Research Institute for Sustainable Humanosphere, Kyoto University, Uji, Kyoto 611-0011, Japan.
Appl Biochem Biotechnol. 2012 Feb;166(3):711-21. doi: 10.1007/s12010-011-9460-3. Epub 2011 Nov 30.
In this paper, we report the combination of a near-infrared (NIR) spectroscopic method with multivariate analysis in order to develop a calibration model of the saccharification ratio of chemically pretreated Erianthus. The regression models clearly depend on the NIR spectral regions, and the information of CH and aromatic framework vibrations contributed most effectively to the alkaline dataset. From interpretations of the regression coefficient, lignin and cellulose were negatively and positively correlated with the saccharification ratio, respectively, and this result was supported by the data from wet chemical analysis. A more complex dataset was obtained from varied chemical pretreatments; here, the saccharification ratio was either small or had no linear correlation with each structural monocomponent. These results enabled the successful construction of the PLS regression model. NIR spectroscopy can be a rapid screening method for the saccharification ratio, and furthermore, can provide information of the key factors influencing the realization of more efficient enzymatic accessibility.
本文报道了将近红外（NIR）光谱法与多元分析相结合，以建立化学预处理后的柳枝稷糖化率校准模型的方法。回归模型明显依赖于 NIR 光谱区域，CH 和芳构化框架振动的信息对碱性数据集的贡献最为有效。从回归系数的解释来看，木质素与纤维素分别与糖化率呈负相关和正相关，这一结果得到了湿化学分析数据的支持。从不同化学预处理得到的更复杂数据集来看，糖化率与每个结构单一组分要么很小，要么没有线性相关性。这些结果使得偏最小二乘回归模型的构建成为可能。NIR 光谱法可以作为一种快速筛选糖化率的方法，并且可以提供影响实现更高效酶可及性的关键因素的信息。
