C. Esquerre et al., J. Near Infrared Spectrosc. 20, 537–546 (2012)Received: 20 March 2012 n Revised: 14 July 2012 n Accepted: 18 July 2012 n Publication: 21 August 2012
Previous research has demonstrated the potential use of near infrared (NIR) hyperspectral imaging for non-destructive monitoring of mushroom quality. The mushroom industry demands economical and high-throughput imaging systems that can reliably classify groups of mushrooms according to quality parameters. Multispectral imaging systems based on the acquisition of just a few (2–10) wavelengths fulfil these criteria. This research concerns the development of a low-cost robust multispectral system for mushroom quality control which can identify slightly damaged mushroom tissue using NIR spectral images. A three step approach was employed: (1) the most suitable pre-treatment was selected; (2) wavelengths with the most stable normalised regression coefficients were identi-fied using ensemble Monte Carlo variable selection (EMCVS); and (3) partial least square discriminant analysis (PLS-DA) models were built using the selected regions (49 nm bandwidth) to simulate a multispectral system. Minimum scaled reflectance spectra produced better results than maximum scaled, mean scaled, median scaled or raw spectra. Five key spectral regions were identified, centred around 971 nm, 1090 nm, 1188 nm, 1384 nm and 1454 nm. A PLS-DA model built using three spectral regions (1090 nm, 1188 nm, 1384 nm) and scaled by the 1454 nm band (minimum reflectance) correctly classified 100% of the physically damaged mushrooms.
IntroductionThe mushroom industry is the main source of employment in the Irish horticultural sector. Improved quality, consistency and timely delivery are key factors in the commercial success of the Irish mushroom industry. The Agaricus bisporus (common white mushroom) species is valued for its white appearance; any surface blemishes or discoloration results in market losses (up to 50%). For this mushroom species, colour is a critical factor in consumer acceptability. Mushroom colour may be adversely affected by the development of dark patches due to polyphenol oxidase activity, often triggered by mechanical stress during mushroom harvesting and handling.1,2 Hyperspectral imaging in the visible–near infrared (vis–NIR) (450–950 nm) wavelength range has been successfully demonstrated for monitoring colour
and enzymatic activity in mushrooms.3–5 However, to identify subtle tissue damage which is not visually evident, it is neces-sary to exploit the extended near infrared (NIR) wavelength range, where past research has indicated the importance of the first overtone water band, around 1300–1600 nm, for detec-tion of surface damage. This is due mainly to the high moisture content of mushrooms (> 90% wet basis); changes in mushroom quality are correlated with changes in their water absorbance pattern.6,7 Traditionally, NIR spectroscopy has provided spectral information from a small area, the average of a few points (for example, 3–10) or the mean spectrum of a larger area. This is not so useful when objects are non-uniform and the features of interest are restricted to a relatively small but unknown part of
Wavelength selection for development of a near infrared imaging system for early detection of bruise damage in mushrooms (Agaricus bisporus)
Carlos Esquerre,a,* Aoife A. Gowen,a Gerard Downeya,b and Colm P. O’Donnella
aUCD School of Biosystems Engineering, University College Dublin, Dublin 4, Ireland. E-mail: [email protected] Food Research Centre, Ashtown, Dublin 15, Ireland
Special Issue on NIR Imaging
538 Wavelength Selection for Imaging System to Detect Early Bruising in Mushrooms
the object.8 In such cases, NIR spectroscopy may not scan the feature of interest, or the dilution effect of the mean spectra can make it undetectable. NIR hyperspectral imaging overcomes this limitation by providing spectral information from small areas (pixels) within objects. NIR spectral imaging potentially allows for real-time detection of quality defects in mushrooms during processing and packaging.4,6,7,9–11 However, there are factors that limit its adoption in real-time monitoring in a process line, such as the time required for the acquisition, processing and clas-sification of hyperspectral images. Multispectral systems offer a compromise by combining a few (for example 2–10) wavelength bands that can provide valuable information on product attrib-utes.12–14 Various strategies have previously been used to iden-tify the most appropriate variables for a multi spectral imaging system. Some examples include visual inspection of the spectra, correlation analysis, principal component analysis (PCA), partial least squares (PLS), PLS discriminant analysis (PLS-DA) and independent component analysis.13,15 Ensemble Monte Carlo variable selection (EMCVS) is a method recently proposed to identify the most informative variables by evaluating the stability of regression vectors producing reproducible results and, where possible, improving the predictive ability of the model.16
The present study describes part of an automated system for the grading of mushrooms based on reflectance imaging in the NIR wavelength range. The objective was to identify a small number of key wavelengths that can discriminate slightly damaged tissue (not detectable to the naked eye) from sound tissue in mushrooms using NIR spectral imaging.
Materials and methodsMushroomsClosed cap Agaricus bisporus Sylvan A15 strain (Sylvan Spawn Ltd, Peterborough, UK) mushrooms, grown in the usual commercial way at Teagasc Kinsealy Research Centre (Dublin, Ireland) were selected for this study. For each of the three repetitions of the experiment, 120 mushrooms were selected at random and transported in specially designed protective trays to the laboratory. The trays held the mushrooms by the stipe, avoiding any contact between caps. The batches of mushrooms measured (B1, B2, B3) were picked on three different dates over a six-month period.
TreatmentHyperspectral imaging of mushroom samples was carried out within 3 h of picking. To simulate damage caused by vibrations during transport, mushrooms were shaken in groups of ten for 30 s in a plastic box at 400 rpm immediately before scanning on day 0 (pick up day) and before packing in punnets for analysis on day 1. Thirty undamaged and 30 damaged mushrooms were imaged on day 0; the remaining mushrooms (30 undamaged and 30 damaged) were packed in punnets in groups of ten, covered with PVC film and stored for one day at 4°C before imaging. Scanning order was as follows: ten damaged mushrooms followed by ten undamaged mushrooms. The PVC film was pierced with a pen at each corner of the punnet to avoid condensation.
Near infrared hyperspectral imaging systemSpectral images of samples were acquired with a line scanning system (DV Optics, Italy) over the wavelength range of 880 nm to 1720 nm at 7 nm intervals and stored in ENVI format using Spectral Scanner (DV Optics, Italy) software. The main compo-nents of the system, including the illumination source, diffuser, moving base, optics, spectrograph and camera are shown in Figure 1. The illumination source for the two first repetitions of the experiment consisted of a single tungsten halogen bulb, with the light conducted through a fibre-optic assembly to the diffuser. For the third repetition, five tungsten halogen bulbs, evenly spaced inside a diffuser, were used in order to increase the dynamic range in the detector. An InGaAs SU320M-1.7RT camera (Sensors Unlimited, Inc, USA) operated at 50 Hz with a 320 × 240 pixels sensor array. The moving base speed was set at 20 mm s–1 to obtain pixels of approximately 0.3 m × 0.3 mm while the scanning line was about 16 cm long.
