潜伏期柑橘黄龙病宿主糖代谢及近红外光谱特征

doi:10.3969/j.issn.1004-1524.2019.03.12

浙江农业学报 ›› 2019, Vol. 31 ›› Issue (3): 428-435.DOI: 10.3969/j.issn.1004-1524.2019.03.12

潜伏期柑橘黄龙病宿主糖代谢及近红外光谱特征

孟幼青¹, 翁海勇^2,3, 岑海燕^2,3,*, 李红叶⁴, 何勇^2,3

1.浙江省植物保护检疫局,浙江杭州 310020;
2.浙江大学生物系统工程与食品科学学院,浙江杭州 310058;
3.农业农村部光谱检测重点实验室,浙江杭州310058;
4.浙江大学农业与生物技术学院,浙江杭州 310058

收稿日期:2018-07-23 出版日期:2019-03-25 发布日期:2019-04-08
通讯作者: *岑海燕,Email:hycen@zju.edu.cn
作者简介:孟幼青(1963—),女,浙江诸暨人,高级农艺师,主要从事柑橘检疫性有害生物研究。E-mail: yqmeng_77@163.com
基金资助:
浙江省重点研发计划(2015C02007)

Carbohydrate metabolism and near-infrared spectral characteristics in asymptomatic Huang-longbing infected leaves

MENG Youqing¹, WENG Haiyong^2,3, CEN Haiyan^2,3,*, LI Hongye⁴, HE Yong^2,3

1. Plant Protection and Quarantine Bureau of Zhejiang, Hangzhou 310020, China;
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China;
3. Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China;
4. College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China

Received:2018-07-23 Online:2019-03-25 Published:2019-04-08

摘要/Abstract

摘要： 柑橘黄龙病(Citrus Huanglongbing)是柑橘最具有毁灭性的病害,传染性极强,严重影响柑橘产业的健康发展。及时发现潜伏期未显症的病树并挖除,能够更加有效地阻碍黄龙病的传播。为此,特研究潜伏期染病叶片的糖代谢以及叶片组织光学特性的变化规律。结果表明,淀粉、蔗糖、葡萄糖和果糖在染病未显症叶片中已经出现了异常累积,分别是健康叶片的3.58、2.16、3.41、1.70倍,同时,近红外光谱的反射率出现了升高的趋势。进一步采用Random Frog算法选择前6个敏感波段(1 015、1 331、1 065、1 334、1 022、951 nm),并结合Naïve Bayes(NB)模型对潜伏期未显症柑橘黄龙病叶片进行判别,得到了97.5%的分类正确率,对阳性样本的漏检率为0,说明采用近红外高光谱技术能够实现对未显症柑橘黄龙病的检测,可为田间柑橘黄龙病的快速普查提供新的方法。

关键词: 柑橘黄龙病, 糖代谢, 近红外高光谱

Abstract: Citrus Huanglongbing (HLB) is one of the most devastating diseases to citrus production, and can spread to various citrus species. It is necessary to establish a more efficient way to detect HLB disease and then timely remove the infection source in latent period. This research was aimed to investigate the impact of bacterium on host carbohydrate metabolism and the potential of hyperspectral imaging for citrus HLB disease in latent period. The results showed that starch, sucrose, glucose and fructose were excessively accumulated in HLB infected leaves with 3.58, 2.16, 3.41 and 1.70 times, respectively as high as to those in healthy samples. In addition, an increasing reflectance of citrus leaves in near-infrared region was observed when subjected to HLB infection. Six sensitive wavelengths (1 015, 1 331, 1 065, 1 334, 1 022 and 951 nm) were selected by Random Frog, and Naïve Bayes (NB) classifier was used for differentiating asymptomatic HLB infected leaves from healthy ones with the overall classification accuracy of 97.5% and the false negative rate of 0. These results demonstrated the feasibility of near-infrared hyperspectral imaging for HLB disease detection in the asymptomatic period.

Key words: citrus Huanglongbing, carbohydrate metabolism, near-infrared hyperspectral imaging

中图分类号:

S436.661

孟幼青, 翁海勇, 岑海燕, 李红叶, 何勇. 潜伏期柑橘黄龙病宿主糖代谢及近红外光谱特征[J]. 浙江农业学报, 2019, 31(3): 428-435.

