Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4
1. College of Agronomy, Hunan Agricultural University, Changsha 410128, China
2. Hunan Institute of Agricultural Information and Engineering, Changsha 410125, China
3. College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
4. National Energy R & D Center for Non-Food Biomass, China Agricultural University, Beijing 100193, China
5. Hunan Intelligent Agriculture Engineering Technology Research Center, Changsha 410125, China
6. Hunan Industrial Technology Basic Public Service Platform, Changsha 410125, China
Abstract：Detecting the ash content of biomass raw materials was the basis for efficient energy conversion. However, the traditional high-temperature calcination method was time-consuming and costly, while the near-infrared spectroscopy analysis technology could achieve non-destructive, rapid and low-cost qualitative, and quantitative analysis of unknown samples. This study used 1465 biomass raw material samples of 5 locations and 10 types as the research object. The sample set was divided into 9 sample sets by the “screening classification set method” to construct the ash content model of biomass samples by near-infrared spectroscopy. The main results were as follows: the best principal components of corn straw (M), wheat straw + corn straw + cotton straw (WCM), and wheat straw+weeds+garden leaves (WWL) were 5, 6, and 6, respectively. The R2cv of corn straw (M) was 0.975, the R2p of WCM was 0.983, and the model fitting degree was the highest. The RMSE of the set of Changbai+cotton straw (WC) was 0.588 7 and 0.486 4, respectively. The highest ratio of prediction to deviation (RPDcv) of M was 6.3, and the highest ratio of prediction to deviation (RPDp) of WCM was 7.8. The minimum average relative deviation (ARDcv) of maize straw (M) collection was 6%, the minimum average relative deviation (ARDp) of maize straw and WCM collection was 8%, and the RMSECV/RMSEP of wood (W) collection was 1.01. The R2 range of the set model of ash content of 9 biomass samples was 0.753 8～0.979 4, and there was a good linear relationship between the predicted value and the measured value. Among them, H set (R2=0.942 5), M set (R2=0.979 4) and the WCM set (R2=0.978 7) had the best fitting degree and linear relationship. The R2 of the L set (wood scrap) was the lowest, and its value was 0.753 8. The main factor in judge the influence was that the sample contained impurities such as sediment, adhesive, and paint. In order to solve the problem of raw material detection and evaluation of common biomass power plants, 9 biomass ash collection models were used to predict and evaluate the average relative deviation (ARD) of 11 biomass samples. The grass sample model had a good prediction effect (ARD range was 3.7%～16.5%). The “screening classification set method” was used to divide the sample set to establish the near-infrared spectrum biomass ash content model, and its fitting degree, robustness, and accuracy were higher than those of the full sample set model.
Key words：Biomass samples; Screening classification set method; Near-infrared spectroscopy; Rapid detection; Model construction
郭 歌，张梦玲，巩志杰，张世壮，王晓玉，周仲华，杨 玉，谢光辉. 基于近红外光谱和复杂样品划分集合的生物质灰分含量模型构建[J]. 光谱学与光谱分析, 2023, 43(10): 3143-3149.
GUO Ge, ZHANG Meng-ling, GONG Zhi-jie, ZHANG Shi-zhuang, WANG Xiao-yu, ZHOU Zhong-hua, YANG Yu, XIE Guang-hui. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149.
[1] Karpenstein-Machan M. Critical Reviews in Plant Sciences, 2001, 20(1): 1.
[2] XIE Guang-hui, WANG Xiao-yu, BAO Wei-qing, et al(谢光辉，王晓玉，包维卿，等). Potential of Carbon Emission Reduction From Biowaste for Energy Conversations and Its Management Policy in China(中国废弃生物质能源化利用碳减排潜力与管理政策). Beijing: China Agricultural University Press(北京：中国农业大学出版社), 2020. 216.
[3] Cassida K A, Muir J P, Hussey M A, et al. Crop Science, 2005, 45(2): 682.
[4] Soto-Barajas M C, Zabalgogeazcoa I, González-Martin I, et al. Journal of the Science of Food and Agriculture, 2017, 97(14): 5028.
[5] YAN Yan-lu, CHEN Bin, ZHU Da-zhou, et al(严衍禄，陈 斌，朱大洲，等). Near Inferared Spectroscopy-Principles, Technologies and Applications(近红外光谱分析的原理技术与应用). Beijing: China Light Industry Press(北京：中国轻工业出版社), 2013. 272.
[6] Dowell F E, Maghirang E B, Graybosch R A, et al. Cereal Chemistry, 2009, 86(3): 251.
[7] Via B K, Zhou C, Acquah G, et al. Sensors, 2014, 14(8): 13532.
[8] Blanco M, Villarrova I. Trends in Analytical Chemistry, 2002, 21(4): 240.
[9] Pojic M, Mastilovic R, Palic R, et al. Food Chemistry, 2010, 123(3): 800.
[10] Ye X P, Liu S, Kline L, et al. Fast Biomass Compositional Analysis Using Fourier Transform Near-Infrared Technique, 2006 ASABE Annual International Meeting, Paper Number: 066155.
[11] LIU Li-ying, CHEN Hong-zhang(刘丽英，陈洪章). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(2): 275.
[12] National Energy Administration(国家能源局). NB/T 34057—2017. Determination of Chemical Composition of Lignocellulosic Biomass Raw Materials(木质纤维素类生物质原料化学成分的测定), 2017.
[13] Shen F, Wu Q, Shao X, et al. Journal of Food Science & Technology, 2018, 55(3): 1175.
[14] WU Zhi-sheng, SHI Xin-yuan, XU Bing, et al(吴志生，史新元，徐 冰，等). China Journal of Chinese Materia Medica(中国中药杂志), 2015, 40(14): 2774.
[15] Cozzolino D, Kwiatkowski M J, Parker M, et al. Analytica Chimica Acta, 2004, 513: 73.
[16] Tao G C, Lestander T A, Geladi P, et al. Renewable and Sustainable Energy Reviews, 2012, 16(5): 3481.
