以收获年份不同、收获地点不同的112份乌拉尔甘草种子为材料,其硬实率分布范围为0.3%-99.3%,根据3:1的比例划分校正集和检验集,采用近红外光谱技术结合定量偏最小二乘法对其硬实率进行了分析。 研究结果表明,光谱范围采用5000-6000cm-1,主成分数为6时,校正集和检验集的决定系数分别为90.85%和91.51%,相关系数分别为0.9532和0.9579,平均绝对误差分别为7.73%和6.96%,标准差分别为9.98和9.57。 采用该方法建模时,即使采用不同的建模样品,校正集和检验集的决定系数均在90%以上,校正标准差和预测标准差在10.00左右,平均绝对误差在7.90%左右。 该研究旨在利用近红外光谱法提出乌拉尔甘草种子硬实率的快速检测方法,以促进硬实种子在生产上的利用。

With 112 licorice seed samples with different hard rates ranging from 0.3% to 99.3%,harvested in different years from 2002 to 2007 and from different locations of China including Xinjiang municipality,Ningxia province,Inner-Mongolia municipality,Gansu province,Shanxi province and Heilongjiang province,a model for determining hard rate of licorice seeds was tried to be built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). All the seeds samples were divided into two groups: calibration set (including 84 samples) and validation set (including 28 samples). The influences of different spectral regions,different main components and different calibration samples on the prediction results were compared. The result indicated that the spectral regions of 4 000-8 000,5 000-9 000,5 000-8 000,5 000-7 000 and 5 000-6 000 cm-1 all had satisfied and similar prediction results,then 5 000-6 000 cm-1 was regarded as the optimum spectral region for building the model because of its faster operation speed. The model with 6 main components had better relative high determination coefficient (R2) and low standard errors and absolute errors. With the spectral range from 5 000 to 6 000 cm-1 and 6 main components,there was a better fitting between the predictive value and true value. Determination coefficients(R2)of calibration and validation sets are 90.23% and 91.24%,the coefficients of correlation are 0.953 2 and 0.957 9,the standard errors are 10.31 and 9.72,and the average absolute errors are 8.01% and 7.45% respectively. Even with different calibration samples,the models have high determination coefficient (R2 over 90%),low standard errors (about 10.00) and low absolute errors (about 7.90%). The building of NIR model for determining hard rate of licorice seeds could promote the application of hard seeds in cultivation.