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

Abstract

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

CLC Number:

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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References 30

[1]	R.A. Wing, M.D. Purugganan, Q.F. Zhang , et al. The rice genome revolution: from an ancient grain to Green Super Rice[J]. Nature Reviews Genetics, 2018. 19(8):505-517.
[2]	杨成林, 王丽妍 . 分析中国水稻高产栽培技术创新与实践[J]. 农业与技术, 2018. 38(8):128.
	Yang C L, Wang L Y . Analysis on Innovation and Practice of Rice High Yield Cultivation Techniques in China[J]. Agriculture and Technology, 2018(8):128.
[3]	M.B. Liu, X.L. Liu, Y. Liu , et al. Detection of Crude Protein, Crude Starch, and Amylose for Rice by Hyperspectral Reflectance[J]. Spectroscopy Letters, 2014. 47(2):101-106.
[4]	S. Yan, X. Wang, J. Huang , et al. Study on the method and model of rice quality monitoring based on hyperspectral data [C]. In Fifth International Conference on Agro-geoinformatics. 2016.
[5]	N.K. Bhullar, W. Gruissem . Nutritional enhancement of rice for human health: The contribution of biotechnology[J]. Biotechnology Advances, 2013. 31(1):50-57.
[6]	王玉珠, 林伟锋, 陈中 , 等. 大米理化指标与米饭品质相关性的研究[J]. 现代食品科技, 2011. 27(11):1312-1315.
	Wang Y Z, Ling W F, Chen Z , et al. Study on the correlation between physicochemical indexes and rice quality[J]. Modern Food Science and Technology, 2011. 27(11):1312-1315.
[7]	M. Martin, M.A. Fitzgerald . Proteins in Rice Grains Influence Cooking Properties[J]. Journal of Cereal Science, 2002. 36(3):285-294.
[8]	T. Tsukaguchi, T. Taniguchi, R. Ito , et al. The effects of nitrogen uptake before and after heading on grain protein content and the occurrence of basal- and back-white grains in rice (Oryza sativa L.)[J]. Plant Production Science, 2016. 19(4):1-10.
[9]	王仪春, 张小明, 石春海 , 等. 稻米直链淀粉含量测定方法的探讨[J]. 中国农学通报, 2001. 17(5):30-32.
	Wang Y C, Zhang X M, Shi C H , et al. Study on the Method of Determining Amylose Content in Rice[J]. Chinese agricultural science bulletin, 2001. 17(5):30-32.
[10]	L.J. Zhong, F.M. Chen . Method for determining amylose content of fresh rice sample[J]. Journal of Zhejiang University, 2002. 28(1):33-36.
[11]	彭波, 孙艳芳, 韩秋 , 等. 水稻种子蛋白质含量检测方法的比较分析[J]. 江苏农业科学, 2018,46(4):22-28.
	Peng B, Sun Y F, Han Q , et al. Comparative Analysis of Protein Content Detection Methods in Rice Seeds[J]. Jiangsu Agricultural Sciences, 2018,46(4):22-28.
[12]	V. Metodos, S.De, M.P.Marcelo , et al. Fecal collection methods for the determination of protein digestibility in bullfrogs[J]. Ciencia Rural, 2015. 45(8):1492-1495.
[13]	邵春甫, 李长文, 王珊 , 等. 近红外光谱应用于高粱中总淀粉、直链淀粉与支链淀粉的定量分析[J]. 粮油食品科技, 2016. 24(2):60-64.
	Shao C F, Li C W, Wang S , et al. Quantitative Analysis of Total Starch, Amylose and Amylopectin in Sorghum by Near Infrared Spectroscopy[J]. Science and Technology of Cereals, oils and Foods, 2016. 24(2):60-64.
[14]	王凡, 李永玉, 彭彦昆 , 等. 马铃薯多品质参数可见/近红外光谱无损快速检测[J]. 光谱学与光谱分析, 2018. 38(12):3736-3742.
	Wang F, Li Y Y, Peng Y K , et al. Rapid Nondestructive Detection of Potato Multi-Quality Parameters by Visible/Near Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2018. 38(12):3736-3742.
[15]	曹晓宁, 田翔, 赵小娟 , 等. 基于近红外光谱法快速检测藜麦淀粉含量[J]. 江苏农业科学, 2017. 45(4):147-149.
	Cao X N, Tian X, Zhao X J , et al. Rapid Detection of Quinoa Starch Content Based on Near Infrared Spectroscopy[J]. Jiangsu Agricultural Sciences, 2017. 45(4):147-149.
