KOREAN JOURNAL OF CROP SCIENCE (한국작물학회지)

The Korean Society of Crop Science (한국작물학회)
권호 표지
DOI QR코드

DOI QR Code

Statistical Analysis of Amylose and Protein Content in Landrace Rice Germplasm Collected from East Asian Countries Based on Near-Infrared Reflectance Spectroscopy (NIRS)

근적외선분광분석에 의한 동아시아 지역 재래종 벼 유전자원의 아밀로스 및 단백질 함량 변이분석

  • Oh, Sejong (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Choi, Yu Mi (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Yoon, Hyemyeong (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Lee, Sukyeung (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Yoo, Eunae (National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA) ;
  • Lee, Myung Chul (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) ;
  • 윤혜명 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • 이수경 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • 유은애 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • 이명철 (농촌진흥청 국립농업과학원 농업유전자원센터) ;
  • ;
  • 채병수 (농촌진흥청 국립농업과학원 농업유전자원센터)
  • Received : 2019.04.04
  • Accepted : 2019.06.10
  • Published : 2019.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

A statistical analysis of 4,380 non-glutinous landrace rice germplasm collected from four East Asian countries namely South Korea (1,032), North Korea (994), Japan (800), and China (528) was conducted using normal distribution, variability index value (VIV), analysis of variation (ANOVA), and Duncan's multiple range test (DMRT) based on a data obtained from Near-Infrared Reflectance Spectroscopy (NIRS) analysis. In normal distribution, the average protein content was 8.2%, and the non-glutinous rice amylose, ranging over 10%, was found to be 22.0%. Protein content in most gremplasm was between 5.4 and 10.9%, and amylose content was between 15.0 and 28.9%. The VIV was 0.50 for protein, and 0.81 for non-glutinous rice amylose content. The average amylose content was 23.34% in Chinese, 21.55% in South Korean, 21.45% in Japanese, and 20.48% in North Korean resources, while the average protein content was found to be 9.02% in Chinese, 8.06% in Japanese, 8.04% in North Korean, and 7.99% in South Korean resources. ANOVA of amylose and protein content showed significant differences at p=0.01. The F-test value for amylose content was 94.92, and for protein content was 81.82 compared to the critical value of 3.79. DMRT of amylose and protein content revealed significant differences (p<0.01). Among the various germplasm obtained from different countries, that from North Korean had the lowest level of amylose content, whereas that from South Korea had the lowest level of protein content than all other resources. Chinese resources had the highest level of amylose and protein content. It is recommended to use these results in breeding fields.

본 연구는 선행연구에서 개발된 근적외선 분광분석(NIRS) 예측모델을 활용하여 측정된 국내외 재래종 메벼 유전자원의 아밀로스 및 단백질 함량 자료를 통계처리 하여 자원의 지리적 특성과 성분 함량에 대한 정확한 정보를 제공하기 위해 실시하였다. 1. 정규분포분석 결과 메벼 유전자원의 아밀로스 평균은 22.0%였고, 단백질 평균은 8.2%였으며 전체 자원의 95%를 차지하는 자원들의 함량범위는 아밀로스의 경우 15.0-28.9%, 단백질은 5.4-10.9%였다. 자원의 다양성지수는 아밀로스의 경우 0.81, 단백질은 0.50이었다. 2. ANOVA, DMRT에 사용된 자원 수는 한국 자원의 경우 1,032, 북한은 994, 일본은 800, 중국은 528자원이었다. 국가별 아밀로스 평균함량은 중국 자원의 경우 23.34%, 한국 자원은 21.55%, 일본 자원은 21.45%, 북한 자원은 20.48%였다. 단백질 평균함량은 중국 자원의 경우 9.02%, 일본 자원은 8.06%, 북한 자원은 8.04%, 한국 자원은 7.99%였다. ANOVA 결과 벼 유전자원의 아밀로스 및 단백질 함량은 국가별 차이가 있었고 1% 유의수준에서 차이가 인정되었다. 3. DMRT 결과 국가별 아밀로스 함량은 한국과 일본, 북한, 중국의 세 집단으로 나눌 수 있었으며 각 집단 간 아밀로스 함량차이는 1% 유의수준에서 차이가 인정되었다. 단백질 함량의 경우 한국, 일본, 북한과 중국의 두 집단으로 나눌 수 있었으며 각 집단 간 단백질 함량차이는 1% 유의수준에서 차이가 인정되었다. 북한 자원은 가장 낮은 아밀로스 평균함량을 나타냈고, 한국 자원은 가장 낮은 단백질 평균함량을 나타냈다. 이에 비해 중국 자원은 가장 높은 아밀로스와 단백질 평균함량을 나타냈다. 이러한 지리적 분포에 따른 벼 자원 간 함량차이는 각 지역별 자원 선호도와 품종 특성이 반영된 결과라고 할 수 있다.

