Skip to main content
Log in

Comparison of Visible and Long-wave Near-Infrared Hyperspectral Imaging for Colour Measurement of Grass Carp (Ctenopharyngodon idella)

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

Abstract

This study was conducted to investigate the potential of hyperspectral imaging technique in a rapid and non-invasive manner for measuring colour distribution of grass carp fillets during cold storage. The quantitative calibration models were established between the spectral data extracted from the hyperspectral images and the measured colour reference values by partial least squares regression (PLSR) and least squares support vector machines (LS-SVM). The performance of two spectral ranges of 400–1,000 and 1,000–2,500 nm was compared to select the best spectral range for further colour analysis of grass carp fillets. The LS-SVM model using the whole spectral range possessed better performance than the PLSR model for predicting colour components of L* and a* with higher coefficients of determination (R 2 P) of 0.916 and 0.905 and lower root-mean-square errors of prediction (RMSEPs) of 2.876 and 2.253, respectively. Seven (466, 525, 590, 620, 715, 850 and 955 nm) and five (465, 585, 660, 720 and 950 nm) optimal wavelengths carrying the most important and sensitive information were recognized and selected using successive projections algorithm (SPA) for predicting L* and a*, with R 2 P values of 0.912 and 0.891 being obtained from the optimized SPA-LS-SVM models established based on the selected valuable wavelengths. In addition, the visualization maps of colour distribution of the examined fish fillets were acquired. The overall results of this study demonstrated that hyperspectral imaging technique in the spectral range of 400–1,000 nm has the potential to be used as an objective and promising tool for rapid and non-destructive measurement of colour distribution of grass carp fillets.

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

Access this article

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

Similar content being viewed by others

References

  • Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS regression). Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 97–106.

    Article  Google Scholar 

  • Araújo, M. C. U., Saldanha, T. C. B., Galvão, 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  Google Scholar 

  • Cawley, G. C., & Talbot, N. L. (2002). Improved sparse least-squares support vector machines. Neurocomputing, 48(1), 1025–1031.

    Article  Google Scholar 

  • Chauchard, F., Cogdill, R., Roussel, S., Roger, J., & Bellon-Maurel, V. (2004). Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems, 71(2), 141–150.

    Article  CAS  Google Scholar 

  • Cheng, J.-H., Dai, Q., Sun, D.-W., Zeng, X.-A., Liu, D., & Pu, H.-B. (2013a). Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends in Food Science & Technology, 34(1), 18–31.

    Article  CAS  Google Scholar 

  • Cheng, J.-H., Qu, J.-H., Sun, D.-W., & Zeng, X.-A. (2014a). Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage. Food Research International, 56, 190–198.

    Article  Google Scholar 

  • Cheng, J.-H., Sun, D.-W., Zeng, X.-A., & Pu, H.-B. (2013b). Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. Innovative Food Science and Emerging Technologies. doi:10.1016/j.ifset.2013.10.013.

    Google Scholar 

  • Cheng, J., Sun, D.-W., Zeng, X.-A., & Liu, D. (2013c). Recent advances in methods and techniques for freshness quality determination and evaluation of fish and fish fillets: a review. Critical Reviews in Food Science and Nutrition. doi:10.1080/10408398.2013.769934.

    Google Scholar 

  • Cheng, J. H., Sun, D. W., Han, Z., & Zeng, X. A. (2014b). Texture and structure measurements and analyses for evaluation of fish and fillet freshness auality: a review. Comprehensive Reviews in Food Science and Food Safety, 13(1), 52–61.

    Article  Google Scholar 

  • Costa, C., Antonucci, F., Menesatti, P., Pallottino, F., Boglione, C., & Cataudella, S. (2012). An advanced colour calibration method for fish freshness assessment: a comparison between standard and passive refrigeration modalities. Food and Bioprocess Technology, 6(1), 2190–2195.

    Google Scholar 

  • Cui, Z. W., Xu, S. Y., & Sun, D.-W. (2004). Effect of microwave-vacuum drying on the carotenoids retention of carrot slices and chlorophyll retention of Chinese chive leaves. Drying Technology, 22(3), 563–575. doi:10.1081/DRT-120030001.

    Article  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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Du, C. J., & 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.

    Article  Google Scholar 

  • ElMasry, G., Sun, D.-W., & Allen, P. (2011). 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 

  • 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 

  • Francis, F. (1995). Quality as influenced by color. Food Quality and Preference, 6(3), 149–155.

    Article  Google Scholar 

  • Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Rodriguez-Mendez, M. L., Gomes, A. A., Araújo, M. C. U., & Galvão, R. K. (2012). Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection. Talanta, 89, 286–291.

