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基于無參數(shù)高效算法的近紅外光譜模型傳遞研究
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北京市自然科學(xué)基金項(xiàng)目(4222043)、國(guó)家自然科學(xué)基金青年科學(xué)基金項(xiàng)目(61807001),、北京工商大學(xué)青年教師科研啟動(dòng)基金項(xiàng)目(QNJJ2022-41)和北京工商大學(xué)研究生科研能力提升計(jì)劃項(xiàng)目


Near Infrared Spectroscopy Calibration Transfer Based on Parameter-free and Efficient Calibration Enhancement Algorithm
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    摘要:

    模型傳遞可解決不同近紅外光譜儀間多元校正模型無法共享的問題,。以食用油為研究對(duì)象,對(duì)其酸值和過氧化值模型進(jìn)行傳遞分析,。在主機(jī)上建立偏最小二乘多元校正模型,,利用無參數(shù)高效模型傳遞(PFCE)算法中NS-PFCE無標(biāo)樣算法和FS-PFCE有標(biāo)樣算法分別實(shí)現(xiàn)模型傳遞,,探討了標(biāo)準(zhǔn)化樣品數(shù)量對(duì)模型傳遞效果的影響,。并與經(jīng)典的3種有標(biāo)樣傳遞算法和2種無標(biāo)樣傳遞算法進(jìn)行對(duì)比。結(jié)果表明,,經(jīng)NS-PFCE無標(biāo)樣傳遞后,,從機(jī)酸值與過氧化值預(yù)測(cè)集均方根誤差分別從0.613mg/g和16.153mmol/kg下降到0.275mg/g和9.523mmol/kg;而經(jīng)FS-PFCE有標(biāo)樣傳遞后,,從機(jī)酸值與過氧化值預(yù)測(cè)集均方根誤差分別下降到0.274mg/g和8.945mmol/kg,。且隨著標(biāo)準(zhǔn)化樣品數(shù)量的增加,經(jīng)PFCE算法傳遞后預(yù)測(cè)集均方根誤差越低,。無參數(shù)高效模型傳遞算法聯(lián)合應(yīng)用單一的無標(biāo)樣算法和有標(biāo)樣算法兩種傳遞方式,,增強(qiáng)了傳遞模型的適應(yīng)性和包容性,,同時(shí)有效地降低主機(jī)光譜與從機(jī)光譜之間的差異,實(shí)現(xiàn)了不同光譜儀間校正模型的共享,。

    Abstract:

    Calibration transfer can solve the problem that multivariate calibration models cannot be shared among different near-infrared spectrometers. Taking edible oil as the research object, transfer analysis of its acid value and peroxide value model was conducted. The partial least squares multivariate correction model was established on the master spectrometers, and the calibration transfer was realized by using the parameter-free and efficient calibration enhancement (PFCE) calibration transfer algorithm in NS-PFCE without standard sample transfer and FS-PFCE with standard sample transfer, and the dependence of calibration transfer on the number of standardization samples was explored. In addition, it was compared with three calibration transfer algorithms with standard sample, which were slope/bias (S/B), direct standardization (DS) and piecewise direct standardization (PDS), and two calibration transfer algorithms without standard sample, which were finite impulse response (FIR) and stability competitive adaptive reweighted sampling (SCARS). The results suggested that after the NS-PFCE without standard sample algorithm was transferred, the root mean square error of prediction (RMSEP) of the acid value and peroxide value was decreased from 0.613mg/g and 16.153mmol/kg to 0.275mg/g and 9.523mmol/kg,respectively. Furthermore, after the FS-PFCE with standard sample algorithm was transferred, the root mean square error of prediction (RMSEP) of the acid value and peroxide value was dropped to 0.274mg/g and 8.945mmol/kg, respectively. Specifically, the increase of the number of standardized samples, the root mean square error of prediction (RMSEP) was lower. The parameter-free and efficient calibration enhancement (PFCE) algorithm combined a single transfer method without a standard sample and a standard sample, which enhanced the adaptability and inclusiveness of the transfer model. And PFCE algorithm effectively reduced the difference between the master spectrum and the slave spectrum, and also realized the calibration model sharing between different spectrometers.

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劉翠玲,徐金陽,孫曉榮,張善哲,昝佳睿.基于無參數(shù)高效算法的近紅外光譜模型傳遞研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2023,54(2):396-402. LIU Cuiling, XU Jinyang, SUN Xiaorong, ZHANG Shanzhe, ZAN Jiarui. Near Infrared Spectroscopy Calibration Transfer Based on Parameter-free and Efficient Calibration Enhancement Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(2):396-402.

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  • 收稿日期:2022-04-07
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  • 在線發(fā)布日期: 2022-04-30
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