Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR
Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR
复制标题
使用 Py-MBMS 和 NIR 预测不同玉米基因型的细胞壁特性和对碱预处理解构的响应
DOI：
10.1007/s12155-016-9798-z
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发表时间：
2016
影响因子：
3.6
通讯作者：
Hodge, David
中科院分区：
文献类型：
--
作者：
Li, Muyang;Williams, Daniel L.;Heckwolf, Marlies;de Leon, Natalia;Kaeppler, Shawn;Sykes, Robert W.;Hodge, David
在这项工作中，我们探索了几种表型表征方法提取不同玉米基因型中植物细胞壁特性信息的能力，目的是确定可用于预测植物对生物质转化为生物燃料过程中解构反应的方法。具体来说，对玉米多样性组进行了两种高通量生物量表征方法，即热解分子束质谱法 (py-MBMS) 和近红外 (NIR) 光谱法以及化学计量模型，以预测许多植物细胞壁特性以及未经预处理或温和碱性预处理后葡萄糖的酶解产率。将这些模型与根据量化属性开发的多元线性回归 (MLR) 模型进行比较。我们能够证明，与来自热解的特定质谱离子以及 NIR 光谱区域二阶导数的特征区域的直接相关性在其预测能力方面与香豆酸含量的偏最小二乘 (PLS) 模型相当，而通过交叉验证评估，对于 Klason 木质素含量和硫代酸解的愈创木基单体释放，与光谱数据的直接相关性优于 PLS。使用 py-MBMS 或 NIR 光谱预测水解产量的 PLS 模型优于基于未预处理生物质的量化特性的 MLR 模型。然而，使用两种高通量表征方法的 PLS 模型无法预测碱性预处理后的水解，而基于量化特性的 MLR 模型则可以。这可能是量化特性的结果，包括对预处理生物质的一些评估，而 py-MBMS 和 NIR 仅利用未经处理的生物质。
In this work, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant’s response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These were compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models forp-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.
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