杂交稻种宜香725纯度的可见-近红外反射光谱鉴定

提出了一种基于可见-近红外光谱技术快速、无损鉴定杂交稻种纯度的新方法.以FieldSpec(R)3地物光谱仪采集纯度在90%～99%范围内的杂交稻种(宜香725)光谱数据90份,随机分成校正集(75份)和检验集(15份).根据其在380～2 400 nm的反射光谱,以偏最小二乘算法(PLS)建立了回归模型,并比较了不同光谱预处理方法对模型的影响.分析表明采用一阶导数结合标准归一化处理能最有效地提取光谱信息,此时PLS模型校正集决定系数与检验集决定系数分别为0.988 4与0.922 7,校正标准误差(SEC)与预测标准误差(SEP)分别为0.002 5与0.006 6.将经一阶导数结合标准归一化处理后的光谱进行PCA降维,以前20个主成份(含原始光谱86.09%的特征信息)为输入变量,建立杂交稻种纯度鉴定的BP-ANN模型.分析表明BP-ANN模型校正集决定系数与检验集决定系数分别为0.995 2与0.936 9,SEC与SEP分别为0.001 7与0.006 1,具有比PLS模型更高的精度.结果表明以可见-近红外技术进行杂交稻种纯度的快速、无损鉴定是可行的,且PCA结合BP-ANN是一种优选方法.

Purity Measurement of Hybrid Rice Seed Yixiang 725 with Visible-Near Infrared Reflectance Spectra

A rapid and non-invasive method was put forward to measure the purity of hybrid rice seed by visible-near infrared reflectance spectra. Ninety hybrid rice seed samples (Yixiang 725) with the purity of 90%-99% were collected using a FieldSpec3 visible-near infrared spectometer. All samples were divided randomly into two groups, one group with 75 samples used as calibrated set, and the other with 15 samples used as validated set. Based on the spectra in the range of 380-2 400 nm, the regression model was established using the PLS (partial least square), and different spectra pretreatment methods were compared. The study showed that spectra information can be extracted thoroughly by the pretreatment method of first derivative combined with standard normal variate, with the SEC (standard error of calibration) of 0.002 5, SEP (standard error of prediction) of 0.006 6, and determination coefficients of 0.988 4 (calibration set) and 0.922 7 (validation set) respectively. The spectra, which were pretreated with the method of first derivative combined standard normal variate, were analyzed by principal component analysis (PCA). The top 20 principal components, which were computed by PCA and accounted for 86.09% variation of the original spectral information, were used to build BP-ANN model for measuring the purity of hybrid rice seed as the new variables. The study showed that the SEC and SEP of BP-ANN model were 0.001 7 and 0.006 1, and the determination coefficients of that were 0.995 2 (calibration set) and 0.936 9 (validation set) respectively. Therefore, the predictive power of BP-ANN model was better than that of PLS model. Results indicated that it was feasible to measure the purity of the hybrid rice seed by visible-near reflectance spectra as a rapid and non-contact way, and PCA combined with BP-ANN was a preferred method.