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

Component Analysis of Biochar Based on Near Infrared Spectroscopy Technology

XIE Yue ^1,2,3, ,
LI Fei-Yue ^1,2 ,
FAN Xing-Jun ¹ ,
HU Shui-Jin ³ ,
XIAO Xin ¹ ,
WANG Jian-Fei ¹

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: 1 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).

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.
- Near infrared spectroscopy,
- Spectrum,
- Biochar,
- Fixed carbon,
- Volatile matter,
- Ash
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