Sun Qun, Li Jun-Hui, Wang Jian-Hua, Sun Bao-Qi
Department of Plant Genetic and Breeding, College of Agriculture and Biotechnology, China Agricultural University/Key Laboratory of Crop Genomics and Genetic Improvement of Ministry of Agriculture/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Oct;29(10):2669-72.
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.
为无损破解甘草等豆科植物硬实种子与软实种子的判别难题,尝试运用具有判别性偏最小二乘法(DPLS)的近红外反射光谱法构建甘草单粒种子硬度判别模型。将244粒甘草种子分为三组:校正集(120个样本)、验证集(60个样本)和预测集(64个样本),每组硬实种子和软实种子数量相同。为尽可能消除人为误差,设计了特制的样品杯用于光谱采集。在每次光谱采集过程中,固定种子和光纤探头的位置。比较了不同重复次数、不同光谱区域和不同校正样本对识别率的影响。结果表明,四次重复可显著提高模型的识别率,校正集、验证集和预测集样本四次重复模型的识别率分别为95.83%、95.00%和96.88%,而一次重复时分别为93.33%、91.67%和82.81%。4000至80000 cm(-1)光谱区域的模型优于其他区域,校正集、验证集和预测集样本的识别率分别为95.53%、95.94%和94.53%。即使使用不同样本,预测率均超过90%。预测集样本中硬实种子和软实种子的识别率分别为92.50%和96.56%。对不同大小和颜色种子的预测表明,该模型不适用于过大或过小的种子,尤其是黑色种子。近红外光谱为快速、准确、无损判别甘草单粒种子硬度提供了新途径,将推动作物种子硬度机理研究。