Pork quality and marbling level assessment usinga hyperspectral imaging system

Jun Qiao c, Michael O. Ngadi a,*, Ning Wang a, Claude Gariepy b, Shiv.O. Prasher a

a Department of Bioresource Engineering, McGill University, Macdonald Campus, Ste-Anne-de-Bellevue, Que., Canada H9X 3V9b Agriculture and Agri-food Canada, Saint-Hyacinthe, Que., Canada J2S 8E3

c China Agricultural University, Beijing 100083, PR China

Abstract

Pork quality is usually evaluated subjectively based on color, texture and exudation characteristics of the meat. In this study, a hyper-spectral imaging-based technique was evaluated for rapid, accurate and objective assessment of pork quality. In addition, marbling levelwas also automatically determined. The system was able to extract spectral characteristics of pork samples. Appropriate spatial featureswere obtained for marbling distribution in pork meat. Existing marbling standards were scanned, and indices of the marbling scores wereformulated by co-occurrence matrix. The principal component analysis (PCA) method was used to compress the entire spectral wave-lengths (430–1000 nm) into 5, 10 and 20 principal components (PCs), which were then clustered into quality groups. Artificial neuralnetwork was used to classify these groups. Results showed that reddish, firm and non-exudative (RFN) and reddish, soft and exudative(RSE) samples were successfully grouped; the total corrected ratio was 75–80%. The feed-forward neural network model yielded cor-rected classification as 69% by 5 PCs and 85% by 10 PCs. Angular second moment was successfully used to determine marbling scoresexcepting the score at 10.0. Forty samples were sorted and the result showed that the samples’ marbling score ranged from 3.0 to 5.0.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Hyperspectral imaging; Pork quality; Marbling; PCA; Cluster analysis; Neural network

1. Introduction

Canada is one of the largest pork exporters in the world.With market expansion and segmentation, the Canadianprocessing industry needs efficient technologies for assess-ment of pork quality in order to maintain its leading posi-tion. Quality of fresh pork varies greatly. Traditionally,pork quality used to be classified into three categoriesbased on color, texture (firmness) and exudation (driploss). Pork meat that is classified as RFN (reddish pink,firm and non-exudative) has a desirable color, and normaltexture and water-holding capacity (WHC). PSE (palepinkish gray, very soft and exudative) meat has an undesir-able appearance and lack firmness due to excessive driploss. DFD (dark purplish red, very firm and dry) meats

have a firm and sticky surface with high WHC, very littleor no drip loss, and very high pH. Over the years two othercategories namely RSE and PFN have arisen.

RSE (redish, soft and exudative) has a normal color, buta soft texture and an exudative character similar to PSE(Kauffman, Cassens, Scherer, & Meeker, 1992). PFN ispale, firm and non-exudative (Nam, Du, Jo, & Ahn,2002). PFN and RSE have been recognized recently asmajor quality defects in Canada, which account for>13% in all defects compared to PSE (13%) and DFD(10%) (Murray, 2000). According to the author, exudativepork can induce an economic loss of $5 per carcass. An effi-cient and effective quality assessment system is urgentlyneeded for the meat industry to identify the defects rapidlyand objectively.

Marbling is the intermingling of fat with lean in a mus-cle and it is usually assessed subjectively by visual assess-ment. Marbling scores encompasses size, number and

distribution of fat particles (Jeremiah, 1998). Visual stan-dards grade the marbling in seven scores by the NationalPork Board (NPB, 1999). Some research works werereported for assessing beef or pork marbling by machinevision (Faucitano, Huff, Teuscher, Gariepy, & Wegner,2005; Shiranita, Hayashi, Otsubo, Miyajima, & Takiyama,2000; Tan, 2004; Toraichi et al., 2002; Yoshikawa et al.,2000). Shiranita, Miyajima, and Takiyama (1998) used aco-occurrence matrix to extract standard texture vectorsfrom beef samples sorted by professional grader and suc-cessfully classified the unevaluated samples.

Hyperspectral imaging techniques can provide not onlyspatial information, as regular imaging systems, but alsospectral information for each pixel within an image. Thisinformation will form a three-dimensional ‘‘hypercube”

which can be analyzed to ascertain minor and/or subtlephysical and chemical features of an object. Thus, a hyper-spectral image can be used to detect physical and geometriccharacteristics such as color, size, shape, and texture. It canalso be used to extract some intrinsic chemical and molec-ular information (such as water, fat, protein, and otherhydrogen-bonded constituent) from a product. Recently,several hyperspectral imaging research were reported onquality assessment for meat, fruit and vegetables. Kim,Chen, and Mehl (2001) introduced a hyperspectral reflec-tance and fluorescence imaging system for food qualityand safety. Cheng et al. (2004) developed a method toinspect damage of cucumber by hyperspectral image. Asimilar approach was successfully developed to inspectthe contamination of chicken carcasses (Kim, Kim, Chen,& Kong, 2004; Yang, Chao, & Chen, 2005).

