Application of Near Infrared Spectroscopy to Qualitative Identification and Quantitative Determination of Puccinia striiformis f. sp tritici and P. recondita f. sp tritici

作者：Li Xiao long; Ma Zhan hong; Zhao Long lian; Li Jun hui; Wang Hai guang^*

来源：Spectroscopy and Spectral Analysis, 2014, 34(3): 643-647.

DOI：10.3964/j.issn.1000-0593(2014)03-0643-05

• Near infrared spectroscopy
• Puccinia strii formis f. sp tritici
• Puccinia recondita f. sp tritici
• Qualitative identification
• Quantitative determination

To realize qualitative identification and quantitative determination of Puccinia strii formis f. sp. tritici (Pat) 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%similar to 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 similar to 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 (R-2), 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 R-2, 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.

• 出版日期2014-3
• 单位中国农业大学