The system was turned on and allowed to stabilise for around 30 min before calibration at the start of each scanning session. The integration time was 8.6 ms and the following two-point calibration procedure was implemented: first, 50 scan lines of a black reference (Ib) were acquired and averaged by taking a measurement after covering the spectrograph lens with a cap; then a white tile with a known reflectance (Rw) was placed on the moving base and used as a “white” reference (Iw) by aver-aging 50 scan lines and, finally, the signal from the sample (Is) was converted and stored as reflectance (R) according to:
(1)w b
s bwI I
I IR R
--
=
ChemometricsENVI format files were imported into Matlab version 2008a (The MathWorks, USA). All further data analysis was performed using in-house functions and scripts written in Matlab code, including some functions from the Statistics Toolbox (The
Wavelength selection for development of a near infrared imaging system for early detection of bruise damage in mushrooms (Agaricus bisporus) Carlos Esquerre, Aoife A. Gowen, Gerard Downey and Colm P. O’Donnell
10 Table 3. Partial least squares discriminant analysis model performance for selected wavelengths ranges scaled by 350 1454 nm band. Subscripts c, t, and v refer to calibration, tuning and validation respectively. 351
Bands Pixel wise Mushroom wise (nm)
Calibration Tuning Calibration+Tuni
ng Validation
LVa
Accub
Accdc
Gc
Accu
Accd
Gt
Accu
Accd
Gpooled
Accu
Accd
Gv
971,1090,1188,1384
4 0.699
0.766
0.732
0.702
0.759
0.730
0.758
0.967
0.856
0.733
1.000
0.856
1090,1188,1384
3 0.700
0.765
0.732
0.701
0.761
0.730
0.742
0.975
0.850
0.700
1.000
0.837
anumber of latent variables 352 bAccuracy for undamaged class 353 cAccuracy for damaged class 354 355
356 Figure 1. Near infrared spectral imaging system; (a) illumination source, (b) diffuser, (c) moving base, (d) optics 357 (mirror and lens), (e) spectrograph, and (f) camera. 358
359 Figure 1. Near infrared spectral imaging system; (a) illumina-tion source, (b) diffuser, (c) moving base, (d) optics (mirror and lens), (e) spectrograph, and (f) camera.
539C. Esquerre et al., J. Near Infrared Spectrosc. 20, 537–546 (2012)
MathWorks, USA) and PLS Toolbox (Eigenvector, USA). Image background was removed from the hyper spectral images using a mask. The mask was generated using the minimum reflectance value of each pixel spectrum in an image: a threshold was selected iteratively by analysing the corre-sponding histogram and drawing a tentative mask image, with all pixels below the threshold being set to 0. For each of the three batches, a threshold was selected and used for all images in that batch.
In order to obtain representative samples of undamaged and damaged mushroom tissue, five regions of interest of 5 × 5 pixels in size were selected from score images built with the three first principal component (PC) images. Spectra from each image were scrutinised for outliers before sampling; a spectrum was labelled as an outlier and removed from the dataset if its associated Hotelling statistic (T2) was greater than the critical value (T2
crit) calculated by Equation (2), where npc is the number of components, n is the number of spectra in the dataset, F(0.05,npc,n–npc) is the F statistic with a = 0.05, npc and n–npc are degrees of freedom. The number of compo-nents (npc) was the lowest number of components explaining more than 99% of the variability in the dataset.
( ) ( )2 2
0.05, , 0.05, , (2)1crit npc n npc npc n npc
nT T npc Fn npca= - a= -
æ ö- ÷ç ÷= =ç ÷ç ÷ç -è ø
Thus, 125 spectra per mushroom were selected. Special care was taken to obtain these spectra from uniform areas spanning the whole mushroom surface.
In order to select representative calibration, tuning and validation sets of spectra for model building and testing, the following procedure was carried out. First, mushroom spectra from the three batches (B1, B2, B3) were sorted according to scanning order. Then, one out of every three mushrooms was randomly selected and placed in the validation set. The spectra of the remaining mushrooms were pooled and then randomly split (50 : 50) into calibration and tuning sets. PLS-DA models were built with the calibration set and the most suitable number of latent variables was selected using the tuning set as follows. The geometric mean of the accuracy of each class (G), calculated on the tuning set, for models built with 1 to 20 latent variables was compared with the maximum value of G (Gmax) minus a penalty of 0.005, selecting the lowest number of latent variables with a corresponding G value greater than Gmax – 0.005.
Classification models were developed in three stages. First, the most suitable pre-treatment was selected using PLS-DA models built on the full wavelength range, second, the wavelength bands with the most stable regression coefficients were identified and, finally, PLS-DA models were built using the selected 49 nm band-width regions of the spectra. In addition to classifying pixel-wise, whole mushrooms were classified as damaged if more than 50% of pixels on their surface were classified as damaged.
Spectral pre-treatmentsHyperspectral imaging (HSI) of agricultural products usually suffers from spectral variability induced by sample shape
and morphology.17 Spectral pre-treatments to overcome this problem have been demonstrated in previous work, in which a technique was developed to assess the performance of such pre-treatments on idealised objects.18
Although numerous pre-treatments are available for over-coming the variation in HSI spectra caused by morphology, many of these require information from the full spectrum and are not feasible for multispectral imaging systems (for example, asymmetric least squares).18 Therefore, the pre-treatments evaluated in this study were limited to: mean scaling, median scaling, minimum scaling and maximum scaling of R and log(1/R).
The geometric mean of the classification accuracy in each class (G) was used to compare pre-treatments and select the most suitable pre-treatment producing the largest geometric mean (G) of the accuracy (Acc) for both classes [Equation (3)]:
* (3)u dG Acc Acc=
where u and d refer to undamaged and damaged classes, respectively. Accuracy was defined as the ratio of the number of spectra correctly identified from one class to the total number of spectra in that class. G is a robust measure of the model performance and is not affected by the prevalence of each class. In addition, G is a non-linear measurement, requiring high classification accuracy in both classes to achieve a high score.19
Variable selectionEnsemble Monte Carlo variable selection (EMCVS) was applied to remove uninformative wavelengths from the dataset studied. This procedure selects the most informative variables in a dataset based on PLS regression coefficients. Compared to other variable selection methods, EMCVS increases consist-ency of variable selection and reduces processing time. This method is described in more detail elsewhere,15 but its main features are summarised here. From the calibration set X matrix [size: n rows (sample spectra) × p columns (wave-lengths)], 10% of the spectra were randomly selected. From this data a PLS-DA model was built and the regression vector, b was calculated. This was repeated 100 times and a normal-ised regression coefficient, cj, for each wavelength variable was calculated as follows:
1,2, 3,..., (4)( )
jj
jc j p
s
b= =
b
100
1(5)
100ij
ji=
æ öb ÷ç ÷çb = å ÷ç ÷ç ÷çè ø
( )0.5
100
1(6)
100 1ij j
ji
s=
æ öb - b ÷ç ÷çb = ÷åç ÷ç ÷- ÷çè ø where b
–j and s(bj) are the mean and standard deviation of the
regression coefficient of the jth variable over all 100 runs and bij is
540 Wavelength Selection for Imaging System to Detect Early Bruising in Mushrooms
the regression coefficient for the jth variable in the ith PLS model (I = 1…100). The procedure was repeated 500 times and the mean of the standardised regression coefficient (cmj) for each wave-length [Equation (7)] was compared with a threshold (cthreshold) in order to eliminate variables which, in more than 50% of itera-tions, had normalised regression coefficients below cthreshold. The value of cthreshold was tuned by comparing G of the tuning set of the regression models built with retained variables obtained by using a number of thresholds up to the maximum value of cmj.
500
1(7)
500ij
ji
ccm
=
æ ö÷ç ÷ç= å ÷ç ÷ç ÷çè ø
Higher absolute values imply a more stable regression coefficient.
Multispectral analysisMultispectral systems using relatively broad bandwidths (20–50 nm) are common in the NIR wavelength range, although some narrow band pass filters of 10 nm and LEDs with full width at half maximum of 13 nm are available in the market.20 In order to approximate a multispectral system, for the selected vari-ables, 49 nm bandwidth variables were calculated, averaging reflectance of seven consecutive wavebands.
Results and discussionNIR spectral interpretationMushroom spectra shown in Figures 2 and 3 have similar peaks and features to the spectra described in previous work in the same range.6,21 A peak with maximum at 1454 nm in log(1/R) was the most prominent characteristic and can be attributed to water, corresponding to the first overtone of O–H
stretching (2 n1) plus the combination of the first overtone of the O–H bending and antisymmetric O–H stretching band (2 n2 + n3). In addition, it was possible to observe: (i) a small peak at 978 nm which may be ascribed to the second overtone of the O–H stretching band (due to water content); (ii) overlapping peaks at 1160 nm and 1230 nm that may be attributed to the combination of the first overtone of the O–H stretching and the O–H bending band (2 n1,3 + n2) and second overtone of the C–H stretching band; (iii) a shoulder at 1342 nm that may be related to a combination of symmetric stretching (n1), bending (n2) and rotation of the H2O molecule plus a combination of C–H stretching with C–H deformation (carbohydrate content); and (iv) a shoulder at 1580 nm attributable to strongly bound water.22–25 Features present at 1434 nm and 1462–1464 nm in spectral data previously reported were not so evident in this sample set,6 perhaps due to the difference in spectral resolution and window size employed in the derivative proce-dure. In the second derivative log (1/R) spectra (Figure 3) the main difference between undamaged and damaged mush-rooms occurred at 1426–1433 nm; this difference could arise from extrusion of cellular content in damaged mushrooms.6,21 Larger positive and negative peaks in the second derivative spectra of damaged mushrooms (Figure 3) may be explained by an increase in the light pathlength in damaged tissue due to disruption, coalescence of hyphae and exudates of intra-cellular content.
Changing the illumination source improved the quality of B3 spectra, reducing the noise and spread of the data (Figure 2). However, in Figure 2(c) it is also possible to observe some spectral aberrations in B3 spectra, with different slopes at the extremes related to the offset of each spectrum. These characteristics increase the challenge for a model built with B1 or B2 spectra to predict properly the damage in B3 mush-rooms. Therefore, the calibration, tuning and validation sets
Wavelength selection for development of a near infrared imaging system for early detection of bruise damage in mushrooms (Agaricus bisporus) Carlos Esquerre, Aoife A. Gowen, Gerard Downey and Colm P. O’Donnell
10 Table 3. Partial least squares discriminant analysis model performance for selected wavelengths ranges scaled by 1454 nm band. Subscripts c, t, and v refer to calibration, tuning and validation respectively.
Bands Pixel wise Mushroom wise (nm)
Calibration Tuning Calibration+Tuni
ng Validation
LVa
Accub
Accdc
Gc
Accu
Accd
Gt
Accu
Accd
Gpooled
Accu
Accd
Gv
971,1090,1188,1384
4 0.699
0.766
0.732
0.702
0.759
0.730
0.758
0.967
0.856
0.733
1.000
0.856
1090,1188,1384
3 0.700
0.765
0.732
0.701
0.761
0.730
0.742
0.975
0.850
0.700
1.000
0.837
anumber of latent variables bAccuracy for undamaged class cAccuracy for damaged class Figure 1. Near infrared spectral imaging system; (a) illumination source, (b) diffuser, (c) moving base, (d) optics (mirror and lens), (e) spectrograph, and (f) camera.
Figure 2. Near infrared spectra for a random sub‐sample of 500 spectra from (a) batch B1, (b) batch B2 and (c) batch B3 mushrooms.
(a) (b) (c)
log(
1/R
) (a
rbitr
ary
uni
ts)
log(
1/R
) (a
rbitr
ary
uni
ts)
log(
1/R
) (a
rbitr
ary
uni
ts)
Wavelength (nm) Wavelength (nm) Wavelength (nm)
Figure 2. Near infrared spectra for a random sub-sample of 500 spectra from (a) batch B1, (b) batch B2 and (c) batch B3 mushrooms. (Colour version of these figures can be found on our website www.impublications.com/jnirs.)
541C. Esquerre et al., J. Near Infrared Spectrosc. 20, 537–546 (2012)
were selected across the three batches as described in the materials and methods section.
