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用Shenk's方法提高芥酸NIR模型預測精度
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    摘要:

    在光譜測量的過程中,由于光譜儀器本身的精度和測量環(huán)境等因素,,經(jīng)常會出現(xiàn)光譜的漂移,、線性或非線性改變,。以基于近紅外光譜分析建立菜籽油中芥酸預測模型為例,,將多元校正Shenk's算法用于修正同臺儀器測量的光譜差異,,大大提高了校正模型的預測精度,。結(jié)果表明,,對于光譜修正后再建模預測,,預測均方差從2.263下降到1.569,,平均相對誤差從4.6%下降到3.218%,相關系數(shù)也由0.780提高到了0.913,,模型的預測精度顯著提高,。

    Abstract:

    As some existed influencing factors resulting from the precise and the measure environments of spectrum instrument, the spectrum drifts, the linear or nonlinear conversion can be found in the process of the measure. Prediction errors will be made if we directly apply the results of the sample's measurement in the construction and prediction of the model. Therefore, multivariate calibration algorith——Shenk's algorithm was adopted to correct the discrepancy of the erucic acid near-infrared spectrums and it was indicated that predication precise would be improved greatly, root mean square errors of prediction (RMSEP) has been reduced to 1.569 from 2.263, and average relative error was dropped to 3.218% from 4.6%, with the relative coefficient improved to 0.913 from 0.780.

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陳斌,吳繼明.用Shenk's方法提高芥酸NIR模型預測精度[J].農(nóng)業(yè)機械學報,2007,38(8):101-104.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(8):101-104.

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