In this study, the potential of hyperspectral imagingtechniques was exploited for pork quality and marblingassessment. The specific objectives were to evaluate differ-ent groups of pork quality based on spectral informationacquired from the hyperspectral imaging system using prin-ciple components analysis (PCA) and cluster analysis;develop models for classifying the quality levels by artificialneural network (ANN); and to estimate marbling scores ofpork by image texture indices extracted from digitized meatmarbling standard.

Fig. 1. The hyperspectral imaging system.

2. Materials and methods

2.1. Sample preparation

Fresh pork loins were obtained from a local commercialpacking plant from Quebec, Canada, in November, 2005.All the samples from the different quality groups wereselected by a trained employee before being shipped tothe Instrumentation Laboratory at McGill University,Que., Canada.

The loin samples were cut into 1 cm thick chops in thelaboratory. A total of forty samples were used throughoutthe experiment, including RFN (10), PSE (10), PFN (10),and RSE (10). The experiment was conducted at room tem-

perature between 20 and 22 �C. Samples were allowed tobloom for 20 min before spectral analyses.

2.2. Spectral image collection and processing

2.2.1. Hyperspectral imaging systemThe hyperspectral imaging system consisted of a line-

scan spectrograph (ImSpector, V10E, 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(Dorner 2200 series, Donner Mfg. Corp., USA), an enclo-sure, a data acquisition and preprocessing software (Spec-traCube, Auto Vision Inc., CA, USA), and a PC as shownin Fig. 1. The ImSpector collected spectral images in awavelength range of 400–1000 nm with a spectral resolu-tion of 2.8 nm and a spot radius <9 lm. Two fiber-opticlight-guiding branches from the DC illuminator weremounted on the enclosure as a light source. The conveyerwas driven by a stepping motor with a user-defined speed(MDIP22314, Intelligent motion system Inc., USA). Theenclosure (450 � 600 � 750 mm) was constructed withanodized aluminum columns enclosing all sensitive compo-nents and providing a rigid platform.

2.2.2. Image acquisition and processing

During image acquisition, pork samples were trans-ported individually by the conveyor to the field of view(FOV) of the ImSpector with an optimized velocity of1.58 mm/s. The convey speed was selected by trial-and-error to avoid distortion on image size and spatial resolu-tion and fit the predetermined camera exposure time. TheFOV is a line area with 6 cm in length and 180 lm in width.The image acquisition process was controlled by the Spec-traCube software. When a sample came into the FOV, ahyperspectral image was taken and the image was sent tothe PC through a USB port for storage. The images werestored in a raw format for processing. A hyperspectralimage was taken for each sample.

To correct the images from the dark current of the cam-era, a dark image and a white image were obtained by cover-ing the lens with a cap and taking image of a standard whitereference The image was acquired at different wavelengths

using the SpectraCube software. All the spectral images werepreprocessed through the following three procedures usingthe ENVI software (V. 4.1, Research System Inc., boulder,CO, USA).

A relative reflectance was calculated with Eq. (1):

I ¼ Io � BW � B

ð1Þ

where I was the relative-reflectance of an image; Io was theoriginal image; B was the dark image, and W was the whiteimage.

The selection of region of interest (ROI) for a sampleimage is critical and has profound impact on the perfor-mance of prediction models. Spectroscopic systems usuallyhave very small spatial field of view. Therefore they can beeasily affected by the selection of ROI. Hyperspectral imag-ing systems acquire abundant spatial information whilecollecting spectral information. The ROI could be definedeasily. In this study, based on observations of all sampleimages, a circular ROI with a size of 10,000 pixels aroundthe center of each sample was used. The mean spectrum ofthe ROI was used to represent the spectral features of thesample image.

After that, each spectrum was smoothed by a 10-pointsmean filter, and their second derivatives were calculated tocorrect multiplicative scatter, avoid the overlapping peaksand also correct the baseline (Geesink et al., 2003). Theoperation was performed using MATLAB 7.0 (The Math-Works, Inc., Mass., USA).

