Acquah Gifty E, Via Brian K, Fasina Oladiran O, Adhikari Sushil, Billor Nedret, Eckhardt Lori G
Forest Products Development Center, School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama, United States of America.
Center for Bioenergy and Bioproducts, Department of Biosystems Engineering, Auburn University, Auburn, Alabama, United States of America.
PLoS One. 2017 Mar 2;12(3):e0172999. doi: 10.1371/journal.pone.0172999. eCollection 2017.
The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2-0.92; RPD- 3.58) and lignin (R2-0.82; RPD- 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon.
本研究的目的是探究热重（TG）数据的化学计量学建模作为一种替代方法，用于估算木质纤维素生物质的化学组成和近似组成（即挥发物、固定碳和灰分含量）。由于这些特性会影响转化途径、加工成本、产品产量和/或质量，因此能够快速测定进入工艺流的生物质原料的这些特性，对于生物转化技术的成功和效率将是有用的。本研究开发的38分钟长的方法能够从相同的TG数据中同时预测森林衍生生物质的化学性质和近似性质。传统上，必须进行两个单独的实验才能获得此类信息。此外，用归一化TG数据构建的化学计量学模型优于通过TG数据传统反卷积开发的模型。PLS和PCR模型在预测生物质的挥发物含量（R2 - 0.92；RPD - 3.58）和木质素含量（R2 - 0.82；RPD - 2.40）方面特别稳健。化学计量学在TG数据中的应用还使得在本研究中能够预测一些单糖。对从化学计量学模型获得的主成分载荷的阐释也为木质纤维素生物质化学成分的热分解行为提供了一些见解。例如，挥发物和纤维素以及固定碳和木质素的载荷相似。研究结果表明，这些化学性质和热反应性性质之间共享共同的潜在变量。本研究的结果支持了文献报道，即热稳定性较差的多糖是挥发物产率的原因，而更难降解的木质素及其较高的元素碳百分比则导致固定碳的产率。
