Skip to main content
SpringerLink
Log in

A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13224))

  • 1250 Accesses

Abstract

Vibrational spectroscopy can be used for rapid nutrient assessment of horticultural produce as a means of quality control. Most commonly, spectral data are calibrated against chemical reference data, which are acquired through resource-intensive analytical methods, using partial least squares regression (PLSR). Recently, genetic algorithms (GAs) have been applied to assist PLSR to construct high-performing models through feature selection and latent variable selection. The current approach relies on manually pre-processed data, which requires human expertise and produces inherent biases. To address this limitation, this paper aims to develop a new GA method for automatically selecting the most appropriate pre-processing techniques for specific tasks to bypass manual pre-processing. The results for infrared spectroscopy show the potential of this approach in out-performing manual pre-processing, while the Raman spectroscopy results are competitive, which demonstrates the utility of the approach in terms of saving time and resources.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Noh, M.F.M., et al.: Recent techniques in nutrient analysis for food composition database. Molecules 25(19), 4567 (2020)

    Article  Google Scholar 

  2. Crocombe, R.A.: The future of portable spectroscopy. In: Portable Spectroscopy and Spectrometry 1: Applications, vol. 2, pp. 545–571 (2021)

    Google Scholar 

  3. Cortés, V., Blasco, J., Aleixos, N., Cubero, S., Talens, P.: Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: a review. Trends Food Sci. Technol. 85, 138–148 (2019)

    Article  Google Scholar 

  4. Yang, D., Ying, Y.: Applications of Raman spectroscopy in agricultural products and food analysis: a review. Appl. Spectrosc. Rev. 46(7), 539–560 (2011)

    Article  Google Scholar 

  5. Tahir, H.E., et al.: Recent progress in rapid analyses of vitamins, phenolic, and volatile compounds in foods using vibrational spectroscopy combined with chemometrics: a review. Food Anal. Methods 12(10), 2361–2382 (2019)

    Article  Google Scholar 

  6. Killeen, D.P., Andersen, D.H., Beatson, R.A., Gordon, K.C., Perry, N.B.: Vibrational spectroscopy and chemometrics for rapid, quantitative analysis of bitter acids in hops (humulus lupulus). J. Agric. Food Chem. 62(52), 12521–12528 (2014)

    Article  Google Scholar 

  7. McIntyre, S.M., Ma, Q., Burritt, D.J., Oey, I., Gordon, K.C., Fraser-Miller, S.J.: Vibrational spectroscopy and chemometrics for quantifying key bioactive components of various plum cultivars grown in New Zealand. J. Raman Spectrosc. 51(7), 1138–1152 (2020)

    Article  Google Scholar 

  8. Amuah, C.L.Y., Teye, E., Lamptey, F.P., Nyandey, K., Opoku-Ansah, J., Adueming, P.O.-W.: Feasibility study of the use of handheld NIR spectrometer for simultaneous authentication and quantification of quality parameters in intact pineapple fruits. J. Spectroscopy 2019, 1–9 (2019)

    Google Scholar 

  9. Robinson, D., et al.: Genetic algorithm for feature and latent variable selection for nutrient assessment in horticultural products. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 272–279 (2021)

    Google Scholar 

  10. Zhu, X., Tao, X., Lin, Q., Duan, Y.: Technical development of Raman spectroscopy: from instrumental to advanced combined technologies. Appl. Spectrosc. Rev. 49(1), 64–82 (2014)

    Article  Google Scholar 

  11. Paterova, A., Lung, S., Kalashnikov, D.A., Krivitsky, L.A.: Nonlinear infrared spectroscopy free from spectral selection. Sci. Rep. 7(1), 42608 (2017)

    Article  Google Scholar 

  12. Manley, M., Baeten, V.: Chapter 3 - spectroscopic technique: near infrared (NIR) spectroscopy. In: Sun, D.-W. (ed.) Modern Techniques for Food Authentication, 2nd edn., pp. 51–102. Academic Press (2018)

