快速检测生物质燃料特性对农林废弃物能源化利用具有重要意义。本研究利用光谱分析技术建立了松木、杉木和棉杆三类农林废弃物生物质的水分、灰分、挥发分、固定碳和热值预测模型。这些模型交叉校验决定系数均高于0.88。应用潜变量神经网络建模法后，水分平均决定系数达到了0.95。结果表明，应用光谱分析技术结合化学计量学方法，可完全替代传统的工业分析方法，为农林废弃物能源化利用提供一种快速检测生物质燃料特性的技术手段。

Rapid analysis of biomass fuel is of great importance to the energy utilization of agricultural and forestry waste. The models for predicting the moisture, ash, volatile matter, fixed carbon and calorific value of three kinds of agricultural and forestry waste such as pine wood, cedar wood and cotton stalk are established by using a visible and near-infrared spectroscopy. All of these models have a determination coefficient greater than 0.88 after cross validation. When the artificial neural network (ANN) modeling with several latent variables is used, the models have the average determination coefficient of up to 0.95 for moisture. The result shows that the visible and near-infrared spectroscopy combined with chemometrics can be used to replace the traditional analysis methods in industry completely and can provide a new method for the rapid detection of the biomass fuel properties of agricultural and forestry waste.