基于高光谱的水稻精米品质参数测量技术研究

doi:10.19788/j.issn.2096-6369.190205

摘要/Abstract

摘要：

【目的】水稻精米直链淀粉和蛋白质含量,是衡量稻米加工特性的重要参数。本研究旨在使用高光谱技术来无损检测其含量。【方法】以水稻核心种质资源材料中106个材料为研究对象,使用可见光-近红外高光谱成像技术获取各波段下光谱指数。【结果】高光谱数据经过数据重整、图像提取及分析等一系列操作后,可以得到初级指数和复杂指数。初级指数与直链淀粉和蛋白质含量模型的R2分别为0.823和0.837,五倍交叉验证结果显示模型稳定性良好。同时,通过复杂指数与直链淀粉和蛋白质含量的相关系数结果也可以看出,500-800nm区域间,高光谱复杂指数与直链淀粉和蛋白质含量相关系数较高。【结论】基于高光谱成像技术可以同时准确估测精米直链淀粉和蛋白质含量,为后续水稻种质资源批量品质分析打下研究基础。

关键词: 高光谱, 水稻, 精米, 直链淀粉, 蛋白质, 线性回归, 相关系数, 植物表型组学

Abstract:

[Objective] The objective of this study is to develop a novel non-destructive and rapid optical method for the measurement of amylose and protein content in rice, which can be used to measure the processing characteristics of rice after cooking. [Method] A new measuring technique based on the acquisition and analysis of near-infrared hyperspectral images was developed to acquire hyperspectral indices within various wavelengths of 106 polished rice accessions in rice core germplasm resources. [Result]The model for stepwise linear regression analysis showed that the R2 of the amylose and protein content models were 0.823 and 0.837, respectively, and the five-fold cross-validation results demonstrated the stability of the models. In the wavelength range of 500-800nm, the correlation coefficient between them was higher, meanwhile, the more the index varied, the less information was available, which showed that among massive spectral data, the information concentration area was very limited.[Conclusion] The novel method based on hyperspectral imaging technology can estimate amylose and protein content non-destructively and accurately, which is the basis for further analysis of the grain quality of generous core rice accessions.

Key words: hyperspectral, rice, polished rice, amylose content, protein content, linear regression, correlation coefficient, plant phenom

中图分类号:

戴国新,陈国兴,杨万能,冯慧. 基于高光谱的水稻精米品质参数测量技术研究[J]. 农业大数据学报, 2019, 1(2): 51-63.

Guoxin Dai,Guoxin Chen,Wanneng Yang,Hui Feng. Measurement Technology of Quality Parameters of Rice Grain Based on Hyperspectral Imaging on the Visible-near Infrared[J]. Journal of Agricultural Big Data, 2019, 1(2): 51-63.

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