Biodiesel produced from renewable sources is known to reduce carbon footprint compared to fossil diesel and thus, has a more significant potential for compression ignition engine applications. Unlike diesel, biodiesel can be produced from various sources and, thereby, subjected to composition variability. A priori knowledge of engine fuel properties of biodiesel would allow a careful feedstock choice for producing biodiesel for automotive and stationary engine applications. Regression models to predict engine fuel properties of biodiesel from its composition using Chromatographic and Spectroscopic-based approaches are explored in the present study to develop the most suitable method. Regression models are developed using composition and Fourier Transform Infrared spectra of seventy biodiesel samples. The composition of the seventy biodiesel samples measured using gas chromatography is correlated with the properties using multilinear regression. In the Spectroscopic-based approach, the seventy biodiesel samples' mid-infrared spectra are correlated with the properties using partial least square regression. The developed regression models based on Chromatographic and Spectroscopic approaches are validated using 33 external validation biodiesel samples. The results obtained show that the calorific value, cetane number, density, and kinematic viscosity of biodiesel samples are predicted with a mean absolute percentage error of 1.16%, 4%, 0.76%, and 2.6%, respectively, using the Chromatographic approach while it is 3.17%, 4.59%, 0.73%, and 3.66%, respectively with Spectroscopic method. The chromatographic approach, which resulted in better prediction than the Spectroscopic method, also captures biodiesel composition variations on the engine fuel properties.

众所周知，与化石柴油相比，由可再生资源生产的生物柴油可减少碳足迹，因此在压燃式发动机应用中具有更大的潜力。与柴油不同，生物柴油可以从各种来源生产，因此会受到成分变化的影响。生物柴油发动机燃料特性的先验知识将允许谨慎选择原料来生产用于汽车和固定发动机应用的生物柴油。本研究探索了使用基于色谱和光谱的方法从生物柴油的成分预测发动机燃料特性的回归模型，以开发最合适的方法。回归模型是使用七十个生物柴油样品的成分和傅立叶变换红外光谱开发的。使用气相色谱法测量的七十个生物柴油样品的组成与使用多元线性回归的特性相关联。在基于光谱的方法中，使用偏最小二乘回归将七十个生物柴油样品的中红外光谱与特性相关联。基于色谱和光谱方法开发的回归模型使用 33 个外部验证生物柴油样品进行验证。获得的结果表明，使用色谱法预测生物柴油样品的热值、十六烷值、密度和运动粘度的平均绝对百分比误差分别为 1.16%、4%、0.76% 和 2.6%。光谱法分别为 3.17%、4.59%、0.73% 和 3.66%。色谱法，