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人工神經(jīng)網(wǎng)絡(luò)NIR定量分析方法及其軟件實(shí)現(xiàn)
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    在Visual C++環(huán)境中采用面向?qū)ο蠹夹g(shù)，開發(fā)了PCA-MBP-NIR定量分析模型軟件。通過40份小麥樣品的原始光譜、加噪光譜(信噪比為14 dB)與含水率所建立的PLS-NIR與PCA-MBP-NIR模型，對(duì)10份未知小麥樣品的原始光譜、加噪光譜分別進(jìn)行含水率的PLS-NIR與PCA-MBP-NIR預(yù)測(cè)分析。分析表明，對(duì)于含噪聲的光譜，與PLS建模相比，使用PCA-MBP-NIR對(duì)未知樣品預(yù)測(cè)結(jié)果具有更高的相關(guān)系數(shù)，更低的預(yù)測(cè)誤差標(biāo)準(zhǔn)差。

    Abstract:

    An artificial neural network of matrix back propagation(MBP-ANN) combined with principal component analysis(PCA) for near infrared spectroscopy (NIR) quantitative analysis method is presented, and its principles is analyzed. A PCA-MBP-NIR quantitative analysis software system is developed based on object oriented programming technology in environment of Microsoft Visual C++. The PCA-MBP-NIR model and partial least square(PLS) NIR model are built between the moisture and raw spectrum of 40 wheat samples, and the two models are also built for noise spectrum (rmax=14dB) in the same way. The moisture of 10 unknown wheat samples are predicted by this model. Results show that, using PCA-MBP-NIR method instead of PLS-NIR for noise spectrum, the correlation coefficient of predicted values and standard values of unknown samples can be increased, and the root mean square deviation (RMSD) can be decreased.

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祝詩(shī)平.人工神經(jīng)網(wǎng)絡(luò)NIR定量分析方法及其軟件實(shí)現(xiàn)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2007,38(1):108-111.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(1):108-111.

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