Evaluation of Spanish cooked ham by NIR Hyperspectral imaging
Pau Talens1*, Leticia Mora2, Noha Morsy3, Douglas F Barbin3, Gamal ElMasry3, DaWen sun3
1
Departamento de Tecnología de Alimentos. Universitat Politècnica de València. Camino de
Vera, s/n 46022. Valencia. Spain
2
Ashtown Food Research Centre, Teagasc. Ashtown, Dublin 15, Ireland.
3
FRCFT Group, Biosystems Engineering, University College Dublin. National University of
Ireland, Belfield, Dublin 4, Ireland
*Corresponding author. E-mail: pautalens@tal.upv.es
Abstract
The objective of this study was to investigate the potential use of hyperspectral imaging
technique in the NIR spectral region of 900–1700nm for the evaluation and quality
classification of Spanish cooked ham. In this study, four types of ham of different qualities
manufactured according to Spanish quality regulations for cooked meat products were used.
Hyperspectral images were acquired for ham slices and multivariate analysis using partial
least squares-discriminant analysis (PLS-DA) was applied to the spectral data to develop
statistical models for overall classification of ham qualities. The PLS-DA model using some
optimal wavelengths (966, 1061, 1148, 1256, 1373 and 1628 nm) successfully classified the
examined hams to different quality categories. The results revealed the potentiality of NIR
hyperspectral imaging technique as an objective and non-destructive method for the
evaluation and classification of cooked hams.
Key words: Spanish cooked ham, hyperspectral imaging, partial least-squares regression,
classification.
1. Introduction.
Cooked ham is a meat product with high levels of consumption in Spain and other countries.
The Spanish Government has established quality regulations for cooked meat products with
the aim to define the conditions and characteristics that must be complied by all Spanish
cooked hams (BOE, 1983). This quality regulation also classifies and categorizes the cooked
ham in three main categories: extra category cooked ham, first category cooked ham, and
second category cold cut ham. Main differences among these categories are based on the
protein and water contents of the cooked hams (Table 1).
In the industry, the quality of ham in terms of chemical composition is generally assessed by
experienced personnel using analytical techniques that are time consuming and sampledestructive such as the gravimetric measurements of water or the nitrogen determination
methods that include Kjeldahl, Dumas, and combustion methods. In this sense,
hyperspectral imaging system (HIS) is an emerging technique that integrates both
conventional imaging and spectroscopy technologies for attaining spatial and spectral
information of the product. This technique has been proved to be useful in quality evaluation
and classification of different types of products such as fruits and vegetables (Cubero et al.,
2011; Rajkumar et al., 2012) or meat (ElMasry et al., 2011; Kamruzzaman et al., 2011;
Barbin et al., 2012). This technique is considered as low time-consuming, non-destructive,
and requiring minimum of human intervention. HSI could also be a good alternative to
conventional analysis of the chemical composition of a high-value product such as Spanish
cooked ham since by using this technique it is possible to capture internal constituents
gradients within the product which is necessary in non-homogeneous multi-components
systems such as cooked hams.
Table 1. Specifications for quality classification of Spanish cooked hams according to
the Quality Regulation for cooked meat products (BOE n159, 5 th July of 1983).
Category
Extra
First Category Second Category
Minimum protein
Water/protein ratio
4.13
4.68
content of 14%*
Total sugar content
1.5% maximum 2% maximum
3% maximum
Agar agar, alginate,
0.5%
0.2% maximum
nd
carragenate
maximum
Starch
negative
negative
2.5% maximum
Total phosphate amounts
7500ppm maximum
*plus 1% maximum of added proteins; nd: non-detected
The main objective of the present work was to investigate the potential application of NIR
hyperspectral imaging technique for evaluating and classifying Spanish cooked hams
according to their quality. The objective was achieved by (1) establishing a NIR hyperspectral
imaging system with a spectral region of 910-1700 nm, (2) building a robust calibration model
using partial least-squares discriminant analysis (PLS-DA), and (3) identifying the optimal
wavelengths that are most useful for the differentiation between cooked hams and classifying
the different quality hams with the selected wavelengths.
2. Materials and methods
2.1. Samples.
Four types of Spanish cooked ham of the different categories were purchased from a
Spanish market. Ham 1 and ham 2 were extra category and main difference between both
was that ham 1 was a low fat content extra category ham. Ham 3 and ham 4 were first
category and second category, respectively. All hams were accurately labelled according to
their category. The ham blocks were stored in a fridge at 4ºC before slicing. Before image
acquisition, the ham blocks were removed from the fridge and kept for 30 min at room
temperature. Cooked ham pieces were sliced in 1 cm thick slices for the hyperspectral image
analysis. The total number of slices obtained was 62. Each slice was then imaged
individually in the hyperspectral imaging system.
2.2. Chemical analysis of fat, water and protein content.
Each ham slice was blended and fat, water and protein content was analysed. The
intramuscular fat and water content for each slice was determined using the chemical
composition CEM analysis system described by Bostian et al., (1985). The protein content
was measured according to the method of Sweeney and Rexroad (1987) on the LECO FP328 total nitrogen determinator (LECO R Corporation, St. Joseph, MI).
2.3. Image acquisition and image processing.
A line-scan hyperspectral imaging system in the NIR range of 890-1750 nm with 256 spectral
bands was used for acquiring hyperspectral images of ham slices. The system consists of
five parts: 1) a spectrograph (ImSpector N17E, Specim, Spectral Imaging Ltd, Oulu, Finland),
2) a camera (Xeva 992, Xenics Infrared Solutions, Belgium) with a standard C-mount lens, 3)
an illumination source (two 500W halogen lamps (Lowel Light Inc., NY, USA), 4) a translation
stage (MSA15R-N, AMT-Linearways, SuperSlides & Bushes Corp., India) operated by
stepping motor (GPL- DZTSA-1000-X, Zolix Instrument Co. Ltd, China) to move the sample,
and 5) a computer supported with SpectralCube data acquisition software (Spectral Imaging
Ltd., Finland). For the image acquisition each slice of the ham blocks was placed on the
translation stage and moved at a constant speed of 2.8 cm/s. The movement of the
translation stage was synchronized with the image acquisition by the SpectralCube software
to obtain spectral images with a spatial resolution of 0.58 mm/pixel. The system scans the
sample line by line and the reflected light is dispersed by the spectrograph and captured by
the area CCD array detector of the camera in spatial-spectral axes. The camera has 320 x
256 (spatial x spectral) pixels and the spectral resolution is 6 nm in the spectral range of
890–1750 nm. Once the hyperspectral image has been acquired, it is send to the computer
for storage in a raw format before being processed.
The raw images were processed using the Environment for Visualizing Images software
(ENVI 4.6.1) (Research Systems Inc., Boulder Co., USA). Because the response of the CCD
detector in the ranges of 897–910 and 1700–1753 nm was rather low and the resulting
spectral images at these two particular ranges were rather noisy, the hyperspectral images
were resized to the spectral range of 910 nm to 1700 nm with a total of 237 bands.
To remove the effect of dark current of the camera sensor from the acquired images (R 0),
white and dark reference images were concurrently captured. The white reference image
(RW ) was acquired from a white Teflon calibration tile and the dark reference image (R D) was
obtained by turning off the light source along with completely closing the lens of the camera
with its opaque cap. A relative reflectance image (R) was then calculated using the following
equation:
R
R0  RD
Rw  RD
(eq. 1)
Final images with a dimension of 320 pixels × 500 pixels × 237 bands were obtained and
subsequently used to extract the spectral information.
2.4. Region of interest selection and spectral data extraction
The spectral data were extracted from the lean region of each ham slice. Therefore, a
segmentation routine was applied before extracting spectral data to separate the lean part of
ham from the background and the fat or gelatin covering layer. The process started by
subtracting a low-reflectance band from a high-reflectance band followed by a simple
thresholding. This step produces a segmented image for the whole ham sample including the
lean part, gelatin and fat portions of the sample. Again, segmentation was performed for
detecting the gelatin and fat by simple thresholding to produce a binary image of fat and
gelatin pixels. The lean portion was isolated by subtracting the second segmented image
from the first segmented image to produce a mask containing only lean part in a black
background. The isolated lean portion was then used as the main region of interest (ROI) to
extract the average spectral data from only the lean part of the ham samples and avoid fat
and other undesired components that can affect the prediction values. The extracted spectral
data were then arranged in an X-matrix of 62 spectra. All extraction routines were performed
using the software Matlab 7.11.0.584(R2010b) (The Mathworks Inc., Natick, MA, USA).
2.4. Spectral data analysis
Partial least-squares discriminant analysis (PLS-DA) developed with leave-one-out crossvalidation, was applied to the spectral data set (X-matrix of 62 spectra) to build a qualitative
model for ham classification. The PLS-DA was conducted using The Unscrambler v9.7
(CAMO Software AS, OSLO, Norway).
3. Results and discussion.
3.1. Cooked ham analysis
The protein, water, and fat content of the Spanish hams were measured using traditional
methods in order to guarantee the category of the purchased hams. Figure 1 shows the
average and standard deviation values of protein, water and fat content and water/protein
ratio of the commercial hams. Values obtained in traditional analysis completely agree in
what it was expected for extra category, first category, and second category Spanish cooked
hams. As it is shown in the figure 1, water/protein ratio values of ham 1 and 2 were under the
4.13 value indicated in legislation. Ham 3 measured water/protein ratio value was higher than
cooked ham 1 and 2 ratio, but also below the 4.68 specified in the Regulation. In second
category Spanish cooked hams, a minimum of 14% of meat protein in total product is
allowed, and it is possible to add until 1% of external proteins which means a final amount of
15% of protein. Also the protein concentration of cooked ham 4 was according to the
legislation. As expected, protein content on hams 1 and 2 (extra category) was higher than
the determined in ham 3 (first category) whereas ham 4 (second category) showed the
lowest protein values. On the other hand, it has been observed that water content increases
while decreasing the category of the cooked ham probably because higher quality cooked
hams are generally made with lower levels of brine injection (Casiraghi et al., 2007). The
content of fat is not considered in the Quality Regulation but a considerable difference
between ham 1 (low fat content) and the rest of the hams was observed. Thus, nonsignificative differences were observed in the amounts of protein and water between cooked
hams 1 and 2 as well as in the fat content of cooked hams 2, 3 and 4. However, significant
differences (p<0.01) between extra quality (hams 1 and 2), first quality (ham 3) and second
quality (ham 4) were detected in protein and water content.
Protein (%)
Water (%)
25
20
80
a
a
78
b
c
15
10
74
5
72
0
b
a
a
Ham1
Ham2
76
a
70
Ham1
Ham2
Ham3
Ham4
Fat (%)
Ham3
Ham4
Water/Protein ratio
5
6
c
5
b
4
b
b
3
4
b
a
a
Ham1
Ham2
3
2
a
2
1
1
0
0
Ham1
Ham2
Ham3
Ham4
Ham3
Ham4
Figure 1. Average and standard deviation values of protein, water and fat content and
water/protein ratio of commercial hams. a,b,cDifferent letters in the same column mean
significant differences (p<0.01).
3.2. Classification of cooked hams.
For the PLS-DA, a dummy Y-variable was assigned to each ham class, 1 for ham1, 2 for
ham 2, 3 for ham 3 and 4 for ham 4. Performance of the classification model was evaluated
using the root-mean-square error of calibration (RMSEC), the root-mean-square error of
cross-validation (RMSECV), the coefficients of determination (R2), and the numbers of the
latent variables (#LV). The result of the PLS-DA model, using the whole spectral range
consisting of 237 wavelengths, is shown in figure 2. The validation test gave similar result as
the calibration set, and present low values of RMSEC (0.176) and RMSECV (0.198) and high
R2 (0.973 for calibration and 0.966 for validation), with 5 latent variables, indicating good
performance of the model for ham classification. The weighted -coefficients resulting from
the PLS-DA model were used for identifying the optimal wavelengths (966, 1061, 1148,
1256, 1373 and 1628 nm). Once the optimal wavelengths were selected, the spectral dataset
was reduced from 62 samples × 237 wavelengths to 62 samples × 6 wavelengths and PLSDA was again conducted on the reflectance spectral data using only the optimum
wavelengths instead of the full spectral range (Figure 3). In this case the values of RMSEC,
RMSECV and R2 were respectively 0.216 and 0.956 for calibration and 0.237 and 0.952 for
validation, with 3 latent variables.
5
PLS Model Line
Predicted class (%)
4
Ideal Model Line
3
2
1
0
0
1
2
3
4
5
Assigned class (%)
Figure 2. PLS-DA plot of assigned and predicted class values for training and
validation sets using the whole spectral range.
5
PLS Model Line
Predicted class (%)
4
Ideal Model Line
3
2
1
0
0
1
2
3
4
5
Assigned class (%)
Figure 3. PLS-DA plot of assigned and predicted class values for training and
validation sets using the optimum wavelengths.
Figure 4 shows the score plot of the first and the second principal components of PLS-DA
using the 6 optimal wavelengths for the spectra of all Spanish cooked ham.
0.04
PC 2
0.02
Ham 2
Ham 3
Ham 4
0.00
Ham 1
-0.02
-0.04
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
PC 1
Figure 4. Score plot of the first and second principal components of PLS-DA model
using the 6 optimal wavelengths for the spectra of all Spanish cooked ham.
The principal components resulting from PLS-DA, which explained 98.96% (94.81% +
4.15%) of the total variance among the samples, can be used to classify the hams. The first
principal component separates the hams samples in tree groups, Ham1 and Ham2 (extra
category), Ham4 (first category) and Ham3 (second category). The ham extra category are
located in the negative section of PC1 whereas the first and the second category are located
in the positive section of PC1. In addition the second principal component clearly separates
the ham samples into two groups, Ham1 in the negative part of PC2 and the other 3 hams in
the positive part of the PC2. These results suggest that PC1 could be related with the water
and protein content whereas PC2 could be related with the fat content of the hams, indicating
that it is possible to classify the Spanish cooked ham on the basis of the water, protein and
fat content.
4. Conclusions
Spectral analysis accompanied by developing a multivariate model was carried out for
classification of Spanish hams. The PLS-DA model developed using some optimal
wavelengths (966, 1061, 1148, 1256, 1373 and 1628 nm) was successfully used to classify
the examined hams to different quality categories. This study demonstrated the potential
capability of NIR hyperspectral imaging as an objective, rapid, and non-destructive technique
for the authentication and classification of Spanish cooked hams.
5. Acknowledgement
Author Pau Talens acknowledges the Spanish Ministry of Education for the financial support
to do a period abroad at University College Dublin, National University of Ireland (Orden
EDU/3378/2010 de 21 de diciembre).
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