2.3. Data analysis

2.3.1. PCA and cluster

Principal components analysis (PCA) is a method forcompressing variables. The method generates a new set ofvariables named principal components. Principal compo-nent (PC) is a linear combination of the original variables.All the principal components are orthogonal to each otherso there is no redundant information (The MathWorks,

Fig. 2. Acquired hyperspectral images, the colored images were obtained

Inc., Mass. USA). Park, Chen, Hruschka, Shackelford,and Koohmaraie (2001) successfully used PCA to analyzespectral data for beef tenderness prediction. In this study,several PCs were generated from the spectral data of porksamples for cluster analysis and ANN.

Clusters can be described as continuous regions in anl-dimensional space containing a relatively high density ofpoints and separated from other regions of high densitypoints in the same space by regions of relatively low densitypoints (Theodoridis & Koutroumbas, 2003). In otherwords, cluster analysis is a way to partition a set of objectsinto groups, or clusters, in such a way that the profiles ofobjects in the same cluster are very similar and the profilesof objects in different clusters are quite distinct. The simi-larity of pork samples was observed, and the subjectivelyclassified groups were confirmed using cluster analysis.

2.3.2. Feed-foreword neural network

A feed-forward neural network (FFN) models wereestablished with the PCs as the inputs and the quality levelsas the outputs. The output includes two nodes to identifythe quality levels as: RFN (1,�1); PFN (�1, 1); PSE(�1,�1) and RSE (1,1). One hidden layer with 10 neuronswas structured. The FFN was designed, trained, and testedusing the MATLAB Neural Networks Toolbox (TheMathWorks, Inc., Mass. 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. Two third of the samples were randomlyselected as training (calibration) set, while the rest of thesamples were used as validation set for FFN.

2.4. Marbling classification

The marbling levels of pork are numerated into sevenscores from 1.0 (devoid) to 6.0 and 10.0 (abundant) accord-ing to the pork marbling standards (NPB, 1999). In this

by combined images at wavelengths of 460 nm, 580 nm and 720 nm.

study, the marbling standards were digitalized by the samesystem and image texture indices were extracted from thedigitalized images by co-occurrence matrix (Shiranitaet al., 1998). The image texture index of angular secondmoment (ASM) was used as a reference to estimate themarbling scores of meat sample images.

The contrast of lean meat and marbling was comparedwithin images manually; a significant contrast image in661 nm was selected and used to estimate the marblingscores. The greatest internal rectangular surface of the loineye area was selected as a ROI, the ASM were calculatedfrom it.

3. Results and discussions

3.1. Acquired hyperspectral images and spectral features

Acquired hyperspectral images in different quality levelsare shown in Fig. 2. The colored images were obtained bycombining images at wavelength of 720 nm (red), 580 nm(green), and 460 nm (blue). Hence, an appearance closerto the natural pork color was obtained with the combinedwavelengths. The images in near-infrared range showed abetter resolution, so these images may be used to observechemical attributes of samples. RFN and RSE were darkerthan PFN and PSE. RSE sample also showed more mar-bling than others. Marbling and lean contrasted more atwavelengths between 580 and 720 nm, especially at 661 nm.

Fig. 3 shows the typical spectral curves of the four qual-ity levels. PFN and PSE showed a higher reflectance thanthat of RFN and RSE due the strong reflection of palecolor. Water absorbing bands were clearly observed at750 and 950 nm. The second derivative corrected the mul-

Fig. 3. Typical spectra reflectance and its second derivative of differentquality levels pork samples.

tiplicative scatter. It avoided the overlapping peaks andalso corrected the baseline. The significant differences inthe range from 500 to 600 nm are attributed to differencesin green color components, whereas the differences at 850–950 nm were mainly due to water content or other chemicalfeatures. The differences in spectral data suggested a possi-bility of classifying the quality levels of pork samples usingthe spectral features. The second derivative data was usedto generate PCs to classify the quality levels.

3.2. PCA and cluster analysis

The weights of the principle components 1 (PC1) and 2(PC2) at different wavelengths are illustrated in Fig. 4.Almost the entire wavebands contributed to PC1 sincethere showed a greater weight of over 0.035 from 460 to945 nm. Information on color, water content, etc. is col-lected in this waveband range. Also, two significant con-tributing wave ranges namely 430–465 nm (weight greaterthan 0.04) and 780–864 nm (weight less than �0.04) wereobserved.

Fig. 4. Weights of the principle components 1 (PC1) and 2 (PC2) atdifferent wavelengths.

-15

-10

-5

0

5

10

15

20

-40 -20 0 20 40 60

PC1(28%)

PC

2 (1

2%)

PFN PSE RSERFN

PSEPFN

RFN

RSE

Fig. 5. Principle component scores for the four quality levels of porksamples with principle component 1 (PC1) and 2 (PC2).