    Article  CAS  Google Scholar 

  • He, H.-J., Wu, D., & Sun, D.-W. (2013a). Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Science and Emerging Technologies, 18, 237–245.

    Article  Google Scholar 

  • He, H.-J., Wu, D., & Sun, D.-W. (2013b). Non-destructive spectroscopic and imaging techniques for quality evaluation and assessment of fish and fish products. Critical Reviews in Food Science and Nutrition. doi:10.1080/10408398.2012.746638.

    Google Scholar 

  • He, H.-J., Wu, D., & Sun, D.-W. (2014). Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets. Journal of Food Engineering, 126, 156–164.

    Article  Google Scholar 

  • Hu, Z. H., & Sun, D.-W. (2000). CFD simulation of heat and moisture transfer for predicting cooling rate and weight loss of cooked ham during air-blast chilling process. Journal of Food Engineering, 46(3), 189–197.

    Article  Google Scholar 

  • Iqbal, A., Sun, D.-W., & Allen, P. (2013). Prediction of moisture, colour and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. Journal of Food Engineering, 117(1), 42–51.

    Article  CAS  Google Scholar 

  • Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry, 141(1), 389–396.

    Article  CAS  Google Scholar 

  • Khojastehnazhand, M., Khoshtaghaza, M. H., Mojaradi, B., Rezaei, M., Goodarzi, M., & Saeys, W. (2014). Comparison of visible-near infrared and short wave infrared hyperspectral imaging for the evaluation of rainbow trout freshness. Food Research International, 56(2), 25–34.

    Article  CAS  Google Scholar 

  • Maleki, M., Mouazen, A., Ramon, H., & De Baerdemaeker, J. (2007). Multiplicative scatter correction during on-line measurement with near infrared spectroscopy. Biosystems Engineering, 96(3), 427–433.

    Article  Google Scholar 

  • McCaig, T. (2002). Extending the use of visible/near-infrared reflectance spectrophotometers to measure colour of food and agricultural products. Food Research International, 35(8), 731–736.

    Article  CAS  Google Scholar 

  • Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41(3), 285–295.

    Article  Google Scholar 

  • Menesatti, P., Costa, C., & Aguzzi, J. (2010). Quality evaluation of fish by hyperspectral imaging. Hyperspectral Imaging for Food Quality Analysis and Control, 273–294. San Diego, California, USA: Academic Press/Elsevier

  • Mery, D., Pedreschi, F., & Soto, A. (2012). Automated design of a computer vision system for visual food quality evaluation. Food and Bioprocess Technology, 6, 2093–2108.

    Article  Google Scholar 

  • Misimi, E., Mathiassen, J., & Erikson, U. (2007). Computer vision‐based sorting of Atlantic salmon (Salmo salar) fillets according to their color level. Journal of Food Science, 72(1), S030–S035.

    Article  CAS  Google Scholar 

  • Moreira, E. D. T., Pontes, M. J. C., Galvão, R. K. H., & Araújo, M. C. U. (2009). Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection. Talanta, 79(5), 1260–1264.

    Article  CAS  Google Scholar 

  • Pathare, P. B., Opara, U. L., & Al-Said, F. A.-J. (2013). Colour measurement and analysis in fresh and processed foods: a review. Food and Bioprocess Technology, 6(1), 36–60.

    Article  CAS  Google Scholar 

  • Qiao, J., Wang, N., Ngadi, M., Gunenc, A., Monroy, M., Gariépy, C., & Prasher, S. (2007). Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. Meat Science, 76(1), 1–8.

    Article  CAS  Google Scholar 

  • Quevedo, R., Aguilera, J., & Pedreschi, F. (2010). Color of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology, 3(5), 637–643.

    Article  Google Scholar 

  • Rinnan, A., van den Berg, F., & Engelsen, S. B. (2009). Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry, 28(10), 1201–1222.

    Article  CAS  Google Scholar 

  • Sivertsen, A. H., Heia, K., Hindberg, K., & Godtliebsen, F. (2012). Automatic nematode detection in cod fillets (Gadus morhua L.) by hyperspectral imaging. Journal of Food Engineering, 111(4), 675–681.

    Article  Google Scholar 

  • Sivertsen, A. H., Heia, K., Stormo, S. K., Elvevoll, E., & Nilsen, H. (2011). Automatic nematode detection in cod fillets (Gadus morhua) by transillumination hyperspectral imaging. Journal of Food Science, 76(1), S77–S83.

    Article  CAS  Google Scholar 

  • Sone, I., Olsen, R. L., Sivertsen, A. H., Eilertsen, G., & Heia, K. (2012). Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging. Journal of Food Engineering, 109(3), 482–489.

