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基于可見/近紅外光譜譜區(qū)有效波長的梨品種鑒別
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國家高技術(shù)研究發(fā)展計劃（863計劃）資助項目（2012AA101901）;中國博士后科學(xué)基金資助項目(2012M520193);2012年北京市農(nóng)林科學(xué)院博士后基金資助項目


Variety Identification of Pears Based on Effective Wavelengths in Visible/Near Infrared Region
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    摘要:

    基于最小二乘支持向量機(jī)（LS—SVM）建模方法,，提出應(yīng)用梨在可見/近紅外光譜譜區(qū)的有效波長（EW）進(jìn)行其品種鑒別的新方法,。用210個樣本作為建模定標(biāo)集，30個樣本進(jìn)行預(yù)測。根據(jù)偏最小二乘法分析載荷圖和回歸系數(shù)圖選擇鑒別梨品種的有效波長,，并建立EW與最小二乘支持向量機(jī)相結(jié)合的EW—LS—SVM模型，同時與應(yīng)用逆反饋人工神經(jīng)網(wǎng)絡(luò)（BP-ANN）建立的EW—BP-ANN模型進(jìn)行判別準(zhǔn)確率的比較,。結(jié)果表明,，應(yīng)用LS—SVM和BP-ANN建立的模型對建模樣本和預(yù)測集樣本的判別準(zhǔn)確率分別為100%和93.3%。研究表明,，應(yīng)用EW—LS—SVM模型進(jìn)行梨品種鑒別是可行的,。

    Abstract:

    Based on least squares—support vector machine (LS—SVM), the effective wavelength (EW) in visible/near infrared (Vis/NIR) region was proposed as a new approach for the variety discrimination of pears. 210 pear samples were used for the calibration set, while 30 samples for the validation set. After partial least squares (PLS) analysis, the EWs were selected according to the X-loading weights and regression coefficients, and an EW—LS—SVM model was developed for the variety discrimination. This model was compared with EW—BP-ANN model by using back-propagation artificial neural network (BP-ANN).Results showed that the same recognition accuracies (100% for the calibration set, 93.3% for the validation set) were obtained for EW—LS—SVM and EW—BP-ANN models, respectively. Studies show that it is feasible to use EW—LS—SVM model for the variety discrimination of pears.

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李江波,趙春江,陳立平,黃文倩.基于可見/近紅外光譜譜區(qū)有效波長的梨品種鑒別[J].農(nóng)業(yè)機(jī)械學(xué)報,2013,44(3):153-157,179. Li Jiangbo, Zhao Chunjiang, Chen Liping, Huang Wenqian. Variety Identification of Pears Based on Effective Wavelengths in Visible/Near Infrared Region[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(3):153-157,179.

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  • 在線發(fā)布日期: 2013-02-25
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