Skip to main content
SpringerLink
Log in

Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration

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

Abstract

This study was carried out for rapid and noninvasive determination of water distribution within beef during dehydration using time series hyperspectral imaging (TS-HSI). Hyperspectral images (380–1,700 nm) of beef slices were acquired at different periods of dehydration process. The spectra of beef were extracted from the TS-HSI images using image segmentation process. Principal component analysis was conducted to obtain an overview of the systematic spectral variations during dehydration. Instead of the traditional data mining strategies to cope with the large multivariate data structures in the TS-HSI images, the selection of effective wavelengths was conducted for the first time to reduce the computational burden of the TS-HSI data and predigest calibration modeling. On the basis of the effective wavelengths identified by using successive projections algorithm (SPA), three spectral calibration algorithms of partial least squares regression, least squares support vector machines, and multiple linear regression (MLR) were compared. The SPA-MLR model with Spectral Set I was considered to be the best for determining water content of beef slice. The model led to a coefficient of determination (\( r_V^2 \)) of 0.953 and root mean square error estimated by cross-validation of 1.280 %. The visualization of water distribution within beef slice during dehydration was finally generated by transferring the quantitative model to each pixel in the image to determine water content in all spots of the beef sample. Kinetic analysis of the TS-HSI images was also conducted for the first time to analyze spectral changes of beef during dehydration. The results demonstrate that TS-HSI has the potential of quantitatively visualizing water content of beef rapidly and noninvasively during dehydration in a reasonable accuracy.

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
Fig. 10

Similar content being viewed by others

References

  • AOAC. (1984). Official methods of analysis of the association of official agricultural chemists. Washington: AOAC.

    Google Scholar 

  • Araujo, M. C. U., Saldanha, T. C. B., Galvao, R. K. H., Yoneyama, T., Chame, H. C., & Visani, V. (2001). The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems, 57(2), 65–73.

    Article  CAS  Google Scholar 

  • Balabin, R. M., & Lomakina, E. I. (2011). Support vector machine regression (SVR/LS-SVM)—An alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst, 136(8), 1703–1712.

    Article  CAS  Google Scholar 

  • Balabin, R. M., & Smirnov, S. V. (2011). Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data. Analytica Chimica Acta, 692(1–2), 63–72.

    Article  CAS  Google Scholar 

  • Barbin, D., ElMasry, G., Sun, D. W., & Allen, P. (2012a). Predicting quality and sensory attributes of pork using NIR hyperspectral imaging. Analytica Chimica Acta. doi:10.1016/j.aca.2012.01.004.

  • Barbin, D. F., Elmasry, G., Sun, D. W., & Allen, P. (2012b). Near-infrared hyperspectral imaging for grading and classification of pork. Meat Science, 90(1), 259–268.

    Google Scholar 

  • Chang, S. F., Huang, T. C., & Pearson, A. M. (1996). Control of the dehydration process in production of intermediate-moisture meat products: A review. Advances in Food and Nutrition Research, 39, 71–161.

    Article  CAS  Google Scholar 

  • ElMasry, G., & Wold, J. P. (2008). High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. Journal of Agricultural and Food Chemistry, 56(17), 7672–7677.

    Article  CAS  Google Scholar 

  • ElMasry, G., Iqbal, A., Sun, D. W., Allen, P., & Ward, P. (2011a). Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system. Journal of Food Engineering, 103(3), 333–344.

    Article  Google Scholar 

  • ElMasry, G., Sun, D. W., & Allen, P. (2011b). Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International, 44(9), 2624–2633.

    Article  Google Scholar 

  • ElMasry, G., Sun, D. W., & Allen, P. (2012). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 110(1), 127–140.

    Article  Google Scholar 

  • Gowen, A. A., Marini, F., Esquerre, C., O'Donnell, C., Downey, G., & Burger, J. (2011). Time series hyperspectral chemical imaging data: Challenges, solutions and applications. Analytica Chimica Acta, 705(1–2), 272–282.

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Kamruzzaman, M., ElMasry, G., Sun, D. W., & Allen, P. (2011b). Prediction of some quality attributes of lamb meat using NIR hyperspectral imaging and multivariate analysis. Analytica Chimica Acta. doi:10.1016/j.aca.2011.11.037.

  • Keey, R. B. (1972). Drying: Principles and practice. Oxford: Pergamon Press.

    Google Scholar 

  • Kobayashi, K., Matsui, Y., Maebuchi, Y., Toyota, T., & Nakauchi, S. (2010). Near infrared spectroscopy and hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts. Journal of Near Infrared Spectroscopy, 18(5), 301–315.

    Article  CAS  Google Scholar 

  • Mathlouthi, M. (2001). Water content, water activity, water structure and the stability of foodstuffs. Food Control, 12(7), 409–417.