[16]	彭玉梅, 刘华强, 朱学栋 , 等. 近红外光谱技术测定水稻直链淀粉含量研究[J]. 农业科学与技术, 2017. 18(4):729-732.
	Peng Y M, Liu H Q, Zhu X D , et al. Determination of Amylose Content in Rice by Near Infrared Spectroscopy[J]. Agricultural Science & Technology, 2017. 18(4):729-732.
[17]	张新玉, 王颖杰, 刘若西 , 等. 近红外光谱技术应用于玉米单籽粒蛋白质含量检测分析的初步研究[J]. 中国农业大学学报, 2017. 22(5):25-31.
	Zhang X Y, Wang Y J, Liu R X , et al. Preliminary Study on the Application of Near Infrared Spectroscopy in the Detection and Analysis of Protein Content in Single Grain of Maize[J]. Journal of China Agricultural University, 2017. 22(5):25-31.
[18]	梁秀英, 李小昱, 杨万能 , 等. 奇异数据筛选法在玉米籽粒蛋白质近红外光谱检测中的应用[J]. 激光生物学报, 2015. 24(1):38-45.
	Liang X Y, Li X Y, Yang W N , et al. Application of Singular Data Screening Method in Near Infrared Spectroscopy Detection of Maize Grain Protein[J]. Acta Laser Biology Sinica, 2015. 24(1):38-45.
[19]	张松, 冯美臣, 杨武德 , 等. 基于近红外光谱的冬小麦籽粒蛋白质含量检测[J]. 生态学杂志, 2018. 37(4):1276-1281.
	Zhang S, Feng M C, Yang W D , et al. Detection of Protein Content in Winter Wheat Grain Based on Near Infrared Spectroscopy[J]. Chinese Journal of Ecology, 2018. 37(4):1276-1281.
[20]	张丽君, 刘龙龙, 马名川 , 等. 燕麦蛋白质近红外定量模型的创建及其在育种中的应用[J]. 安徽农业科学, 2017. 45(8):10-13.
	Zhang L J, Liu L L, Ma M C , et al. Establishment of Near Infrared Quantitative Model of Oat Protein and Its Application in Breeding[J]. Journal of Anhui Agricultural Sciences, 2017. 45(8):10-13.
[21]	K.S. Andrew, G.F. Jelle, M. Onisimo , et al. Forage quality of savannas-simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery[J]. Remote Sensing of Environment, 2010. 114(1):64-72.
[22]	C. Zhang, F. Liu, W.W. Kong , et al. Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves[J]. Sensors, 2015. 15(7):16576-16588.
[23]	于宏威, 王强, 石爱民 , 等. 高光谱成像技术结合化学计量学可视化花生中蛋白质含量分布[J]. 光谱学与光谱分析, 2017. 37(3):853-858.
	Yu H W, Wang Q, Shi A M . et al. Visualization of Protein Distribution in Peanut by Hyperspectral Imaging and Chemometrics[J] Spectroscopy and Spectral Analysis, 2017. 37(3):853-858.
[24]	冯慧, 熊立仲, 陈国兴 , 等. 基于高光谱成像和主成分分析的水稻茎叶分割[J]. 激光生物学报, 2015. 24(1):31-37.
	Feng H, Xiong L Z, Chen G X , et al. Rice stem and leaf segmentation based on hyperspectral imaging and principal component analysis[J]. Acta Laser Biology Sinica, 2015. 24(1):31-37.
[25]	H. Feng, G.X. Chen, L.Z. Xiong , et al. Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline[J]. Frontiers in Plant Science, 2017. 8:1238-1248.
[26]	X.M. Sun, Q.F. Zhou, Q.X. He , et al. Hyperspectral Variables in Predicting Leaf Chlorophyll Content and Grain Protein Content in Rice[J]. Acta Agronomica Sinica, 2005. 31(7):844-850.
[27]	X. Xie, X. Li, S. Shen , et al. Prediction Model of Grain Crude Protein and Amylose Content with Canopy Spectral Reflectance in Rice [C]. International Conference on Multimedia Technology. 2010.
[28]	Y. Liu, Y.L. Tang, J.F. Huang , et al. Contents of crude protein, crude starch and amylase in rice flour by hyperspectral data[J]. In AGRIS since, 2010. 9(41):2617-2623.
[29]	M.B. Liu, X.L. Li, Y. Liu , et al. Detection of Crude Protein, Crude Starch, and Amylose for Rice by Hyperspectral Reflectance[J]. Spectroscopy Letters, 2014,47(2):101-106.
[30]	N. Otsu . A Threshold Selection Method from Gray-Level Histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 2007. 9(1):62-66.

Measurement Technology of Quality Parameters of Rice Grain Based on Hyperspectral Imaging on the Visible-near Infrared

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