Detection of Puccinia striiformis f. sp. tritici Latent Infections in Wheat Leaves Using Near Infrared Spectroscopy Technology
LI Xiao-long1, MA Zhan-hong1, ZHAO Long-lian2, LI Jun-hui2, WANG Hai-guang1*
1. College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, China 2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Abstract：To realize the early detection of P. striiformis f. sp. tritici latent infections in wheat leaves while no disease symptoms appear, a qualitative model for identification of the wheat leaves in the incubation period of stripe rust was built using near infrared reflectance spectroscopy (NIRS) technology combined with qualitative partial least squares (DPLS). In this study, 30 leaf samples infected with P. striiformis f. sp. tritici were collected each day during the eleven-day incubation period. And 30 healthy leaf samples and 30 leaf samples showing disease symptoms infected with P. striiformis f. sp. tritici, were also collected as controls. In total, there were 390 leaf samples that were divided into thirteen categories. Near infrared spectra of 390 leaf samples were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using cross verification method. The effects of different spectral ranges, samples for building the model, preprocessing methods of spectra and number of principal components on NIRS prediction results for qualitative identification were investigated. The optimal identification results were obtained for the model built in the combined spectral region of 5 400～6 600 and 7 600～8 900 cm-1 when the spectra were divided into the training set and the testing set with the ratio equal to 4∶1, “scatter correction” was used as the preprocessing method and the number of principal components was 14. Accuracy rate, misjudgment rate and confusion rate of the training set were 95.51%, 1.28% and 3.21%, respectively. And accuracy rate, misjudgment rate and confusion rate of the testing set were 100.00%, 0.00% and 0.00%, respectively. The result showed that using near infrared reflectance spectroscopy technology, P. striiformis f. sp. tritici latent infections in wheat leaves could be detected as early as one day after inoculation (or 11 days before symptoms appearing) and the number of days when the leaf has been infected could also be identified. The results indicated that the method using near infrared reflectance spectroscopy technology proposed in this study is feasible for the identification of wheat leaves latently infected by P. striiformis f. sp. tritici. A new method based on NIRS was provided for the early detection of wheat stripe rust in this study.
李小龙1，马占鸿1，赵龙莲2，李军会2，王海光1* . 基于近红外光谱技术的小麦条锈病菌潜伏侵染的检测 [J]. 光谱学与光谱分析, 2014, 34(07): 1853-1858.
LI Xiao-long1, MA Zhan-hong1, ZHAO Long-lian2, LI Jun-hui2, WANG Hai-guang1* . Detection of Puccinia striiformis f. sp. tritici Latent Infections in Wheat Leaves Using Near Infrared Spectroscopy Technology . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(07): 1853-1858.
[1] LI Zhen-qi, ZENG Shi-mai(李振岐, 曾士迈). Wheat Rusts in China(中国小麦锈病). Beijing: China Agriculture Press(北京: 中国农业出版社), 2002. 1. [2] Wan A, Zhao Z, Chen X, et al. Plant Disease, 2004, 88(8): 896. [3] Wan A M, Chen X M, He Z H. Australian Journal of Agricultural Research, 2007, 58(6): 605. [4] CAI Cheng-jing, WANG Hai-guang, AN Hu, et al(蔡成静, 王海光, 安 虎, 等). Journal of Northwest Sci-Tech Univ. of Agri. and For.·Nat. Sci. Ed.(西北农林科技大学学报·自然科学版), 2005, 33(Supp.): 31. [5] LI Jing, CHEN Yun-hao, JIANG Jin-bao, et al(李 京, 陈云浩, 蒋金豹, 等). Science & Technology Review(科技导报), 2007, 25(6): 23. [6] Winton L M, Stone J K, Watrud L S, et al. Phytopathology, 2002, 92(1): 112. [7] Zhao J, Wang X J, Chen C Q, et al. Plant Disease, 2007, 91(12): 1669. [8] Wang X J, Zheng W M, Buchenauer H, et al. European Journal of Plant Pathology, 2008, 120(3): 241. [9] Wang X J, Tang C L, Chen J L, et al. Journal of Phytopathology, 2009, 157(7-8): 490. [10] PAN Juan-juan, LUO Yong, HUANG Chong, et al(潘娟娟, 骆 勇, 黄 冲, 等). Acta Phytopathologica Sinica(植物病理学报), 2010, 40(5): 504. [11] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄, 赵龙莲, 韩东海, 等). Basis and Application of Near Infrared Reflectance Spectroscopy(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2005. 2. [12] Chen Q S, Zhao J W, Liu M H, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46(3): 568. [13] Alamprese C, Casale M, Sinelli N, et al. LWT-Food Science and Technology, 2013, 53(1): 225. [14] Sankaran S, Mishra A, Ehsani R, et al. Computers and Electronics in Agriculture, 2010, 72(1): 1. [15] WU Di, FENG Lei, ZHANG Chuan-qing, et al(吴 迪, 冯 雷, 张传清, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2007, 26(4): 269. [16] FENG Lei, CHEN Shuang-shuang, FENG Bin, et al(冯 雷, 陈双双, 冯 斌, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(1): 139. [17] Gachon C, Saindrenan P. Plant Physiology and Biochemistry, 2004, 42(5): 367. [18] Mercado-Blanco J, Collado-Romero M, Parrilla-Araujo S, et al. Physiological and Molecular Plant Pathology, 2003, 63(2): 91.
