Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Zhejiang 311400, PR China.
School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Apr 15;213:111-117. doi: 10.1016/j.saa.2019.01.060. Epub 2019 Jan 16.
The use of quick attenuated total reflectance infrared (ATR-IR) spectroscopy and near infrared (NIR) spectroscopy to predict extractives content (EC) in heartwood of E. bosistoana with partial least squares regression (PLSR) models was studied. Different spectra pre-processing methods and variable selection were tested for calibration optimisation. While variable selection substantially improved the NIR-PLSR models, only small effects were observed for spectra pre-processing methods and ATR-IR-PLSR models. Both of the NIR-PLSR and ATR-IR-PLSR models yielded reliably EC results with high R and low root mean square error (RMSE). NIR based models performed better (RMSE 0.9%) than ATR-IR based models (RMSE 1.6%). Analysis showed that the models were based on IR signals assigned to chemical structures known from eucalyptus heartwood extracts. Combined with PLSR and variable selection, both, ATR-IR and the NIR spectroscopy, can be used to quickly predict EC in E. bosistoana, a measure needed in tree breeding and the quality control of for durable timber.
利用快速衰减全反射红外（ATR-IR）光谱和近红外（NIR）光谱，通过偏最小二乘回归（PLSR）模型预测蓝桉心材中的抽出物含量（EC）。研究了不同的光谱预处理方法和变量选择对校准优化的影响。虽然变量选择显著改善了 NIR-PLSR 模型，但对光谱预处理方法和 ATR-IR-PLSR 模型的影响较小。NIR-PLSR 和 ATR-IR-PLSR 模型都能可靠地预测 EC，具有较高的 R 和较低的均方根误差（RMSE）。基于 NIR 的模型表现优于基于 ATR-IR 的模型（RMSE 为 0.9%，而 ATR-IR 的为 1.6%）。分析表明，这些模型基于从蓝桉心材提取物中已知的化学结构分配的 IR 信号。结合 PLSR 和变量选择，ATR-IR 和 NIR 光谱都可以用于快速预测蓝桉 EC，这是树木育种和耐用木材质量控制所必需的措施。
