Detection of crude protein, crude starch, and amylose for rice by hyperspectral reflectance

Spectroscopy Letters

Volumn 47, Issue 2, 2014, Pages 101-106

  Liu, Ming Bo ^a   Li, Xiao Li ^a   Liu, Yun ^a   Huang, Jing Feng ^b   Tang, Yan Lin ^a  

^a GUIZHOU UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

Amylose; Crude protein; Crude starch; Rice; Spectral matching

Indexed keywords

EID: 84890898518     PISSN: 00387010     EISSN: 15322289     Source Type: Journal    
DOI: 10.1080/00387010.2013.779283     Document Type: Article

References (21)

1. Card, D. H.; Peterson, D. L.; Matson, P. A. Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy. Remote Sens. Environ. 1988, 26(2), 123.
2. Kokaly, R. F. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sens. Environ. 2001, 75(2), 153.
3. LaCapra, V. C.; Melack, J. M.; Gastil, M. Remote sensing of Foliar chemistry of inundated rice with imaging spectrometry. Remote Sens. Environ. 1996, 55(1), 50.
4. Niu, Z.; Chen, Y. H.; Sui, H. Z. Mechanism analysis of leaf biochemical concentration by high spectral remote sensing. J. Remote Sens. 2000, 4(2), 125.
5. Wang, J. H.; Huang, W. J.; Zhao, C. J. The inversion of leaf biochemical components and grain quality indicators of winter wheat with spectral reflectance. J. Remote Sens. 2003, 7(4), 277.
6. Konishi, N.; Shiga, H.; Asaka, D. The application of remote sensing technology in high quality rice production. Measurement 2000, 7, 14.
7. Curran, P. J.; Dungan, J. L.; Gholz, H. L. Estimating the Foliar biochemical concentration of leaves with reflectance spectrometry. Remote Sens. Environ. 2001, 76(3), 349.
8. Kokaly, R. F.; Clark, R. N. Spectroscopic determination of leaf biochemistry using band-depth analysis of features and stepwise multiple linear regression. Remote Sens. Environ. 1999, 67 (3), 267.
9. Wu, J. G.; Shi, C. H.; Zhang, X. M. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy. Field Crops Res. 2002, 75(1), 1.
10. Yadav, B. K.; Jindal, V. K. Monitoring milling quality of by image analysis. Computers Electronics Agric. 2001, 33(1), 19.
11. Barton, F. E.; Himmelsbach, D. S.; McClung, A. M. Rice quality by spectroscopic analysis: Precision of three spectral regions. Cereal Chem. 2000, 77(5), 669.
12. Chen, N.; Luo, Y. K.; Zhu, Z. W. Correlation between eating quality and physic-chemical properties of high grain quality rice. Chin. J. Rice Sci. 1997, 11(2), 71.
13. Shuso, K.; Motoyasu, N.; Kazuhiro, T. Development of an automatic rice-quality inspection system. Computers Electronics Agric. 2003, 40(1-3), 115.
14. Xue, L. H.; Cao, W. X.; Li, Y. X. Relationship between canopy spectral reflectance characteristics and grain quality traits in rice. Chin. J. Rice Sci. 2004, 18(5), 431.
15. Tang, Y. L.; Huang, J. F.; Wang, R. C. Study on estimating the contents of crude protein and crude starch in rice panicle and paddy by hyperspectra. Sci. Agric. Sinica 2004, 37 (9), 1282.
16. Liu, Y.; Tang, Y. L.; Huang, J. F. Contents of crude protein, crude starch and amylose in rice flour by hyperspectral data. Sci. Agric. Sinica 2008, 41(9), 2617.
17. Yi, C. P.; Yao, H. Y. Isolation of rice protein and rice starch. Food Machin. 2004, 20(6), 18.
18. Hong, Y.; Gu, Z. B.; Liu, X. X. Extraction and determination of pure amylose and amylopection. Sci. Technol. Food Indust. 2004, 25(4), 86.
19. Curran, P. J. Remote sensing of Foliar chemistry. Remote Sensing Environ. 1989, 30(3), 271.
20. Wang, J. N.; Zhang, B.; Liu, J. G. Hyperspectral data mining-toward target recognition and classification. J. Image Graphics 1999, 4(11), 957.
21. Davies, A. M. C.; Grant, A. Near infra-red analysis of food. Int. J. Food Sci. Technol. 1987, 22(3), 191.