Department of Agriculture and Biosystems Engineering, Iowa State University , Ames, Iowa 50011, United States.
J Agric Food Chem. 2012 Aug 29;60(34):8314-22. doi: 10.1021/jf3012807. Epub 2012 Aug 14.
Four near-infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single-seed attributes: weight (g), protein (%), and oil (%). Using partial least-squares (PLS) and four preprocessing methods, the attribute that was significantly most easily predicted was seed weight (RPD > 3 on average) and protein the least. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system, with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no single one gave the best overall performances but weight measurements were often more successful with raw spectra, whereas protein and oil predictions were often enhanced by SNV and SNV + detrending.
重量（g）、蛋白质（%）和油（%）。使用偏最小二乘（PLS）和四种预处理方法，最容易显著预测的属性是种子重量（平均 RPD > 3），而蛋白质则最难预测。所有仪器的性能均存在差异。油和蛋白质预测的性能与仪器采样系统相关，使用从多个种子角度采集的光谱进行预测效果最佳。这得益于光谱采集过程中种子的旋转或翻滚，而不是静态采样方法。在所使用的预处理方法中，没有一种方法能给出最佳的整体性能，但对于重量测量，原始光谱通常更有效，而对于蛋白质和油的预测，SNV 和 SNV + 去趋势通常会增强效果。
