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| Research on Optimization of Wheat Seed Germination Rate NIR Model Based on Si-cPLS |
| WU Jing-zhu1，2, DONG Wen-fei1, DONG Jing-jing1, CHEN Yan1, MAO Wen-hua1，3, LIU Cui-ling1 |
1. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
2. State Key Laboratory of Soil Plant Machinery System Technology, Beijing 100083, China
3. Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China |
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Abstract 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.
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Received: 2015-11-06
Accepted: 2016-04-14
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[1] HE Zhong-hu, XIA Xian-chun, CHEN Xin-min, et al(何中虎，夏先春，陈新民, 等). Acta Agronomica Sinica(作物学报), 2011, 37(2): 202.
[2] ZHI Ju-zhen, BI Xin-hua, DU Ke-min, et al(支巨振, 毕辛华, 杜克敏, 等). Rules for Agricultural Seed Testing-Germination Test(农作物种子检验规程-发芽试验), GB/T 3543.4—1995.
[3] WANG Chun-hua, HUANG Ya-wei, WANG Ruo-lan(王春华, 黄亚伟, 王若兰). Science and Technology of Cereals, Oils and Foods(粮油食品科技), 2013, 21(6): 73.
[4] DAI Zi-yun, LIANG Xiao-hong, ZHANG Li-juan, et al(戴子云, 梁小红, 张利娟, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(10): 2642.
[5] LI Yan-kun, SHAO Xue-guang, CAI Wen-sheng(李艳坤, 邵学广, 蔡文生). Chemical Journal of Chinese Universities (高等学校化学学报) 2007, 28(2): 246.
[6] Li Yankun, Jing Jing. Chemometrics and Intelligent Laboratory Systems, 2014, 130: 45.
[7] Liu Ke, Chen Xiaojing, Limin Li, et al. Analytica Chimica Acta, 2015, 858(1): 16.
[8] Li Yankun, Shao Xueguang, Cai Wensheng. Talanta, 2007, 72(1): 217.
[9] Wang Xiaofei, Bao Yanfei, Liu Guili, et al. Procedia Engineering, 2012, 29(4): 2285.
[10] ZHANG Ling-li, GUO Yue-xia, SONG Xi-yue(张玲丽, 郭月霞, 宋喜悦). Seed(种子), 2008, 10: 52.
[11] Roberto Kawakami Harrop Galvo, Mário César Ugulino Araujo, Gledson Emídio José, et al. Talanta, 2005, 67: 736.
[12] ZOU Xiao-bo，HUANG Xiao-wei，SHI Ji-yong, et al(邹小波, 黄晓玮, 石吉勇, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2012, 43(9): 155.
[13] Zuo Xiaobo, Fang Sheng, Liang Xianli. Advance Journal of Food Science & Technology, 2014, 6(11): 1209. |
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