Skip to main content
SpringerLink
Log in

Application of Near-Infrared (NIR) Hyperspectral Imaging System for Protein Content Prediction in Chickpea Flour

  • Conference paper
  • First Online:
Agriculture-Centric Computation (ICA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1866))

Included in the following conference series:

  • 150 Accesses

Abstract

Chickpea flour, being high in protein content, is used in several culinary preparations to make protein rich foods. Dumas method is typically used to measure the protein content of chickpea flour, but it is time-consuming, expensive, and labor-intensive. The protein content of chickpea flour was predicted using near-infrared (NIR) hyperspectral imaging in this research. To produce chickpea flour, eight chickpea varieties with varying levels of protein were ground into powder. NIR reflectance hyperspectral imaging was carried out on chickpea flour powder samples between 900 and 2500 nm spectral range. The protein content of twenty-four samples of chickpea flour (8 var \(\times \) 3 replications) was measured using the Dumas combustion method. The measured reference protein content (dependent variables) and the spectral data (independent variables) of the chickpea flour samples were correlated. Out of total 24 samples, the calibration model was built using 16 powder samples, while the prediction model was built using 8 powder samples. With orthogonal signal correction (OSC)+standard normal variate (SNV) preprocessing, the optimal protein prediction model was obtained using PLSR, which yielded correlation coefficient of prediction (R2p) and root mean square error of prediction (RMSEP) values of 0.934 and 1.006, respectively. Further, competitive adaptive reweighted sampling (CARS) selected 11 feature wavelengths from the studied spectrum and produced the best PLSR model with R2p and RMSEP of 0.944 and 0.889, respectively. As a result, the best prediction model for protein prediction in chickpea flour was obtained by combining PLSR, OSC+SNV and CARS selected wavelength.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Food and Agriculture Organization (fao). Faostat statistical database of the united nation food and agriculture organization (fao) Statistical division, Rome (2020)

    Google Scholar 

  2. Barker, B.: Understanding protein in pulses. Pulse advisor. Saskatchewan pulse growers, p. 1 (2019)

    Google Scholar 

  3. Diaz-Contreras, L.M., Erkinbaev, C., Paliwal, J.: Non-destructive and rapid discrimination of hard-to-cook beans. Can. Biosyst. Eng. 60, 7.1–7.8 (2018)

    Google Scholar 

  4. Saha, D., Manickavasagan, A.: Chickpea varietal classification using deep convolutional neural networks with transfer learning. J. Food Process Eng. 45(3), e13975 (2022)

    Article  Google Scholar 

  5. Aporaso, N., Whitworth, M.B., Fisk, I.D.: Protein content prediction in single wheat kernels using hyperspectral imaging. Food Chem. 240, 32–42 (2018)

    Article  Google Scholar 

  6. Grasso, N., Lynch, N.L., Arendt, E.K., O’Mahony, J.A.: Chickpea protein ingredients: a review of composition, functionality, and applications. Compr. Rev. Food Sci. Food Saf. 21(1), 435–452 (2022)

    Article  Google Scholar 

  7. Boye, J.I., et al.: Comparison of the functional properties of pea, chickpea and lentil protein concentrates processed using ultrafiltration and isoelectric precipitation techniques. Food Res. Int. 43(2), 537–546 (2010)

    Article  Google Scholar 

  8. Boye, J., Zare, F., Pletch, A.: Pulse proteins: processing, characterization, functional properties and applications in food and feed. Food Res. Int. 43(2), 414–431 (2010)

    Article  Google Scholar 

  9. Day, L.: Proteins from land plants-potential resources for human nutrition and food security. Trends Food Sci. Technol. 32(1), 25–42 (2013)

    Article  Google Scholar 

  10. Schutyser, M.A.I., Pelgrom, P.J.M., Van der Goot, A.J., Boom, R.M.: Dry fractionation for sustainable production of functional legume protein concentrates. Trends Food Sci. Technol. 45(2), 327–335 (2015)

    Article  Google Scholar 

  11. Sharma, S., Pradhan, R., Manickavasagan, A., Thimmanagari, M., Dutta, A.: Evaluation of nitrogenous pyrolysates by PY-GC/MS for impacts of different proteolytic enzymes on corn distillers solubles. Food Bioprod. Process. 127, 225–243 (2021)

    Article  Google Scholar 

  12. Saha, D., Senthilkumar, T., Singh, C.B., Manickavasagan, A.: Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network. J. Food Compos. Anal. 120, 105290 (2023)

    Article  Google Scholar 

  13. Saha, D., Manickavasagan, A.: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: a review. Curr. Res. Food Sci. 4, 28–44 (2021)

    Article  Google Scholar 

  14. Huang, M., Tang, J., Yang, B., Zhu, Q.: Classification of maize seeds of different years based on hyperspectral imaging and model updating. Comput. Electron. Agric. 122, 139–145 (2016)

    Article  Google Scholar 

  15. Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G., Frias, J.M.: Hyperspectral imaging-an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 18(12), 590–598 (2007)

    Article  Google Scholar 

  16. Saha, D., Senthilkumar, T., Sharma, S., Singh, C.B., Manickavasagan, A.: Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed. J. Food Compo. Anal. 115, 104938 (2023)

    Article  Google Scholar 

  17. Pullanagari, R.R., Li, M.: Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. J. Food Eng. 289, 110177 (2021)

    Article  Google Scholar 

  18. Sun, J., Ma, B., Dong, J., Zhu, R., Zhang, R., Jiang, W.: Detection of internal qualities of hami melons using hyperspectral imaging technology based on variable selection algorithms. J. Food Process Eng. 40(3), e12496 (2017)

