用最小二乘支持向量机的可见-近红外光谱测定蜂花粉贮存时间
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浙江省新苗人才计划资助项目


STORAGE PERIOD DETERMINATION OF BEE POLLEN BY VISIBLENEAR INFRARED SPECTROSCOPY WITH LEAST SQUARESSUPPORT VECTOR MACHINES
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    摘要:

    为了探索一种快速有效的蜂花粉新鲜程度检测方法,利用可见近红外光谱技术结合最小二乘支持向量机(LSSVM)对蜂花粉的贮存时间进行了检测.选择常温环境中贮存时间为4~50天(共计47天)的茶花蜂花粉作为研究对象,将全光谱数据作为输入变量建立了LSSVM模型.结果显示,该LSSVM模型预测效果较好,预测相关系数rp达到了0.996,预测标准误差(SEP)和预测均方根误差(RMSEP)的值分别为1.310和1.308,优于偏最小二乘法(PLS)和主成分回归(PCR)的预测结果,说明基于LSSVM的可见近红外光谱技术能够很好地对花粉贮存时间进行检测.同时对不同贮存时间段花粉的预测效果进行了比较,发现该LSSVM模型适用于对第11~50天范围的贮存时间进行检测.

    Abstract:

    In order to investigate a fast and efficient method determining the freshness of bee pollen, visible and nearinfrared (VisNIR) reflectance spectroscopy with least squaressupport vector machines (LSSVM) was applied to determine storage period of bee pollen. The Camellia bee pollens stored for 4~50(47) days at room temperature were investigated. Spectra were collected by an ASD Fieldspec spectrometer as the input variables to build the LSSVM model. Results show that the prediction performance of LSSVM model is better than partial least square (PLS) and principal component regression (PCR). Its correlation coefficient of prediction set (rp) is 0.996, standard error of prediction (SEP) is 1.310, and root mean square error of prediction (RMSEP) is 1.308. It is concluded that VisNIR spectroscopy with LSSVM is a feasible method to determine the storage period of bee pollen. Moreover, the results for different storage periods were compared. It is shown that the storage periods between 11~50 can be well determined by LSSVM.

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金航峰,黄凌霞,吴迪,金佩华,楼程富.用最小二乘支持向量机的可见-近红外光谱测定蜂花粉贮存时间[J].红外与毫米波学报,2010,29(3):216~219]. JING Hang-Feng, HUANG Ling-Xia, WU Di, JIN Pei-Hua, LOU Cheng-Fu. STORAGE PERIOD DETERMINATION OF BEE POLLEN BY VISIBLENEAR INFRARED SPECTROSCOPY WITH LEAST SQUARESSUPPORT VECTOR MACHINES[J]. J. Infrared Millim. Waves,2010,29(3):216~219.]

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  • 收稿日期:2009-02-06
  • 最后修改日期:2009-06-28
  • 录用日期:2009-07-15
  • 在线发布日期: 2010-07-19
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