Selection of pre-treatmentsTable 1 shows the geometric mean (G) of accuracy of both classes (i.e. undamaged mushrooms and damaged mush-rooms) corresponding to the R and log(1/R) spectra and different pre-treatments. Pixel-wise classification results are shown for the calibration (Gc) and tuning (Gt) sets, while mushroom-wise classification is shown for the joint calibration+tuning set (Gpooled) and validation set (Gv). Considering each pre-treat-ment used, the differences between Gc and Gt were generally very small. This may be attributed to the fact that both came from a large population (30,000 pixels) and were representa-tive of the dataset. In addition, Gpooled and Gv were similar to each other in most cases. Minimum scaling of the reflec-tance (R) spectra produced the best classification results. This pre-treatment normalised each spectrum with respect to the maximum absorption value, which occurred at around 1454 nm, where the first overtone of O–H stretching is present. As previously mentioned, this wavelength is related to water in the sample.25 Minimum scaled log(1/R) produced the worst results because the minimum value of the spectrum was close to zero and, in some cases, was negative. Five latent variables were required to achieve a good classification in the R spectra. This relatively high number may result from some features remaining in the spectra, such as morphology effects, due, for instance, to variations in the size of the mushroom, position of the pixel or experimental conditions, such as sample and air
temperature, which were not completely suppressed by the minimum scaling pre-treatment. Based on these results, the subsequent variable selection step was applied to minimum scaled R spectra.
Variable selectionThe most stable regression coefficients for minimum scaled R spectra, as assessed by the absolute values of normalised mean regression coefficient cm (Figure 4) were located in five regions of the NIR spectrum: 971 nm (second overtone of O–H bond stretching), 1076–1125 nm (high reflectance area), 1167–1216 nm (second overtone of C–H bond stretching), 1370–1398 nm (first overtone of C–H combinations) and 1426–1489 nm (first overtone of O-H bond stretching). A PLS-DA model built with these selected 30 wavelength bands and four latent variables achieved a good classification performance (Gc = 0.770, Gt = 0.726, Gpooled = 0.900, Gv = 0.913) (Table 2). This was in agreement with previous work in which differences in water-related bands were the most prominent wavebands to discriminate between undamaged and damaged mushroom tissue in the NIR spectral range.6,21 Also shown in Table 2 is the classification performance of PLS-DA after succes-sive iteration of the EMCVS selection procedure. In previous work, it was found that subsequent iterations of this procedure could improve model performance.16 In this case, the model performance slightly worsened while the reduction in the number of variables was modest. Interestingly, the waveband at 971 nm was not selected in the 3rd iteration. Based on these results, pseudo multispectral systems employing five (971 nm,
Figure 3. Mean spectra for 60,000 randomly-selected pixels of each class of mushrooms from batch 1 (B1); blue and green lines cor-respond to undamaged and damaged mushrooms respectively. (a) log(1/R) spectra (b) second derivative spectra (window size of nine data points).
542 Wavelength Selection for Imaging System to Detect Early Bruising in MushroomsTa
ble
1. G
eom
etri
c m
ean
(G) o
f acc
urac
y fo
r bo
th c
lass
es (u
ndam
aged
and
dam
aged
) of m
ushr
oom
s. S
ubsc
ript
s c,
t an
d v
corr
espo
nd to
cal
ibra
tion,
tuni
ng a
nd v
alid
atio
n se
ts. 5
0% o
f mus
h-ro
om s
urfa
ce.
Pix
el w
ise
Mus
hroo
m w
ise
Cal
ibra
tion
Tuni
ngC
alib
ratio
n+Tu
ning
Valid
atio
n
Spec
tra
Pre
-tre
atm
ent
LVa
Acc ub
Acc dc
G cAc
c uAc
c dG t
Acc u
Acc d
G pool
edAc
c uAc
c dG v
R
—5
0.76
00.
800
0.78
00.
765
0.79
20.
778
0.81
70.
883
0.84
90.
817
0.93
20.
