您好,欢迎访问中国农业科学院 机构知识库!

Research on Optimization of Wheat Seed Germination Rate NIR Model Based on Si-cPLS

文献类型: 外文期刊

作者: Wu Jing-Zhu 1 ; Dong Wen-Fei 1 ; Dong Jing-Jing 1 ; Chen Yan 1 ; Mao Wen-Hua 1 ; Liu Cui-Ling 1 ;

作者机构: 1.Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 100048, Peoples R China

2.State Key Lab Soil Plant Machinery Syst Technol, Beijing 100083, Peoples R China

3.Chinese Acad Agr Mechanizat Sci, Beijing 100083, Peoples R China

关键词: Near infrared spectroscopy;Characteristic spectrum;Consensus modeling;Germination rate

期刊名称:SPECTROSCOPY AND SPECTRAL ANALYSIS ( 影响因子:0.589; 五年影响因子:0.504 )

ISSN: 1000-0593

年卷期: 2017 年 37 卷 4 期

页码:

收录情况: SCI

摘要: To improve the detecting accuracy and robustness of wheat seed germination rate with near infrared spectroscopy technique, single PLS model and consensus PLS model(cPLS) developed on the full-spectral were compared and analyzed, thus, the Si-cPLS model which developed on the characteristic spectral regions was put forward. There were 84 samples partitioned into 66 training samples and 18 prediction samples using SPXY method. By randomly selecting 50 samples from training set as calibration set, a series of sub PLS models were build. 100 PLS sub models satisfying predefined criterion were selected and combined one cPLS model by averaging all predicted results. With this basis Si-cPLS model were developed on characteristic spectral regions selected with synergy interval method. Statistics on 50 repeat prediction of wheat seed germination by full-spectral PLS model, full-spectral cPLS model, and Si-cPLS model showed that, the mean correlation coefficient(R) were 0. 901, 0. 922 and 0. 936 respectively, the mean RMSEP were 13. 735%, 12. 533% and 10. 273% respectively with standard deviation of RMSEP of 1. 144%, 0. 096% and 0. 080% respectively. Results showed that cPLS model was more stable and reliable than single PLS model, While Si-cPLS could further increase the stability and prediction accuracy of cPLS model.

  • 相关文献

[1]Research on Fast Detecting Tomato Seedlings Nitrogen Content Based on NIR Characteristic Spectrum Selection. Wu Jing-zhu,Wang Feng-zhu,Wang Li-li,Zhang Xiao-chao,Mao Wen-hua. 2015

[2]Can rice genetic diversity reduce Echinochloa crus-galli infestation?. Chen, X.,Yu, L..

[3]Cloning and characterization of a maize SnRK2 protein kinase gene confers enhanced salt tolerance in transgenic Arabidopsis. Ying, Sheng,Zhang, Deng-Feng,Li, Hui-Yong,Liu, Ying-Hui,Shi, Yun-Su,Song, Yan-Chun,Wang, Tian-Yu,Li, Yu,Ying, Sheng,Li, Hui-Yong,Liu, Ying-Hui.

[4]Study on Germination Rate of Zoysia (Zoysia japonica Steud.) Seeds Using Near Infrared Reflectance Spectroscopy. Dai Zi-yun,Liang Xiao-hong,Zhang Li-juan,Fan Bo,Puyang Xue-hua,Han Lie-bao,Mao Wen-hua. 2013

[5]The Influence of Different Seed Dressing Treatments on Wheat Seedlings Growth under Water Stress Conditions. Wu, Xueping,Zha, Yan,Li, Yinkun,Cai, Dianxiong,Gao, Zhiqiang,Lu, Mei. 2012

[6]Quantitative trait loci mapping and stability for low temperature germination ability of rice. Ji, S. L.,Jiang, L.,Wang, Y. H.,Zhang, W. W.,Liu, X.,Liu, S. J.,Chen, L. M.,Wan, J. M.,Zhai, H. Q.,Wan, J. M..

[7]Study on Pretreatment Algorithm of Near Infrared Spectroscopy. Xiaoli Wang,Guomin Zhou. 2011

[8]The Determination of Total N, Total P, Cu and Zn in Chicken Manure Using Near Infrared Reflectance Spectroscopy. Yiwei Dong,Yongxing Chen,Dazhou Zhu,Yuzhong Li,Chunying Xu. 2011

[9]Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten. Lu, Chengxu,Zhang, Yinqiao,Wei, Chongfeng,Mao, Wenhua,Jiang, Xunpeng,Zhou, Xingfan,Zhang, Naiqian. 2017

[10]Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression. Zhang, Guang-Cai,Yan, Xiang-Mei,Zhou, Ping,Lin, Guo-Lin,Zhou, Chong-Jun,Liu, Ning,Han, Xiao-Ri,Zhang, Guang-Cai,Li, Zhuang,Cheng, Cun-Gang,Yan, Xiang-Mei. 2012

[11]Prediction of black tea fermentation quality indices using NIRS and nonlinear tools. Dong, Chunwang,Zhao, Jiewen,Chen, Quansheng,Dong, Chunwang,Zhu, Hongkai,Wang, Jinjin,Yuan, Haibo. 2017

[12]Feasibility of Using NIR Spectroscopy to Detect Melamine in Milk. Dong Yi-wei,Liu Ya-wei,Wang Ya-nan,Huang Jin-li,Sun Bao-li,Fan Zhong-nan,Tu Zhen-hua,Zhu Da-zhou. 2009

[13]Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie.

[14]A Feasibility Study on the Discrimination of the Propolis Varieties Based on Near Infrared Spectroscopy. Yang Juan,Chen Lan-zhen,Xue Xiao-feng,Wu Li-ming,Li Yi,Wu Zhao-bin,Yang Juan,Chen Lan-zhen,Xue Xiao-feng,Wu Li-ming,Li Yi,Wu Zhao-bin,Zhang Yan-nan,Zhao Jing. 2016

[15]Variable Selection Based Cotton Bollworm Odor Spectroscopic Detection. Lu, Chengxu,Gai, Shasha,Luo, Min,Zhao, Bo. 2016

[16]Effect of the Near Infrared Spectrum Resolution on the Nitrogen Content Model in Green Tea. Yang Dan,Liu Xin,Zhang Ying-bin,Yin Peng,Liu Hong-gang. 2013

[17]Short communication: Identification of geographical indication tea with Fisher's discriminant classification and principal components analysis. Zhou, Jian,Cheng, Hao,Wang, Liyuan,Liu, Xu,Lu, Wenyuan,He, Wei. 2009

[18]Parameter optimization in soluble solid content prediction of entire bunches of grape based on near infrared spectroscopic technique. Yu, Jing,Sun, Xiangyu,Huang, Weidong,Wang, Hui.

[19]Classification of Chinese honeys according to their floral origin by near infrared spectroscopy. Ye, Zhihua,Chen, Lanzhen,Zhao, Jing,Xue, Xiaofeng,Wang, Jiahua,Sun, Qian,Vander Heyden, Yvan.

[20]Using near infrared spectroscopy to predict the physical traits of Bos grunniens meat. Zhang, Li,Sun, Baozhong,Xie, Peng,Li, Haipeng,Su, Huawei,Sha, Kun,Huang, Caixia,Lei, Yuanhua,Liu, Xuan,Wang, Huan.

作者其他论文 更多>>