Tillman Zofia, Gray Kevin, Wolfrum Edward
National Renewable Energy Laboratory, Golden, USA.
Biotechnol Biofuels Bioprod. 2024 Aug 14;17(1):112. doi: 10.1186/s13068-024-02558-6.
Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance.
We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model.
We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.
与传统测量技术（如气相或液相色谱法（GC或LC））相比，使用近红外（NIR）光谱等技术对生物质转化过程进行快速监测，由于无需使用溶剂和制备方法，以及无需将样品转移到外部实验室进行分析评估，因此可以显著更快，且劳动力、资源和能源消耗更低。本研究的目的是确定使用近红外光谱结合多元统计建模对生物质转化过程进行快速监测的可行性，并研究（1）按过程位置对原始数据集中的样品进行子集划分，以及（2）减小校准模型中使用的光谱范围对模型性能的影响。
我们针对从甘蔗渣中生产并纯化木寡糖（XOS）化合物的生物质转化过程中多个点的可溶性木寡糖（XOS）、木糖单体和总固体浓度建立了多元校准模型。使用来自过程流中多个位置的样品建立的单个模型，通过标准统计量度测量显示出可接受的性能。然而，与单个模型相比，我们表明根据过程位置分离校准样品建立的单独模型性能有所提高。我们还表明，将对样品光谱的理解与简单的多元分析工具相结合，可以得到一个光谱范围显著更小的校准模型，其性能与全范围模型基本相当。
我们证明了使用近红外光谱结合多元统计对过程流中多个点的可溶性木寡糖（XOS）、木糖单体和总固体浓度进行实时监测是可行的。按过程位置对样品群体进行分离可提高模型性能。使用包含最相关光谱特征的减小光谱范围的模型与全范围模型表现出非常相似的性能，这强化了在开始多元建模之前进行稳健探索性数据分析的重要性。