Keywords

JMHHBK_2019_v64n2_70_f0001.png 이미지

Fig. 1. Density function of probability for protein contents on landrace rice germplasm.

JMHHBK_2019_v64n2_70_f0002.png 이미지

Fig. 2. ANOVA procedure

JMHHBK_2019_v64n2_70_f0003.png 이미지

Fig. 3. Relationship between the formula (C=A+B; A=C−B; B=C−A).

JMHHBK_2019_v64n2_70_f0004.png 이미지

Fig. 4. General concept of calculating SSB (A), SSW (B) and SST (C=SSB+SSW) with mean values ($\overline{X_1}$, $\overline{X_4}$, $\overline{X_t}$) in normal distribution for ANOVA.

JMHHBK_2019_v64n2_70_f0005.png 이미지

Fig. 5. Procedure of Duncan’s multiple range test.

JMHHBK_2019_v64n2_70_f0006.png 이미지

Fig. 6. Correlation plots between NIRS data, amylose (A) and protein content (B) in the milled brown rice germplasm.

JMHHBK_2019_v64n2_70_f0007.png 이미지

Fig. 7. Normal distribution and probability density of amylose content in total landrace rice germplasm (n=4,948).

JMHHBK_2019_v64n2_70_f0008.png 이미지

Fig. 8. Normal distribution and probability density of amylose content in waxy-type landrace rice germplasm (n=568).

JMHHBK_2019_v64n2_70_f0009.png 이미지

Fig. 9. Normal distribution and probability density of protein content in waxy-type landrace rice germplasm (n=568).

JMHHBK_2019_v64n2_70_f0010.png 이미지

Fig. 10. Normal distribution and probability density of nonglutinous rice amylose content in landrace rice germplasm (n=4,380).

JMHHBK_2019_v64n2_70_f0011.png 이미지

Fig. 11. Normal distribution and probability density of non-glutinous amylose content in landrace rice germplasm of South Korea (A), North Korea (B), Japan (C) and China (D).

JMHHBK_2019_v64n2_70_f0012.png 이미지

Fig. 12. Normal distribution and probability density of nonglutinous protein content in landrace rice germplasm (n=4,380).

JMHHBK_2019_v64n2_70_f0013.png 이미지

Fig. 13. Normal distribution and probability density of non-glutinous protein content in landrace rice germplasm from South Korea (A), North Korea (B), Japan (C) and China (D).

Table 1. Distribution of landrace rice germplasm based on geographical origin.

JMHHBK_2019_v64n2_70_t0001.png 이미지

Table 2. Frequency distribution table of protein contents of landrace rice germplasm.

JMHHBK_2019_v64n2_70_t0002.png 이미지

Table 3. ANOVA formula definition.

JMHHBK_2019_v64n2_70_t0003.png 이미지

Table 4. Descending arrangement of average values ($\overline{X}$) in each group and the determination of Studentized range p-value.

JMHHBK_2019_v64n2_70_t0004.png 이미지

Table 5. Calculation of LSR value (at significance level α=0.01).

JMHHBK_2019_v64n2_70_t0005.png 이미지

Table 6. Creating DMRT table by comparing with $\overline{X}$ and | $\overline{X}$ - LSR0.01 | value in each group.

JMHHBK_2019_v64n2_70_t0006.png 이미지

Table 7. Interpretation of DMRT result.

JMHHBK_2019_v64n2_70_t0007.png 이미지

Table 8. External validation results of NIRS equation model for the amylose and protein content in the milled brown rice.

JMHHBK_2019_v64n2_70_t0008.png 이미지

Table 9. Statistics data table for ANOVA test on non-glutinous rice amylose content based on geographical origin.

JMHHBK_2019_v64n2_70_t0009.png 이미지

Table 10. ANOVA table of non-glutinous rice amylose contents in landrace rice germplasm from various countries.

