Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system

Journal of Food Engineering 103 (2011) 333–344

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Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng

Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system Gamal ElMasry a, Abdullah Iqbal a, Da-Wen Sun a,⇑, Paul Allen b, Paddy Ward b a b

FRCFT Group, Biosystems Engineering, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland Ashtown Food Research Centre, Teagasc, Ashtown, Dublin 15, Ireland

a r t i c l e

i n f o

Article history: Received 18 August 2010 Received in revised form 16 October 2010 Accepted 31 October 2010 Available online 11 November 2010 Keywords: Hyperspectral imaging Turkey ham Principle component analysis Image processing Wavelength selection

a b s t r a c t This study was carried out to develop a hyperspectral imaging system in the near infrared (NIR) region (900–1700 nm) to assess the quality of cooked turkey hams of different ingredients and processing parameters. Hyperspectral images were acquired for ham slices originated from each quality grade and then their spectral data were extracted. Spectral data were analyzed using principal component analysis (PCA) to reduce the high dimensionality of the data and for selecting some important wavelengths. Out of 241 wavelengths, only eight wavelengths (980, 1061, 1141, 1174, 1215, 1325, 1436 and 1641 nm) were selected as the optimum wavelengths for the classification and characterization of turkey hams. The data analysis showed that it is possible to separate different quality turkey hams with few numbers of wavelengths on the basis of their chemical composition. The results revealed the potentiality of NIR hyperspectral imaging as an objective and non-destructive method for the authentication and classification of cooked turkey ham slices. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Due to market expansion the ham processing industry needs non-invasive, efficient and subjective technologies for ham quality assessment. Ham quality grading is usually performed objectively by human quality inspector based on grading standards (e.g. NPB, 1999). Turkey ham producers should deliver formulated products to meet special requirements of consumers of superior quality. Examples of such requirements may be an improvement in moisture retention, cooking yields, slicing properties, mouthfeel and juiciness of final products. In practice, the quality of turkey ham is normally assessed either subjectively by experienced personnel or by some chemical techniques. However, these methods are time-consuming and destructive which make it unsuitable for a fast-paced production or processing environment. Therefore, a rapid, objective and non-destructive technique is needed for fast classification and characterization of turkey ham quality. In general, the optical properties of meat have been discussed several decades ago for the quality control of meat (Swatland, 1994). Imaging and spectroscopic are two valuable and proven technologies that provide useful information about the quality of food products being examined and the effects of different processing regimes (Scotter, 1997; Brosnan and Sun, 2004; Schlüter et al., ⇑ Corresponding author. Tel.: +353 1 7167342; fax: +353 1 7167493. E-mail address: [email protected] (D.-W. Sun). URLs: http://www.ucd.ie/refrig, http://www.ucd.ie/sun (D.-W. Sun). 0260-8774/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2010.10.031

2009; Kumar and Mittal, 2009; Pallottino et al., 2010; Quevedo et al., 2010). Spectroscopic techniques in the ultraviolet (UV), visible (VIS) and near infrared (NIR) regions of the electromagnetic spectrum have been developed dramatically and have been shown to be a rapid and effective tool for ham quality assessment, allowing the determination of several parameters simultaneously with a greater frequency of data acquisition (Scotter, 1997; Garcia-Rey et al., 2005; Gangidi and Proctor, 2009). The unrivalled combination of simplicity, accuracy and expeditiousness as well as the limited level of sample preparation makes spectroscopy one of the most popular techniques for determining the essential properties of agricultural products, but less for meat products as compared to plant materials. Indeed, spectroscopy is one of the major optical techniques that has been intensively used in quality evaluation of different types of meat such as pork (Hoving-Bolink et al., 2005; Barlocco et al., 2006; Geesink et al., 2003; Savenije et al., 2006), beef (Ripoll et al., 2008; Anderson and Walker, 2003), lamb (Cozzolino et al., 2000; Andrés et al., 2007), poultry (Windham et al., 2003; Viljoen et al., 2005) and fish (Mengshi et al., 2003; Quevedo and Aguilera, 2010). Moreover, the application of spectroscopic techniques was extended to all meat products including hams (Møller et al., 2003; Laursen et al., 2008; Garcia-Rey et al., 2005) and some of them was successfully implemented in on-line industrial applications (Tøgersen et al., 1999). Unfortunately, with spectroscopic technique it is not possible to provide compositional gradients because the measurement focuses only on a relatively small part of the specimen being analyzed to

