共识径向基神经网络应用于近红外光谱法测定三七中总黄酮
NIRS Determination of Total Flavonoids in Panax Notoginseng with Consensus Radial Basis Function Neural Network
摘 要
将共识策略结合径向基神经网络用于近红外光谱法测定三七中总黄酮的含量中。首先采用离散小波变换对近红外光谱进行预处理,去除噪声并压缩数据。继而采用共识径向基神经网络建立校正模型。结果表明:共识策略可以使模型更稳定、更准确。
共识径向基神经网络
三七
总黄酮
NIRS
Consensus radial basis function neural network
Panax notoginseng
Total flavonoids
Abstract
Consensus strategy was applied to radial basis function neural network and used in NIRS determination of total flavonoids in panax notoginseng. Firstly, the spectra were preprocessed using discrete wavelet transform for noise filtering and data compression. Then, consensus radial basis function neural network was used for establishing the calibration model. It was shown by the results that the consensus models were more stable and accurate than the conventional regression models.
中图分类号 O657.33 DOI 10.11973/lhjy-hx201606004
所属栏目 试验与研究
基金项目 云南省教育厅一般项目(2012Y414)
收稿日期 2015/6/13
修改稿日期
网络出版日期
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备注杨晓丽(1980-),女,辽宁沈阳人,副教授,博士,主 要从事计算化学研究工作。
引用该论文: YANG Xiao-li,HE Qiong. NIRS Determination of Total Flavonoids in Panax Notoginseng with Consensus Radial Basis Function Neural Network[J]. Physical Testing and Chemical Analysis part B:Chemical Analysis, 2016, 52(6): 635～638
杨晓丽,何琼. 共识径向基神经网络应用于近红外光谱法测定三七中总黄酮[J]. 理化检验－化学分册, 2016, 52(6): 635～638
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参考文献
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【3】BORGGARD C, THODBERG H. Optimal minimal neural interpretation of spectra［J］. Analytical Chemistry, 1992,64(5):545-551.
【4】VAPNIK V. The nature of statistical learning theory［M］. New York: Springer-Verlag, 1995.
【5】GELADI P, KOWALSKI B R. Partial least squares regression: a tutorial［J］. Analytical Chimica Acta, 1986,185:1-17.
【6】TONG Wei-da, HONG Hui-xiao, FANG Hong, et al. Decision forest: combining the predictions of multiple independent decision tree models［J］. Journal of Chemical Information and Computer Sciences, 2003,43(2):525-531.
【7】JAWORSKI A, WIKIEL K, WIKIEL H. Application of multiblock and hierarchical PCA and PLS models for analysis of AC voltammetric data［J］. Electroanalysis, 2005,17(15/16):1477-1485.
【8】OPITZ D W, SHAVLIK J W. Actively searching for an effective neural network ensemble［J］. Connection Science, 1996,8(3/4):337-353.
【9】LI Yan-kun, SHAO Xue-guang, CAI Wen-sheng. A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples［J］. Talanta, 2007,72(1):217-222.
【10】SU Zhen-qiang, TONG Wei-da, SHI Le-ming, et al. A partial least squares-based consensus regression method for the analysis of near-infrared complex spectra data of plant samples［J］. Analytical Letter, 2006,39(9):2073-2083.
【11】GRAMATICA P, PILUTTI P, PAPA E. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling［J］. Journal of Chemical Information and Computer Sciences, 2003,44(5):1794-1802.
【12】崔秀明.三七中黄酮成分的含量测定［J］.中草药, 2002,33(7):611-612.
【13】LANG M, GUO H, ODEGARD J E, et al. Noise reduction using an undecimated descrete wavelet transform［J］. Signal Processing Letters, 1996,3(1):10-12.
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【16】BILLINGS S A, WEI Hua-liang, BALIKHIN M A. Generalized multiscale radial basis function networks［J］. Neural Networks, 2007,20:1081-1094.
【17】MARINARO M, SCARPETTA S. On-line learning in RBF neural networks: a stochastic approach［J］. Neural Networks, 2000,13:719-729.
【18】朱明星,张德龙.RBF网络基函数中心选取算法的研究［J］.安徽大学学报(自然科学版), 2000,24(1):72-78.
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