Skip to main content
SpringerLink
Log in

Using Wavelet Textural Features of Visible and Near Infrared Hyperspectral Image to Differentiate Between Fresh and Frozen–Thawed Pork

  • Original Paper
  • Published:
Food and Bioprocess Technology Aims and scope Submit manuscript

Abstract

In this study, wavelet textural analysis was applied to hyperspectral images in the visible and near-infrared (VIS/NIR) region (400–1,000 nm) for differentiation between fresh and frozen–thawed pork. The spectral data of acquired hyperspectral images were analyzed using partial least squares (PLS) regression and five wavelengths (462, 488, 611, 629, and 678 nm) were selected as the feature wavelengths by the regression coefficients from the PLS model. The fourth-order daubechies wavelet (“db4”) was used to serve as the wavelet mother function for wavelet textural extraction of the feature images at the above selected feature wavelengths with the wavelet decomposition level from 1 to 4. Four textural features were calculated in the horizontal, vertical, and diagonal orientations at each level. Forty-eight textural features were extracted from each feature image and used to differentiate between fresh and frozen–thawed pork samples by least-squares support vector machine (LS-SVM) model. Wavelet texture extracted from all five feature images at first decomposition level was identified as optimal wavelet texture combination, resulting in the highest classification accuracy for the LS-SVM models (98.48 % for the training set and 93.18 % for the testing set). Based on the texture combination, the quality attributes of pork meat could be predicted with correlation coefficients of calibration (r c ) of 0.982 and 0.913, and correlation coefficients of prediction (r p ) of 0.845 and 0.711 for pH and thawing loss, respectively. The results showed the possibility of developing a fast and reliable hyperspectral system for discrimination between fresh and frozen–thawed pork samples based on wavelet texture in the VIS/NIR wavelength range.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Barbin, D., Sun, D.-W., & Su, C. (2013). NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen-thawed porcine longissimus dorsi muscles. Innovative Food Science & Emerging Technologies, 18, 226–236.

    Article  CAS  Google Scholar 

  • Benjakul, S., Visessanguan, W., Thongkaew, C., & Tanaka, M. (2003). Comparative study on physicochemical changes of muscle proteins from some tropical fish during frozen storage. Food Research International, 36(8), 787–795.

    Article  CAS  Google Scholar 

  • Bossu, J., Gee, C., Jones, G., & Truchetet, F. (2009). Wavelet transform to discriminate between crop and weed in perspective agronomic images. Computers and Electronics in Agriculture, 65(1), 133–143.

    Article  Google Scholar 

  • Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.

    Article  Google Scholar 

  • Braga, R. A., Horgan, G. W., Enes, A. M., Miron, D., Rabelo, G. F., & Filho, J. (2007). Biological feature isolation by wavelets in biospeckle laser images. Computers and Electronics in Agriculture, 58(2), 123–132.

    Article  Google Scholar 

  • Carballo, J., Cofrades, S., Solas, M. T., & Jimenez-Colmenero, F. (2000). High pressure/thermal treatment of meat batters prepared from freeze-thawed pork. Meat Science, 54(4), 357–364.

    Article  CAS  Google Scholar 

  • Choudhary, R., Paliwal, J., & Jayas, D. S. (2008). Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images. Biosystems Engineering, 99(3), 330–337.

    Article  Google Scholar 

  • Choudhary, R., Mahesh, S., Paliwal, J., & Jayas, D. S. (2009). Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering, 102(2), 115–127.

    Article  Google Scholar 

  • Cozzolino, D., Barlocco, N., Vadell, A., Ballesteros, F., & Gallieta, G. (2003). The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle. Lebensmittel-Wissenschaft Und-Technologie-Food Science and Technology, 36(2), 195–202.

    Article  CAS  Google Scholar 

  • Delgado, A. E., Sun, & D.-W. (2002). Desorption isotherms for cooked and cured beef and pork. Journal of Food Engineering, 51(2), 163--170.

  • Delgado, A. E., Zheng, L., & Sun, D.-W. (2009). Influence of ultrasound on freezing rate of immersion-frozen apples. Food and Bioprocess Technology, 2(3)263--270.

  • Du, CJ., Sun, & D.-W. (2005). Comparison of three methods for classification of pizza topping using different colour space transformations. Journal of Food Engineering, 68(3), 277--287.

  • Durek, J., Bolling, J. S., Knorr, D., Schwagele, F., & Schluter, O. (2012). Effects of different storage conditions on quality related porphyrin fluorescence signatures of pork slices. Meat Science, 90(1), 252–258.

