不同预处理可见-近红外光谱对农作物秸秆热值预测的影响

热值是反映农作物剩余物作为燃料利用潜力的重要参数。运用氧弹方法测试热值费时费力。基于可见-近红外光谱分析技术，分别对采用了不同预处理方法处理的光谱建立了5种农作物秸秆的偏最小二乘法（PLR）和主成分回归方法（PCR）热值模型，分析了预测模型的准确性与稳定性。结果显示，10点平滑的PLR模型效果最好，预测相关系数R2和预测标准差RMSEP分别为0.853 7和0.443 4。过多的平滑点处理产生了过平滑现象，导致模型性能变坏，采用了微分处理和多元散射校正（MSC）处理的光谱预测模型性能未见明显提高。研究结果可为热值快速测试设备的研发提供基础数据和数学模型优化支持。 

Suitability of Vis-near Infrared Spectra Pretreatments for Predicting Calorific Value of Agricultural Wastes

Measurement of calorific value using oxygen bomb calorimeter is time consuming.Visible-near infrared spectra technology was employed to collect spectral characteristic of agricultural crop residues.Differential spectral pretreatments such as smoothing，derivation，multiplicative scatter correction（MSC）and standard normal variate（SNV）were used to improve predicting models of calorific value based on partial least squares regression（PLR）and principal component regression（PCR），respectively.Results showed that the model pretreated with 10-point smoothing exhibited the highest correlation coefficient（R2）and root mean square error of prediction（RMSEP）of 0.853 7and 0.443 4，respectively.Predicting model based on PLR exhibited relative higher estimating accuracy than that of PCR.Also，PLR showed more stability in prediction than that of PCR.Excessive smoothing was observed when more than 15 points smoothened.Derivation and MSC pretreatments showed no enhancement for the model’s estimating performance.Results provided basic parameters and theoretical model for inventing biomass rapid GCV detection device.