Abstract
Citrus huanglongbing (HLB) as a devastating disease seriously affects the advance in agriculture, so early detection or accurate diagnosis is the key to control its spread. This paper reported a method for early, rapid, and non-destructive detection of HLB using microscopic confocal Raman (MCR). The spectra of healthy (HE), HLB-asymptomatic (HA), and HLB-symptomatic (HS) leaves were very different at 730–810 cm−1, 866 cm−1, 942 cm−1, 1082 cm−1, 1250 cm−1, 1455 cm−1, and 1510–1630 cm−1, which could be clearly distinguished mutually. Meanwhile, the spectra of relative compounds inside leaves were connected to further analyze spectral differences. The contents of glucose, sucrose, carotene, and chlorophyll in HA leaves were distinctly decreased, but increased in starch and polyphenols compared with HE leaves. In addition, three types of leaves could be well classified by principal component analysis (PCA) whose cumulative percentage variance (CPV) accounted for about 91.01% (three principal components). Partial least square discriminant analysis (PLS-DA) also demonstrated the good clustering effect with an accuracy of 97.2%. Finally, BP-artificial neural network (BP-ANN) model was utilized to evaluate datasets (75% for training, 25% for testing). The low root mean square errors (RMSE 0.0616) and high squared correlation coefficients (R2 0.9598) values showed the high prediction accuracy and stability of the classification model. These results indicated that MCR had excellent practical values for horticulturists to constantly and early detect HLB, which was conducive to prevent and timely control of the spread of HLB.
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Acknowledgments
The authors would like to express their gratitude to the National Navel Orange Engineering Research Center (China) for supplying samples.
Funding
This work was supported by the National Science Foundation of China (61575065). The authors would like to thank the MOE Key Laboratory of Laser Life Science & Laboratory of Photonic Chinese Medicine for funding this research.
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Kangkang Wang declares that he has no conflict of interest. Yubo Liao declares that he has no conflict of interest. Yaoyong Meng declares that he has no conflict of interest. Xianzhi Jiao declares that she has no conflict of interest. Wei Huang declares that she has no conflict of interest. Timon Cheng-yi Liu declares that he has no conflict of interest.
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Highlights
• Verified a method for early, rapid, and non-destructive detection of HLB with MCR.
• Raman spectra of HE, HA, and HS leaves differ clearly from each other.
• The better classification (PCA) and clustering (PLS-DA) effects compared with other methods.
• BP-ANN model could accurately predict the kinds of samples.
• It might promote the protection and safety of the citrus industry.
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Wang, K., Liao, Y., Meng, Y. et al. The Early, Rapid, and Non-Destructive Detection of Citrus Huanglongbing (HLB) Based on Microscopic Confocal Raman. Food Anal. Methods 12, 2500–2508 (2019). https://doi.org/10.1007/s12161-019-01598-1
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DOI: https://doi.org/10.1007/s12161-019-01598-1