    Google Scholar 

  13. Brian Dyer, R., Woodruff, W.H.: Vibrational Spectroscopy. American Cancer Society (2011)

    Google Scholar 

  14. Hashimoto, K., Badarla, V.R., Kawai, A., Ideguchi, T.: Complementary vibrational spectroscopy. Nat. Commun. 10(1), 4411 (2019)

    Article  Google Scholar 

  15. Hackshaw, K.V., Miller, J.S., Aykas, D.P., Rodriguez-Saona, L.: Vibrational spectroscopy for identification of metabolites in biologic samples. Molecules 25(20), 4725 (2020)

    Article  Google Scholar 

  16. Rosipal, R., Krämer, N.: Overview and recent advances in partial least squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 34–51. Springer, Heidelberg (2006). https://doi.org/10.1007/11752790_2

    Chapter  Google Scholar 

  17. Liland, K.H., Rukke, E.-O., Olsen, E.F., Isaksson, T.: Customized baseline correction. Chemometr. Intell. Lab. Syst. 109(1), 51–56 (2011)

    Article  Google Scholar 

  18. Zhang, Z.-M., Chen, S., Liang, Y.-Z.: Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135(5), 1138–1146 (2010)

    Article  Google Scholar 

  19. Shen, X., et al.: Study on baseline correction methods for the Fourier transform infrared spectra with different signal-to-noise ratios. Appl. Opt. 57(20), 5794–5799 (2018)

    Article  Google Scholar 

  20. Peng, J., Peng, S., Jiang, A., Wei, J., Li, C., Tan, J.: Asymmetric least squares for multiple spectra baseline correction. Anal. Chim. Acta 683(1), 63–68 (2010)

    Article  Google Scholar 

  21. Lasch, P.: Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging. Chemom. Intell. Lab. Syst. 117, 100–114 (2012)

    Article  Google Scholar 

  22. Barnes, R.J., Dhanoa, M.S., Lister, S.J.: Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 43(5), 772–777 (1989)

    Article  Google Scholar 

  23. Isaksson, T., Næs, T.: The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl. Spectrosc. 42(7), 1273–1284 (1988)

    Article  Google Scholar 

  24. Kolodziej, M., et al.: Classification of aggressive and classic mantle cell lymphomas using synchrotron Fourier transform infrared microspectroscopy. Sci. Rep. 9(1) (2019)

    Google Scholar 

  25. Press, W.H., Teukolsky, S.A.: Savitzky-Golay smoothing filters. Comput. Phys. 4(6), 669 (1990)

    Article  Google Scholar 

  26. Pétillot, L., et al.: Calibration transfer for bioprocess Raman monitoring using Kennard stone piecewise direct standardization and multivariate algorithms. Eng. Rep. 2(11), e12230 (2020)

    Google Scholar 

  27. Li, H., Xu, Q., Liang, Y.: libPLS: an integrated library for partial least squares regression and discriminant analysis. PeerJ PrePrints 2, e190v1 (2014)

    Google Scholar 

  28. Byrne, H.J., Knief, P., Keating, M.E., Bonnier, F.: Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells. Chem. Soc. Rev. 45, 1865–1878 (2016)

    Article  Google Scholar 

  29. Huang, J., Romero-Torres, S., Moshgbar, M.: Practical considerations in data pre-treatment for NIR and Raman spectroscopy. Am. Pharm. Rev. 13, 116–127 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

    Demelza Robinson, Qi Chen, Bing Xue & Mengjie Zhang

  2. The New Zealand Institute for Plant and Food Research Limited, Nelson, New Zealand

    Daniel Killeen

  3. Department of Food Science, University of Otago, Dunedin, New Zealand

    Keith C. Gordon

Authors
  1. Demelza Robinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bing Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Killeen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Keith C. Gordon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mengjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Chen .

Editor information

Editors and Affiliations

  1. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  2. Universidad Complutense de Madrid, Madrid, Spain

    J. Ignacio Hidalgo

  3. Edinburgh Napier University, Edinburgh, UK

    Kehinde Oluwatoyin Babaagba

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Robinson, D., Chen, Q., Xue, B., Killeen, D., Gordon, K., Zhang, M. (2022). A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-02462-7_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02461-0

  • Online ISBN: 978-3-031-02462-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation