Sheng Kui-Chuan, Shen Ying-Ying, Yang Hai-Qing, Wang Wen-Jin, Luo Wei-Qiang
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2012 Oct;32(10):2805-9.
Rapid determination of biomass feedstock properties is of value for the production of biomass densification briquetting fuel with high quality. In the present study, visible and near-infrared (Vis-NIR) spectroscopy was employed to build prediction models of componential contents, i. e. moisture, ash, volatile matter and fixed-carbon, and calorific value of three selected species of agricultural biomass feedstock, i. e. pine wood, cedar wood, and cotton stalk. The partial least squares (PLS) cross validation results showed that compared with original reflection spectra, PLS regression models developed for first derivative spectra produced higher prediction accuracy with coefficients of determination (R2) of 0.97, 0.94 and 0.90, and residual prediction deviation (RPD) of 6.57, 4.00 and 3.01 for ash, volatile matter and moisture, respectively. Good prediction accuracy was achieved with R2 of 0.85 and RPD of 2.55 for fixed carbon, and R2 of 0.87 and RPD of 2.73 for calorific value. It is concluded that the Vis-NIR spectroscopy is promising as an alternative of traditional proximate analysis for rapid determination of componential contents and calorific value of agricultural biomass feedstock
快速测定生物质原料特性对于生产高质量的生物质致密成型燃料具有重要价值。在本研究中，采用可见近红外（Vis-NIR）光谱法建立了三种选定的农业生物质原料（即松木、雪松和棉秆）的成分含量（即水分、灰分、挥发物和固定碳）及热值的预测模型。偏最小二乘法（PLS）交叉验证结果表明，与原始反射光谱相比，基于一阶导数光谱建立的PLS回归模型具有更高的预测准确性，灰分、挥发物和水分的决定系数（R2）分别为0.97、0.94和0.90，剩余预测偏差（RPD）分别为6.57、4.00和3.01。固定碳的R2为0.85，RPD为2.55；热值的R2为0.87，RPD为2.73，均取得了良好的预测准确性。得出结论：Vis-NIR光谱法有望作为传统近似分析的替代方法，用于快速测定农业生物质原料的成分含量和热值。
