利用近红外光谱技术结合定性偏最小二乘法(DPLS)和定量偏最小二乘法(QPLS)分别实现了小麦条锈病菌和叶锈病菌的定性识别和定量测定。 获取两种锈菌单一夏孢子样品各50个以及条锈病菌纯度为25%～100%的混合样品120个。 采集样品光谱后, 将两类样品均按2∶1的比例分为建模集和检验集, 在4 000～10 000 cm-1内采用内部交叉验证法建模。 散射校正预处理方法下、 主成分数为3时, 定性识别模型的建模集和检验集识别准确率均为10000%。 “极差归一+散射校正”预处理方法下、 主成分数为6时, 定量测定模型建模集的决定系数(R2)、 校正标准差(SEC)、 平均相对误差(AARD)分别为9936%, 231%, 894%, 检验集的R2、 预测标准差(SEP)、 AARD分别为9937%, 229%, 540%。 结果表明, 利用该方法对这两种锈菌定性和定量分析是可行的。 本研究为植物病原菌的定性识别和定量分析提供了一种基于近红外光谱技术的新方法。

To realize qualitative identification and quantitative determination of Puccinia striiformis f. sp. tritici (Pst) and P. recondita f. sp. tritici (Prt), a qualitative identification model was built using near infrared reflectance spectroscopy (NIRS) combined with distinguished partial least squares (DPLS), and a quantitative determination model was built using NIRS combined with quantitative partial least squares (QPLS). In this study, 100 pure samples including 50 samples of Pst and 50 samples of Prt were obtained, and 120 mixed samples including three replicates of mixed urediospores of the two kinds of pathogen in different proportions (the content of Pst was within the range of 25%～100% with 2.5% as the gradient) were obtained. Then the spectra of the samples were collected using MPA spectrometer, respectively. Both pure samples and mixed samples were divided into training set and testing set with the ratio equal to 2∶1. Qualitative identification model and quantitative determination model were built using internal cross-validation method in the spectral region 4 000～10 000 cm-1 based on the training sets from pure samples and mixed samples, respectively. The results showed that the identification rates of the Pst-Prt qualitative identification model for training set and testing set were both up to 100.00% when scatter correction was used as the preprocessing method of the spectra and the number of principal components was 3. When ‘range normalization + scatter correction’ was used as the preprocessing method of the spectra and the number of principal components was 6, determination coefficient (R2), standard error of calibration(SEC) and average absolute relative deviation(AARD) of the Pst-Prt quantitative determination model for training set were 9936%, 231% and 894%, respectively, and R2, standard error of prediction (SEP) and AARD for testing set were 9937%, 229% and 540%, respectively. The results indicated that qualitative identification and quantitative determination of Pst and Prt using near infrared spectroscopy technology are feasible and that the Pst-Prt qualitative identification model and the Pst-Prt quantitative determination model built in this study were reliable and stable. A new method based on NIRS was provided for qualitative identification and quantitative determination of plant pathogen in this study.