    Article  Google Scholar 

  19. Li, Y., Ma, B., Li, C., Guowei, Yu.: Accurate prediction of soluble solid content in dried hami jujube using swir hyperspectral imaging with comparative analysis of models. Comput. Electron. Agric. 193, 106655 (2022)

    Article  Google Scholar 

  20. Laborde, A., Puig-Castellví, F., Jouan-Rimbaud Bouveresse, D., Eveleigh, L., Cordella, C., Jaillais, B.: Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and multivariate curve resolution. Food Control 119, 107454 (2021)

    Article  Google Scholar 

  21. Senthilkumar, T., Jayas, D.S., White, N.D.G., Fields, P.G., Grafenhan, T.: Detection of fungal infection and ochratoxin a contamination in stored barley using near-infrared hyperspectral imaging. Biosyst. Eng. 147, 162–173 (2016)

    Article  Google Scholar 

  22. Senthilkumar, T., Jayas, D.S., White, N.D.G.: Detection of different stages of fungal infection in stored canola using near-infrared hyperspectral imaging. J. Stored Prod. Res. 63, 80–88 (2015)

    Article  Google Scholar 

  23. Cruz-Tirado, J.P., Fernández Pierna, J.A., Rogez, H., Fernandes Barbin, D., Baeten, V.: Authentication of cocoa (theobroma cacao) bean hybrids by NIR-hyperspectral imaging and chemometrics. Food Control 118, 107445 (2020)

    Article  Google Scholar 

  24. Florián-Huamán, J., Cruz-Tirado, J.P., Fernandes Barbin, D., Siche, R.: Detection of nutshells in cumin powder using NIR hyperspectral imaging and chemometrics tools. J. Food Compos. Anal. 108, 104407 (2022)

    Article  Google Scholar 

  25. Panda, B.K., et al.: Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis. J. Food Eng. 318, 110889 (2022)

    Article  Google Scholar 

  26. Li, H., Liang, Y., Qingsong, X., Cao, D.: Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal. Chim. Acta 648(1), 77–84 (2009)

    Article  Google Scholar 

  27. Tao, F., et al.: A rapid and nondestructive method for simultaneous determination of aflatoxigenic fungus and aflatoxin contamination on corn kernels. J. Agric. Food Chem. 67(18), 5230–5239 (2019)

    Article  Google Scholar 

  28. Liu, C., Huang, W., Yang, G., Wang, Q., Li, J., Chen, L.: Determination of starch content in single kernel using near-infrared hyperspectral images from two sides of corn seeds. Infrared Phys. Technol. 110, 103462 (2020)

    Article  Google Scholar 

  29. Yun, Y.-H., et al.: A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration. Anal. Chim. Acta 807, 36–43 (2014)

    Article  Google Scholar 

  30. Yao, K., et al.: Non-destructive detection of egg qualities based on hyperspectral imaging. J. Food Eng. 325, 111024 (2022)

    Article  Google Scholar 

  31. Balabin, R.M., Lomakina, E.I.: 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 (2011)

    Article  Google Scholar 

  32. Kucha, C.T., Liu, L., Ngadi, M., Gariépy, C.: Assessment of intramuscular fat quality in pork using hyperspectral imaging. Food Eng. Rev. 13, 274–289 (2021)

    Article  Google Scholar 

  33. Kennard, R., Stone, L.: Computer aided design of experiments. Technometrics 11, 137–148 (1969)

    Article  MATH  Google Scholar 

  34. Mishra, G., Srivastava, S., Panda, B.K., Mishra, H.N.: Rapid assessment of quality change and insect infestation in stored wheat grain using FT-NIR spectroscopy and chemometrics. Food Anal. Meth. 11, 1189–1198 (2018)

    Article  Google Scholar 

  35. Williams, P.C., Sobering, D.C.: How do we do it: a brief summary of the methods we use in developing near infrared calibration. In: Davis, A.M.C., Williams, P. (eds.) Near Infrared Spectroscopy: The Future Waves, NIR Publications, Chichester, pp. 185–188 (1996)

    Google Scholar 

  36. Qiao, M., et al.: Determination of hardness for maize kernels based on hyperspectral imaging. Food Chem. 366, 130559 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICAR-Central Institute of Post-Harvest Engineering and Technology (CIPHET), Ludhiana, Punjab, 141004, India

    Dhritiman Saha

  2. Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, Leth-bridge, T1K1L6, AB, Canada

    T. Senthilkumar & Chandra B. Singh

  3. School of Engineering, University of Guelph, Guelph, N1G 2W1, ON, Canada

    Annamalai Manickavasagan

Authors
  1. Dhritiman Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Senthilkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chandra B. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Annamalai Manickavasagan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dhritiman Saha .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Ropar, Ropar, India

    Mukesh Kumar Saini

  2. Indian Institute of Technology Ropar, Ropar, India

    Neeraj Goel

  3. Indian Institute of Technology Guwahati, Guwahati, India

    Hanumant Singh Shekhawat

  4. Polytechnic University of Valencia, Valencia, Spain

    Jaime Lloret Mauri

  5. Saint Louis University, St. Louis, MO, USA

    Dhananjay Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saha, D., Senthilkumar, T., Singh, C.B., Manickavasagan, A. (2023). Application of Near-Infrared (NIR) Hyperspectral Imaging System for Protein Content Prediction in Chickpea Flour. In: Saini, M.K., Goel, N., Shekhawat, H.S., Mauri, J.L., Singh, D. (eds) Agriculture-Centric Computation. ICA 2023. Communications in Computer and Information Science, vol 1866. Springer, Cham. https://doi.org/10.1007/978-3-031-43605-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43605-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43604-8

  • Online ISBN: 978-3-031-43605-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation