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基于傅里叶变换红外光谱和siPLS-GA-PLS的水稻叶片氮素含量预测研究

Keywords: 水稻,,傅里叶变换红外光谱,协同偏最小二乘法,遗传—偏最小二乘法

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Abstract:

傅里叶变换中红外光谱谱区宽,搜索空间大,需要采用高效率和高质量的算法进行波长选择.敏感波段及其组合的选择是简化分析模型和提高模型预测精度的关键技术之一.本研究以水稻孕穗期叶片干样的中红外光谱透射率和叶片氮素含量为数据源,通过协同偏最小二乘算法(siPLS)从宽谱区中初选出波段范围1583.3~992.2cm-1,再采用迭代遗传算法(GA)从中选出了84个水稻叶片氮素含量预测的敏感波段.研究结果显示以此敏感波段建立的偏最小二乘回归模型的预测均方根误差(RMSEP)和水稻叶片总氮含量的测量值与预测值之间的相关系数分别为0.1186和0.9120,该预测结果明显优于协同偏最小二乘法(siPLS)和光谱指数NFSA的预测结果,说明傅里叶变换红外光谱技术结合siPLS-GA-PLS算法能够实现水稻叶片氮素含量的预测.

References

[1]  【1】朱西存,赵庚星,隋学艳,雷彤,孙顶国, "基于光谱分析技术的苹果花钾素含量估测研究",红外 31, 19-23(2010)
[2]  【2】修丽娜,刘湘南,刘美玲, "镉污染水稻高光谱诊断分析与建模",光谱学与光谱分析 31, 192-196(2011)
[3]  【3】刘晓,唐文婷,李倩,岳明, "用傅里叶变换红外光谱研究增强UV-B辐射对PSII蛋白结构的影响",光谱学与光谱分析 31, 65-68(2011)
[4]  【1】PAN Rui-Chi. Rice Physiology [M]. Beijing: Science Press (潘瑞炽. 水稻生理 .北京:科学出版社), 1979:87—95.
[5]  【2】Zhou Q, Sun S Q, Yu L, et al. Sequential changes of main components in different kinds of milk powders using two-dimensional infrared correlation analysis[J]. Journal of Molecular Structure, 2006, 799: 77—84.
[6]  【3】WU Di, CAO Fang, FENG Shui-Juan, et al . Application of infrared spectroscopy technique to protein content fast measurement in milk powder based on support vector machines [J]. Spectroscopy and Spectral Analysis (吴迪, 曹芳, 冯水 娟, 等.基于支持向量机算法的红外光谱技术在奶粉蛋 白质含量快速检测中的应用. 光谱学与光谱分析 ), 2008, 28 (5):1071—1075.
[7]  【4】Zhou Q F, Shen Z Q, Wang R H. Fourier transform infrared spectral difference of leaf tips in rice related to nitrogen fertilizer rates[J]. Acta Botanica Sinica, 2002, 44 :547— 550.
[8]  【5】Leardi R, Gonzalez A L. Genetic algorithm applied to feature selection in PLS regression: how and when to use them [J]. Chemometrics and Intelligent Laboratory Systems. 1998, 41: 195—207.
[9]  【6】Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data set[J]. Journal of Chemometrics, 2000, 14: 643—655.
[10]  【7】Li L, Cheng Y B, Ustin S, et al. Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression[J]. Advances in Space Research, 2008, 41 :1755—1763.
[11]  【8】CHU Xiao-Li, YUAN Hong-Fu, LU Wan-Zhen. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique[J]. Progress in Chemistry (褚小立, 袁洪福, 陆婉珍.近红外分析中光 谱预处理及波长选择方法进展与应用. 化学进展 ), 2004, 16 (4):528—542.
[12]  【9】Barnes R J, Dhanoa M S, Lister S J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra[J]. Applied Spectroscopy, 1989, 43 (5): 772—777.
[13]  【10】SHEN De-Yan. Application of Infrared Spectral Technique in the Analysis for Polymer Materials [M].Beijing: Science Press(沈德言. 红外光谱法在高分子研究中的应用. 北 京: 科学出版社), 1982:57—75.

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