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砂糖橘可溶性總糖可見—近紅外光譜無損檢測
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國家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)專項(xiàng)資金資助項(xiàng)目(農(nóng)科教發(fā)[2007]14號(hào));華南農(nóng)業(yè)大學(xué)校長基金資助項(xiàng)目(2009K005)


Nondestructive Examination of Sugar Content of Intact ‘Shatangju’ with Visible-near Infrared Spectroscopy Based on Variables Selection
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    摘要:

    在波長450~2500nm范圍提取189個(gè)砂糖橘的漫反射光譜,使用sym8小波的3層分解對(duì)其進(jìn)行去噪預(yù)處理,引入連續(xù)投影算法(SPA)對(duì)光譜進(jìn)行壓縮,從2051個(gè)波長中初步提取14個(gè)優(yōu)選波長,以這14個(gè)波長建立的多元線性回歸模型(MLR)的預(yù)測相關(guān)系數(shù)為0.8855,預(yù)測均方根誤差為0.5111,效果優(yōu)于全譜偏最小二乘模型(PLS)。通過貢獻(xiàn)值進(jìn)一步篩選,提取11個(gè)特征波長,以這11個(gè)特征波長建立的MLR模型、PLS模型和BP神經(jīng)網(wǎng)絡(luò)模型(BPNN)都與14個(gè)優(yōu)選波長建立的相應(yīng)模型效果相當(dāng)。結(jié)果表明,連續(xù)投影算法結(jié)合貢獻(xiàn)值篩選可以將波長變量數(shù)縮減到全譜變量的0.54%,簡化定量模型的結(jié)構(gòu),增強(qiáng)模型精度和穩(wěn)健性;同時(shí)被選擇的波長物理意義明確,模型解釋能力增強(qiáng)。

    Abstract:

    The reflectance spectra of 189 samples within 450~2500nm were collected. Firstly, the spectra were denoised using the orthogonal wavelet functions sym8 (level was 3). And then the spectra variables were compressed to 14 variables by successive projections algorithm (SPA). The MLR model with 14 variables as inputs could result in that prediction correlation coefficient was 0.8855 and prediction root mean square error was 0.5111. Then the group of wavelengths derived from SPA was screened by their contributions to the total sugar content. After screening on contribution, the number of wavelength variables dropped to 11. Finally, the MLR, PLS and BPNN calibration models were built with 11 wavelength variables as inputs and compared. The results demonstrated that wavelength variables were decreased to 0.54 % of the original variables by SPA and screening on contribution, and this could help to make the model more concise and robust. 

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代芬,洪添勝,岳學(xué)軍,張昆,洪涯.砂糖橘可溶性總糖可見—近紅外光譜無損檢測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2011,42(4):133-138. Dai Fen, Hong Tiansheng, Yue Xuejun, Zhang Kun, Hong Ya. Nondestructive Examination of Sugar Content of Intact ‘Shatangju’ with Visible-near Infrared Spectroscopy Based on Variables Selection[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(4):133-138.

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