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基于模擬退火波長優(yōu)化的草莓堅(jiān)實(shí)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(shù)檢測草莓堅(jiān)實(shí)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(yù)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(jiān)實(shí)度數(shù)據(jù),，并采用標(biāo)準(zhǔn)正交變換,、多元散射校正、一階導(dǎo)數(shù)、二階導(dǎo)數(shù)等方法對原始光譜進(jìn)行預(yù)處理；最后,，利用模擬退火算法優(yōu)選與草莓堅(jiān)實(shí)度高度相關(guān)的波數(shù)點(diǎn)變量，結(jié)合偏最小二乘法建立草莓堅(jiān)實(shí)度預(yù)測模型,。結(jié)果表明：經(jīng)過標(biāo)準(zhǔn)正交變換預(yù)處理后,，采用模擬退火算法優(yōu)選出24個波數(shù)點(diǎn)，在主成分?jǐn)?shù)為5時,，建立的偏最小二乘模型具有最佳預(yù)測效果,，模型校正集樣本相關(guān)系數(shù)rc為0.9342，校正均方根誤差為0.665N/cm2；預(yù)測樣本相關(guān)系數(shù)rp為0.9197,，預(yù)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(yù)測草莓堅(jiān)實(shí)度的精度和魯棒性，并降低預(yù)測模型復(fù)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(jiān)實(shí)度近紅外光譜檢測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),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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