Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA

Lju Jinming; Li Nan; Zhen Feng; Xu Yonghua; Li Wenzhe; Sun Yong

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Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA

机译：使用近红外光谱与BIPLS和GSA联合厌氧发酵原料的快速检测厌氧发酵原料的碳氮比

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Near-infrared spectroscopy (NIRS) is an efficient method for detecting the content of carbon and nitrogen in many materials, which solves the problems of the time-consuming and high-cost traditional chemical analysis method. To quickly detect the carbon-nitrogen ratio (C/N) for the anaerobic fermentation (AF) feedstock using NIRS, a genetic simulated annealing algorithm (GSA) is presented based on a genetic algorithm combined with a simulated annealing algorithm. By combining GSA with backward interval partial least squares (BiPLS), we construct a BiPLS-GSA algorithm to optimize the characteristic wavelength variables of NIRS; this algorithm significantly reduced the number of wavelength variables involved in modeling and effectively improved the detection accuracy and efficiency of the model. The determination coefficients, root mean squared error, mean relative error (MRE) and residual predictive deviation for the validation set in the BiPLS-GSA regression model were 0.9067, 7.6676, 5.5274%, and 3.5626, respectively. Meanwhile, compared to the entire spectrum model, the MRE was decreased by 16.54% in the BiPLS-GSA-based model. The research in this paper improves the adaptability of the prediction model based on optimizing sensitive wavelength variables for C/N, which provides a new way for rapid and accurate measurement of the C/N of AF feedstock. (C) 2019 Optical Society of America

机译：近红外光谱（NIRS）是用于检测许多材料中碳和氮含量的有效方法，其解决了耗时和高成本的传统化学分析方法的问题。为了使用NIRS快速检测厌氧发酵（AF）原料的碳 - 氮比（C / N），基于与模拟退火算法组合的遗传算法给出了遗传模拟退火算法（GSA）。通过将GSA与向后区间部分最小二乘（BIPLS）组合，我们构建了BIPLS-GSA算法，以优化NIR的特征波长变量;该算法显着降低了建模中涉及的波长变量的数量，有效地提高了模型的检测精度和效率。在BIPLS-GSA回归模型中验证集的验证系数，均方根误差，平均相对误差（MRE）和残差预测偏差分别为0.9067,7.6676,5.5274％和3.5626。同时，与整个频谱模型相比，基于BIPLS-GSA的模型中，MRE在16.54％下降了16.54％。本文的研究提高了基于优化C / N的敏感波长变量的预测模型的适应性，这为AF原料的C / N的C / N的快速和准确测量提供了一种新的方式。（c）2019年光学学会

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《Applied optics》 |2019年第18期|共8页
作者
Lju Jinming; Li Nan; Zhen Feng; Xu Yonghua; Li Wenzhe; Sun Yong;
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Northeast Agr Univ Coll Engn Dept Agr Biol Environm &

Energy Engn Harbin 150030 Heilongjiang Peoples R China;

Northeast Agr Univ Coll Engn Dept Agr Biol Environm &

Energy Engn Harbin 150030 Heilongjiang Peoples R China;

Chinese Acad Sci CAS Key Lab Renewable Energy Guangzhou Inst Energy Convers Guangzhou 510640 Guangdong Peoples R China;

Northeast Agr Univ Sch Elect &

Informat Harbin 150030 Heilongjiang Peoples R China;

Northeast Agr Univ Coll Engn Dept Agr Biol Environm &

Energy Engn Harbin 150030 Heilongjiang Peoples R China;

Northeast Agr Univ Coll Engn Dept Agr Biol Environm &

Energy Engn Harbin 150030 Heilongjiang Peoples R China;

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Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA

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