Prediction of drip-loss, pH, and color for pork using ahyperspectral imaging technique

J. Qiao a,c, N. Wang a,*, M.O. Ngadi a, A. Gunenc a, M. Monroy a,C. Gariepy b, S.O. Prasher a

a Department of Bioresource Engineering, McGill University, Macdonald Campus, MS1-027, 21,111 Lakeshore Road, Ste-Anne-de-Bellevue,

Que., Canada H9X 3V9b Agriculture and Agri-food Canada, 3600 Casavant West, Saint-Hyacinthe, Que., Canada J2S 8E3

c China Agricultural University, Beijing 100083, China

Received 10 February 2006; received in revised form 24 June 2006; accepted 26 June 2006

Abstract

Many subjective grading methods with poor repeatability and tedious procedures are still widely used in meat industry. In this study,a hyperspectral-imaging-based technique was investigated to evaluate its potentials for objective determination of pork quality attributes.The system extracted spectral and spatial characteristics simultaneously to determinate the quality attributes, drip loss, pH, and color, ofpork meat. Six feature band images were selected for predicting the drip loss (459, 618, 655, 685, 755 and 953 nm), pH (494, 571,637, 669,703 and 978 nm) and color (434, 494, 561, 637, 669 and 703 nm), respectively. Two intensity indices of the band images were used asinputs to establish neural network models to predict the quality attributes. The results showed that with the hyperspectral-imaging sys-tem, the drip loss, pH, and color of pork meat could be predicted with correlation coefficients of 0.77, 0.55 and 0.86, respectively. Porkmeat could be classified based on their exudative characteristics and color successfully.

Keywords: Pork quality; Exudative characteristics; Feature band images selection; Neural Network

1. Introduction

With market expansion and segmentation, the pork pro-cessing industry needs non-invasive, efficient and effectivetechnologies for meat assessment. The quality of pork var-ies greatly. Reddish, firm and non-exudative (RFN or nor-mal) pork used to stand in between pale, soft and exudative(PSE) and dark, firm and dry (DFD) meat as extremes of apH-related axis. However, this physico-chemical order,where color, texture and water-holding capacity are posi-tively related, is challenged. RSE, a new class of pork qual-ity with an acceptable color but a soft texture and anexudative character comparable to PSE, has been reported(Kauffman, Cassens, Scherer, & Meeker, 1992). There are

also known cases of pale but firm and non-exudative(PFN) pork (Nam, Du, Jo, & Ahn, 2002). Pork qualitygrading is usually performed objectively by human qualityinspector based on pork grading standards (e.g. NPB,1999). Therefore, it becomes very important to developnon-invasive technologies that can rapidly measure thetechnological quality of the meat considering.

The water holding capacity of a pork meat indicates itsaptitude in further processing. The statistical data showthat exudative pork meats can induce an economic lossof $5 per carcass (Murray, 2000). To accurately identifythe exudative pork meats, new quality categories of porkquality were defined. RSE (radish, soft and exudative)has normal color, but a soft texture and an exudative char-acter similar to PSE (Kauffman et al., 1992). Its ultimatepH value is higher than PSE (Van Laack & Kauffman,1999). PFN is pale, firm and non-exudative (Nam et al.,

2002). PFN and RSE have been recognized recently asmajor quality defects in Canada, which account for>13% in all defects comparing to PSE (13%) and DFD(10%) (Murray, 2000). Based on a prediction of a majorCanadian pork processor, the RSE incidence may reachas high as 30% (Fournaise & Davies, 2003). Rapid andobjective techniques to characterize exudative property ofpork meat are urgently needed.

The use of optical properties of meat for quality con-trol has been discussed several decades ago (Swatland,1994). As one of the major optical applications, spectros-copy is widely used to detect the chemical attributes ofpork meat (Swatland, 1995). Savenije, Greesink, Van-der-Palen, and Hemke (2006) developed models to predictpH (r = 0.63–0.84), drip loss (r = 0.56–0.59) and colorvalue L* (r = 0.79–0.89) using wavelength range of 400–1100 nm with modified partial least square (MPLS)method. Chan, Walker, and Mills (2002) predictedWHC (capillary method and press method) and pH usingpartial least square (PLS) and achieved r of 0.71 and 0.58,standard error of prediction (SEP) of 2.45 and 0.16,respectively. Brøndum et al. (2000) archived a correlationcoefficients were 0.72 (SEP = 2.14) and 0.64 (SEP = 2.43)for drip loss prediction using visual (VIS) and near infra-red (NIR) reflectance spectrophotometer, respectively.Josell, Martinsson, Borggaard, Andersen, and Tornberg(2000) predicted pH with r of 0.57 using a VIS andNIR spectroscopy (400–2200 nm) with seven-factor PLS-based models. Forrest et al. (2000) predicted drip losswith r of 0.84, using a wavelength range of 900–1800 nm. Geesink et al. (2003) predicted the drip loss withr of 0.74 using two selected wavelengths, 1404 and2178 nm.

Hyperspectral imaging techniques can provide not onlyspatial information, as regular imaging systems, but alsospectral information for each pixel in an image. This infor-mation will form a three-dimension ‘‘hypercube’’ whichcan be analyzed to ascertain minor and/or subtle physicaland chemical features in an object. Thus, a hyperspectralimage can be used to detect physical and geometric charac-teristics such as color, size, shape, and texture. It can alsobe used to extract some intrinsic chemical and molecularinformation (such as water, fat and protein) from a prod-uct. Recently, several hyperspectral imaging researcheswere reported on quality assessment for meat and fruit veg-etables. Kim, Chen, and Mehl (2001) introduced a hyper-spectral reflectance and fluorescence imaging system forfood quality and safety. Cheng et al. (2004) developed amethod to inspect damage of cucumber by hyperspectralimage. A hyperspectral imaging system was successfullydeveloped to inspect the contamination of chicken car-casses (Kim, Kim, Chen, & Kong, 2004; Yang, Chao, &Chen, 2005).

In this study, the potentials of hyperspectral imagingtechniques will be exploited for evaluating exudative char-acteristics of pork meat based on drip loss, pH, and color.The specific objectives includes selecting feature bands at

which variations of drip loss, pH and color were high-lighted and developing artificial-neural-network (ANN)-based prediction models to predict drip loss, pH and colorusing feature band images.

2. Materials and methods

2.1. Sample preparation

A total of 80 fresh pork samples from the central por-tion of the loin were obtained at 24 h post-mortem froma commercial cutting house (Olymel, Quebec, Canada)from August to November of 2005. All the samples wereselected from different quality categories by the companyinspectors before shipped to the Instrumentation Labora-tory at McGill University, Quebec, Canada.

The loin samples were cut into chops in the laboratorywith a thickness of 1 cm. The 80 samples included RFN(20), PFN (20), PSE (20), and RSE (20). The overall exper-iment was conducted under room temperature between 20and 22 �C.

2.2. Measurement of quality attributes

Drip loss, pH, and color were measured after 24 h ofpostmortem at loin eye area only. Drip loss was determinedas a percentage of weight loss after 2 days storage at 4 �C(Lundstrom & Malmfors, 1985). pH was measured at sixdifferent locations across the sample surface with a pHmeter (AB15, Fisher scientific Inc., USA). The average ofthe six pH readings was used to represent the ultimatepH value of the sample. Color reflectance (L*) was mea-sured after a one hour after bloom period with a MinoltaChromameter (CR-300, Minolta Crop., Japan). Six read-ings of L* were also obtained and averaged on each sampleacross the surface of the Longissimus muscle. The averagedL* value was used as a color indicator of the sample.

2.3. Spectral image collection and feature bands selection

2.3.1. Hyperspectral imaging system

The hyperspectral imaging system consisted of a line-scan spectrograph (ImSpector, VI0E, Spectra ImagingLtd, Finland), a CMOS camera (BCi4-USB-M40LP, Vec-tor International, Belgium), a DC illuminator (Fiber-LitePL900-A, Dolan-Jenner Industries Inc, USA), a conveyer(Donner 2200 series, Donner Mfg. Corp., USA), an enclo-sure, a data acquisition and preprocessing software (Spec-traCube, Auto Vision Inc., USA), and a PC as shown inFig. 1. The ImSpector collected spectral images in a wave-length range of 400–1000 nm with a spectral resolution of2.8 nm and a spot radius <9 lm. Two fiberoptic light-guid-ing branches (the back and front angle of the brancheswere about 45�) from the DC illuminator were mountedon the enclosure as a light source. The conveyer was drivenby a stepping motor with a user-defined speed(MDIP22314, Intelligent motion system Inc., USA). The

Fig. 1. The hyperspectral imaging system.

enclosure (450 · 600 · 750 mm) was constructed withanodized aluminum columns enclosing all sensitive compo-nents and providing a rigid platform.

2.3.2. Image acquisition and pre-processing

During image acquisition, pork samples were conveyedone at a time to the field of view (FOV) of the ImSpectorwith an optimized velocity of 1.58 mm/s. The convey speedwas selected by trial-and-error to avoid distortion on imagesize and spatial resolution and fit the predetermined cam-era exposure time. The FOV is a line area with 6 cm inlength and 180 lm in width. The image acquisition processwas controlled by the SpectraCube software. Upon enter-ing the FOV, a hyperspectral image of the sample wastaken and the image was sent to the PC through a USBport for storage. The images were stored in a raw formatbefore being processed. One hyperspectral image was takenfor each sample.

To correct the images due to dark current effect of cam-era, and to obtain relative reflectance, a dark image and awhite image were obtained by covering the lens with a capand taking an image from a white reference (Teflon whiteboard with 99% reflectance, Edmund Optics Inc., Barring-ton, NJ, USA,).

All the spectral images were preprocessed through thefollowing four procedures using the ENVI software (V.4.1, Research System, Inc., USA) and MATLAB 7.0(The MathWorks, Inc., USA):

1. Calculate a relative reflectance:

I ¼ I0 � BW � B

; ð1Þ

where I was the relative-reflectance of an image; I0 was theoriginal image; B was the dark image, and W was the whiteimage.2. Locate the center of a lion-eye and define a circular

region of interest (ROI) as 10,000 pixels around thecenter.

3. Calculate the mean relative reflectance for each image byaveraging the spectral responses of each pixel in theROI. To avoiding the low ratio of signal to noise, thewavelength range of 430–980 nm was used for furtherprocessing. In total, 80 mean reflectance spectra wereobtained from 80 samples.

4. Smooth each spectrum with a 20 points mean filter andcalculating its first derivative to correct multiplicativescatter and baseline drifts and avoid the overlappingpeaks (Geesink et al., 2003; Xing, Landahl, Lammertyn,Vrindts, & Baerdemaeker, 2003).

2.3.3. Selection of feature band images

Simple correlation analysis was conducted between thespectral response at each wavelength from 430 to 980 nmand corresponding drip loss, pH, and color, respectively.The wavelengths at which the highest correlation coeffi-cient (r) was found were selected as feature wavebands.The feature band images were then extracted from the ori-ginal spectral images at these feature wavebands.

The feature waveband selection can be affected bymany factors, such as dark current effect, illumination sta-bility, conveyor vibration noise, and the system noise ofcamera and spectrograph. In this study, measures weretaken to remove dark-current effects, stabilize lightingconditions and conveyor moving speed, and optimizecamera exposure time in order to achieve optimal featurewavebands.

2.3.4. Calculation of the average intensity of the feature band

images

The average intensity of whole loin-eye area at each fea-ture band images were used to predict drip loss, pH, andcolor.

The following masking method was developed in orderto locate the loin-eye area in a feature band image. The fea-ture band image at 685 nm was used to make a mask due toits significant contrast between the loin-eye area and the

Table 1Drip loss, pH and color L* measured with conventional methods

Drip loss (%) pH Color (L*)

Mean 6.37 5.56 58.40Standard deviation (SD) 2.97 0.12 5.41Minimum 1.55 5.29 44.67Maximum 14.46 6.06 67.89Range 12.91 0.77 23.22SD/range (%) 23.01 15.58 23.30

Table 2The standard error of mean for drip loss, pH and color L measured withthe conventional methods

RFN PFN PSE RSE

Drip loss (%) 0.32a (5.31)b 0.32 (6.20) 0.41 (8.18) 0.41 (5.78)PH 0.06 (5.62) 0.05 (5.53) 0.07 (5.49) 0.09 (5.62)Color (L*) 0.37 (56.09) 0.42 (61.19) 0.44 (63.67) 0.40 (52.66)

a The standard error of mean.b The sample mean of the corresponding.

background. The intensity histogram valley method(Amoroso, Ardizzone, Morreale, & Storniolo, 1999) wasused to segment the lean meat against fat and background.With multiple processes of erosion and filling-holes, theloin-eye area and the other small lean area around the loinwere extracted (Fig. 2). A particle filter (6000 pixels) wasused to remove the small lean meat area and the loin-eyearea was masked. The masking operation was done by acommercial software, WinROOF v3.12 (MITANI Corpo-ration, Japan).

For each feature band image, the average intensity (R)of the masked loin-eye area was calculated and a nor-malized ratio, R 0, was defined using the followingequation:

R0i ¼RiPni¼1Ri

; i ¼ 1; . . . ; n;

n is the number of the feature band images: ð2Þ

Both R and R 0 were used as intensity features to developprediction models for the drip loss, pH, and color of meats.As a relative index, R 0 was less affected by system noise.From the 80 feature band images of the meat samples,60 were randomly selected as a training set (calibrationset) and remaining 20 used as a validation set (predictionset).

2.4. Prediction of drip loss, pH and color

Artificial neural network is widely used for developingmodels under both linear and nonlinear phenomenon.Two sets of feed-forward neural network (FNN) predictionmodels were established to predict drip loss, pH and colorof meat samples. Model set 1 used the intensity feature of R

as input, while Model set 2 used R 0 as input for the FNNmodels. For both model sets, the drip loss, pH and colorwere the outputs. One hidden layer with 10 neurons wasstructured. The models was designed, trained, and testedusing the MATLAB Neural Networks Toolbox (TheMathWorks, Inc., MA, USA). The transfer function ofTansig was used for the hidden layer and Pureline for out-put layer, also learning function of Learngdm and perfor-mance function Mse were used. The training algorithm ofBayesian regularization (Trainbr) was used in order toavoid over fitting.

Fig. 2. Masking operations: (a) band image at 685 nm; (b) image after binarizatfilling holes and further erosion; (e) image mask obtained after particle remov

3. Results and discussions

3.1. Measured meat quality attributes

Table 1 shows results for drip loss, pH, and color mea-sured with conventional methods. The ranges in thesedata are fairly wide and the ratios of standard devia-tion/range for the three attributes were 16–23%, whichmade the data set valuable to develop meaningful predic-tion models.

Table 2 shows the standard error of mean (SEM) fordrip loss, pH and color L* measurements for each qualityclass. SEMs were not significant to the corresponding meanvalues. Hence, the sampling data could be accepted for fur-ther analysis. Intercorrelation among measured drip loss,pH and color L* was presented in Table 3. Color L* topH showed an opposite correlation for four quality classes.Drip loss to pH and color L* showed a contrary for paleand reddish samples. The phenomenon might be due tothe pale samples lost much water with a greater drip loss

ion using histogram valley method; (c) image after erosions; (d) image aftering.

and a lower pH than that of the reddish sample (NPB,1998).

3.2. Acquisition of hyperspectral images and extraction of

spectral features

The hyperspectral images obtained from a RFN sampleare shown in Fig. 3. The upper leftmost image was a com-bined spectral images at three wavelengths located in thered (700 nm), green (525 nm), and blue (460 nm) region.Hence, it appeared similar to its natural color. When thewavebands increased, the intensity of the 14 band imagesvaried.

Four areas (A1 (red), A2 (green), A3 (blue), and A4 (yel-low)) were marked at different locations on the combinedimage, e.g. lean meat area inside loin-eye (A1 and A2), leanmeat area outside loin-eye (A3), and fat area (A4) (Fig. 3).The mean reflectance spectra for the four areas were calcu-lated, respectively, and are shown in Fig. 4. Significant dif-ferences were observed for the reflectance from lean areas(A1, A2, and A3) vs. that from fat area (A4). Large varia-

Table 3Intercorrelation coefficients among drip loss, pH and color L* measuredwith the conventional methods

RFN PFN PSE RSE

Drip loss to pH �0.38 0.10 0.55 �0.65Drip loss to color L* 0.20 �0.23 �0.38 0.52pH to color L* �0.37 �0.63 �0.47 �0.81

Fig. 3. Hyperspectral images of RFN

tions were also found among the mean spectra of A1, A2and A3. Spectral features were therefore not uniformwithin a meat image. Selection of ROI for a sample imagebecomes critical and has profound impacts on the perfor-mance of prediction models. Spectroscopic systems usuallyhave very small spatial field of view; therefore it can be eas-ily affected by the selection of ROI. Hyperspectral imagingsystems acquire abundant spatial information while collect-ing spectral information. The size and location of a ROIcould be defined easily.

The average spectra and its first derivative for each ofthe four meat quality classes are shown in Fig. 5. Asexpected, the reflectance of PSE and PFN were greaterthan those of RFN and RSE due to their lighter color.The reflectance on the wavebands from 540 to 580 nmare associated with respiratory pigment, while the wave-

sample at different wavebands.

Fig. 4. Mean spectral responses of A1, A2 A3 and A4 in Fig. 3.

Fig. 5. Average spectra and its first derivative for RFN, PFN, PSE andRSE.

Fig. 6. Correlation coefficients between spectral reflectance, drip loss, pHand color.

bands in 760, 850 and 970 nm would be related to watercontent of the samples as the other reports mentioned(Cozzolino, Murray, Scaife, & Paterson, 2000; Kawano,2003). The multiplicative scatter and baseline were cor-rected by the first derivative, and the variations wereenhanced at 480–500, 520–580, 900–920 and 960–980 nm.

3.3. Prediction models

3.3.1. Feature wavebands and feature band images

The results from the simple correlation analysis showedthat high correlation coefficients (r) were found at 459, 618,655, 685, 755, and 953 nm for drip loss, 494, 571, 637, 669,703, and 978 nm for pH, and 434, 494, 561, 637, 669, and703 for color. Four identical feature bands correlated bothwith pH and color. Fig. 6 also shows that L* value (reflec-tance) and pH are negatively correlated at all wavelengths.In total, 14 feature wavebands were used to extract the fea-ture band images.

3.3.2. Prediction of the drip loss, pH and color

Table 4 shows the predicted results from two sets of theFNN models.

Except for drip loss, Model 2 (R 0) provided better resultsthan those from Model 1 (R). R 0 as a relative index may

Table 4Predicted drip loss, pH and color using the two sets of FNN models

Quality attribute Selected wavebands (nm) Model 1 (

SEC

Drip loss (%) 459, 618, 655, 685, 775, 953 1.12pH 494, 571, 637, 669, 703, 978 0.07L* 434, 494, 561, 637, 669, 703 2.09

reduce the influence of illumination and enhance differ-ences among data. Both sets of the models provided similarcorrelation coefficients (r > 0.77), which was higher thanthose reported by Savenije et al. (2006) (r > 0.58), Geesinket al. (2003) (r > 0.74), Chan et al. (2002) (r > 0.71) andBrøndum et al. (2000) (r > 0.72). The predicted r for pHwas slightly lower than those reported by Chan et al.(2002) (r > 0.58) and Josell et al. (2000) (r > 0.57). The r

of L* was 0.86, which was slightly higher than thatobtained by Savenije et al. (2006) (r > 0.84).

In order to establish prediction models, only 14 featureband images were selected among wavelengths rangingfrom 430 to 980 nm. The prediction results derived fromthe hyperspectral imaging system in this study comparewell, if not better, with those obtained with high-cost spec-troscopic systems with much wider wavelength range.These results show the potentials of lower-cost hyperspec-tral imaging technique in online pork quality assessment.

Prediction results for drip loss and pH also show abilityto segregate pork samples according to their exudative char-acter and pH value. In Fig. 7, the exudative groups (PSEand RSE) and non-exudative groups (RFN and PFN) ofmeat samples were completely classified using a singlethreshold (T1) (Fig. 7a). Such a finding represents a defini-tive advantage for online or non-contact pork quality deter-mination as exudative character and indirectly the softnessof the lean are more difficult to appreciate from the dis-tance, particularly in the case of RSE. Within exudativesamples, further online color measurements would be easilytaken to separate PSE from RSE. Same procedures can beused within non-extrudative groups as well. On the otherhand, using two thresholds (T1 and T2) to subgroups differ-ing in ultimate pH values (Fig. 7b) could almost entirelysegregate PFN from PSE. The PFN group was locatedabove threshold, T1, while the PSE group was under thresh-

R) Model 2 (R 0)

SEP r SEC SEP r

2.61 0.78 1.17 2.34 0.770.15 0.53 0.07 0.21 0.553.21 0.79 1.76 3.89 0.86

Fig. 7. Classification of pork samples according to their exudative character and pH value. (a) Classify exudative and non-exudative meats with onethreshold (T1); (b) classify reddish and pale meat groups according to pH using two thresholds (T1 and T2).

old T2. RFN and RSE (reddish group) were mixed togetherand located between T1 and T2. As mentioned previously,pH has a strong correlation with color L* value. From thesamples used in this study, Fig. 7b shows that the reddishand pale meats could also be classified based on pH.

4. Conclusions

This study shows the potentials of a hyperspectral imag-ing system in the extraction of spectral information for pre-diction of the technological quality parameters of pork:drip loss, pH and color value L*. Six sensitive wavelengthbands were selected based on simple correlation analysisfor each attributes. A total of 14 feature band images wereextracted for each meat sample. Two intensity indices (Rand R 0) of the feature band images were used to establishFNN-based prediction models to predict drip loss, pHand color.

The result showed that the FNN models with R 0 as input(Model 2) outperformed those with R as inputs (Model 1).The prediction correlation coefficients (r) using Model 2were 0.77, 0.55 and 0.86 for drip loss, pH and color,respectively.

Using predicted drip loss and pH, both exudative (PSE,RSE) and pale (PSE, PFN) subgroups of pork were suc-cessfully segregated from their respective counterpartsusing predicted drip loss or pH, respectively, and applyingsimple threshold techniques.

Acknowledgments

Sincerely appreciate Canadian National Science andEngineering Research Council for providing financial sup-port and Olymel S.E.C./L.P., Quebec, Canada for provid-ing samples for this research.

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