    Article  CAS  Google Scholar 

  • Sun, D.-W. (1997). Thermodynamic design data and optimum design maps for absorption refrigeration systems. Applied Thermal Engineering, 17(3), 211–221.

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Sun, D.-W. (2010). Hyperspectral imaging for food quality analysis and control: Academic Press/Elsevier, San Diego, California, USA, 496 pp., ISBN: 978-0-12-374753-2 (2010).

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

    Article  Google Scholar 

  • Sun, D.-W., & Brosnan, T. (1999). Extension of the vase life of cut daffodil flowers by rapid vacuum cooling. International Journal of Refrigeration-Revue Internationale Du Froid, 22(6), 472–478. doi:10.1016/S0140-7007(99)00011-0.

    Article  Google Scholar 

  • Sun, D.-W., & Hu, Z. H. (2003). CFD simulation of coupled heat and mass transfer through porous foods during vacuum cooling process. International Journal of Refrigeration-Revue Internationale Du Froid, 26(1), 19–27. doi:10.1016/S0140-7007(02)00038-5.

  • Sun, D.-W., & Woods, J. L. (1993). The moisture-content relative-humidity equilibrium relationship of wheat - a review. Drying Technology, 11(7), 1523–1551. doi:10.1080/07373939308916918.

    Article  CAS  Google Scholar 

  • Sun, D.-W., & Woods, J. L. (1994a). Low-temperature moisture transfer characteristics of wheat in thin-layers. Transactions of the ASAE, 37(6), 1919–1926.

    Article  Google Scholar 

  • Sun, D.-W., & Woods, J. L. (1994b). The selection of sorption isotherm equations for wheat-based on the fitting of available data. Journal of Stored Products Research, 30(1), 27–43.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Suykens, J. A., De Brabanter, J., Lukas, L., & Vandewalle, J. (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1), 85–105.

    Article  Google Scholar 

  • Suykens, J. A., Vandewalle, J., & De Moor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14(1), 23–35.

    Article  CAS  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.

    Article  Google Scholar 

  • Wang, L. J., & 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. doi:10.1016/S0924-2244(01)00077-2.

    Article  CAS  Google Scholar 

  • Wu, D., & Sun, D.-W. (2012). Colour measurements by computer vision for food quality control—a Review. Trends in Food Science & Technology, 29(1), 5–20.

    Article  CAS  Google Scholar 

  • Wu, D., & Sun, D.-W. (2013a). Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. Talanta, 116(11), 266–276.

    Article  CAS  Google Scholar 

  • Wu, D., & Sun, D.-W. (2013b). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta, 111(7), 39–46.

    Article  CAS  Google Scholar 

  • Wu, D., Sun, D.-W., & He, Y. (2014). Novel non-invasive distribution measurement of texture profile analysis (TPA) in salmon fillet by using visible and near infrared hyperspectral imaging. Food Chemistry, 145, 417–426.

    Article  CAS  Google Scholar 

  • Wu, J., Peng, Y., Li, Y., Wang, W., Chen, J., & Dhakal, S. (2012). Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique. Journal of Food Engineering, 109(2), 267–273.

    Article  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. doi:10.1016/S0260-8774(01)00022-X.

    Article  Google Scholar 

  • Zheng, L. Y., & Sun, D.-W. (2006). Innovative applications of power ultrasound during food freezing processes - a review. Trends in Food Science & Technology, 17(1), 16–23.

    Article  CAS  Google Scholar 

  • Zhu, F., Zhang, D., He, Y., Liu, F., & Sun, D.-W. (2013a). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food and Bioprocess Technology, 6(1), 2931–2937.

    Article  CAS  Google Scholar 

  • Zhu, F., Zhang, H., Shao, Y., He, Y., & Ngadi, M. (2013b). Mapping of fat and moisture distribution in Atlantic salmon using near-infrared hyperspectral imaging. Food and Bioprocess Technology. doi:10.1007/s11947-013-1228-z.

    Google Scholar 

  • Zou, X., Zhao, J., Povey, M. J., Holmes, M., & Hanpin, M. (2010). Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta, 667(1), 14–32.

    CAS  Google Scholar 

Download references

Acknowledgments

The authors were grateful to the Guangdong Province Government (China) for its 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) and the Foundation for the Author of National Excellent Doctoral Dissertation of South China University of Technology.

Author information

Authors and Affiliations

Authors

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

Cheng, JH., Sun, DW., Pu, H. et al. Comparison of Visible and Long-wave Near-Infrared Hyperspectral Imaging for Colour Measurement of Grass Carp (Ctenopharyngodon idella). Food Bioprocess Technol 7, 3109–3120 (2014). https://doi.org/10.1007/s11947-014-1325-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-014-1325-7

Keywords

Navigation