Skip to main content
SpringerLink
Log in

Mapping the Distribution of Additives Within Polymer Films Through Near-Infrared Spectroscopy and Hyperspectral Imaging

  • Chapter
  • First Online:
Food Packaging Materials

Part of the book series: Methods and Protocols in Food Science ((MPFS))

  • 161 Accesses

Abstract

Near-infrared (NIR) spectroscopy and hyperspectral imaging allow the study of spectral and spatial distribution of multiple chemical components in large sample areas. This technique is fast, non-destructive, contactless, and does not require sample preparation. The NIR spectrum of each sample pixel is acquired, resulting in a data cube that contains two spatial dimensions (x and y) and one spectral dimension (z), providing the spectral profiles of every part of the sample. This technique, for example, can provide significant information about the distribution of additives into polymer matrices with potential to be used as a tool for real-time quality control. Herein, the stepwise application of this method is demonstrated for determination of spatial and spectral distributions of film components, showcasing the plasticization of a biodegradable packaging.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Pfaendner R (2006) How will additives shape the future of plastics? Polym Degrad Stab 91(9):2249–2256. https://doi.org/10.1016/j.polymdegradstab.2005.10.017

    Article  CAS  Google Scholar 

  2. Rabello M (2000) Aditivação de polímeros. Artliber

    Google Scholar 

  3. Rabello M, De Paoli M (2013) Aditivaçao De Termoplasticos. Artliber

    Google Scholar 

  4. Ambrogi V, Carfagna C, Cerruti P, Marturano V (2017) Chapter 4 – Additives in polymers. In: Jasso-Gastinel CF, Kenny JM (eds) Modification of polymer properties. William Andrew Publishing, pp 87–108. https://doi.org/10.1016/B978-0-323-44353-1.00004-X

    Chapter  Google Scholar 

  5. Groh KJ, Backhaus T, Carney-Almroth B, Geueke B, Inostroza PA, Lennquist A, Leslie HA, Maffini M, Slunge D, Trasande L, Warhurst AM, Muncke J (2019) Overview of known plastic packaging-associated chemicals and their hazards. Sci Total Environ 651:3253–3268. https://doi.org/10.1016/j.scitotenv.2018.10.015

    Article  CAS  PubMed  Google Scholar 

  6. Harper CA, Harper C (2006) Handbook of plastics technologies: the complete guide to properties and performance. McGraw-Hill Education

    Google Scholar 

  7. Han J-W, Ruiz-Garcia L, Qian J-P, Yang X-T (2018) Food packaging: a comprehensive review and future trends. Compr Rev Food Sci Food Saf 17(4):860–877. https://doi.org/10.1111/1541-4337.12343

    Article  PubMed  Google Scholar 

  8. Avolio R, Castaldo R, Avella M, Cocca M, Gentile G, Fiori S, Errico ME (2018) PLA-based plasticized nanocomposites: effect of polymer/plasticizer/filler interactions on the time evolution of properties. Compos Part B 152:267–274. https://doi.org/10.1016/j.compositesb.2018.07.011

    Article  CAS  Google Scholar 

  9. Terra LR, Roque JV, Pola CC, Gonçalves IM, Soares NFF, Teófilo RF (2020) Study of chemical compound spatial distribution in biodegradable active films using NIR hyperspectral imaging and multivariate curve resolution. J Chemom 34(1):e3193. https://doi.org/10.1002/cem.3193

    Article  CAS  Google Scholar 

  10. Amigo JM, Babamoradi H, Elcoroaristizabal S (2015) Hyperspectral image analysis. A tutorial. Anal Chim Acta 896:34–51. https://doi.org/10.1016/j.aca.2015.09.030

    Article  CAS  PubMed  Google Scholar 

  11. Fraser DG, Jordan RB, Künnemeyer R, McGlone VA (2003) Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy. Postharvest Biol Technol 27(2):185–196. https://doi.org/10.1016/S0925-5214(02)00058-3

    Article  Google Scholar 

  12. Li Q, Tang Y, Yan Z, Zhang P (2017) Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution. Spectrochim Acta A Mol Biomol Spectrosc 180:154–160. https://doi.org/10.1016/j.saa.2017.03.019

    Article  CAS  PubMed  Google Scholar 

  13. Li N, Taylor LS (2016) Nanoscale infrared, thermal, and mechanical characterization of telaprevir–polymer miscibility in amorphous solid dispersions prepared by solvent evaporation. Mol Pharm 13(3):1123–1136. https://doi.org/10.1021/acs.molpharmaceut.5b00925