MENG Youqing, WENG Haiyong, CEN Haiyan, LI Hongye, HE Yong. Carbohydrate metabolism and near-infrared spectral characteristics in asymptomatic Huang-longbing infected leaves[J]. , 2019, 31(3): 428-435.

参考文献

[1] SAUER A V, ZANUTTO C A, NOCCHI P T R, et al. Seasonal variation in populations of ‘Candidatus Liberibacter asiaticus’ in citrus trees in Paraná State, Brazil[J]. Plant Disease, 2015, 99(8):1125-1132.
[2] 许美容, 戴泽翰, 孔维文, 等. 基于分子技术的柑橘黄龙病研究进展[J]. 果树学报, 2015, 32(2):322-334.
XU M R,DAI Z H, KONG W W, et al.Citrus Huanglongbing research based on molecular biology techniques[J]. Journal of Fruit Science, 2015, 32(2): 322-334. (in Chinese with English abstract)
[3] 程春振, 曾继吾, 钟云, 等. 柑橘黄龙病研究进展[J]. 园艺学报, 2013, 40(9):1656-1668.
CHENG C Z, ZENG J W,ZHONG Y, et al.Research progress on citrus Huanglongbing disease[J]. Acta Horticulturae Sinica, 2013, 40(9): 1656-1668. (in Chinese with English abstract)
[4] LEE J A, HALBERT S E, DAWSON W O, et al.Asymptomatic spread of huanglongbing and implications for disease control[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(24): 7605-7610.
[5] MARTINELLI F, IBANEZ A M, REAGAN R L, et al.Stress responses in citrus peel: comparative analysis of host responses to Huanglongbing disease and puffing disorder[J]. Scientia Horticulturae, 2015, 192: 409-420.
[6] WHITAKER D C, GIURCANU M C, YOUNG L J, et al.Starch content of citrus leaves permits diagnosis of Huanglongbing in the warm season but not cool season[J]. Hortscience A Publication of the American Society for Horticultural Science, 2014, 49(6): 757-762.
[7] CHIYAKA C, SINGER B H, HALBERT S E, et al.Modeling huanglongbing transmission within a citrus tree[J]. Proceedings of the National Academy of Sciences, 2012, 109(30):12213-12218.
[8] 程保平, 赵弘巍, 彭埃天, 等. 柑橘黄龙病的传播介体-柑橘木虱在广东果园的发生调查[J]. 植物保护, 2016, 42(1):189-192.
CHENG B P, ZHAO H W, PENG A T, et al.Occurrence of Huanglongbing disease and its vetor-citrus psylla in citrus orchard in Guangdong Province[J]. Plant Protection, 2016, 42(1): 189-192. (in Chinese with English abstract)
[9] 林亚玉, 殷幼平, 陈世钦, 等. 三种分子检测体系的比较及柑橘果园黄龙病监测[J]. 植物保护学报, 2012, 39(6): 503-507.
LIN Y Y, YIN Y P, CHEN S Q, et al.Comparative of citrus Huanglongbing molecular detection approaches and monitoring of the orchards[J]. Acta Phytophylacica Sinica, 2012, 39(6): 503-507. (in Chinese with English abstract)
[10] KNAUER U, MATROS A, PETROVIC T, et al.Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images[J]. Plant Methods, 2017, 13(1): 47.
[11] DAVOUD A, MOHAMMAD M, ALFREDO H.Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements[J]. Remote Sensing, 2014, 6(6):5107-5123.
[12] MISHRA A, HEYER A G, MISHRA K B.Chlorophyll fluorescence emission can screen cold tolerance of cold acclimated Arabidopsis thaliana accessions[J]. Plant Methods, 2014, 10(1):38.
[13] BEHMANN J, STEINRÜCKEN J, PLÜMER L. Detection of early plant stress responses in hyperspectral images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 93:98-111.
[14] 饶敏, 桂家祥, 卢占军, 等. 基于近红外技术柑橘黄龙病田间快速检测方法研究[J]. 植物保护, 2017 (6):139-142.
RAO M, GUI J X, LU Z J, et al.Fast determination of Huanglongbing disease in the field by near infrared spectroscopy[J]. Plant Protection, 2017 (6): 139-142. (in Chinese with English abstract)
[15] 翁海勇, 岑海燕, 何勇. 直接校正算法的柑橘溃疡病高光谱模型传递[J]. 光谱学与光谱分析, 2018, 38(1):235-239.
