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Construction of Database System on Amylose and Protein Contents Distribution in Rice Germplasm Based on NIRS Data

벼 유전자원의 아밀로스 및 단백질 성분 함량 분포에 관한 자원정보 구축

  • Oh, Sejong (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Choi, Yu Mi (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Lee, Myung Chul (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Lee, Sukyeung (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Yoon, Hyemyeong (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Rauf, Muhammad (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Chae, Byungsoo (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA)
  • 오세종 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • 최유미 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • 이명철 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • 이수경 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • 윤혜명 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • ;
  • 채병수 (농촌진흥청 국립농업과학원 농업유전자원센터)
  • Received : 2019.02.26
  • Accepted : 2019.03.21
  • Published : 2019.04.30

Abstract

This study was carried out to build a database system for amylose and protein contents of rice germplasm based on NIRS (Near-Infrared Reflectance Spectroscopy) analysis data. The average waxy type amylose contents was 8.7% in landrace, variety and weed type, whereas 10.3% in breeding line. In common rice, the average amylose contents was 22.3% for landrace, 22.7% for variety, 23.6% for weed type and 24.2% for breeding line. Waxy type resources comprised of 5% of the total germplasm collections, whereas low, intermediate and high amylose content resources share 5.5%, 20.5% and 69.0% of total germplasm collections, respectively. The average percent of protein contents was 8.2 for landrace, 8.0 for variety, and 7.9 for weed type and breeding line. The average Variability Index Value was 0.62 in waxy rice, 0.80 in common rice, and 0.51 in protein contents. The accession ratio in arbitrary ranges of landrace was 0.45 in amylose contents ranging from 6.4 to 8.7%, and 0.26 in protein ranging from 7.3 to 8.2%. In the variety, it was 0.32 in amylose ranging from 20.1 to 22.7%, and 0.51 in protein ranging from 6.1 to 8.3%. And also, weed type was 0.67 in amylose ranging from 6.6 to 9.7%, and 0.33 in protein ranging from 7.0 to 7.9%, whereas, in breeding line it was 0.47 in amylose ranging from 10.0 to 12.0%, and 0.26 in protein ranging from 7.0 to 7.9%. These results could be helpful to build database programming system for germplasm management.

Keywords

Amylose;Database;NIRS (Near-Infrared Reflectance Spectroscopy);Protein;Rice germplasm

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Fig. 1. Relationship between probability density histogram and normal distribution.

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Fig. 2. The process and findings of standardization of normal distribution.

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Fig. 3. Outputting number of accessions in random data range by standardization of normal distribution.

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Fig. 4. Correlation plots between NIRS data and amylose (A) and protein content (B) in the milled brown rice germplasm.

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Fig. 5. Normal distribution and probability density of amylose content in landrace (A1, A2), rice variety (B1, B2), weed type (C1, C2) and breeding line (D1, D2) germplasm.

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Fig. 6. Normal distribution and probability density of protein content in landrace (A), rice variety (B), weed type (C) and breeding line (D) germplasm.

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Fig. 7. Number of accessions obtained from the probability density using the process of standard normal distribution of waxy type amylose content in landrace (n=688).

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Fig. 8. Number of accessions obtained from the probability density using the process of standard normal distribution of common rice amylose content in landrace (n=4,260).

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Fig. 9. Number of accessions obtained from the probability density using the process of standard normal distribution of protein content in landrace (n=4,948).

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Fig. 10. Number of accessions obtained from the probability density using the process of standard normal distribution of waxy type amylose content in rice variety (n=617).

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Fig. 11. Number of accessions obtained from the probability density using the process of standard normal distribution of common rice amylose content in rice variety (n=5,540).

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Fig. 12. Number of accessions obtained from the probability density using the process of standard normal distribution of protein content in rice variety (n=6,157).

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Fig. 13. Number of accessions obtained from the probability density using the process of standard normal distribution of waxy type amylose content in weed type (n=418).

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Fig. 14. Number of accessions obtained from the probability density using the process of standard normal distribution of common rice amylose content in weed type (n=5,788).

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Fig. 15. Number of accessions obtained from the probability density using the process of standard normal distribution of protein content in weed type (n=6,206).

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Fig. 16. Number of accessions obtained from the probability density using the process of standard normal distribution of waxy type amylose content in breeding line (n=596).