    Article  CAS  PubMed  Google Scholar 

  14. Manley M (2014) Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chem Soc Rev 43(24):8200–8214. https://doi.org/10.1039/C4CS00062E

    Article  CAS  PubMed  Google Scholar 

  15. Calvini R, Ulrici A, Amigo JM (2020) Growing applications of hyperspectral and multispectral imaging. In: Data handling in science and technology, vol 32. Elsevier, pp 605–629

    Google Scholar 

  16. De Juan A, Piqueras S, Maeder M, Hancewicz T, Duponchel L, Tauler R (2014) Chemometric tools for image analysis. In: Infrared and Raman spectroscopic imaging. Wiley-VCH, Weinheim, pp 57–110. https://doi.org/10.1002/9783527678136.ch2

    Chapter  Google Scholar 

  17. Wu D, Sun D-W (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—Part I: fundamentals. Innovative Food Sci Emerg Technol 19:1–14. https://doi.org/10.1016/j.ifset.2013.04.014

    Article  CAS  Google Scholar 

  18. Zhang C, Jiang H, Liu F, He Y (2017) Application of near-infrared hyperspectral imaging with variable selection methods to determine and visualize caffeine content of coffee beans. Food Bioprocess Technol 10(1):213–221. https://doi.org/10.1007/s11947-016-1809-8

    Article  CAS  Google Scholar 

  19. Prats-Montalbán JM, de Juan A, Ferrer A (2011) Multivariate image analysis: a review with applications. Chemom Intell Lab Syst 107(1):1–23. https://doi.org/10.1016/j.chemolab.2011.03.002

    Article  CAS  Google Scholar 

  20. ElMasry G, Sun D-W, Allen P (2012) Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. J Food Eng 110(1):127–140. https://doi.org/10.1016/j.jfoodeng.2011.11.028

    Article  Google Scholar 

  21. 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 Sci Emerg Technol 16:316–325. https://doi.org/10.1016/j.ifset.2012.07.007

    Article  CAS  Google Scholar 

  22. Rodrigues e Brito L, Braz A, Saldanha Honorato R, Pimentel MF, Pasquini C (2019) Evaluating the potential of near infrared hyperspectral imaging associated with multivariate data analysis for examining crossing ink lines. Forensic Sci Int 298:169–176. https://doi.org/10.1016/j.forsciint.2019.02.043

    Article  CAS  PubMed  Google Scholar 

  23. Piqueras S, Duponchel L, Tauler R, de Juan A (2011) Resolution and segmentation of hyperspectral biomedical images by multivariate curve resolution-alternating least squares. Anal Chim Acta 705(1):182–192. https://doi.org/10.1016/j.aca.2011.05.020

    Article  CAS  PubMed  Google Scholar 

  24. Adam E, Mutanga O, Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetl Ecol Manag 18(3):281–296. https://doi.org/10.1007/s11273-009-9169-z

    Article  Google Scholar 

  25. Lee H, Kim MS, Song Y-R, Oh C-S, Lim H-S, Lee W-H, Kang J-S, Cho B-K (2017) Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging. J Sci Food Agric 97(4):1084–1092. https://doi.org/10.1002/jsfa.7832

    Article  CAS  PubMed  Google Scholar 

  26. Sun H, Zhang S, Chen C, Li C, Xing S, Liu J, Xue J (2019) Detection of the soluble solid contents from fresh jujubes during different maturation periods using NIR hyperspectral imaging and an artificial bee colony. J Anal Methods Chem 2019:5032950. https://doi.org/10.1155/2019/5032950

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Alexandrino GL, Poppi RJ (2013) NIR imaging spectroscopy for quantification of constituents in polymers thin films loaded with paracetamol. Anal Chim Acta 765:37–44. https://doi.org/10.1016/j.aca.2012.12.017

    Article  CAS  PubMed  Google Scholar 

  28. Amigo JM, Cruz J, Bautista M, Maspoch S, Coello J, Blanco M (2008) Study of pharmaceutical samples by NIR chemical-image and multivariate analysis. TrAC Trends Anal Chem 27(8):696–713. https://doi.org/10.1016/j.trac.2008.05.010

    Article  CAS  Google Scholar 

  29. Carneiro RL, Poppi RJ (2014) Infrared imaging spectroscopy and chemometric tools for in situ analysis of an imiquimod pharmaceutical preparation presented as cream. Spectrochim Acta A Mol Biomol Spectrosc 118:215–220. https://doi.org/10.1016/j.saa.2013.08.104

    Article  CAS  PubMed  Google Scholar 

  30. de la Ossa MAF, Amigo JM, García-Ruiz C (2014) Detection of residues from explosive manipulation by near infrared hyperspectral imaging: a promising forensic tool. Forensic Sci Int 242:228–235. https://doi.org/10.1016/j.forsciint.2014.06.023

    Article  CAS  Google Scholar 

  31. de la Ossa MAF, García-Ruiz C, Amigo JM (2014) Near infrared spectral imaging for the analysis of dynamite residues on human handprints. Talanta 130:315–321. https://doi.org/10.1016/j.talanta.2014.07.026

    Article  CAS  PubMed  Google Scholar 

  32. Silva CS, Pimentel MF, Honorato RS, Pasquini C, Prats-Montalbán JM, Ferrer A (2014) Near infrared hyperspectral imaging for forensic analysis of document forgery. Analyst 139(20):5176–5184. https://doi.org/10.1039/C4AN00961D

    Article  CAS  PubMed  Google Scholar 

  33. Weinstock BA, Janni J, Hagen L, Wright S (2006) Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis. Appl Spectrosc 60(1):9–16. https://doi.org/10.1366/000370206775382631

    Article  CAS  PubMed  Google Scholar 

  34. ElMasry G, Wang N, ElSayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J Food Eng 81(1):98–107. https://doi.org/10.1016/j.jfoodeng.2006.10.016

    Article  CAS  Google Scholar 

  35. Forchetti DAP, Poppi RJ (2017) Use of NIR hyperspectral imaging and multivariate curve resolution (MCR) for detection and quantification of adulterants in milk powder. LWT Food Sci Technol 76:337–343. https://doi.org/10.1016/j.lwt.2016.06.046

    Article  CAS  Google Scholar 

  36. Zhao J, Vittayapadung S, Chen Q, Chaitep S, Chuaviroj R (2009) Nondestructive measurement of sugar content of apple using hyperspectral imaging technique. Maejo Int J Sci Technol 3(1):130–142

    CAS  Google Scholar 

  37. Taghizadeh M, Gowen A, Ward P, O’Donnell CP (2010) Use of hyperspectral imaging for evaluation of the shelf-life of fresh white button mushrooms (Agaricus bisporus) stored in different packaging films. Innovative Food Sci Emerg Technol 11(3):423–431. https://doi.org/10.1016/j.ifset.2010.01.016

    Article  CAS  Google Scholar 

  38. Specim (2015) SisuChema chemical imaging analyzer. https://www.specim.fi/wp-content/uploads/2020/03/SisuCHEMA_2_2015.pdf. Accessed 12 Sept 2020

  39. Cortés V, Blasco J, Aleixos N, Cubero S, Talens P (2019) Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: a review. Trends Food Sci Technol 85:138–148. https://doi.org/10.1016/j.tifs.2019.01.015

    Article  CAS  Google Scholar 

  40. Pasquini C (2003) Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. J Braz Chem Soc 14:198–219

    Article  CAS  Google Scholar 

  41. Pasquini C (2018) Near infrared spectroscopy: a mature analytical technique with new perspectives – a review. Anal Chim Acta 1026:8–36. https://doi.org/10.1016/j.aca.2018.04.004

    Article  CAS  PubMed  Google Scholar 

  42. Vidal M, Amigo JM (2012) Pre-processing of hyperspectral images. Essential steps before image analysis. Chemom Intell Lab Syst 117:138–148. https://doi.org/10.1016/j.chemolab.2012.05.009

    Article  CAS  Google Scholar 

  43. Amigo JM, Santos C (2020) Chapter 2.1 – Preprocessing of hyperspectral and multispectral images. In: Amigo JM (ed) Data handling in science and technology, vol 32. Elsevier, pp 37–53. https://doi.org/10.1016/B978-0-444-63977-6.00003-1

    Chapter  Google Scholar 

  44. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2):109–130. https://doi.org/10.1016/S0169-7439(01)00155-1

    Article  CAS  Google Scholar 

  45. Leardi R, Lupiáñez González A (1998) Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom Intell Lab Syst 41(2):195–207. https://doi.org/10.1016/S0169-7439(98)00051-3

