Skip to main content
SpringerLink
Log in

Method Development of Near-Infrared Spectroscopy Approaches for Nondestructive and Rapid Estimation of Total Protein in Brown Rice Flour

  • Protocol
  • First Online:
Rice Grain Quality

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1892))

Abstract

Rice varietal development and improvement programs are constantly seeking means to shorten the breeding cycle in order to deliver new, consumer-acceptable rice varieties to farmers and to consumers. Advances in molecular biology technologies have enabled breeders to use high-throughput genotyping to screen breeding lines. However, current phenotyping technologies, particularly for rice cooking and eating properties, have yet to match the efficiency of genotyping methodologies. A high-throughput and cost-effective phenotyping suite is essential because without phenotype, the value of genotypic information cannot be maximized. In this book chapter, we explore the application of near-infrared spectroscopy (NIRS), a high-throughput and nondestructive approach in characterizing rice grains, primarily describing method development and validation, instrument calibration, upgrading, and maintenance. We then focus on estimating protein content (PC) in brown rice as a case study because (1) PC is an attribute that contributes to the cooking behavior and the eating properties of cooked rice; and (2) proteins contain chemical bonds that can easily be detected by NIRS.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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. Cuevas RP, Pede VO, McKinley J, Velarde O, Demont M (2016) Rice grain quality and consumer preferences: a case study of two rural towns in the Philippines. PLoS One 11(3):e0150345

    Article  Google Scholar 

  2. Cuevas RP, Daygon VD, Corpuz HM, Reinke RF, Waters DLE, Fitzgerald MA (2010) Melting the secrets of gelatinisation temperature in rice. Func Plant Biol 37:439–447

    Article  CAS  Google Scholar 

  3. Cagampang GB, Perez CM, Juliano BO (1973) A gel consistency test for eating quality of rice. J Sci Food Agric 24:1589–1594

    Article  CAS  Google Scholar 

  4. Juliano BO, Perez CM, Resurreccion AP (2009) Apparent amylose content and gelatinization temperature types of Philippine rice accessions in the IRRI Gene Bank. Phil Agric Sci 92(1):106–109

    Google Scholar 

  5. Tanaka J, Hayashi T, Iwata H (2016) A practical, rapid generation-advancement system for rice breeding using simplified biotron breeding system. Breed Sci 66(4):542–551

    Article  Google Scholar 

  6. Janwan M, Sreewongchai T, Sripichitt P (2013) Rice breeding for high yield by advanced single seed descent method of selection. J Plant Sci 8(1):24–30

    Article  Google Scholar 

  7. Spindel J, Begum H, Akdemir D, Virk P, Collard BCY, Redoña E, Atlin G, Jannink J-L, McCouch SR (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet 11(2):e1004982

    Article  Google Scholar 

  8. Varshney RK, Terauchi R, McCouch SR (2014) Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biol 12(6):e1001883

    Article  Google Scholar 

  9. Rahim HA, Ibrahim S (2013) Near infrared spectroscopy measurement: the assessement of amylose content in rice grain. Sensors Transducers 156:251–258

    Google Scholar 

  10. Bokobza L (1998) Near infrared spectroscopy. J Near Infrared Spectrosc 6:3–17

    Article  CAS  Google Scholar 

  11. Pasquini C (2003) Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. J Brazilian Chem Soc 14(2):198–219

    Article  CAS  Google Scholar 

  12. Agelet LE, Hurburgh CR Jr (2014) Limitations and current applications of Near Infrared Spectroscopy for single seed analysis. Talanta 121:288–299

    Article  Google Scholar 

  13. Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N (2007) A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharma Biomed Anal 44(3):683–700

    Article  CAS  Google Scholar 

  14. Luypaert J, Massart DL, Heyden YV (2007) Near-infrared spectroscopy applications in pharmaceutical analysis. Talanta 72(3):865–883

    Article  CAS  Google Scholar 

  15. Butz P, Hofmann C, Tauscher B (2005) Recent developments in noninvasive techniques for fresh fruit and vegetable internal quality analysis. J Food Sci 70:R131–R141

