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Chemical Composition and Calorific Value Prediction of Wheat Straw at Different Maturity Stages Using Near-Infrared Reflectance Spectroscopy

机译:利用近红外反射光谱技术预测不同成熟期小麦秸秆的化学成分和热值预测

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摘要

Rapid assessment of wheat straw at different maturity stages will help to reveal its growth mature and enable better process control that will optimize the sustainable value-added usage. This study explored the potential of near-infrared reflectance (NIR) spectroscopy to quantitatively and qualitatively analyze the multiple chemical composition and calorific value of wheat straw at different maturity stages. Partial least-squares (PLS) and genetic algorithm and partial least-squares (GA-PLS) models were used for NIR spectroscopy analysis. Results showed that PLS models and GA-PLS models could be both used for the estimation of chemical composition and calorific value of wheat straw at three maturity stages, and the GA-PLS method reduced the spectral variables for modeling and provided sensitive spectral variables correlated well with chemical composition and calorific value. NIR spectroscopy could successfully detect the contents of water-soluble carbohydrates (WSC), crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), ash, and nitrogen (N). It was also able to quantify dry matter (DM), cellulose (Cel), moisture (Moist), volatile matter (VM), higher heating value (HHV), and lower heating value (LHV). The NIR models for hemicellulose (Hem), lignin (Lig), carbon (C), sulfur (S), and hydrogen (H) had moderate accuracy, and could be used for qualitative analysis, whereas for fixed carbon (FC), it was suitable for screening purposes.
机译:快速评估不同成熟期的小麦秸秆将有助于揭示其成熟度,并实现更好的过程控制,从而优化可持续的增值用途。这项研究探索了近红外反射(NIR)光谱技术在定量和定性分析不同成熟阶段小麦秸秆的多种化学成分和热值方面的潜力。偏最小二乘(PLS)和遗传算法以及偏最小二乘(GA-PLS)模型用于近红外光谱分析。结果表明,PLS模型和GA-PLS模型均可用于估算三个成熟期小麦秸秆的化学成分和热值,GA-PLS方法减少了用于建模的光谱变量,并提供了敏感的光谱变量具有化学成分和热值。近红外光谱技术可以成功检测水溶性碳水化合物(WSC),粗蛋白(CP),酸性洗涤剂纤维(ADF),中性洗涤剂纤维(NDF),灰分和氮(N)的含量。它还能够量化干物质(DM),纤维素(Cel),水分(湿气),挥发物(VM),较高的发热量(HHV)和较低的发热量(LHV)。半纤维素(Hem),木质素(Lig),碳(C),硫(S)和氢(H)的NIR模型具有中等准确度,可用于定性分析,而对于固定碳(FC),它可用于定性分析适用于筛选。

著录项

  • 来源
    《Energy & fuels》 |2014年第novaadeca期|7474-7483|共10页
  • 作者单位

    Biomass Resources and Utilization Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China;

    Biomass Resources and Utilization Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China;

    Biomass Resources and Utilization Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China;

    Biomass Resources and Utilization Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China;

    Biomass Resources and Utilization Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China,China Agricultural University (East campus), Box 191, Beijing 100083, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 00:40:34

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