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基于特征光譜的草莓品種快速鑒別
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國(guó)家自然科學(xué)基金資助項(xiàng)目(31271620);江蘇省科技支撐計(jì)劃資助項(xiàng)目(BE2011346);江蘇省農(nóng)機(jī)三項(xiàng)工程資助項(xiàng)目(NJ2011—46)


Discrimination of Strawberries Varieties Based on Characteristic Spectrum
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    摘要:

    應(yīng)用近紅外光譜技術(shù)對(duì)草莓品種快速鑒別進(jìn)行了研究,。通過(guò)比較7種預(yù)處理方法,確定SNV+MAF+2D組合預(yù)處理方法最佳,。采用相關(guān)系數(shù)閾值法提取了14個(gè)與草莓品種信息相關(guān)的特征光譜,。建立了基于548~562nm范圍內(nèi)14個(gè)特征光譜的PLS—ANN,、PLSR,、PCR 3種校正模型,。經(jīng)預(yù)測(cè)集樣品驗(yàn)證,,主成分?jǐn)?shù)為9時(shí)的PLS—ANN模型最優(yōu),,其預(yù)測(cè)相關(guān)系數(shù)和預(yù)測(cè)均方根誤差分別為0.9677和0.461,。結(jié)果表明,通過(guò)提取少量特征光譜建立PLS—ANN校正模型能實(shí)現(xiàn)草莓品種的快速鑒別,。

    Abstract:

    Near infrared spectroscopy (NIRS) technology was applied to discriminate the variety of strawberries rapidly. Compared with seven kinds of preprocessing methods, “SNV+MAF+2D” was ascertained as the optimal scheme. Totally 14 characteristic spectrums related to strawberries information were extracted by the correlation coefficient and threshold value method. Three kinds of correction models of PLS—ANN, PLSR and PCR based on wavelength ranging from 548nm to 562nm were established. Verified by the prediction set samples, the optimal correction model was PLS—ANN with nine principal components. Prediction correlation coefficientand root mean square error of prediction(RMSEP) of the PLS—ANN correction model was 0.9677 and 0.461. The results showed that a few characteristic spectrum extracted to establish PLS—ANN correction model would achieve rapid discrimination of strawberries.

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閆潤(rùn),王新忠,邱白晶,史德林,孔鵬飛.基于特征光譜的草莓品種快速鑒別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2013,44(9):182-186. Yan Run, Wang Xinzhong, Qiu Baijing, Shi Delin, Kong Pengfei. Discrimination of Strawberries Varieties Based on Characteristic Spectrum[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(9):182-186.

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  • 在線發(fā)布日期: 2013-09-11
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