    Article  CAS  Google Scholar 

  16. Givens DI, De Boever JL, Deaville ER (1997) The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans. Nutr Res Rev 10:83–114

    Article  CAS  Google Scholar 

  17. Martens H, Martens M (2001) What makes NIR data so information-rich? In: Martens H, Martens M (eds) Multivariate analysis of quality: an introduction. Wiley, Chichester, pp 415–417

    Google Scholar 

  18. Infrasoft International (1995) NIRS 2 Version 3.10 Routine Operation and Calibration Development Software for Near Infrared Instruments, State College PA, USA

    Google Scholar 

  19. Villareal CP, Dela Cruz NM, Juliano BO (1994) Rice amylose analysis by near-infared transmittance spectroscopy. Cereal Chem 71(3):292–296

    CAS  Google Scholar 

  20. Delwiche SR, Bean MM, Miller RE, Webb BD, Williams PC (1995) Apparent amylose content of milled rice by near-infrared reflectance spectroscopy. Cereal Chem 72(2):182–187

    CAS  Google Scholar 

  21. Delwiche SR, McKenzie KS, Webb BD (1996) Quality characteristics in rice by near-infrared reflectance analysis of whole-grain milled samples. Cereal Chem 73(2):257–263

    CAS  Google Scholar 

  22. Rahim HA, Ibrahim S (2013) Using near-infrared spectroscopy to investigate the amylose content in rice. Appl Mech Mater 239–240:163–166

    Google Scholar 

  23. Lee H-S, Choi Y-M, Lee Y-Y, Ma K-H, Gwag J-G, Lee JR, Yoon Y-T, Cho Y-G, Lee S-Y (2014) Selecting high amylose rice germplasm combined with NIR spectroscopy at the RDA genebank conserved. Plant Breed Biotechnol 2(4):380–385

    Article  Google Scholar 

  24. Bao J-S, Cai YZ, Corke H (2001) Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy. J Food Sci 66(7):936–939

    Article  CAS  Google Scholar 

  25. Shu Q, Wu D, Xia Y, Gao M, McClung AM (1999) Apparent amylose content of rice by near infrared reflectance analysis of ground milled samples. Chinese J Rice Sci 13(3):189–192

    Google Scholar 

  26. Wu JG, Shi CH (2007) Calibration model optimization for rice cooking characteristics by near infrared reflectance spectroscopy (NIRS). Food Chem 103(3):1054–1061

    Article  CAS  Google Scholar 

  27. Rash J, Meullenet J-FC (2010) Apparent amylose content prediction using near infrared spectroscopy of individual and bulk rice kernels. AAES Res Ser:312–321

    Google Scholar 

  28. Martin M, Fitzgerald MA (2002) Proteins in rice grains influence cooking properties. J Cereal Sci 36(3):285–294

    Article  CAS  Google Scholar 

  29. Juliano BO (2003) Rice chemistry and quality. Philippine Rice Research Institute, Munoz, p 492

    Google Scholar 

  30. Gomez KA (1979) Effect of environment on protein and amylose content of rice. In: Workshop on chemical aspects of rice grain quality. Rice Grain Res, pp 59–68

    Google Scholar 

  31. Shi C, Zhu J, Yang X, Yu Y, Wu J (1999) Genetic analysis for protein content in indica rice. Euphytica 107:135–140

    Article  Google Scholar 

  32. Osborne BG (2006) Applications of near infrared spectroscopy in quality screening of early-generation material in cereal breeding programmes. J Near Infrared Spectrosc 14:93–101

    Article  CAS  Google Scholar 

  33. Williams PC (1975) Application of near infrared reflectance spectroscopy to analysis of cereal grains and oilseeds. Cereal Chem 52:561–576

    Google Scholar 

  34. Kawamura S, Natsuga M, Itoh K (1999) Determination of undried rough rice constituent content using near-infrared transmission spectroscopy. Trans Am Soc Agric Eng 42(3):813–818

