乌拉尔甘草单粒种子硬实特性的近红外光谱分析

以乌拉尔甘草种子为材料，采用近红外光谱结合定性偏最小二乘法对244粒种子(硬实种子和非硬实种子比例为1∶1)的硬实性进行了鉴别研究,并特制一样品杯用于单粒种子的光谱采集，以降低人为误差。研究结果表明，4次重复平均光谱所建模型鉴别率显著高于单次光谱所建模型，光谱范围采用4 000～8 000 cm-1时模型效果较好，校正集、检验集、预测集样本的鉴别率分别为95.53%，95.94%和94.53%，采用不同建模样品所建模型其预测准确率均在90%以上，硬实种子和非硬实种子的预测准确率分别为92.50%和96.56%。种子大小和颜色均会影响模型的鉴别率，种子颜色的影响相对更大。

Identification of Hardness of Licorice Single Seed Using Near Infrared Spectroscopy

To break the dilemma on judging hard seeds and soft seeds of licorice and other legume families nondestructively, a distinguishing model for the hardness of licorice single seed was tried to be built by near infrared reflectance spectroscopy with distinguished partial least squares(DPLS). A total of 244 licorice seeds were divided into three groups： calibration set (120 samples), validation set (60 samples) and prediction set (64 samples), and each group has the same number of hard seeds and soft seeds. To eliminate the human error as far as possible, a specially made sample cup was designed for spectrum acquisition. Then the locations of the seed and the fiber-optic probe were fixed during each spectrum acquisition process. The influences of different replicate time, different spectral region and different calibration samples on the identification rate were compared. The result indicated that four replicates could increase the identification rate of the model significantly, the identification rates of the model of four replicates in calibration, validation and prediction set samples were 95.83%, 95.00% and 96.88% respectively, while that of one replicate were 93.33%, 91.67% and 82.81% respectively. The model of the spectral region between 4 000 and 80 000 cm ^-1 was better than that of other regions, and the identification rate in calibration, validation and prediction set samples were 95.53%, 95. 94% and 94. 53% respectively. Even with different samples, the predication rates were all more than 90%. The identification rates of hard seed and soft seed in prediction set samples were 92.50% and 96.56% respectively. The prediction for seeds with different size and different color showed that this model was not suitable for bigger and smaller seeds, especially not for black seeds. NIR offered a new way to distinguish the hardness of licorice singe seed quickly, precisely and nondestructively, which will advance the study on the mechanism of hardness of crop seeds.