    Article  Google Scholar 

  • Naganathan, G. K., Grimes, L. M., Subbiah, J., Calkins, C. R., Samal, A., & Meyer, G. E. (2008a). Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction. Sensing and Instrumentation for Food Quality and Safety, 2, 178–188.

    Article  Google Scholar 

  • Naganathan, G. K., Grimes, L. M., Subbiah, J., Calkins, C. R., Samal, A., & Meyer, G. E. (2008b). Visible/near-infrared hyperspectral imaging for beef tenderness prediction. Computers and Electronics in Agriculture, 64(2), 225–233.

    Article  Google Scholar 

  • Peng, Y. K., Zhang, J., Wang, W., Li, Y. Y., Wu, J. H., Huang, H., et al. (2011). Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles. Journal of Food Engineering, 102(2), 163–169.

    Article  Google Scholar 

  • Prieto, N., Roehe, R., Lavin, P., Batten, G., & Andres, S. (2009). Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Science, 83(2), 175–186.

    Article  CAS  Google Scholar 

  • Van Arsdel, W. B. (1963). Food dehydration. Westport: AVI Publishing Company, Inc.

    Google Scholar 

  • Van Der Weerd, J., & Kazarian, S. G. (2007). Multivariate movies and their applications in pharmaceutical and polymer dissolution studies. In Grahn & Geladi (Eds.), Techniques and applications of hyperspectral image analysis (pp. 221–260). Chichester: Wiley.

    Chapter  Google Scholar 

  • Watson, E. L., & Harper, J. C. (1987). Elements of food engineering. New York: Van Nostrand Reinhold.

    Google Scholar 

  • Wold, J. P., O'Farrell, M., Hoy, M., & Tschudi, J. (2011). On-line determination and control of fat content in batches of beef trimmings by NIR imaging spectroscopy. Meat Science, 89(3), 317–324.

    Article  CAS  Google Scholar 

  • Wu, D., He, Y., Feng, S. J., & Sun, D. W. (2008). Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM. Journal of Food Engineering, 84(1), 124–131.

    Article  CAS  Google Scholar 

  • Wu, D., Chen, X. J., Shi, P. Y., Wang, S. H., Feng, F. Q., & He, Y. (2009a). Determination of alpha-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination. Analytica Chimica Acta, 634(2), 166–171.

    Article  CAS  Google Scholar 

  • Wu, D., He, Y., Shi, J. H., & Feng, S. J. (2009b). Exploring near and midinfrared spectroscopy to predict trace iron and zinc contents in powdered milk. Journal of Agricultural and Food Chemistry, 57(5), 1697–1704.

    Article  CAS  Google Scholar 

  • Wu, D., He, Y., Nie, P. C., Cao, F., & Bao, Y. D. (2010a). Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Analytica Chimica Acta, 659(1–2), 229–237.

    Article  CAS  Google Scholar 

  • Wu, J. H., Peng, Y. K., Chen, J. J., Wang, W., Gao, X. D., & Huang, H. (2010b). Study of spatially resolved hyperspectral scattering images for assessing beef quality characteristics. Spectroscopy and Spectral Analysis, 30(7), 1815–1819.

    CAS  Google Scholar 

  • Wu, D., Chen, X. J., Zhu, X. G., Guan, X. C., & Wu, G. C. (2011a). Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver. Analytical Methods, 3(8), 1790–1796.

    Article  CAS  Google Scholar 

  • Wu, D., Nie, P. C., Cuello, J., He, Y., Wang, Z. P., & Wu, H. X. (2011b). Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder. Journal of Food Engineering, 102(3), 278–286.

    Article  CAS  Google Scholar 

  • Wu, D., Nie, P. C., He, Y., & Bao, Y. D. (2012a). Determination of calcium content in powdered milk using near and mid-infrared spectroscopy with variable selection and chemometrics. Food and Bioprocess Technology, 5, 1402–1410.

    Article  CAS  Google Scholar 

  • Wu, D., Shi, H., Wang, S., He, Y., Bao, Y., & Liu, K. (2012b). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chimica Acta, 726, 57–66.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This study was supported by 863 National High-Tech Research and Development Plan (Project No:2011AA100705), Natural Science Foundation of China (31072247), Specialized Research Fund for the Doctoral Program of Higher Education (20100101120084), and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China

    Di Wu, Nanfei Wang, Pengcheng Nie & Yong He

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

    Di Wu & Da-Wen Sun

  3. Department of Mathematics, Zhejiang University, Hangzhou, 310027, China

    Songjing Wang

  4. Management Bureau of Agricultural Machine of Haining, Haining, 314400, China

    Jiansong Yao

Authors
  1. Di Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Songjing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nanfei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pengcheng Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong He
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Da-Wen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jiansong Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong He.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, D., Wang, S., Wang, N. et al. Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration. Food Bioprocess Technol 6, 2943–2958 (2013). https://doi.org/10.1007/s11947-012-0928-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-012-0928-0

Keywords

Search

Navigation