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produce average values of composition. In reality, there are some vital cases where spatial distribution of quality parameters is needed especially in meat products like ham to evaluate processing factors. On the other hand, imaging techniques in the form of computer or machine vision systems has the capability to provide superior spatial information. Therefore, imaging techniques have been applied for visual evaluation of meat and meat products and for rapidly identifying quality parameters on the processing line with the minimum of human intervention (Brosnan and Sun, 2004; Du and Sun, 2004; Yang et al., 2009; Fathi et al., 2009; Kaya et al., 2008). Although external attributes such as size, shape, colour, surface texture and external defects can easily be evaluated by imaging techniques, the compositional attributes such as moisture, fat and protein contents are very difficult to be determined with this relatively simple imaging approach due to very limited spectral information (Du and Sun, 2004). Thus, hyperspectral imaging has been introduced to integrate both spectroscopic and imaging techniques in one system for providing both spectral and spatial information simultaneously. Hyperspectral imaging technique provides spatial information, as regular imaging systems, along with spectral information for each pixel in the image. This information then form a three-dimension ‘‘hypercube’’ which can be analyzed to ascertain minor and/or subtle physical and chemical features in a sample. Recently, several hyperspectral imaging research works were reported on quality assessment of meat which accentuated the ability of hyperspectral imaging technique to predict different quality traits in beef, pork, poultry and fish (Naganathan et al., 2008a,b; Qiao et al., 2007b; Park et al., 2007; Sivertsen et al., 2009). It was successfully implemented for predicting beef tenderness (Cluff et al., 2008; Peng and Wu, 2008), classification and prediction of marbling, colour, texture and exudation, drip loss, pH in pork (Qiao et al., 2007a,b,c), contaminants and tumor detection in chicken (Lawrence et al., 2004; Nakariyakul and Casasent, 2004; Park et al., 2006) and assessment of water and fat contents in fish fillets (ElMasry and Wold, 2008). To our knowledge, no research endeavourers have been reported yet for characterizing turkey ham qualities using hyperspectral imaging. Therefore, it is of our interest to implement this technology for the quality evaluation of turkey hams. The overall objective of this study was thus to investigate the potential of hyperspectral imaging in the NIR spectral region of 900–1700 nm for the classification and characterization of turkey hams. Specific objectives were to:  Develop a hyperspectral imaging system in the NIR spectral region (900–1700 nm).  Identify the spectral region or optimal wavelengths that are most useful for the differentiation of different quality turkey hams.  Classify the different quality turkey hams with the selected wavelengths.  Develop image processing algorithms for the visualization of ham qualities in all pixels within an image to form chemical images or classification maps. 2. Materials and methods 2.1. Turkey ham samples For the experimental work of this investigation, four types of turkey hams were prepared in Teagasc Ashtown Food Research Centre, Dublin 15, Ireland. The hams were prepared from whole butterfly turkey breast trimmed of fat and membrane using different levels of brine injection to create four different ham blocks: premium (B1), medium–high (B2), medium–low (B3) and low

Table 1 Specifications for turkey hams parameters during ham processing. Formulation/ process

Types/quality Block1 (premium)

Block2 (medium– high)

Block3 (medium–low)

Block4 (low)

Brine injection rate

10%

20%

30%

40%

6.00% 2.4 2.4 – – – – 89.2

4.30% – – 2.16 1.3 – – 92.24

4.20% – – 2.27 – 1.4 1.05 91.08

90

180

360

6.0 540

6.0 1080

8.0 2880

Brine formulations Salt 11.00% 2.4 S.T.P.Pa Dextrose 2.9 STPP HI.M273 – b Carrageenan – Fairgel 410 – Pea starchc – Water 83.7 Processing parameters Tumbling 45 time (min) Rev/min 6.0 No. of revs. 275

a Sodium tripolyphosphate (Redbrook Ingredient Services Ltd., Mulhuddart, Co. Dublin, Ireland). b Carrageenan and potassium chloride (Chemcolloids Ltd., Congleton, Cheshire, UK). c Pea starch (Coscura, Provital Industries SA, Belgium).

quality hams (B4). The brine formulation, level of injection and other processing conditions are shown in Table 1. During preparing all four quality hams, a pre-forming tumbling was carried out at 6 rpm for 10 min. The injected and tumbled turkey ham was formed, vacuum packed and pressed into shape using pressure moulds before steam cooking. The vacuum tumbling process helps in distributing the brine evenly inside the muscle. The duration of vacuum tumbling was augmented in case of lower quality ham products (B2, B3 and B4) in order to bind the individual muscles together during cooking in which the extracted proteins are denatured, thus making the ham slices more compact. The hams were cooked at 84 °C to a core temperature of 74 °C. All of the turkey ham blocks were chilled in a fridge at 4 °C before slicing. Before image acquisition, the ham blocks were removed from the fridge and kept for 30 min in the room temperature. Each block was then sliced by a mechanical slicer to slices of 10 mm in thickness. The number of slices was 70 slices in total with 16, 18, 18 and 18 slices for the premium (block1), medium–high (block2), medium–low (block3) and low quality (block4) class, respectively. Each slice was then imaged individually in the hyperspectral imaging system line by line as explained below. 2.2. Spectral image collection 2.2.1. Hyperspectral imaging system A laboratory NIR hyperspectral imaging system shown in Fig. 1 was assembled to acquire hyperspectral images for turkey ham slices. The hyperspectral imaging system consists of a spectrograph (ImSpector N17E, Specim, Spectral Imaging Ltd., Oulu, Finland), a high performance camera (Xeva 992, Xenics Infrared Solutions, Belgium), an illumination unit consisting for two 500W halogen lamps (Lowel Light Inc., NY, USA), a translation stage operated by stepper motor (GPL-DZTSA-1000-X, Zolix Instrument Co. Ltd., China) and a computer supported with SpectralCube data acquisition software (Spectral Imaging Ltd., Finland) which controls the motor speed, exposure time, binning mode, wavelength range and image acquisition. The camera has 320  256 (spatial  spectral) pixels with a spatial resolution of 0.578 mm/pixel and a spectral resolu-