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Fig. 17. Number of accessions obtained from the probability density using the process of standard normal distribution of common rice amylose content in breeding line (n=9,402).

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Fig. 18. Number of accessions obtained from the probability density using the process of standard normal distribution of protein content in breeding line (n=9,998).

Table 1. Origin distribution of rice germplasm used in the analysis of NIRS

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Table 2. External validation results of NIRS equation model for the amylose and protein content in the milled brown rice

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Table 3. Classification of rice germplasm according to amylose content by NIRS

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Acknowledgement

Supported by : 농촌진흥청

References

  1. Abrams, S.M., J.S. Shenk, M.O. Westerhaus and F.E. Barton. 1987. Determination of forage quality by near-infrared reflectance spectroscopy: Efficiency of broad based calibration equations. J. Diary Sci. 70:806-813. https://doi.org/10.3168/jds.S0022-0302(87)80077-2
  2. Ahn, H.G. and Y.H. Kim. 2012. Discrimination of Korean domestic and foreign soybeans using near-infrared reflectance spectroscopy. Korean J. Crop Sci. 57(3):296-300 (in Korean). https://doi.org/10.7740/kjcs.2012.57.3.296
  3. Bagchi, T.B., S. Sharma and K. Chattopadhyay. 2016. Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran. Food Chemistry 191:21-27. https://doi.org/10.1016/j.foodchem.2015.05.038
  4. Cho, K.C., H.S. Park, S.H. Lee, J.H. Choi, S. Seo and G.J. Choi. 2012. Transfer and validation of NIRS calibration models for evaluating forage quality in Italian ryegrass silages. J. Lives Hous. & Env. 18(suppl.):81-90 (in Korean).
  5. Chun, J.U. 2010. Rapid measure of color and catechins contents in processed tea using NIRS. Korean J. Plant Res. 23(4): 386-392 (in Korean).
  6. Clarke, M.A., E.R. Arias and C. McDonald-Lewis. 1992. Near Infrared Analysis in The Sugarcane Factory, Ruspam Commun. Inc., Sugary Azucar Press, LA (USA). pp. 244-264.
  7. Delwiche, S.R., K.S. McKenzie and B.D. Webb. 1996. Quality characteristics in rice by near-infrared relfecance analysis of whole grain muilled samples. J. of Cereal Chemistry 73: 257-263.
  8. Han, C.S., K.S. Yon and J.R. Warashina. 1998. Development of a constituent prediction model of domestic rice using nearinfrared reflectance analyzer(II)-Prediction of brown and milled rice protein content and brown rice yield from undried paddy. J. Korean Soc. Agri. Machinary 23:253-258 (in Korean).
  9. Hwang, H.G., R.K. Cho, J.K. Sohn and S.K. Lee. 1994. Rapid evaluation of chemical components of rice grain using near infrared spectroscopy. Korean J. Crop Sci. 39(1):7-14 (in Korean).
  10. Jeong, J.M., J.U. Jeung, S.B. Lee, M.K. Kim, B.K. Kim and J.K. Sohn. 2013. Physicochemical properties of rice endosperm with different amylose contents. Korean J. Crop Sci. 58(3): 274-282 (in Korean). https://doi.org/10.7740/kjcs.2013.58.3.274
  11. Jiang, H.Y., Y.J. Zhu, L.M. Wee, J.R. Dai, T.M. Song, Y.L. Yan and S.J. Chen. 2007. Analysis of protein, starch and oil content of single intact kernels by near infrared reflectance spectroscopy (NIRS) in maize. Plant Breeding 126:492-497. https://doi.org/10.1111/j.1439-0523.2007.01338.x
  12. Kawamura, S., M. Natsuga, K. Takekura and K. Itoh. 2003. Development of an automatic rice-quality inspection system. Comput. Elect. Agr. 40:115-126. https://doi.org/10.1016/S0168-1699(03)00015-2
