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引用本文:   谢越, 李飞跃, 范行军, 胡水金, 肖新, 汪建飞. 基于近红外光谱技术的生物炭组分分析. 分析化学, 2018, 46(4): 609-615. doi:  10.11895/j.issn.0253-3820.171084 [复制]

Citation:   XIE Yue, LI Fei-Yue, FAN Xing-Jun, HU Shui-Jin, XIAO Xin, WANG Jian-Fei. Component Analysis of Biochar Based on Near Infrared Spectroscopy Technology. Chinese Journal of Analytical Chemistry, 2018, 46(4): 609-615. doi: 10.11895/j.issn.0253-3820.171084 [复制]

基于近红外光谱技术的生物炭组分分析

通讯作者:  肖新, xiaoxin8088@126.com

收稿日期: 2017-07-11

接受日期: 2018-02-01

出版日期: 2018-04-01

基金项目: 本文系国家自然科学基金项目(Nos.21607002,41705107)、安徽省自然科学基金项目(Nos.1508085QD74,1604a702011,16030701102)、农业部生物有机肥创制重点实验室开放课题(No.BOFC2015KB04)、2016年高校中青年骨干人才重点项目(No.gxfxZD2016182)和安徽省科技重大专项(No.16030701102)资助

Component Analysis of Biochar Based on Near Infrared Spectroscopy Technology

Corresponding author:  XIAO Xin, xiaoxin8088@126.com

Received Date:  2017-07-11

Accepted Date:  2018-02-01

Published Date:  2018-04-01

Fund Project:  This work was supported by the National Natural Science Foundation of China (Nos.21607002, 41705107) and the Natural Science Foundation of Anhui Province, China (Nos.1508085QD74, 1604a0702011, 16030701102)

建立了基于近红外光谱技术的生物炭组分快速定量分析方法。采集了163个样品在10000~3800 cm-1范围内的近红外光图谱,测定了样品中的固定碳(Fixed carbon,FC)、挥发分(Volatile matter,VM)和灰分(Ash)3种组分含量。在优化建模波段,确定最佳因子数,采用多元散射校正与二阶导数光谱法对原始光谱预处理后,利用偏最小二乘法(Partial least squares,PLS)构建了生物炭样品中3种组分的模型,并对模型的预测性能进行了评价。结果表明,PLS模型具有良好的预测能力,FC、VM和Ash的真实值和预测值的相关系数(Predicted coefficient,Rp2)分别达到0.9423,0.9517和0.9265,预测均方差(Root mean square error of prediction,RMSEP)值分别为0.1074,0.1201和0.1243,相对预测误差(Ratio of prediction to deviation,RPD)值分别为3.51,4.28和2.03。模型对FC和VM的精度较高,可以作为定量分析方法。根据RPD值,模型对Ash的预测精度较差,需要进一步提高模型预测精准度。本方法为生物炭组分的定量分析提供一种快速有效的技术手段。

关键词:   近红外, 光谱, 生物炭, 固定碳, 挥发分, 灰分
Key words:   Near infrared spectroscopy, Spectrum, Biochar, Fixed carbon, Volatile matter, Ash
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基于近红外光谱技术的生物炭组分分析

谢越, 李飞跃, 范行军, 胡水金, 肖新, 汪建飞

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