This study utilizes advanced wavenumber selection techniques to improve the prediction of amylose content in grounded rice samples with near-infrared spectroscopy. Four different wavenumber selection techniques, i.e. covariate selection (CovSel), variable combination population analysis (VCPA), bootstrapping soft shrinkage (BOSS) and variable combination population analysis-iteratively retains informative variables (VCPA-IRIV), were used for model optimization and key wavenumbers selection. The results of the several wavenumber selection techniques were compared with the predictions reported previously on the same data set. All the four wavenumber selection techniques improved the predictive performance of amylose in rice samples. The best performance was obtained with VCPA, where, with only 11 wavenumbers-based model, the prediction error was reduced by 19% compared to what reported previously on the same data set. The selected wavenumbers can help in development of low-cost multi-spectral sensors for amylose prediction in rice samples.

这项研究利用先进的波数选择技术，利用近红外光谱法改善了地面大米样品中直链淀粉含量的预测。四种不同的波数选择技术，即协变量选择（CovSel），变量组合总体分析（VCPA），自举软收缩（BOSS）和变量组合总体分析-迭代地保留信息变量（VCPA-IRIV），用于模型优化和关键波数选择。将几种波数选择技术的结果与先前在同一数据集上报告的预测进行了比较。四种波数选择技术均提高了大米样品中直链淀粉的预测性能。使用VCPA可获得最佳性能，其中只有11个基于波数的模型，与先前在同一数据集上报告的结果相比，预测误差减少了19％。选择的波数可以帮助开发低成本的多光谱传感器，用于预测稻米样品中的直链淀粉。