  13. Kim, C.E., M.Y. Kang and M.H. Kim. 2012. Comparison of properties affecting the palatability of 33 commercial brands of rice. Korean J. Crop Sci. 57(3):301-309 (in Korean). https://doi.org/10.7740/kjcs.2012.57.3.301
  14. Kim, H.I. 2004. Comparison of Korean and Japanese rice by NIR and chemical analysis. J. East Asian Soc. Dietary Life 14(2):135-144 (in Korean).
  15. Kim, H.J., and S.H. Kang. 2017. Ethnobotany, phytochemistry, pharmacology of the Korean Campanulaceae: a comprehensive review. Korean J. Plant Res. 30(2):240-264 (in Korean). https://doi.org/10.7732/kjpr.2017.30.2.240
  16. Kim, H.J., S.Y. Kim, Y.S. Lee and Y.H. Kim. 2014. Determination of baicalein contents in Scutellaria baicalensis by NIRS. Korean J. Plant Res. 27(4):286-292 (in Korean). https://doi.org/10.7732/kjpr.2014.27.4.286
  17. Kim, J.S., M.H. Song, J.E. Choi, H.B. Lee and S.N. Ahn. 2008a. Quantification of protein and amylose contents by near-infrared reflectance spectroscopy in aroma rice. Korean J. Food Sci. Technol. 40(6):603-610 (in Korean).
  18. Kim, J.S., Y.H. Cho, J.G. Gwag, K.H. Ma, Y.M. Choi, J.B. Kim, J.H. Lee, T.S. Kim, J.K. Cho and S.Y. Lee. 2008b. Quantitative analysis of amylose and protein content of rice germplasm in RDA-genebank by near-infrared reflectance spectroscopy. Korean J. Crop Sci. 53(2):217-223 (in Korean).
  19. Kim, K.H., C.S. Kang, I.D. Choi, H.S. Kim, J.N. Hyun and C.S. Park. 2016. Analysis of grain characteristics in Korean wheat and screening wheat for quality using near infrared reflectance spectroscopy. Korean J. Breed. Sci. 48(4):442-449 (in Korean). https://doi.org/10.9787/KJBS.2016.48.4.442
  20. Kim, S.S. and H.C. Cha. 2017. Comparison of the total phenolic and flavonoid contents and antioxidant activities of four kinds of sand dune plants living in Taean, Korea. Korean J. Plant Res. 30(1):8-16 (in Korean). https://doi.org/10.7732/kjpr.2016.30.1.008
  21. Kwon, Y.R., M.H. Baek, D.C. Choi, J.S. Choi and Y.G. Choi. 2005. Determination of calibration curve for total nitrogen contents analysis in fresh rice leaves using visible and nearinfrared spectroscopy. Korean J. Crop Sci. 50(6):394-399 (in Korean).
  22. Kwon, Y.R., S.H. Cho, Y.E. Song, J.H. Lee and C.H. Cho. 2006. Nondestructive measurement of chemical compositions in polished rice and brown rice using NIR spectra of hulled rice acquired in transmittance and reflectance modes. Korean J. Crop Sci. 51(5):451-457 (in Korean).
  23. Lee, I., J.C. Joo, C.S. Lee, G.Y. Kim, D.Y. Woo and J.H. Kim. 2017. Evaluation of the water quality changes in agricultural reservoir covered with floating photovoltaic solar-tracking system. J. Korean Soc. Environ. Eng. 39(5):255-264 (in Korean). https://doi.org/10.4491/KSEE.2017.39.5.255
  24. Lee, J.I., K. Kim, J.C. Shin, E.H. Kim, M.H. Lee and Y.J. Oh. 1996. Effects of ripening temperature on quality appearance and chemical quality characteristics of rice grain. J. Agri. Sci. 38(1):1-9 (in Korean).
  25. Lee, K.Y., U. Sim, Y.M. Choi and J.S. Lee. 2018. Nutritional compositions and antioxidant activities of frequently consumed mushrooms in Korea. J. Korean Soc. Food Sci. Nutr. 47(11):1178-1184 (in Korean). https://doi.org/10.3746/jkfn.2018.47.11.1178
  26. Moon, S.S., K.H. Lee and R.K. Cho. 1994. Application of near infrared reflectance spectroscopy in quality evaluation of domestic rice. Korean J. Food Sci. Technol. 26(6):718-725 (in Korean).
  27. Nam, J.I., G.E. Choi, Y.M. Kim and J.I. Park. 2015. Analysis of morphological characteristics and variation among six populations of Pourthiaea villosa (Thunb.) Decne. var. villosa in Korea. Korean J. Plant Res. 28(1):26-34 (in Korean). https://doi.org/10.7732/kjpr.2015.28.1.026
