration andvalidation sets were obtained, each scan taking approximately1 min.

The noise characteristics of the sensor were investigated byacquiring 50 scans of a calibration tile (Ceram Research, Stafford-shire, UK) over a time period of 1 h. Signal to noise ratio was lowestat the upper (950–1000 nm) and lower (400–445 nm) wavelengthranges; in these regions the noise level exceeded 2% of the signal.This is due to decreased CCD detector sensitivity in these wave-length regions. Due to this noise, subsequent analysis of spectrawas performed only on data in the 445–945 nm spectral range.

A 2 point reflectance calibration was performed (Ariana et al.,2006). The bright response (‘W’) was obtained by acquiring ahypercube from a uniform white ceramic tile (the reflectance ofwhich was pre-calibrated against a tile of certified reflectance(Ceram Research, Staffordshire, UK)); the dark response (‘D’) wasacquired by turning off the light source, completely covering thelens with its cap and recording the camera response. This was doneprior to image acquisition at each time point. The corrected reflec-tance value (R) was calculated from the measured signal (‘I’) on apixel-by-pixel basis as shown below:

Ri ¼ ðIi � DiÞ=ðWi � DiÞ

where i is the pixel index, i.e. i = 1, 2, 3,. . .,n and n is the total num-ber of pixels.

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2.3. Data processing and analysis

Fig. 2 shows a flowchart of the data processing and analysisstrategy employed in the study. After acquisition, hyperspectralimages of individual mushrooms were pre-processed by maskingin order to separate the mushroom from the image background.The mask was created by thresholding the mushroom image at840 nm (images at this wavelength provided good contrast be-tween mushroom and background) and setting all background re-gions to zero. Non-zero elements of the image were then extractedand the mean spectrum was calculated for each mushroom. Multi-plicative scatter correction (MSC) is a transformation method usedto compensate it is assumed that each spectrum is determined onone hand by the actual sample characteristics and, on the other bythe particle size. The particle size can be represented by a base-lineeffect and the trend by means of a standard spectrum. It is a row-oriented transformation; thus, the contents of a cell are likely to beinfluenced by its horizontal neighbours. It removes physical effectslike particle size and surface blaze from the spectra, which do notcarry any chemical or physical information. When using MSC, it isassumed that each spectrum is determined on one hand by the ac-tual sample characteristics and, on the other by the particle size.The particle size can be represented by a base-line effect and thetrend by means of a standard spectrum. This method is capableof correcting differences in the base line and in the trend and hasan advantage that the transformed spectra are similar to the origi-nal spectra, and that an optical interpretation is therefore moreeasily accessible. It is important to note that MSC delivers differentresults for the same spectrum if the MSC model was establishedusing different data sets. In the present study, MSC was used as aspectral pre-treatment to reduce the influence of scatter effectsand other sources of variations (e.g. differences in mushroom sam-ple height and shape). The mean spectrum of each mushroom wasused as the target spectrum for applying MSC.

2.3.1. Pixel spectra selection for model buildingAfter application of MSC to the non-background elements of

each image, the distribution of pixel intensity values at 690 nmwas examined (this wavelength was located at the middle of the

Read image

Mask

Pixel spectra selection

Apply MSC

Random spectra extraction

Modelling strategy

Build 3 PLS-DA models

1 for each class

Se

Fig. 2. Flowchart of data processing and an

wavelength range studied). In order to avoid any extreme or outlierpixel spectra, only the subset of pixel spectra lying between the2nd and 98th percentile of this distribution were selected for mod-el building.

2.3.2. Modelling strategyOne thousand spectra from the pre-processed matrix (described

above) were extracted randomly for each individual mushroom;these spectra were combined to build a calibration set containing1000 � 216 spectra (1,08,000 spectra from U class; 54,000 for eachof C and D classes). One thousand spectra from the calibration setrepresenting each class were randomly extracted for model build-ing. Three PLS-DA models were developed; one for each class. Inthe first model, the response variable (Y) for undamaged sampleswas set to one and that for the other 2 classes was set to zero inorder to differentiate between undamaged issues and the other 2classes. The same strategy was used for the other 2 models to dif-ferentiate between casing soil particles and the rest (second mod-el) and discriminate between enzymatic damaged regions and therest (third model). This procedure was repeated 100 times and theresultant 100 regression vectors for each of the models wereaveraged.

2.3.3. Prediction map constructionPrediction maps of samples for calibration and validation sets

were constructed by applying the 3 PLS-DA models to MSC pre-treated hyperspectral images of mushrooms in trays. To reduceprocessing time, hypercubes were reduced to 50% of their originalsize using bi-cubic interpolation. Masking was done to remove im-age background, and each individual mushroom in the image wassegregated from the rest using morphological operations (erosionand dilation). MSC was applied to each mushroom region in theimage separately, using the mean spectrum of that region as a tar-get spectrum. The MSC pretreated hypercube was unfolded into 2-dimensional matrix in which each row was the spectrum of 1 pixel.The 3 constructed models were applied to this matrix and theresultant matrix was refolded to form a prediction image, resultingin 3 prediction images (one for each class). Red green blue (RGB)false colour images of the prediction maps were obtained by con-

Read image

Apply 3 PLS-DA models

Mask

Predicted image

Resize image

Prediction map

Apply MSC

RGB false colour image

lect maximum RGB value for each image

alysis strategy employed in this study.

M. Taghizadeh et al. / Computers and Electronics in Agriculture 77 (2011) 74–80 77

catenating the three prediction images, where the red (R) chan-nel = model for ‘‘U’’ class, the green (G) channel = model for ‘‘C’’class and the blue (B) channel = model for ‘‘D’’ class. The colourchannel with the highest predicted value was used to define thepredicted class of that pixel, e.g. a pixel with R > G > B would be-long to the class defined by the red channel, i.e. ‘‘U’’ class.

2.4. RGB images

RGB false colour images of calibration set were obtained by cal-culating L⁄, a⁄ and b⁄ values from each image spectra using Spectral

400 500 600 700 800 900 10000.1

0.15

0.2

0.25

Wavelength (nm)

Ref

lect

ance

(R

)

UC

D

Fig. 3. MSC pre-treated average reflectance spectra of the 3 classes investigated,where U = undamaged mushroom tissue, C = casing soil, D = enzymatic damage(thick lines) and their corresponding standard deviations (thin lines).

U

C

D

Model 1: U and the rest

Model 2: C and the rest

Model 3: D and the rest

Model 1: U and the rest

Model 2: C and the rest

Model 3: D and the rest

Model 1: U and the rest

Model 2: C and the rest

Model 3: D and the rest

Fig. 4. Prediction maps for the first 6 latent variables for the 3 developed models (one findicates the actual class of the mushroom).

Scanner software (DV Optics, Padua, Italy) and the RGB coordinateswere extracted (Slawomir, 2007). The same modelling strategy asthat described in Fig. 2 was also applied to the RGB images of thecalibration set in order to compare the potential of HSI and RGBtechniques in discriminating between the 3 classes studied.

3. Results and discussion

3.1. Differences in spectral reflectance between classes

The average MSC pre-treated reflectance spectra of the 3 classes(U, C and D) of samples (average of 1,000 spectra randomly se-lected from the calibration set of spectra described in Section2.3.2.) are shown in Fig. 3. It can be seen that the average reflec-tance values for the U class is higher than that of the C and D clas-ses. Moreover, the spectra for U and C classes have similar profiles,while the mean spectra of D class samples exhibit a different pro-file. The difference in reflectance values between different classesis relatively high across the visible wavelength range between445 and 645 nm; this is related to the colour differences betweenthe different groups, also evident in Fig. 1. In the longer wavelengthregion, from 645 to 945 nm, the curves become similar and thespectra of C and D class samples are overlapping. Standard devia-tion curves are similar across the whole wavelength range studied.

3.2. Model development

To estimate the optimal number of latent variables for applyingthe 3 constructed PLS-DA models, prediction images of samplesrepresenting each class for the first 6 latent variables were ob-tained (Fig. 4). It can be seen that in all classes, prediction imagesobtained by applying models containing more than 4 latent vari-ables tend to be noisy and do not seem to add any extra informa-

or each of the 3 classes studied, where the letter in bold before each of the arrows

78 M. Taghizadeh et al. / Computers and Electronics in Agriculture 77 (2011) 74–80

tion. For U and D class samples, models with 4 components wereselected for further analysis while for C class samples, it was notclear whether 3 or 4 latent variables were suitable. Therefore, 3and 4 latent variable models for C class discrimination were furtherinvestigated for their ability to classify pixels belonging to C class.The model containing 4 latent variables misclassified 18% of U classsamples as C class while this value for the model with 3 latent vari-ables was 13.97% which shows better performance of the 3 latentvariable model in comparison with the 4 latent variable model. The4 latent variable model classified 54.88% of casing soil samples as Cclass while this value for the 3 latent variable model was 41.49%.Regarding the fact that the whole surfaces of mushrooms werenot covered by casing soil, the latter value seems to be more rea-sonable. In terms of D class samples, the 3 and 4 latent variablemodels performed similarly well by classifying 89.83% and89.23% of enzymatic damaged samples as D class, respectively.

Fig. 5. Prediction maps for PLS-DA model applied to mushroom

Fig. 6. Prediction maps for different classes using both RGB and

Therefore, the 3 latent variable model was selected for C classdiscrimination.

3.3. Discriminating between different classes using hyperspectralimaging data for calibration set

Fig. 5 demonstrates prediction maps obtained by applying thePLS-DA models (4 latent variables for U and D class samples and3 latent variables for C class samples) to all samples in the calibra-tion set with their corresponding RGB images in order to discrim-inate between mushrooms in U, C and D classes. In this figure red,green and blue are representing regions that have been classifiedas U, C and D respectively. It can be seen that the proposed modelperformed well to identify U and D class mushrooms. In somecases, the existing shadows caused by illumination effects onmushrooms edges have been misclassified as casing soil. However,

s of different classes and their corresponding RGB images.