    Article  CAS  Google Scholar 

  46. Roque JV, Cardoso W, Peternelli LA, Teófilo RF (2019) Comprehensive new approaches for variable selection using ordered predictors selection. Anal Chim Acta 1075:57–70. https://doi.org/10.1016/j.aca.2019.05.039

    Article  CAS  PubMed  Google Scholar 

  47. Teófilo RF, Martins JPA, Ferreira MMC (2009) Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression. J Chemom 23(1):32–48. https://doi.org/10.1002/cem.1192

    Article  CAS  Google Scholar 

  48. Leardi R, Nørgaard L (2004) Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J Chemom 18(11):486–497. https://doi.org/10.1002/cem.893

    Article  CAS  Google Scholar 

  49. de Araújo Gomes A, Galvão RKH, de Araújo MCU, Véras G, da Silva EC (2013) The successive projections algorithm for interval selection in PLS. Microchem J 110:202–208. https://doi.org/10.1016/j.microc.2013.03.015

    Article  CAS  Google Scholar 

  50. Jaumot J, de Juan A, Tauler R (2015) MCR-ALS GUI 2.0: new features and applications. Chemom Intell Lab Syst 140:1–12. https://doi.org/10.1016/j.chemolab.2014.10.003

    Article  CAS  Google Scholar 

  51. ASTM (2013) ASTM D618-21: Standard practice for conditioning plastics for testing

    Google Scholar 

  52. Bassan P (2011) Light scattering during infrared spectroscopic measurements of biomedical samples. The University of Manchester

    Google Scholar 

  53. Amigo JM, Grassi S (2020) Chapter 1.2 – Configuration of hyperspectral and multispectral imaging systems. In: Amigo JM (ed) Data handling in science and technology, vol 32. Elsevier, pp 17–34. https://doi.org/10.1016/B978-0-444-63977-6.00002-X

    Chapter  Google Scholar 

Download references

Acknowledgments

The authors are grateful for the financial support of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. We also thank Cristiane Vidal for the experimental support in the HSI acquisition and Prof. Celio Pasquini for promptly receiving us in the laboratory that he coordinates (Grupo de Instrumentação e Automação em Química Analítica, Instituto de Química, Universidade Estadual de Campinas, Campinas-SP, Brazil) to obtain the images. C. L. G. and C. C. P. gratefully acknowledge funding support from National Science Foundation under award numbers CBET-1756999. C. L. G. and C. C. P. also acknowledge the National Institute of Food Agriculture, US Department of Agriculture, award numbers 2021-67017-33344, 2020-67021-31375, and 2018-672 67016-27578 awarded as a Center of Excellence for financial support.

Author information

Author notes

  1. Jussara V. Roque

    Present address: Chemistry Institute, Universidade Federal de Goias, Goiania, GO, Brazil

Authors and Affiliations

  1. Multivariate Chemical Data Analysis Laboratory, Universidade Federal de Viçosa (UFV), Vicosa, MG, Brazil

    Jussara V. Roque & Reinaldo F. Teófilo

  2. Nanoscale Biological Engineering Laboratory, Iowa State University, Ames, IA, USA

    Cícero C. Pola & Carmen L. Gomes

  3. Laboratory for Theoretical and Applied Chemometrics, Institute of Chemistry, Universidade de Campinas (UNICAMP), Campinas, SP, Brazil

    Larissa R. Terra

  4. Food Packaging Laboratory, Universidade Federal de Viçosa (UFV), Vicosa, MG, Brazil

    Taíla V. Oliveira & Nilda F. F. Soares

Authors
  1. Jussara V. Roque
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cícero C. Pola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Larissa R. Terra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Taíla V. Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Reinaldo F. Teófilo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Carmen L. Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nilda F. F. Soares
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cícero C. Pola .

Editor information

Editors and Affiliations

  1. Department of Materials Engineering, Federal University of São Carlos, São Carlos, Brazil

    Caio Otoni

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Roque, J.V. et al. (2024). Mapping the Distribution of Additives Within Polymer Films Through Near-Infrared Spectroscopy and Hyperspectral Imaging. In: Otoni, C. (eds) Food Packaging Materials. Methods and Protocols in Food Science . Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3613-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3613-8_10

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3612-1

  • Online ISBN: 978-1-0716-3613-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation