The study aims to test the hypothesis that modelling of near-infrared (NIR) spectroscopic data based on a single scatter correction technique is sub-optimal. Better predictive performance of the multivariate analysis method can be obtained when the information from differently scatter corrected data is jointly used. To demonstrate it, an open-source NIR spectroscopy data set related to protein prediction in wheat kernels was used. Two different pre-processing fusion approaches i.e., sequential and parallel fusion, were used for fusing the complementary information from four different scatter correction techniques, namely standard normal variate (SNV), variable sorting for normalisation (VSN), 2nd derivative, and multiplicative scatter correction (MSC). As a comparison, partial least-squares regression (PLSR) was performed on the SNV pre-processed data. The results showed that fusion of scatter correction can improve the predictive performance of NIR spectroscopic models. The results revealed that both sequential and parallel fusion approaches improved the predictive performance compared to the PLSR performed using a single scatter correction technique. The R²_p was improved by up to 3% and the RMSEP was reduced by up to 13% compared to the results obtained with conventional PLSR model developed with a single scatter correction technique.

该研究旨在检验以下假设：基于单一散射校正技术的近红外（NIR）光谱数据建模次优。当联合使用来自不同散射校正数据的信息时，可以获得多变量分析方法的更好的预测性能。为了证明这一点，使用了与小麦籽粒中蛋白质预测有关的开放源NIR光谱数据集。两种不同的预处理融合方法，即顺序融合和并行融合，用于融合来自四种不同散点校正技术的互补信息，即标准正态变量（SNV），归一化变量排序（VSN），二阶导数和乘法散点校正（MSC）。作为比较，对SNV预处理数据执行偏最小二乘回归（PLSR）。结果表明，散射校正融合可以提高近红外光谱模型的预测性能。结果表明，与使用单散射校正技术执行的PLSR相比，顺序和并行融合方法均提高了预测性能。R与使用单散射校正技术开发的常规PLSR模型获得的结果相比，将² _p提高了3％，将RMSEP降低了13％。