    Article  CAS  Google Scholar 

  • Estevez, M., Kylli, P., Puolanne, E., Kivikari, R., & Heinonen, M. (2008). Fluorescence spectroscopy as a novel approach for the assessment of myofibrillar protein oxidation in oil-in-water emulsions. Meat Science, 80(4), 1290–1296.

    Article  CAS  Google Scholar 

  • Fletcher, R. (1987). Practical methods of optimization. Chichester and New York: John Wiley and Sons.

    Google Scholar 

  • Gowen, A. A., Taghizadeh, M., & O’Donnell, C. P. (2009). Identification of mushrooms subjected to freeze damage using hyperspectral imaging. Journal of Food Engineering, 93(1), 7–12.

    Article  Google Scholar 

  • Hoving-Bolink, A. H., Vedder, H. W., Merks, J. W. M., de Klein, W. J. H., Reimert, H. G. M., Frankhuizen, R., et al. (2005). Perspective of NIRS measurements early post mortem for prediction of pork quality. Meat Science, 69(3), 417–423.

    Article  CAS  Google Scholar 

  • Jackman, P., & Sun, D. W. (2013). Recent advances in image processing using image texture features for food quality assessment. Trends in Food Science & Technology, 29(1), 35–43.

    Article  CAS  Google Scholar 

  • Jung, E. Y., Yun, I. R., Go, G. W., Kim, G. D., Seo, H. W., Joo, S. T., et al. (2012). Effects of radix puerariae extracts on physicochemical and sensory quality of precooked pork sausage during cold storage. LWT--Food Science and Technology, 46(2), 556–562.

    Article  CAS  Google Scholar 

  • Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2011). Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of food engineering, 104(3), 332--340.

  • Kamruzzaman, M., Barbin, D., ElMasry, G., Sun, D.-W., & Allen, P. (2012). Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat. Innovative Food Science & Emerging Technologies, 16, 316–325.

    Article  CAS  Google Scholar 

  • Lai, Y., Ni, Y., & Kokot, S. (2011). Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods. Vibrational Spectroscopy, 56(2), 154–160.

    Article  CAS  Google Scholar 

  • Li, C. B., Liu, D. Y., Zhou, G. H., Xu, X. L., Qi, J., Shi, P. L., et al. (2012). Meat quality and cooking attributes of thawed pork with different low field NMR T-21. Meat Science, 92(2), 79–83.

    Article  Google Scholar 

  • Liao, Y., Fan, Y., & Cheng, F. (2012). On-line prediction of pH values in fresh pork using visible/near-infrared spectroscopy with wavelet de-noising and variable selection methods. Journal of Food Engineering, 109(4), 668–675.

    Article  CAS  Google Scholar 

  • Liu, Y. L., & Chen, Y. R. (2001). Two-dimensional visible/near-infrared correlation spectroscopy study of thawing behavior of frozen chicken meats without exposure to air. Meat Science, 57(3), 299–310.

    Article  CAS  Google Scholar 

  • Liu, Y. L., Barton, F. E., Lyon, B. G., Windham, W. R., & Lyon, C. E. (2004). Two-dimensional correlation analysis of visible/near-infrared spectral intensity variations of chicken breasts with various chilled and frozen storages. Journal of Agricultural and Food Chemistry, 52(3), 505–510.

    Article  CAS  Google Scholar 

  • Mamani-Linares, L. W., Gallo, C., & Alomar, D. (2012). Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Science, 90(2), 378–385.

    Article  CAS  Google Scholar 

  • Millar, S. J., Moss, B. W., & Stevenson, M. H. (2000). The effect of ionising radiation on the colour of leg and breast of poultry meat. Meat Science, 55(3), 361–370.

    Article  CAS  Google Scholar 

  • Monroy, M., Prasher, S., Ngadi, M. O., & Karimi, Y. (2010). Pork meat quality classification using visible/near-infrared spectroscopic data. Biosystems Engineering, 107(3), 271–276.

    Article  Google Scholar 

  • Ngadi, M., Xing, J., Gunenc, A., Prasher, S., & Gariepy, C. (2007). Use of visible spectroscopy for quality classification of intact pork meat. Journal of Food Engineering, 82(2), 135–141.

    Article  Google Scholar 

  • Pelckmans, K., Suykens, J. A., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., et al. (2002). LS-SVMlab: a matlab/c toolbox for least squares support vector machines. Tutorial. Leuven: KULeuven-ESAT.

    Google Scholar 

  • Ramos, R. P., do Nascimento, M. Z., & Pereira, D. C. (2012). Texture extraction: an evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms. Expert Systems with Applications, 39(12), 11036–11047.

    Article  Google Scholar 

  • Reum, D., & Zhang, Q. (2007). Wavelet based multi-spectral image analysis of maize leaf chlorophyll content. Computers and Electronics in Agriculture, 56(1), 60–71.