HSI analysis strategy and their corresponding RGB images

M. Taghizadeh et al. / Computers and Electronics in Agriculture 77 (2011) 74–80 79

overall the model showed reasonable performance to separatemushroom surfaces smeared with casing soil from undamaged orenzymatic damaged tissues.

3.4. Comparison of RGB and full spectrum (HSI) image analysis

RGB images of the calibration set were analysed using the samemodelling strategy to compare the potential of HSI and RGB tech-niques in discriminating between the 3 classes studied. Obtainedprediction maps of both methods with their corresponding RGBimages are shown in Fig. 6.

Although, the results obtained by analysis of RGB images werepromising in predicting enzymatic damage (D) on the mushrooms’surface, some of the other 2 classes (U and C) were misclassified asD which showed the lower potential of RGB images analysis incomparison with HSI analysis to discriminate between the 3 clas-ses studied. Moreover, RGB image analysis had a lower ability to

Table 1Average percentages of pixel values classified for each class using the 3 PLS-DA models ob

Actualclass

HSI analysis

First model: U and therest (%)

Second model: C andthe rest (%)

Third model: D and trest (%)

U 81.08 45.18 5.11C 13.97 41.49 5.06D 4.95 13.33 89.83

Fig. 7. Prediction maps for the three test sets and their corresponding RGB image. (a

identify casing soil particles on mushroom surface in comparisonwith HSI analysis.

In order to quantify the performance of the RGB and HSI basedmodels, the percentages of pixel values classified as each of the 3different classes were calculated (Table 1). It can be seen that forthe U class samples, 81.08% and 81.79% of pixels have been classi-fied correctly in HSI and RGB images, respectively which suggestssimilar performance of the first model when applying on bothtypes of images. For D class samples, RGB imaging showed betterperformance by classifying 98.15% of enzymatic damaged regionsas D though HSI method classified 89.83% of enzymatic damagedareas as D which was also reasonable. In terms of C class samples,it should be mentioned the whole surface of mushrooms were notcovered by casing soil and the obtained value of 41.49% by HSImethod was satisfactorily reasonable, while using RGB method74.57% of C class were misclassified as U class which suggestedpoor performance of the model in this case.

tained by both RGB and HSI data.

RGB image analysis

he First model: U and therest (%)

Second model: C andthe rest (%)

Third model: D and therest (%)

81.79 74.57 1.498.88 9.21 0.369.33 16.22 98.15

) Set 1, (b) set 2, (c) set 3 (undamaged), (d) set 3 (natural enzymatic damaged).

80 M. Taghizadeh et al. / Computers and Electronics in Agriculture 77 (2011) 74–80

3.5. Model validation

Three different groups of mushroom were selected in this studyto evaluate the proposed PLS-DA model performance. Predictionimages for mushrooms of the 3 validation sets and their corre-sponding RGB images are shown in Fig. 7.

Although some of the edge regions in the images have beenmisclassified as C class due to illumination/curvature effects, over-all the 3 developed PLS-DA models performed satisfactorily well todiscriminate between the 3 classes studied on the independentvalidation set of mushrooms. In terms of the low grade mushroomsin set 2, it can be seen that the proposed model can identify casingsoil particles even on mushroom stalks. For naturally enzymaticdamaged mushrooms in set 3, it can be seen that even a mushroomwith lower level of damage has been identified amongst all otherdamaged mushrooms which confirms the potential of the pro-posed model.

4. Conclusions

Three PLS-DA models were developed to discriminate betweenthe 3 classes studied (U, C and D). HSI data correlated well withmushroom surface characteristics and the constructed models per-formed well for discrimination purposes. Comparison of HSI anal-ysis with conventional RGB image analysis demonstrated theenhanced capability of HSI in identifying different classes studied,especially in identifying samples contaminated with casing soil.The developed models showed satisfactory results in discriminat-ing between undamaged, casing soil and enzymatic damaged tis-sues on the mushroom validation sets. Currently, grading ofmushrooms by visual inspection is both labour intensive andsomewhat subjective. The introduction of optically based methodssuch as HSI would facilitate more objective quality monitoring.However, the drawbacks of using HSI in this context include highequipment cost and relatively long computational time requiredfor analysis of HSI data; this limits its utilisation in real time qual-ity monitoring. Therefore, in order to facilitate online imagingbased mushroom grading on an industrial scale, a multi-spectralapproach which is cost effective and rapid may be preferable. Fur-ther work is required to identify the optimal wavelength regionsfor the development of such a system.

Acknowledgements

The authors would like to thank Dr. Helen Grogan and Ted Cor-mican (Teagasc Research Station, Kinsealy, Ireland) and Dr. John

Collier and Jenna Warby (Monaghan Mushrooms, Monaghan, Ire-land) for production of mushrooms and technical advice. This re-search was funded by the Irish Government Department ofAgriculture, Fisheries and Food under the Food Institutional Re-search Measure (FIRM).

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