Wageningen Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA, Wageningen, the Netherlands.
Department of Chemistry, University of Rome "La Sapienza", P.le Aldo Moro 5, 00185, Rome, Italy.
Talanta. 2021 Feb 1;223(Pt 1):121693. doi: 10.1016/j.talanta.2020.121693. Epub 2020 Sep 24.
Near-infrared (NIR) spectroscopy of fuels can suffer from scattering effects which may mask the signals corresponding to key analytes in the spectra. Therefore, scatter correction techniques are often used prior to any modelling so to remove scattering and improve predictive performances. However, different scatter correction techniques may carry complementary information so that, if jointly used, both model stability and performances could be improved. A solution to that is the fusion of complementary information from differently scatter corrected data. In the present work, the use of a preprocessing fusion approach called sequential preprocessing through orthogonalization (SPORT) is demonstrated for predicting key quality parameters in diesel fuels. In particular, the possibility of predicting four different key properties, i.e., boiling point (°C), density (g/mL), aromatic mass (%) and viscosity (cSt), was considered. As a comparison, standard partial least-squares (PLS) regression modelling was performed on data pretreated by SNV and 2nd derivative (which is a widely used preprocessing combination). The results showed that the SPORT models, based on the fusion of scatter correction techniques, outperformed the standard PLS models in the prediction of all the four properties, suggesting that selection and use of a single scatter correction technique is often not sufficient. Up to complete bias removal with 50% reduction in prediction error was obtained. The R was increased by up to 8%. The sequential scatter fusion approach (SPORT) is not limited to NIR data but can be applied to any other spectral data where a preprocessing optimization step is required.
燃料的近红外（NIR）光谱可能会受到散射效应的影响，这可能会掩盖光谱中关键分析物对应的信号。因此，在进行任何建模之前，通常会使用散射校正技术来去除散射并提高预测性能。然而，不同的散射校正技术可能会提供互补的信息，因此，如果联合使用，模型的稳定性和性能都可以得到提高。解决这个问题的方法是融合来自不同散射校正数据的互补信息。在本工作中，展示了一种称为顺序预处理正交化（SPORT）的预处理融合方法，用于预测柴油燃料中的关键质量参数。特别是，考虑了预测四个不同关键性质的可能性，即沸点（°C）、密度（g/mL）、芳烃质量（%）和粘度（cSt）。作为比较，对经过 SNV 和二阶导数预处理的数据（这是一种广泛使用的预处理组合）进行了标准偏最小二乘（PLS）回归建模。结果表明，基于散射校正技术融合的 SPORT 模型在预测所有四个性质方面均优于标准 PLS 模型，表明选择和使用单一散射校正技术通常是不够的。通过融合技术可以实现高达 50%的预测误差减少和完全消除偏差。R 值最高可提高 8%。顺序散射融合方法（SPORT）不仅限于 NIR 数据，还可以应用于任何需要预处理优化步骤的其他光谱数据。