  28. Oh, H.S., Y.H. Park and J.H. Kim. 2002. Isoflavone contents, antioxidative and fibrinolytic activities of some commercial cooking-with-rice soybeans. Korean J. Food Sci. Technol. 34(3):498-504 (in Korean).
  29. Oh, S.J., B.S. Chae, M.C. Lee, Y.M. Choi, S.K. Lee, H.C. Ko, M. Rauf and D.Y. Hyun. 2018. Statistical treatment on amylose and protein contents in rice variety germplasm based on the data obtained from analysis of near-infrared reflectance spectroscopy (NIRS). Korean J. Plant Res. 31(5): 498-514 (in Korean). https://doi.org/10.7732/KJPR.2018.31.5.498
  30. Oh, S.J., M.C. Lee, Y.M. Choi, S.K. Lee, M.W. Oh, A. Ali, B.S. Chae and D.Y. Hyun. 2017a. Development of near-infrared reflectance spectroscopy (NIRS) model for amylose and crude protein contents analysis in rice germplasm. Korean J. Plant Res. 30(1):38-49 (in Korean). https://doi.org/10.7732/kjpr.2016.30.1.038
  31. Oh, S.J., M.C. Lee, Y.M. Choi, S.K. Lee, M.W. Oh, A. Ali, B.S. Chae and D.Y. Hyun. 2017b. Fast systemic evaluation of amylose and protein contents in collected rice landrace germplasm using near-infrared reflectance (NIRS). Korean J. Plant Res. 30(4):450-465 (in Korean). https://doi.org/10.7732/KJPR.2017.30.4.450
  32. Oh, S.W., S.M. Lee, S.Y. Park, S.Y. Lee, W.H. Lee, H.S. Cho and Y.S. Yeo. 2016. Rice biotechnology and current development. J. Korean Soc. Int. Agric. 28(1):24-36 (in Korean). https://doi.org/10.12719/KSIA.2016.28.1.24
  33. Peng, S., Q. Tang and Y. Zou. 2009. Current status and challenges of rice production in China. Plant Prod. Sci. 12(1):3-8. https://doi.org/10.1626/pps.12.3
  34. Ramirez, J.A., J.M. Poada, I.T. Handa, G. Hoch, M. Vohland, C. Messier and B. Reu. 2015. Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species. Methods in Ecology and Evolution 6:1018-1025. https://doi.org/10.1111/2041-210X.12391
  35. Shu, Q.Y., D.X. Wu, Y.W. Xia, M.W. Gao and A. McClung. 1999. Calibration optimization for rice apparent amylose content by near-infrared reflectance spectroscopy (NIRS). J. of Zhejiang University (Agriculture & Life Science) 25: 343-346.
  36. Song, Y.E., D.R. Lee, S.H. Cho, K.K. Lee, J.S. Jeong and K.C. Cho. 2013. NIRS calibration equation development and validation for total nitrogen contents field analysis in fresh rice leaves. Korean J. Crop. Sci. 58(3):301-307 (in Korean). https://doi.org/10.7740/kjcs.2013.58.3.301
  37. Song, Y.J., Y.E. Song, N.K. Oh, Y.G. Choi and K.C. Cho. 2006. Relationship between near-infrared reflectance spectra and mechanical sensory score of commercial brand rice produced in Jeonbuk. Korean J. of Crop Sci. 51(s):42-46 (in Korean).
  38. Williams, P. and K. Norris. 1987. Near-Infrared Technology in Agricultural and Food Industries. American Association of Cereal Chemists, Inc., MN (USA). p. 330.
  39. Wu, J.G. and C.H. Shi. 2007. Calibration model optimization for rice cooking characteristics by near infrared reflectance spectroscopy (NIRS). Food Chemistry 103:1054-1061. https://doi.org/10.1016/j.foodchem.2006.07.063
  40. Yoon, M.O., H.S. Lee, K.R. Kim, J.E. Shim and J.Y. Hwang. 2017. Development of processed food database using Korea national health and nutrition examination survey data. Journal of Nutrition and Health 50(5):504-518 (in Korean). https://doi.org/10.4163/jnh.2017.50.5.504
  41. Zhu, X., G. Li and Y. Shan. 2015. Prediction of cadmium content in brown rice using near-infrared spectroscopy and regression modelling techniques. International Journal of Food Science and Technology 50:1123-1129. https://doi.org/10.1111/ijfs.12756