Distribution of the first PC (PC1) and second PC (PC2)of the second derivative are shown in Fig. 5. PC1 showedRFN and RSE samples located in an area less than zero.Almost all PSE and half of the PFN samples are locatedin the area greater than zero. PC2 showed the RFN sam-ples were greater than 9.6 and the other samples werebelow this level. The RFN samples were perfectly classified,and the RSE samples were also easier to sort out. However,

Fig. 6. Dendrogram of clustered diff

Table 1Clustered result by 5, 10 and 20 PCs

PCs Levels Samples Correc-ted

Error Reason1

Reason2

Corrected(%)

5 RFN 10 10 0 100PFN 10 6 4 4 to

RSE60

PSE 10 5 5 2 toPFN

3 toRSE

50

RSE 10 9 1 1 toPFN

90

10 RFN 10 10 0 100PFN 10 5 5 4 to

RSE1 toRFN

50

PSE 10 5 5 3 toRSE

2 toPFN

50

RSE 10 10 0 100

20 RFN 10 10 0 100PFN 10 7 3 3 to

RSE70

PSE 10 5 5 5 toRSE

2 toPFN

50

RSE 10 10 0 100

the PFN samples were mixed with RSE and PSE samples.The explained percentage of PC1 was 28% and PC2 was12%, amounting to a total of 40%. Therefore, 5 PCs, 10PCs and 20 PCs with explained percentage of 62%, 76%and 90% were used as the input for cluster analysis andFFN modeling.

Clustered results of quality groups are shown in Fig. 6and Table 1. The dendrogram of 20 PCs showed that theRFN samples (1–10) were completely grouped into cluster1 due to their strong similarity. All of the RSE samples(31–40) were grouped into cluster 3, however, 3 PSE sam-ples (23, 26 and 28) and 3 PFN samples (17, 19 and 20)were grouped into this cluster. Cluster 4 contained 5 PSEsamples and cluster 2 contained mainly PFN samples.RFN and RSE samples were also successfully grouped irre-

erent quality groups by 20 PCs.

Table 2Classified different quality levels of pork samples by FFN

Sample ID Targets (subjectively) Classified 1(5 PCs )

Classified 2(10 PCs )

2 RFN RFN RFN5 RFN RFN RFN8 RFN RFN RFN

11 PFN PFN PFN14 PFN PFN PFN17 PFN RFN PSE20 PFN PFN RSE23 PSE PFN PSE26 PSE RSE PSE29 PSE PFN RSE32 RSE RSE RSE35 RSE RSE RSE38 RSE RSE RSE

00.00020.00040.00060.0008

0.0010.00120.00140.00160.0018

0.002

M1.0

M2.

0M

3.0M

4.0

M5.0

M6.

0

M10

.0

Marbling scores

AS

M

00.00020.00040.00060.0008

0.0010.00120.00140.00160.0018

0.002

0 02 03 04 0

Samples

AS

M

M1.0

M2.0

M3.0

M4.0

M5.0 (10.0)M6.0

a b

Fig. 7. Extracted textures features for marbling in different scores and classified scores.

spective of whether 5, 10 or 20 PCs were used. Half of thePSE samples were grouped incorrectly. Observing theuncorrected samples, almost all of them were classified toRSE group. This indicates that the uncorrected samplesshowed a stronger similarity with RSE samples. Theseuncorrected samples were located between RSE and PFN(PSE) group (‘‘between samples”). These ‘‘between sam-ples” are difficult to classify subjectively. Classifying the‘‘between samples” based on the pork quality standardsby three inspectors, provided different results. It showedthe limitation of the subjective classification. The total cor-rected ratio was 75–80%.

3.3. Classification by FFN

The sample ID corresponds to the identity of pork sam-ples in Fig. 6. Twenty-seven samples were used to train themodel, whereas thirteen samples were used to test it. Clas-sified results are illustrated in Table 2. The corrected clas-sification using 5 PCs was 69% and 85% using 10 PCs.Again the errors came from the PSE group.

3.4. Marbling classification

The extracted image textures of ASM from marblingscores are shown in Fig. 7a. ASM successfully discrimi-nated the marbling scores except for the standard score# 10.0. The extracted ASM from 40 samples with thestandard score (colored lines) are shown in Fig. 7b. Thesorted results showed that most of the samples were at3.0–5.0. However, the sorted results were higher than thatobtained subjectively with an error around 1.0. The mainreason was probably due to the stronger halation of thefresh samples which increased marbling perception com-pared to the digitalized marbling standards. Large batchsamples should be analyzed to confirm and improve themethod.

4. Summary and conclusions

The study demonstrated potentials of a hyperspectralimaging system to extract the spectral information for theassessment of pork quality. It also illustrates the possibilityof using textural features extracted from digitalized mar-bling standards to determine marbling scores of pork meat.The principle components analysis showed that the RFNsamples were perfectly classified, and the RSE samples wereeasier to classify, but only the PFN samples were mixedwith RSE and PSE samples by PC1 and PC2. The explainedpercentage of 5 PCs, 10 PCs and 20 PCs were of 62%, 76%and 90%, respectively. Cluster analysis showed that theRFN and RSE samples were successfully grouped with atotal corrected ratio as 75 to 80%. The feed-forward neuralnetwork model yielded the corrected classification as 69%by 5 PCs and 85% by 10 PCs. The errors also came fromPSE group. ASM was achieved to distinct the marblingscores except the score of 10.0. The 40 samples were sortedand their scores ranged from 3.0 to 5.0.

Acknowledgement

The financial support of the Canadian National Scienceand Engineering Research Council is gratefullyacknowledged.

References

Cheng, X., Chen, Y. R., Tao, Y., Wang, C. Y., Kim, M. S., & Lefcourt, A.M. (2004). A novel integrated PCA and FLD method on hyperspectralimage feature extraction for cucumber chilling damage inspection.Transactions of the ASAE, 47, 1313–1320.

Faucitano, L., Huff, P., Teuscher, F., Gariepy, C., & Wegner, J. (2005).Application of computer image analysis to measure pork marblingcharacteristics. Meat Science, 69, 537–543.

Geesink, G. H., Schreutelkamp, F. H., Frankhuizen, R., Vedder, H. W.,Faber, N. M., Kranen, R. W., et al. (2003). Prodiction of pork quality

attributes from near infrared reflectance spectra. Meat Science, 65,661–668.

Jeremiah, L. E. (1998). Marbling and pork tenderness. Pork Fact Sheet.Des Moines, IA: National Pork Producers Council and AmericanMeat Science Association.

Kauffman, R. G., Cassens, R. G., Scherer, A., & Meeker, D. L. (1992).Variations in pork quality. Des Moines. IA: National Pork ProducersCouncil.

Kim, I., Kim, M. S., Chen, Y. R., & Kong, S. G. (2004). Detection of skintumors on chicken carcasses using hyperspectral fluorescence imaging.Transactions of the ASAE, 47, 1785–1792.

Kim, M. S., Chen, Y. R., & Mehl, P. M. (2001). Hyperspectral reflectanceand fluorescence imaging system food quality and safety. Transactions

of the ASAE, 44, 721–729.NPB (National pork board) (1999). Pork quality standards. Des Moines,

IA USA.Murray, A. C. (2000). Reducing losses from farm gate to packer:

a Canadian’s perspective. In Proceedings of the first interna-

tional virtual conference on pork quality, Concordia, Brazil: pp.72–84.

Nam, K. C., Du, M., Jo, C., & Ahn, D. U. (2002). Effect of ionizingradiation on quality characteristics of vacuum-packaged normal, pale-soft-exudative, and dark-firm-dry pork. Innovative Food Science and

Emerging Technologies, 3, 73–79.

Park, B., Chen, Y. R., Hruschka, W. R., Shackelford, S. D., &Koohmaraie, M. (2001). Principle component regression of near-infrared reflectance spectra for beef tenderness prediction. Transactions

of the ASAE, 44, 609–615.Shiranita, K., Miyajima, T., & Takiyama, R. (1998). Determination of

meat quality texture analysis. Pattern Recognition Letters, 19,1319–1324.

Shiranita, K., Hayashi, K., Otsubo, A., Miyajima, T., & Takiyama, R.(2000). Grading meat quality by image processing. Pattern Recogni-

tion, 33, 97–104.Tan, J. (2004). Meat quality evaluation by computer vision. Journal of

Food Engineering, 61, 27–35.Theodoridis, S., & Koutroumbas, K. (2003). Pattern recognition. London,

UK: Academic Press, Elsevier Science.Toraichi, K., Kwan, P. W. H., Katagishi, K., Sugiyama, T., Wada, K.,

Mitsumoto, M., et al. (2002). On a fluency image coding system forbeef marbling evaluation. Pattern Recognition Letters, 23, 1277–1291.

Yang, C. C., Chao, K., & Chen, Y. R. (2005). Development ofmultispectral image processing algorithms for identification of whole-some, septicemic, and inflammatory process chickens. Journal of Food

Engineering, 69, 225–234.Yoshikawa, F., Toraichi, K., Wada, K., Ostu, N., Nakai, H., Mitsumoto,

M., et al. (2000). On a grading system for beef marbling. Pattern

Recognition Letters, 21, 1037–1050.