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基于模擬退火波長優(yōu)化的草莓堅實度近紅外光譜檢測
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Strawberry Firmness by NIR Wavelength Selection Based on Simulated Annealing Algorithm
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    摘要:

    為提高近紅外光譜技術檢測草莓堅實度模型的精度和魯棒性,，研究了一種基于模擬退火算法的波長優(yōu)選方法，并找到一種與該算法配套的光譜預處理方法。利用光譜儀和物性儀分別采集草莓樣品近紅外漫反射光譜和堅實度數(shù)據(jù),，并采用標準正交變換,、多元散射校正,、一階導數(shù),、二階導數(shù)等方法對原始光譜進行預處理,；最后,，利用模擬退火算法優(yōu)選與草莓堅實度高度相關的波數(shù)點變量,，結合偏最小二乘法建立草莓堅實度預測模型。結果表明：經(jīng)過標準正交變換預處理后,，采用模擬退火算法優(yōu)選出24個波數(shù)點,，在主成分數(shù)為5時，建立的偏最小二乘模型具有最佳預測效果,，模型校正集樣本相關系數(shù)rc為0.9342,，校正均方根誤差為0.665N/cm2；預測樣本相關系數(shù)rp為0.9197,，預測均方根誤差為0.673N/cm2,。研究表明：模擬退火算法可以提高近紅外模型預測草莓堅實度的精度和魯棒性，并降低預測模型復雜度。

    Abstract:

    In order to improve the accuracy and robust of NIR spectroscopy modules in predicting the firmness of strawberry,，simulated annealing algorithm (SAA) was used to select the wavenumbers in NIR spectra. A preprocessing method was also selected to adapt the SAA. Firstly, 150 strawberries were selected to collect NIR spectra. Secondly, preprocessing methods, such as SNV, MSC, 1st order derivation, 2nd order derivation, were used to denoise the NIR spectra of strawberry. Thirdly, 24 wavenumbers were selected by simulated annealing algorithm. At last, partial least square was employed to establish the calibration models of firmness. The calibration model was obtained with the correlation coefficient rc of 0.9342, the root mean square error of calibration of 0.665N/cm2 and the correlation coefficient rp of 0.9197,，the root mean square error of prediction of 0.673N/cm2. The results show that SAA can improve the robust and accuracy and simplify NIR spectra models.

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石吉勇,殷曉平,鄒小波,趙杰文,鞠時光.基于模擬退火波長優(yōu)化的草莓堅實度近紅外光譜檢測[J].農(nóng)業(yè)機械學報,2010,41(9):99-103. Strawberry Firmness by NIR Wavelength Selection Based on Simulated Annealing Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(9):99-103.

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