873
Mea
n sc
aled
50.
748
0.76
60.
757
0.75
60.
761
0.75
80.
717
0.98
30.
839
0.68
31.
000
0.82
7
Med
ian
scal
ed4
0.76
70.
780
0.77
30.
773
0.77
10.
772
0.80
80.
983
0.89
20.
767
1.00
00.
876
Max
imum
sca
led
50.
769
0.77
70.
773
0.77
50.
776
0.77
60.
792
0.99
20.
886
0.76
71.
000
0.87
6
Min
imum
sca
led
50.
739
0.81
60.
777
0.73
90.
812
0.77
40.
833
0.97
50.
901
0.81
71.
000
0.90
4
Log(
1/R
)
—6
0.75
30.
824
0.78
80.
761
0.82
00.
790
0.82
50.
950
0.88
50.
800
0.94
90.
871
Mea
n sc
aled
40.
741
0.79
80.
769
0.74
80.
797
0.77
20.
842
0.90
00.
870
0.78
30.
932
0.85
5
Med
ian
scal
ed14
0.72
80.
707
0.71
70.
731
0.70
00.
715
0.69
20.
967
0.81
80.
650
1.00
00.
806
Max
imum
sca
led
40.
770
0.77
20.
771
0.77
80.
769
0.77
30.
792
0.90
00.
844
0.75
00.
932
0.83
6
Min
imum
sca
led
200.
801
0.40
90.
572
0.79
60.
403
0.56
70.
808
0.95
80.
880
0.76
70.
949
0.85
3
a num
ber
of la
tent
var
iabl
es
b Accu
racy
for
unda
mag
ed c
lass
c Ac
cura
cy fo
r da
mag
ed c
lass
543C. Esquerre et al., J. Near Infrared Spectrosc. 20, 537–546 (2012)
1090 nm, 1188 nm, 1384 nm and 1454 nm) and four (excluding 971 nm) wavebands were evaluated.
Multispectral approachIn order to approximate a multispectral system, the reflec-tance values of seven consecutive wavebands were averaged and scaled by the minimum absorbing band 1454 nm. PLS-DA was applied to the four scaled bands (centred at 971 nm, 1090 nm, 1188 nm and 1384 nm) and three scaled bands not including 971 nm (Table 3).
Compared with the four scaled bands model, the model with three scaled bands produced a lower Gpooled and Gv. This is due to a reduction in the correct classification of undamaged mushrooms which dropped from 75.8% to 74.2% in the pooled (calibration + tuning) set and from 73.3% to 70.0% in the validation set of mushrooms. However, the iden-tification of damaged mushrooms was better for the three- than for the four-scaled band models, i.e. 97.5% to 96.7% in the pooled (calibration + tuning) set and 100.0% in the valida-tion set. This indicates that a model using only three wave-
Wavelength selection for development of a near infrared imaging system for early detection of bruise damage in mushrooms (Agaricus bisporus) Carlos Esquerre, Aoife A. Gowen, Gerard Downey and Colm P. O’Donnell
11
Figure 4. EMCVS on minimum scaled R spectra (a) cm values, horizontal dotted lines correspond to the selected +threshold/‐threshold (b) Gt calculated with PLS‐DA model built with variables selected using different thresholds, vertical line corresponds to the threshold selected.
1000 1100 1200 1300 1400 1500 1600-8
-6
-4
-2
0
2
4
6
Wavelength (nm)
Cm
(dim
ensi
onle
ss)
(a)
0 1 2 3 4 5 6 70
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Threshold (dimensionless)
Gt (d
imen
sion
less
)
(b)
Figure 4. EMCVS on minimum scaled R spectra (a) cm values, horizontal dotted lines correspond to the selected +threshold/-threshold (b) Gt calculated with PLS-DA model built with variables selected using different thresholds, vertical line corresponds to the threshold selected.
544 Wavelength Selection for Imaging System to Detect Early Bruising in MushroomsTa
ble
2. P
artia
l lea
st s
quar
es d
iscr
imin
ant a
naly
sis
mod
el p
erfo
rman
ce fo
r se
lect
ed w
avel
engt
hs in
suc
cess
ive
itera
tions
of E
MCV
S m
etho
d. S
ubsc
ript
s c,
t, a
nd v
ref
er to
cal
ibra
tion,
tuni
ng
and
valid
atio
n re
spec
tivel
y.
Pix
el w
ise
Mus
hroo
m w
ise
Cal
ibra
tion
Tuni
ngC
alib
ratio
n+Tu
ning
Valid
atio
n
Iter
atio
nN
Wa
LVb
Acc uc
Acc dd
G cAc
c uAc
c dG t
Acc u
Acc d
G pool
edAc
c uAc
c dG v
130
40.
738
0.80
40.
770
0.73
40.
801
0.76
70.
825
0.98
30.
901
0.83
31.
000
0.91
3
224
40.
727
0.79
50.
761
0.72
70.
795
0.76
00.
842
0.97
50.
906
0.81
70.
983
0.89
6
320
30.
724
0.79
40.
758
0.72
30.
794
0.75
80.
825
0.97
50.
897
0.81
70.
983
0.89
6
a num
ber
of w
avel
engt
h se
lect
ed
b num
ber
of la
tent
var
iabl
es
c accu
racy
for
unda
mag
ed c
lass
d ac
cura
cy fo
r da
mag
ed c
lass
Tabl
e 3.
Par
tial l
east
squ
ares
dis
crim
inan
t ana
lysi
s m
odel
per
form
ance
for
sele
cted
wav
elen
gths
ran
ges
scal
ed b
y 14
54 n
m b
and.
Sub
scri
pts
c, t,
and
v r
efer
to c
alib
ratio
n, tu
ning
and
val
ida-
tion
resp
ectiv
ely.
Pix
el w
ise
Mus
hroo
m w
ise
Cal
ibra
tion
Tuni
ngC
alib
ratio
n+Tu
ning
Valid
atio
n
Ban
ds(n
m)
LVa
Acc
ubAc
c dcG
cAc
c uAc
c dG
tA
ccu
Acc
dG
pool
edA
ccu
Acc
dG
v
971,
1090
,118
8,13
844
0.69
90.
766
0.73
20.
702
0.75
90.
730
0.75
80.
967
0.85
60.
733
1.00
00.
856
1090
,118
8,13
843
0.70
00.
765
0.73
20.
701
0.76
10.
730
0.74
20.
975
0.85
00.
700
1.00
00.
837
a num
ber
of la
tent
var
iabl
es
b Accu
racy
for
unda
mag
ed c
lass
c Ac
cura
cy fo
r da
mag
ed c
lass
545C. Esquerre et al., J. Near Infrared Spectrosc. 20, 537–546 (2012)
lengths (1090 nm, 1188 nm and 1384 nm) plus an additional wavelength (1454 nm) for scaling, was able to identify physi-cally damaged mushrooms. Such a system would be useful for industry to identify high quality mushrooms for premium markets.
ConclusionsMost of the observed spectral [log(1/R)] features of mush-rooms were tentatively assigned to water molecule vibration modes due to their high moisture content (ca 90%). The first overtone of the O–H stretching bond in water molecules was the main feature in log(1/R) spectra of mushrooms, with a maximum at 1454 nm, which coincided with the most notice-able difference between undamaged and damaged spectra in both log(1/R) and second derivative spectra. Minimum scaling of R performed best out of pre-treatment methods evaluated. However, a model built with only four bands produced good results in the classification of damaged mushrooms (100% correct classification on the validation set).
AcknowledgementsFinancial support from the Irish Department of Agriculture, Fisheries and Food under the FIRM Programme is gratefully acknowledged. Carlos Esquerre was a Teagasc Walsh Fellow.
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