    Article  Google Scholar 

  • Sen, A. R., & Sharma, N. (2004). Effect of freezing and thawing on the histology and ultrastructure of buffalo muscle. Asian-Australasian Journal of Animal Sciences, 17(9), 1291–1295.

    Google Scholar 

  • Simoniova, A., Rohlik, B.-A., Skorpilova, T., Petrova, M., & Pipek, P. (2013). Differentiation between fresh and thawed chicken meats. Czech Journal of Food Sciences, 31(2), 108–115.

    CAS  Google Scholar 

  • Sun, D.-W., & Woods, J. L. (1993). The moisture-content relative-humidity equilibrium relationship of wheat-a review. Drying Technology, 11(7), 1523--1551.

  • Sun, D.-W., & Woods, J. L. (1994). Low-temperature moisture transfer characteristics of barley-thin-layer models and equilibrium isotherms. Journal of Agricultural Engineering Research, 59(4), 273--283.

  • Sun, D.-W. (1999). Comparison and selection of EMC ERH isotherm equations for rice. Journal of Stored Products Research, 35(3), 249--264.

  • Sun, DW., & Byrne, C. (1998). Selection of EMC/ERH isotherm equations for rapeseed. Journal of Agricultural Engineering Research, 69(4), 307--315.

  • Sun, D.-W., & Woods, J. L. (1997). Simulation of the heat and moisture transfer process during drying in deep grain beds. Drying Technology, 15(10), 2479--2508.

  • Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.

    Article  Google Scholar 

  • Tsai, F., & Philpot, W. (1998). Derivative analysis of hyperspectral data. Remote Sensing of Environment, 66(1), 41–51.

    Article  Google Scholar 

  • Uddin, M., Okazaki, E., Turza, S., Yumiko, Y., Tanaka, M., & Fukuda, Y. (2005). Non-destructive visible/NIR spectroscopy for differentiation of fresh and frozen-thawed fish. Journal of Food Science, 70(8), 506–510.

    Article  Google Scholar 

  • Valous, N. A., Mendoza, F., Sun, D.-W., & Allen, P. (2009). Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat science, 81(1), 132--141.

  • Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.

    Book  Google Scholar 

  • Wang, LJ., & Sun, D.-W. (2001). Rapid cooling of porous and moisture foods by using vacuum cooling technology. Trends in Food Science & Technology, 12(5-6), 174--184.

  • Xia, X., Kong, B., Liu, Q., & Liu, J. (2009). Physicochemical change and protein oxidation in porcine longissimus dorsi as influenced by different freeze-thaw cycles. Meat Science, 83(2), 239–245.

    Article  CAS  Google Scholar 

  • Xu, S. Y., Chen, X. F., Sun, & D.-W. (2001). Preservation of kiwifruit coated with an edible film at ambient temperature. Journal of Food Engineering, 50(4), 211--216.

  • Zhang, X. W., Zhang, L. F., Huang, C. P., Zheng, L. F., & Tong, Q. X. (2011). Fresh and frozen-thawed meat discrimination based on FISS imaging spectral data. Spectroscopy and Spectral Analysis, 31(8), 2187–2190.

    CAS  Google Scholar 

  • Zheng, C. X., Sun, D. W., & Zheng, L. Y. (2006). Recent applications of image texture for evaluation of food qualities—a review. Trends in Food Science & Technology, 17(3), 113–128.

    Article  CAS  Google Scholar 

  • Zhu, F., Zhang, D., He, Y., Liu, F., & Sun, D.-W. (2013). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food and Bioprocess Technology, 10(6), 2931–2937.

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the Guangdong Province Government (China) for support through the program of “Leading Talent of Guangdong Province (Da-Wen Sun)”. This research was also supported by the National Key Technologies R&D Program (2014BAD08B09), the Fundamental Research Funds for the Central Universities (2014ZM0027), and China Postdoctoral Science Foundation (2013M530366).

Author information

Authors and Affiliations

  1. College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, 510641, China

    Hongbin Pu, Da-Wen Sun, Ji Ma, Dan Liu & Jun-hu Cheng

  2. Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland

    Da-Wen Sun

Authors
  1. Hongbin Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Da-Wen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ji Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun-hu Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Da-Wen Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pu, H., Sun, DW., Ma, J. et al. Using Wavelet Textural Features of Visible and Near Infrared Hyperspectral Image to Differentiate Between Fresh and Frozen–Thawed Pork. Food Bioprocess Technol 7, 3088–3099 (2014). https://doi.org/10.1007/s11947-014-1330-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-014-1330-x

Keywords

Search

Navigation