Department of Chemical and Biomolecular Engineering, North Carolina State University, Campus Box 7905, Raleigh, North Carolina 27695, USA.
Biotechnol Bioeng. 2013 Jun;110(6):1654-62. doi: 10.1002/bit.24849. Epub 2013 Feb 7.
In situ Raman spectroscopy was employed for real-time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm(-1) C-C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high- and low-concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two-set calibration models for starch (R(2) = 0.998, percent error = 2.5%) and ethanol (R(2) = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS-based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman-based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present.
原位拉曼光谱被用于通过工业酿酒酵母菌株实时监测玉米醪的同步糖化和发酵（SSF）。根据非常强的 883cm(-1) C-C 伸缩带，建立了用于乙醇的准确单变量校准模型。使用来自八个分批发酵的数据，针对总淀粉、糊精、麦芽三糖、麦芽糖、葡萄糖和乙醇开发了多元偏最小二乘（PLS）校准模型，并使用另一个分批的预测进行了验证。当将校准数据分为高低浓度集时，淀粉、乙醇和糊精模型的预测得到了显著改善。通过排除淀粉模型中含有强乙醇峰的区域并排除乙醇模型中含有强糖峰的区域，避免了乙醇和淀粉模型之间的共线性。用于淀粉（R(2) = 0.998，误差百分比 = 2.5%）和乙醇（R(2) = 0.999，误差百分比 = 2.1%）的两集校准模型提供了比以前发表的任何光谱模型更准确的预测。葡萄糖、麦芽糖和麦芽三糖的模型精度与以前在较简单发酵过程中的工作相当。我们的结果表明，拉曼光谱能够实时原位监测复杂的工业生物质发酵。据我们所知，这是第一个基于 PLS 的在典型工业条件下对玉米醪发酵的化学计量建模，也是第一个基于拉曼的监测在葡萄糖、低聚糖和多糖存在下的发酵过程。