WENG H Y, CEN H Y, HE Y.Hyperspectral model transfer for citrus canker detection based on direct standardization algorithm[J]. Spectroscopy and Spectral Analysis, 2018, 38(1): 235-239. (in Chinese with English abstract)
[16] CEN H Y, WENG H Y, YAO J N, et al.Chlorophyll fluorescence imaging uncovers photosynthetic fingerprint of citrus huanglongbing[J]. Frontiers in Plant Science, 2017 (8): 1509.
[17] 梅慧兰,邓小玲,洪添胜,等. 柑橘黄龙病高光谱早期鉴别及病情分级[J]. 农业工程学报, 2014, 35(9): 140-147.
MEI H L, DENG X L, HONG T S, et al.Early detection and grading of citrus huanglongbing using hyperspectral imaging technique[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 35(9): 140-147. (in Chinese with English abstract)
[18] 马淏,吉海彦,LEE W S.基于高光谱成像技术应用光谱及纹理特征识别柑橘黄龙病[J]. 光谱学与光谱分析, 2016, 36(7): 2344-2350.
MA H, JI H Y, LEE W S.Indetification of the citrus greening disease using spectral and textural features based on hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2016, 36(7): 2344-2350.
[19] 刘燕德,肖怀春,孙旭东,等. 柑桔叶片黄龙病光谱特征选择及检测模型[J]. 农业工程学报, 2018, 34(3): 180-187.
LIU Y D, XIAO H C, SUN X D, et al.Spectral feature selection and discriminant model building for citrus leaf Huanglongbing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(3): 180-187. (in Chinese with English abstract)
[20] LI W, HARTUNG J S, LEVY L.Quantitative real-time PCR for detection and identification of Candidatus Liberibacter species associated with citrus Huanglongbing[J]. Journal of Microbiological Methods, 2006 (1): 104-115.
[21] LI H D, XU Q S, LIANG Y Z.Random frog: an efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification[J].Analytica Chimica Acta, 2012, 740: 20-26.
[22] PANT B, PANT K, PARDASANI K R.Naïve Bayes Classifier for classification of plant and animal miRNA[J]. International Journal of Computer Theory and Engineering, 2010, 2(3): 420-424.
[23] WU W, MALLET Y, WALCZAK B, et al.Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data[J]. Analytica Chimica Acta, 1996, 329(3):257-265.
[24] MACHO S, RIUS A, CALLAO M P, et al.Monitoring ethylene content in heterophasic copolymers by near-infrared spectroscopy: standardisation of the calibration model[J]. Analytica Chimica Acta, 2001, 445(2):213-220.
[25] KIM J S, SAGARAM U S, BURNS J K, et al.Response of sweet orange (Citrus sinensis) to ‘Candidatus Liberibacter asiaticus’ infection: microscopy and microarray analyses[J]. Phytopathology, 2009, 99(1):50-57.
[26] ALBRECHT U, BOWMAN K D.Gene expression in Citrus sinensis(L.) Osbeck following infection with the bacterial pathogen Candidatus Liberibacter asiaticus causing Huanglongbing in Florida[J]. Plant Science, 2008, 175(3):291-306.
[27] ZHAO H B, XU L F, SU T, et al.Melatonin regulates carbohydrate metabolism and defenses against Pseudomonas syringae pv. tomato DC3000 infection in Arabidopsis thaliana[J]. Journal of Pineal Research, 2015, 59(1): 109-119.
[28] MAMANI-LINARES L W, GALLO C, ALOMAR D. Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy[J]. Meat Science, 2011, 90(2):378-385.
[29] LIU D, SUN D W, ZENG X A.Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry[J]. Food and Bioprocess Technology, 2017, 7(2): 307-323.

潜伏期柑橘黄龙病宿主糖代谢及近红外光谱特征

Carbohydrate metabolism and near-infrared spectral characteristics in asymptomatic Huang-longbing infected leaves

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