基于近红外光谱技术的生物炭组分分析

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[J]. 分析化学, 2018, 46(4): 609-615,622. doi: 10.11895/j.issn.0253-3820.171084 shu

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[J]. Chinese Journal of Analytical Chemistry, 2018, 46(4): 609-615,622. doi: 10.11895/j.issn.0253-3820.171084 shu

基于近红外光谱技术的生物炭组分分析

1.
安徽科技学院资源与环境学院, 凤阳 233100;
2.
农业部生物有机肥创制重点实验室, 蚌埠 233400;
3.
美国北卡罗来纳州立大学植物病理学系, 罗利 27695
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

摘要: 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，R_p²）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

English

Component Analysis of Biochar Based on Near Infrared Spectroscopy Technology

1.
College of Resources and Environment, Anhui Science and Technology University, Fengyang 233100, China;
2.
Key Laboratory of Bio-organic Fertilizer Creation, Ministry of Agriculture, Bengbu 233000, China;
3.
Department of Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
Received Date: 11 July 2017
Revised Date: 01 February 2018
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). This work was supported by the National Key Research and Development Program of China (Nos.2016YFD0300806-1,2016 YFD0200309-6),and the Excellent Young Talent Program of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences (No.DLSXZ1605).

Abstract: A rapid quantitative component analysis method for biochar based on near infrared spectroscopy (NIRS) technology was established in this study. Near infrared spectra of 163 samples in 10000-3800 cm^-1 (1000-2632 nm) range were collected, and the contents of fixed carbon (FC), volatile matter (VM) and ash of samples were analyzed. A partial least square (PLS) model for FC, VM and Ash was established in the optimized model spectral ranges. The factors were optimized and the raw spectra were pretreated by multiple scatter correction and second derivative (MSC+SD) method. Finally, the prediction performance of predictive model was evaluated. The results showed that the PLS model had a good prediction ability, and the predicted coefficient R_p² of actual values vs prediction values for FC, VM and Ash were 0.9423, 0.9517 and 0.9265, respectively. Root mean square errors of prediction (RMSEP) were 0.1074, 0.1201 and 0.1243, and ratios of prediction to deviation (RPD) were 3.51, 4.28 and 2.03, respectively. The PLS model has good accuracy and precision for both of FC and VM, and can be used as a quantitative method for FC and VM contents analysis. Nevertheless, PLS model needs to improve the precision for Ash analysis according to RPD value. The method provides a fast and effective technical means for the quantitative analysis of biochar components.

Key words:

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1.
沈阳化工大学材料科学与工程学院沈阳 110142

基于近红外光谱技术的生物炭组分分析

English

Component Analysis of Biochar Based on Near Infrared Spectroscopy Technology

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基于近红外光谱技术的生物炭组分分析

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Component Analysis of Biochar Based on Near Infrared Spectroscopy Technology

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

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