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tion of 6 nm. The actual optical sensitivity of this system ranges from 900 to 1800 nm but only the range of 910–1710 nm was used to avoid low signal-to-noise ratio. The speed of the translation stage was adjusted at 28 mm/s to give the same spatial resolution (0.578 mm/pixel) in horizontal and vertical directions. With this pushbroom configuration the ham slice sample was placed on the translation stage to be scanned line by line using 10 ms exposure time to build a hyperspectral image (I) called ‘hypercube’ with dimension of (x, y, k), where x and y are the spatial dimensions (number of rows and columns in pixels) and k is the number of wavebands. Therefore, the composed hyperspectral image could be viewed either as a separate spatial image I(x, y) at each wavelength (k), or as a spectrum I(k) at every pixel (x, y). Each pixel in the hyperspectral image contains the spectrum of that specific position characterizing the chemical components within that pixel. 2.2.2. Image acquisition Each slice of the tested turkey ham blocks was placed on the translation stage and then moved at a speed of 2.8 cm/s to be scanned line by line. The images were stored in a raw format before being processed. The acquired image consists of several congruent images representing intensities at different wavelength bands with 320 pixels in x-direction, n-pixels in y-direction (based on the length of the sample) and 241 wavelengths in k-direction with 0.7 nm between contiguous bands. The information retained in each hyperspectral image includes both spatial and spectral information from which physical and geometric features such as size, orientation, shape, colour, and texture, as well as chemical/ molecular information such as water, fat, protein contents could be extracted. 2.2.3. Image pre-processing The following three subsequent steps were applied to process the images using ENVI software (ITT visual information solutions, Boulder, CO, USA): 2.2.3.1. Correction of hyperspectral images. After scanning each slice, additional two images were recorded for black and white references. The black one (B) was acquired to remove the effect of dark current of the camera sensor and it was acquired when the light source was turned off and the camera lens was completely covered

with its opaque cap. The white reference image (W) was acquired for a Teflon white surface under the same condition of the raw image. The following equation was then used to calculate the corrected image (I):

I¼

I0  B  100 W B

ð1Þ

where I is the corrected hyperspectral image in a unit of relative reflectance (%); I0 is the original hyperspectral image; B is the dark image (0% reflectance) and W is the white reference image (99.9% reflectance). All the corrected images were then used as the basis for subsequent analysis to extract spectral information, effective wavelength selection, classification, and visualization purposes. 2.2.3.2. Image segmentation. The segmentation steps were developed to isolate the ham slice in a homogenous background with the aid of ENVI software (ITT visual information solutions, Boulder, CO, USA) as shown in Fig. 2. During the segmentation it was of interest to separate the lean part of the turkey ham, the fat covering layer and the background of the image. As shown in Fig. 2, the segmentation started by subtracting the image at wavelength 1415 nm (of a very low reflectance value) from the image at wavelength 940 nm (of a very high reflectance value). This subtraction step resulted in a highly contrasted sample (slice) in a homogeneous background as shown in Fig. 2b. The resulted image is then segmented by a simple thresholding with a value of 0.17 as shown in Fig. 2c. This segmented image is called the ‘complete mask’ or lean mask which contains both the lean part of the turkey ham as well as the fat covering layer. Because the main interest is to isolate the lean part of the ham samples in a separate mask, it was necessary to isolate the lean from the fat covering layer. Therefore, another subtraction was carried out between the image at wavelength of 1215 nm (the fat absorption band of a very low reflectance value) from the image at wavelength of 1270 nm (of higher reflectance value) as shown in Fig. 2d followed by a simple thresholding with a value of 0.04 to isolate the fat layer from the turkey ham parts as shown in Fig. 2e in a separate image called the ‘fat mask’. The fat mask is then subtracted from the complete mask to produce the final mask which is called the ‘ham mask’ appearing as white pixels (ones) in black background (zeros) as shown in Fig. 2f. After masking the whole spectral image with this final

Camera

Spectrograph Lens Illumination Sample Translation

Motor

Computer Frame

Fig. 1. Schematic diagram of the hyperspectral imaging system.

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Fig. 2. Key steps involved in segmenting and masking hyperspectral images of the ham slices. (a) The raw hyperspectral image, (b) subtraction: I940–I1415, (c) complete mask that contains lean and fat portions, (d) subtraction: I1270–I1215, (e) fat mask that contains only fat parts, (f) ham mask containing only the lean part of turkey ham slice which is used as the main region of interest (ROI) and (g) the masked image with only lean part on zero background.

segmented image, the target object (only turkey ham without fat layer) was obtained in a black background as shown in Fig. 2g. 2.2.3.3. Spectral data extraction. The final mask ‘ham mask’ resulting from segmentation step was then used as the main region of interest (ROI) to extract the average spectral data from only the ham parts by ignoring the fat covering layer. Reflectance values of the ham slice in the raw hyperspectral image was then extracted where the pixels in the ham mask are active pixels (ones). Then all reflectance values of all pixels were averaged to produce only one mean value representing each slice. The same routine was repeated for all hyperspectral images of all ham slices. It is also wise to mention that the ‘fat mask’ was also used to extract the spectral data of fat covering layers which will be used to demonstrate the difference between fat and lean spectral signatures as well as for visualization task. 2.3. Data analysis The extracted spectral data from all tested slices were then arranged in a matrix (A) where the rows of this matrix represent the number of samples (70 slices) and the columns represent the number of variables (241 wavelengths). These spectral data extracted from all slices of different ham qualities (four blocks) were analyzed using principal component analysis (PCA). The full cross validation method was used for PCA. In essence, PCA is used to decompose the spectral data into several principal components, which are linear combinations of the original spectral data and can represent the spectral variations that are common to all spectral data. This produces a small set of defined factors that can be used for discrimination, since it provides an accurate description

of the entire dataset. Therefore, PCA breaks apart the spectral data matrix (A) into the most common spectral variations in a principal component matrix called the eigenvectors or loadings (F) that contains the maximum variations common to all spectra in a dataset in addition to the corresponding scaling coefficient matrix called the scores (S) described by the equation below:

A ¼ SF þ ea

ð2Þ

where A is an n  k matrix of spectral data, S is an n  f matrix of score values for all of the spectra, and F is an f  k matrix of eigenvectors. The ea is the residual spectra matrix which represents the error in the model’s ability to predict the spectral data and has the same dimension as the A matrix. Also n is the number of samples or number of spectra, k is the number of wavelengths and f is the number of principal components. Loadings resulting from PCA are independent uncorrelated variables describing the wavelength space on the basis of the main cause of variance in all spectra. Therefore spectra could be characterized either by the reflectance at each wavelength in the wavelength space, or by their score on each loading in the PC space. Plotting the two or three first loadings will direct the similar spectra (samples) to aggregate in the same region of this space as if one quality class, whereas samples with different spectral features will be clustered in other parts of this space as if another quality class. 2.4. Wavelength selection The NIR region (910–1710 nm) employed in the current hyperspectral imaging system of this investigation is particularly attractive because most absorption bands observed in this region arise from overtones and combination bands of carbon–hydrogen (C–

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H) stretching vibrations of molecules. Indeed, the extracted spectral data from ham slice images possess a great degree of dimensionality with redundancy among contiguous variables (wavelengths). Redundancy means that some of the variables are correlated with each other which should be reduced to facilitate and hasten the classification of the tested ham qualities. Principal component analysis is a variable reduction procedure which could be used to minimize the number of variables (241 wavelengths) into a smaller number of principal components that account for most of the variance. The loadings resulting from principal component analysis are considered as an indication of the effective wavelengths that does not suffer from redundancy and contribute to classification of ham samples. The important wavelengths are those contributing most to the loadings on the principal components. Therefore, the variables (wavelengths) having high loading values are good candidates to be effective wavelengths and assigned to characteristic absorption regions that correspond to ham quality attributes. 2.5. Visualization of spectral images Displaying ham slices at every single wavelength can be useful, but provides limited information for spatial distribution of different attributes presented in the tested sample. For example, a single wavelength of the hypercube of a ham slice at 1215 nm would show the fat distribution in the slice but without any relevant information about its quantity. Moreover, this particular wavelength is not able to demonstrate other chemical attributes due to the fact that it is only sensitive to fat absorption band. To quantitatively asses the distribution of certain chemical attributes in all spots of the tested sample, calibration models are needed to build visualization maps of these attributes in what is called ‘chemical image’. Therefore, more wavelengths are needed to build such chemical images to demonstrate how different chemical attributes are distributed in the samples. In fact, the value of hyperspectral images increases dramatically when more information from more than one waveband is simultaneously included. The selected wavelengths could be chosen in regions of the spectrum where there is a great difference in reflectance among the different attributes. One simple way to visualize chemical contents in the sample is to create a pseudo-colour composite image. In this process, images at important wavelengths substitute the red, green and blue channels to form a pseudo RGB image. The intensity of each channel in this image represents the reflectance of the tested sample at the corresponding important wavelengths. Moreover, linear transformation by multivariate image analysis could be used to produce some useful mathematical combinations of images. For instance, principal component analysis (PCA) can arrange the variability in the spectral data, and reassign the variability to a new set of bands called score images. The later approach was used in this study to visualize the distribution of major components within ham slices. In this experiment, the extracted spectral data from all ham slice images (of different qualities) were decomposed by PCA and the loading values were then used to produce score image of tested ham slice. As the loadings retain the variability among spectral data especially in the first PCs, the variability inside one slice can be visualized by using these particular PCs. Therefore, the PCA score images resulting from principal component analysis of the spectral data could also be intuitively used instead of the important wavelengths as inputs to form the pseudo-colour image. The image created from the first PC contains most of the variance of the original data set, meanwhile the subsequent PC score images show less details. By using this method, pixels had similar spectral patterns will tend to be projected in the same location of the PCA space and then will appear in similar colors in the classification image. By applying this color mapping to each pixel in the tested im-

