Biosystems Engineering, Bioresources Research Centre, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
Bioresour Technol. 2011 Apr;102(8):5200-6. doi: 10.1016/j.biortech.2011.01.087. Epub 2011 Feb 24.
The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R(2) = 0.99), 0.13 MJ/kg (R(2) = 0.99), 0.42% (R(2) = 0.58), and 0.57% (R(2) = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.
利用近红外光谱结合偏最小二乘回归预测芒草和短轮伐期柳树的质量指标的潜力进行了研究。水分、热值、灰分和碳含量的预测值与交叉验证的均方根误差分别为 0.90%（R²=0.99）、0.13MJ/kg（R²=0.99）、0.42%（R²=0.58）和 0.57%（R²=0.88）。水分和热值预测模型具有很高的准确性，而碳和灰分模型的准确性则分别为良好和差。结果表明，近红外光谱技术具有预测专用能源作物质量指标的潜力，然而，在实施之前，这些模型必须在更广泛的样本范围内进一步验证。利用这些模型将有助于根据生物质特性优化原料的使用。
