The development of rapid and accurate techniques to predict the composition of crude bio-oils obtained via the pyrolysis of lignocellulosic biomass is a prerequisite for their industrial implementation. Here, we demonstrate the potential of Fourier transform infrared spectroscopy to replace gas chromatography–mass spectrometry (GC-MS) in determining the compositional groups of bio-oils. Using the mid-infrared spectroscopic technique as a predictor, chemometric tools based on partial least-squares regression models were contrasted with GC-MS results to foresee the various families of organic compounds. A broad data set consisting of more than 100 samples obtained from the thermal and catalytic pyrolysis of woody biomass and from the upgrading of bio-oil vapors by catalytic cracking over zeolites and metal oxides was used. The applicability of the developed model was assessed by external validation using the Kennard–Stone algorithm, showing that more than 90 wt % of the bio-oil composition was accurately determined. These results pave the path for the online monitoring of the forthcoming manufacture system of second-generation biofuels through rapid and cost-effective characterization of the pyrolysis bio-oils, thus enabling industrial producers to make timely decisions.

中文翻译：

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通过基于红外光谱的化学计量工具确定生物油的成分

发展快速准确的技术来预测通过木质纤维素生物质热解获得的粗制生物油的组成是其工业应用的先决条件。在这里，我们证明了傅立叶变换红外光谱法在确定生物油成分组中取代气相色谱-质谱法（GC-MS）的潜力。使用中红外光谱技术作为预测指标，将基于偏最小二乘回归模型的化学计量工具与GC-MS结果进行对比，以预测各种有机化合物家族。使用了广泛的数据集，该数据集包含100多个样品，这些样品是从木质生物质的热解和催化热解以及通过沸石和金属氧化物的催化裂化而得到的生物油蒸气的提质获得的。通过使用Kennard-Stone算法进行外部验证，评估了开发模型的适用性，表明已准确确定了超过90 wt％的生物油成分。这些结果通过对热解生物油的快速，经济高效的表征，为即将到来的第二代生物燃料制造系统的在线监控铺平了道路，从而使工业生产商能够及时做出决定。