JMHHBK_2019_v64n2_70_t0010.png 이미지

Table 11. Statistics data table for ANOVA test on non-glutinous rice protein content based on geographical origin.

JMHHBK_2019_v64n2_70_t0011.png 이미지

Table 12. ANOVA table of non-glutinous rice protein contents in landrace rice germplasm from various countries.

JMHHBK_2019_v64n2_70_t0012.png 이미지

Table 13. DMRT table of non-glutinous rice amylose content in landrace germplasm based on countries of origin.

JMHHBK_2019_v64n2_70_t0013.png 이미지

Table 14. DMRT table of non-glutinous protein content in landrace rice germplasm based on countries of origin.

JMHHBK_2019_v64n2_70_t0014.png 이미지

Table 15. Amylose and protein contents of non-glutinous in landrace rice germplasm based on countries of origin.

JMHHBK_2019_v64n2_70_t0015.png 이미지

References

  1. 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. https://doi.org/10.7740/kjcs.2012.57.3.296
  2. 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
  3. Champagne, E.T., K.L. Aett, B.T. Vinyard, B.D. Webb, A.M. McClung and F.E. Barton. 1997. Effects of drying conditions, final moisture content and degree of milling on rice flavor. J. of Cereal Chemistry 74:566-570. https://doi.org/10.1094/CCHEM.1997.74.5.566
  4. 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.
  5. 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. https://doi.org/10.7740/kjcs.2013.58.3.274
  6. Juliano, B.O. 2003. Rice: In Caballero, B., L. Trugo, P. Finglas (eds.), Encyclopedia of Food Sciences and Nutrition (2nd ed.), Academic Press, London, UK. pp. 4995-5001.
  7. Kim, D.J., S.K. Oh, J.H. Lee, M.R. Yoon, I.S. Choi, D.H. Lee and Y.G. Kim. 2012. Changes in quality properties of brown rice after germination. Korean J. Food Sci. Technol. 44(3) : 300-305. https://doi.org/10.9721/KJFST.2012.44.3.300
  8. 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.
  9. Kim, J.S., M.H. Song, J.E. Choi, H.B. Lee and S.N. Ahn. 2008. Quantification of protein and amylose contents by near-infrared reflectance spectroscopy in aroma rice. Korean J. Food Sci. Technol. 40(6) : 603-610.
  10. Kim, K.H., M.H. Heu, S.Z. Park, and H.J. Koh. 1991. New mutants for endorsperm and embryo characters in rice. Korean J. Crop Sci. 36(3) : 197-203.
  11. Kim, S.J. 2016. Analysis of freshness of rice depending on packing material using MANOVA. Korean J. Applied Statistics 29(7) : 1421-1428. https://doi.org/10.5351/KJAS.2016.29.7.1421
  12. 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.
  13. 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. https://doi.org/10.7732/KJPR.2016.30.1.038
  14. 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. https://doi.org/10.7732/KJPR.2017.30.4.450
  15. Oh, S.J., Y.M. Choi, M.C. Lee, S.K. Lee, H.M. Yoon, M. Rauf and B.S. Chae. 2019. Construction of database system on amylose and protein contents distribution in rice germplasm based on NIRS data. Korean J. Plant Res. 32(2) : 124-143. https://doi.org/10.7732/KJPR.2019.32.2.124
  16. Pak, S.I. and T.H. Oh. 2010. The Application of analysis of variance (ANOVA). J. Vaterinary Clinics 27(1) : 71-78.
  17. Park, K.H. 2005. A recent rice breeding strategy in Japan. Korean J. Intl. Agri. 17(1) : 1-5.
  18. Park, S.Z., W.S. Hahn and N.J. Chung. 2016. Comparison of several groups In Biostatistics, KNOU Press, Seoul, Korea. pp. 230-250.
  19. Son, J.R., J.H. Kim, J.I. Lee, J.K. Kim, H.G. Hwang and H.P. Moon. 2002. Trend and further of rice quality evaluation. Korean J. Crop Sci. 47:33-54.
  20. Song, J., J.H. Kim, D.S. Kim, C.K. Lee, J.T. Youn, S.L. Kim and S.J. Suh. 2008. Physicochemical properties of starches in japonica rices of different amylose content. Korean J. Crop Sci. 53(3) : 285-291.
  21. Williams, P. and Norris, K. 1987. Near-Infrared Technology in Agricultural and Food Industries. American Association of Cereal Chemists, Inc., MN (USA). p. 330.