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age, a color distribution map was produced. Different colors in the final classification map mean different quality in the image in proportion to the spectral differences of the components. All of these computations, chemometric analyses and visualization process were executed with programs developed in Matlab 8 (The Mathworks, Inc., Natick, MA, USA). 3. Results and discussion 3.1. Appearance of turkey hams slices The turkey ham samples investigated in this study were manufactured entirely from turkey breast muscles and therefore should have similar visual appearance. The injection of brine ensured a uniform distribution of the constituent ingredients and additives that are necessary to achieve the desired colour and texture pertinent to quality specifications (Casiraghi et al., 2007). The brine injection level and the ingredients used are characteristic of each product and determine the final quality of the cooked hams. However, due to different moisture and brine contents as well as different processing regimes, the evaluated ham slices possess a combination of colored, discolored and pale appearance with a larger degree of color heterogeneity, making its visual characterization or discrimination a difficult task. The perceived dissimilarities among qualities emerge mainly due to brine composition and processing conditions, which includes percentage of brine injection and type and duration of mechanical treatment (tumbling), since the raw material (muscles) and cooking conditions were the same. The real moisture content of the tested ham blocks was measured by taking 5–6 cores from each slice and its corresponding moisture content expressed as wet basis values was determined by using AOAC official method (AOAC, 1998). The average moisture content of the tested ham blocks were 67.5 ± 1.65%, 72.4 ± 1.66%, 72.9 ± 2.74% and 72.9 ± 2.87% for block1, block2, block3 and block4, respectively. However, different visual appearance was noticed even inside each slice indicating heterogeneous distribution of moisture content and the other ingredients within the same slice in the same block. Generally, the surface of all turkey ham slices has randomness in their appearance as a common feature, which is difficult to characterize and describe. Differences can be perceived in the spatial distribution of pores or defects in a slice, but it is rather difficult to assign a distinct pattern of visual colour or texture for a given ham slice. Therefore, it is relevant to visualize the spatial distribution of different ham characteristics in the ham slice to evaluate processing routines. 3.2. Spectral features of turkey hams Although the hyperspectral images were acquired in the NIR range of 900–1800 nm, only spectral data between 910 and 1710 (consisting of 241 wavelengths) were taken into account for the analysis. Outside this range, signal-to-noise ratios were too low, probably due to the attenuation of the CCD detector response in these wavelength bands. The mean spectra of the four tested blocks of turkey ham qualities are shown in Fig. 3. In general the peaks observed in the NIR region of the electromagnetic spectrum are due to overtone vibrations of the molecular bonds between hydrogen and carbon, oxygen, or nitrogen (O–H, C–H, C–O and N–H). Some differences were observed among quality blocks especially between block1 and the other three ham block due to the big difference in moisture content between block1 and the other three blocks. Water is the main component of turkey ham slices ranging for 63.72–78.96%, and the absorption bands observed at 980 and 1440 nm related to O–H third and second stretching overtones are mainly due to water content of the samples. It can be seen from

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Fig. 3. Mean spectra the four tested turkey ham blocks and fat covering layer.

Fig. 3 that block1 (premium quality) differs to a large extent from the other three blocks in their reflectance values throughout the whole spectrum. Moreover, significant variations in spectra among samples from the same ham block were also found. Also, it is noticed from Fig. 3 that when the moisture content increased in a sample the reflectance values of such a sample decreased (absorbance increased). However, there are no particular peaks in the spectrum that can be used to discriminate between ham blocks due to similar chemical composition during preparation. The only difference among ham block spectra is the magnitude of absorbance which makes the discrimination between different ham qualities a difficult procedure. At around 1200 nm there is another peak, representing the absorption band of C–H stretching second overtone (Cozzolino and Murray, 2004) which is related to fat content of the sample. When the fat spectrum is plotted along with the spectra of ham blocks, a very sharp peak at 1215 nm was observed in its spectrum indicating that different composition is presented, which enables the possibility of distinguishing this spectrum from ham spectra. The discrimination of fat spectrum from other spectra will facilitate the visualization of all fat pixels in the final step of classification algorithm. 3.3. Principal component analysis by using all wavelengths Principal component analysis (PCA) is the technique used to interpret spectral data by identifying the most important directions of variability in the multivariate data space and expressing the spectral data in such a way to highlight their similarities and differences. PCA as a mathematical procedure decomposes spectral data into several principal components, which are linear combinations of the original spectral data and can represent the spectral variations that are common to all of the spectral data. In the current study, PCA was carried out for all spectral data extracted from all hyperspectral images of all slices using all wavelengths (variables) in the whole spectral ranges (910–1710 nm). The first few principal components resulting from PCA are usually used to examine the common features among samples and their grouping. So, samples having similar spectral signatures tend to aggregate together in the principal component score plot of the first two or three principal components. As shown in Fig. 4, the spectral data are clustered into three distinct groups based on their spectral properties which reflect their composition. There was a full separation between block1 samples (as the first group), the samples of block2, block3 and block4 (as the second group) and the fat spectra extracted from fat layer pixels (as the third group) as shown in Fig. 4a. It was noticed that there was a little interference between some spectra of block1 samples and some spectra of fat spots. In

addition, there was no distinction among block2, block3 and block4 as if they have the same spectral patterns. The same trend was also recognized when the PCA was repeated by using only the spectral data of ham slices without fat spectra as declared in Fig. 4b, indicating the difficulty of classifying the turkey ham slices to four different groups. The only justification of this result is that the main difference among prepared turkey ham blocks is the moisture content, which was responsible for sample discrimination. Since there were no differences in chemical compositions of the samples in terms of their fat and protein contents (simply because they are prepared from the same muscles of the same turkey species), the big contribution for sample discernment is the moisture content. Therefore, the ham samples are separated into two clusters along the first and the second principal components which explained 99.19% (56.43% + 42.76%) of the variation between the samples – the first cluster is the low moisture content class (that includes block1 samples, premium quality) and the second cluster is the high moisture class (that contains samples from block2, block3 and block4). On the PC1–PC2 score plot presented in Fig. 4a and b, one can see that the high-moisture ham samples have positive scores on PC2, whereas the low-moisture samples are situated on the negative side along PC2 like the fat spectra. However, recognizing fat spectra in a separate class is not only ascribed to their very low moisture content but it is also attributed to the very high fat content compared to all ham sample and observed as a very sharp absorption peak at 1215 nm as shown in Fig. 3. This implies that it is possible to classify and characterize the turkey ham slices on the basis of the moisture and fat contents. Nevertheless, within the same sample it is possible to find different spots with a great variability of moisture contents which should be justified by image visualization. 3.4. Effective wavelengths selection Hyperspectral images have redundant information and often require application of dimensionality reduction by using one or more of multivariate analysis strategies such as PCA. The loadings resulting from principal component analysis are considered as an indication of the effective wavelengths that does not suffer from redundancy and contribute to classification of ham samples. The spectral data of all wavelengths (K = 241 variables) extracted from all samples (n = 70) were first analyzed by PCA, and then the variables (wavelengths) having high loading values at the first three PCs were selected as effective wavelengths for discrimination among ham classes, which have been clustered into distinct classes in the principal component plots. From plots of wavelengths versus the loadings of the first principal component (42.52% of the total variance), the second principal component (35.34% of the total variance) and the third principal component (20.58% of the total variance) as shown in Fig. 5, it was decided to retain those wavelengths situated at the maxima or minima of each plot. No data pre-treatment (e.g. derivatives, standard normal variate or multiplicative scatter correction) was applied to the spectra when PCA analysis was carried out. Eight wavelengths (980, 1061, 1141, 1174, 1215, 1325, 1436, 1641 nm) were then selected as the effective wavelengths which can later be used to discriminate the different turkey ham qualities. It was observed that the peaks (maxima) in the second principal component correspond to valleys (minima) in the third principal component. As expected, two of the selected optimal wavelengths were correspond to the water absorption bands at 980 and 1436 nm (Osborne et al., 1993) as water is the main component of turkey ham slices. The fat absorption band was obviously at 1215 nm (Cozzolino and Murray, 2004; Westad et al., 2008) meanwhile peaks at 1061, 1141, 1174, 1325 and 1641 nm were due to N–H and C–H, O–H stretching overtones and combinations related to protein, fat and water contents (Os-

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339

Fig. 4. Score plots of the first and second principal components of PCA using 241 wavelengths in the whole spectral range (910–1710 nm) for (a) spectra of turkey ham blocks and fat layer and for (b) spectra of only turkey ham blocks.

borne et al., 1993; Park et al., 2001; Cen and He, 2007). Such reduced number of wavelengths would help in decreasing the time required to acquire and process each spectral images. 3.5. PCA using effective wavelengths Once the effective wavelengths are selected, the spectral dataset were then reduced to a matrix with a dimension of n  k, where n is the number of samples (n = 70) and the number of variables is reduced from K = 241 to k = 8 wavelengths (the number of effective wavelengths). Principal component analysis was then carried out in the reduced spectral data and the score plot was created to demonstrate the ability of the selected wavelengths for ham classification. The wavelength (variable) having high loading value of PCA contributes most in discrimination of spectra. These wavelengths could be individual wavelengths (at the highest peak of the loadings) or could be a window or band of wavelengths. As shown in Fig. 6, the selected eight wavelengths were able to classify the four blocks of turkey hams into the same categories of quality with better separation between spectra. When the redundant wavelengths are excluded from the dataset, most of co-linearity problem among variables was alleviated, leading to better clustering of ham sam-

ples to distinct quality classes. The same trend was observed in case of analysing the spectral data of ham blocks and fat spectra as shown in Fig. 6a as well as in case of analysing the spectra of ham blocks alone as shown in Fig. 6b. Compared to Fig. 4a in which the PCA was carried out using the full spectral range of 241 wavelengths, Fig. 6a explicitly asserted that using only effective wavelengths gave better discrimination between samples. The first principal component completely separates the spectra of fat from the spectra of ham block without any interference. The fat spectra are located in the negative section of PC2 whereas all ham samples are located in the positive section of PC2. In addition the second principal component clearly separates the ham samples into two classes, the low-moisture samples in the negative part of PC1 and the high-moisture samples in the positive part of PC1. However, the ham samples could not be separated to more than two classes and there was still no discrimination among block2, block3 and block4 of ham slices. When the PCA was repeated using only the spectra of ham slices (without considering fat spectra) by using the effective eight wavelengths, the obtained results is much better, indicating the possibility of classifying ham samples to three different classes as shown in Fig. 6b. The score plot of PCA presented in Fig. 6b revealed that the tested turkey ham samples pre-

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Fig. 5. Selection of effective wavelengths from eigenvectors of the first three principal components derived from PCA of all turkey ham samples.

Fig. 6. Score plots of the first and second principal components of PCA using the effective wavelengths (980, 1061, 1141, 1174, 1215, 1325, 1436 and 1641 nm) for (a) spectra of turkey ham blocks and fat layer and for (b) spectra of only turkey ham blocks.

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pared in a manner indicated in Table 1 could be hardly classified into three different classes. Ham samples of block1 was obviously distinguished in a clear class as expected meanwhile the rest of the other three ham qualities could be separated into another two classes. The ham samples of block2 and block3 are entirely distinguished from each other as two separated classes meanwhile ham samples of block4 did not belong to a clear class. This is attributed to that moisture content of block4’s slices has a high standard deviation (±2.87) indicating that the same slice contains a wide variation of moisture content values. Therefore, the spectral data extracting from these particular slices are not representative to a certain quality class. Strictly speaking, the usage of only effective wavelengths instead of the whole spectral range not only precluded the problem of co-linearity among contiguous wavelengths but also enhanced the discrimination between ham samples. Compared with Fig. 4b, the ham samples could be classified to three different classes as shown in Fig. 6b. In general, preparing ham qualities of different chemical and physical properties is an essential prerequisite for developing more robust multivariate prediction models. Moreover, extracting pure spectra from these predefined classes of ham qualities will enhance the differentiation among ham blocks. For instance, including samples of different moisture contents instead of the very narrow range of moisture content of block2, block3 and block4 (72.4–72.9%) would help in getting reliable classification accuracy. 3.6. Ham classification by linear discriminant analysis While principal component analysis (PCA) extracts new factors suitable for data representation in a reduced form, the linear discriminant analysis seeks data discrimination both for dimensionality reduction and classification. A prior knowledge for predefined classes of the tested samples is a primordial prerequisite for applying discriminant analysis for differentiation among ham quality classes. Linear discriminant analysis is a supervised classification technique used to classify samples into predefined groups by forming discriminant functions from input variables (wavelengths) to yield a new set of transformed values that provides a more accurate discrimination than any variable (wavelength) alone. A discriminant function is then built using samples with known groups to be used later to classify samples with unknown group membership. In general, the extracted full spectra from different ham quality classes at all wavelengths represent the overall spectral signa-

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tures of these classes and contain information about the whole composition and physical conditions of ham quality classes. However, the spectral information extracted from any wavelength that does not have a reasonable contribution towards ham quality class increases the complexity of the analysis and potentially degrading the classification performance. Therefore, the spectral data at only effective wavelengths (selected from PCA analysis) are believed to contain ham quality class membership. Accordingly, the purpose of the discriminant analysis experienced in the dataset aimed at determining whether the selected effective wavelengths best discriminate between ham quality classes. Thus, the dataset was then organized in a two-dimensional form (n  k) containing the spectral data (X) of the training samples (n = 70) at the effective wavelengths (k = 8 variables) in addition to one column vector (Y) of dummy-variable code of the ham quality class representing each sample’s predefined class. Full cross validation by leave-one-out method was used during developing the discriminant analysis calibration model in which one sample was preserved for validation and the rest of the samples (n  1) were used to build the discriminant analysis calibration model which was then used to predict the class of the validation sample. The same routine was repeated until each sample is used once as the validation data. The results indicated that the linear discriminant analysis calibration model correctly classified 100% of all ham quality classes. Fig. 7 graphically depicts the classification of different quality turkey hams with eight selected wavelengths along the first two discriminant functions. It can be discerned that the selected effective wavelengths have a strong discriminatory power for differentiation between the ham quality blocks. The interference between block2, block3 and block4 is alleviated in this model indicating better separability between these quality classes. Indeed, with the naked eye (of consumers who is interested to purchase) it was impossible to differentiate each other but with the use of hyperspectral imaging it is possible to make this kind of discrimination. The ability of this model to discriminate or identify ham quality classes is based on the spectral responses of chemical and physical attributes of the tested ham samples. However, including more samples with higher variability in these physicochemical entities would produce more accurate models especially when extrapolated with different ham slices of different compositions. The next section will be used to evaluate the possibility of using the same effective wavelengths to visualize the differences in quality distribution within ham slices.

Fig. 7. Linear discriminant analysis of turkey hams using effective wavelengths (980, 1061, 1141, 1174, 1215, 1325, 1436 and 1641 nm) as quality class predictors.

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3.7. Visualization of ham slice components Basically, score images resulting from PCA model have different ability to visualize the main components of the examined ham slices based on the amount of variance captured in each score image. Since PCA performs a significant data compression, the first few score images will indicate the greatest contrast in the spectral composition of image features. Score image created from the first PC contains most of the variance of the original dataset, meanwhile the next PC score image show less details and so on. The subsequent PC score images show great amounts of noise or random variations in intensity due to the fact that the major features of the data have already been captured in the first few PCs. Therefore, the first three PC score images were then combined altogether to form a pseudo-colour image (one score image stands for one colour channel). For simplicity of visualization and interpreting the results, the constructed pseudo-colour image was converted to an indexed image by using a colour map of only limited number of colors instead of using the full colour range. Each row of this colour map specifies the red, green, and blue components of a single colour. Therefore, pixels in the composed pseudo-colour image having same features tend to be indexed with the same values (row) in the color map. Colour map of only four rows was formed to index four different components in the image in which any common compo-

nents in the image will be represented (visualized) in the same colour. One row of the colour map was reserved to the image background with a white colour. The second row was given a red colour to represent the first turkey ham block B1. A green colour was assigned to the third row of the colour map to represent turkey ham blocks B2, B3, and B4 altogether. The fat covering layer was the fourth row in the colour map and would be presented in the final indexed image in a blue colour. The indexed image uses direct mapping of pixel values to the values existing in the colour map. The colour of each image pixel in the indexed image is determined by using the corresponding value in the colour map. Simply speaking, if a pixel in the pseudo-colour image is indexed (based on its value) to the first row of the colour map it will appear in a white colour (as background) in the final visualized image, and if it is indexed to the second row of the colour map it will appear in the final visualized image in a red colour (block1, B1) and so on until all pixels are indexed to the corresponding element in the colour map. The left column of Fig. 8 standing for the real colour images of B1, B2, B3 and B4 (from top to bottom) accentuates the difficulty of discriminating ham slices by simple imaging method or via ordinary visual inspection. The middle column of Fig. 8 is the spectral image of the same ham slices at a single wavelength of 1215 nm. It is obvious to notice that spectral image of B1 is significantly differ-

Fig. 8. Visualization of different turkey ham qualities. (a) Real colour images, (b) spectral images at wavelength of 1215 nm and (c) visualized classification map of ham blocks B1, B2, B3 and B4. Fat spot originated from turkey skin during ham processing is marked with dashed circles in the colour images which are then visualized as blue areas in the final visualized images in the right column.

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ent from those of the other three ham blocks (B2, B3 and B4) in terms of image intensity since B1 looks brighter (higher in reflectance) than the other blocks. This result is explicit by looking to the value of reflectance of B1 in Fig. 2 which is much higher (lowest in absorbance) than those of B2, B3 or B4 throughout the whole spectrum. Moreover, the intensity of spectral image of B1 is more homogenous; meanwhile the spectral images of B2, B3 and B4 is a composite of light and dark regions indicating different ham qualities within the same slice and non-uniform distribution of the ingredients and additives. In addition, the fat pixels in spectral images of all ham block appeared as the darkest object in those spectral images since 1215 nm is the absorption band of fat. More interestingly, some fat flecks originated from turkey skin during ham processing could be presented in the slice and these fat parts also appeared very dark in the spectral image at this wavelength. The right column of Fig. 8 is the final visualized image of the same ham slices using the PCA score image routine explained before. By using this method, pixels had similar spectral patterns appeared in the same colors. Different colors in this classification map imply different quality even inside the image of one slice in proportion to the spectral differences of its components. Due to different moisture and brine contents as well as different processing regimes, the evaluated ham slices have different appearance. For instance, the classification map of B3 implied that, although this particular slice is from a high-moisture group (72.9%) it contains both low moisture parts (in red) as well as high-moisture sections (in green). It is worth also to notice that ham slices of B3 and B4 have mixed qualities and heterogeneous distribution of ham quality. This is ascribed to the high standard deviation of moisture contents in B3 and B4 (2.74% and 2.87%, respectively) indicating that the same slice has moisture content spread out over a large range of values and therefore has both edges of ham qualities. In fact, this distribution could not be seen at all in the real colour images (Fig. 8a) of the same slices. Indeed, the visualization ability of PCA method explained here depends on the quality of the original spectral data extracted from the ham samples under investigation. To achieve more accurate results and more clear classification maps, the spectral data should be extracted from more homogenous samples to reflect the real properties of different ham grades. Furthermore, to see the gradient of such traits in the visualized classification map a multivariate regression techniques such as multiple linear regression (MLR), partial least squares (PLS) or principal component regression (PCR) should be implemented to link the spectral data with its corresponding values of these traits. However, the results recognized here are very sound since the difference in quality distribution within the same slice could be used as an indication to evaluate the processing regimes employed in preparing turkey hams. The classification images obtained from this protocol explained to a great extent the robustness of this technique in quality classification of turkey ham. It is obvious that this technique enables early sorting of products without additional laborious chemical analysis and thereby improves quality management specially if applied at an early stage of processing.

4. Conclusions A hyperspectral imaging system in the near infrared (NIR) region of 900–1700 nm was developed to evaluate the quality of cooked turkey hams. The results revealed that the classification accuracy is increased when eight optimal wavelengths (980, 1061, 1141, 1174, 1215, 1326, 1436 and 1641 nm) were chosen for classification purpose indicating the potentiality of NIR hyperspectral imaging to screen samples on the basis of their spectral information. The results of this study suggest that NIR hyperspec-

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