    Article  Google Scholar 

  35. Sohn M, Barton FE, McClung AM, Champagne ET (2004) Near-infrared spectroscopy for determination of protein and amylose in rice flour through use of derivatives. Cereal Chem 81(3):341–344

    Article  CAS  Google Scholar 

  36. Nørgaard L, Lagerholm M, Westerhaus MO (2013) Artificial neural networks and near infrared spectroscopy: a case study on protein content in whole wheat grain. FOSS, Hillerød, Denmark

    Google Scholar 

  37. Wang H, Peng J, Xie C, Bao Y, He Y (2015) Fruit quality evaluation using spectroscopy technology: a review. Sensors 15:11889–11927

    Article  Google Scholar 

  38. Yoshida S, Forno DA, Cock JH, Gomez KA (1976) Laboratory manual for physiological studies of rice, 3rd edn. International Rice Research Institute, Los Banos

    Google Scholar 

  39. Searle PL (1984) The berthelot or indophenol reaction and its use in the analytical chemistry of nitrogen. A review. Analyst 109:549–568

    Article  CAS  Google Scholar 

  40. Holtzhauer M (2006) Basic methods for the biochemical lab, 1st edn. Springer, Berlin

    Google Scholar 

  41. Cao N (2013) Calibration optimization and efficiency in near infrared spectroscopy. Iowa State University, Ames, p 184

    Google Scholar 

  42. Shenk JS, Westerhaus MO (1991) Population structuring of near infrared spectra and modified partial least squares regression. Crop Sci 31(6):1548–1555

    Article  CAS  Google Scholar 

  43. Baillères H, Davrieux F, Ham-Pichavant F (2002) Near infrared analysis as a tool for rapid screening of some major wood characteristics in a eucalyptus breeding program. Ann For Sci 59:479–490

    Article  Google Scholar 

  44. Agelet LE, Hurburgh CR Jr (2010) A tutorial on near infrared spectroscopy and its calibration. Crit Rev Anal Chem 40:246–260

    Article  CAS  Google Scholar 

  45. Kays SE, Barton FE II, Windham WR (2000) Predicting protein content by near infrared reflectance spectroscopy in diverse cereal food products. J Near Infrared Spectrosc 8:35–43

    Article  CAS  Google Scholar 

  46. Bietz JA (1974) Micro-Kjeldahl analysis by an improved automated ammonia determination following manual digestion. Anal Chem 46:1617–1618

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank the following GQNSL staff for the technical assistance in generating the protein data: Jose Rosales, Edgar Amoloza, Leonel Borebor, Anna Carissa Basilio, and Ruben Chavez. This work has been supported under the CGIAR thematic area Global Rice Agri-Food System CRP, RICE, Stress-Tolerant Rice for Africa and South Asia (STRASA) Phase III, and Australian Centre for International Agricultural Research (Project ID CIM/2016/046) funding.

Author information

Authors and Affiliations

  1. International Rice Research Institute, Los Baños, Laguna, Philippines

    Rosario Jimenez, Lilia Molina, Iman Zarei, Jennine Rose Lapis, Ruben Chavez, Rosa Paula O. Cuevas & Nese Sreenivasulu

Authors
  1. Rosario Jimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lilia Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iman Zarei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jennine Rose Lapis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruben Chavez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rosa Paula O. Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nese Sreenivasulu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nese Sreenivasulu .

Editor information

Editors and Affiliations

  1. Grain Quality and Nutrition Center, International Rice Research Institute, Manila, Philippines

    Nese Sreenivasulu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Jimenez, R. et al. (2019). Method Development of Near-Infrared Spectroscopy Approaches for Nondestructive and Rapid Estimation of Total Protein in Brown Rice Flour. In: Sreenivasulu, N. (eds) Rice Grain Quality. Methods in Molecular Biology, vol 1892. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8914-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8914-0_7

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8912-6

  • Online ISBN: 978-1-4939-8914-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation