Rapid detection of citrus Huanglongbing using Raman spectroscopy and Auto-fluorescence spectroscopy

doi:10.12133/j.smartag.2019.1.3.201812-SA026

Abstract

Abstract:

In order to detect citrus Huanglongbing (HLB, also named citrus greening) quickly, Auto-fluorescence and Raman spectra of HLB leaf samples and healthy ones were collected and analyzed. PLS-DA models based on Auto-fluorescence spectra, Raman spectra and mixed spectra were established and compared respectively. Finally, ROC curves of the three models were drawn, and the performance of the models were further evaluated by using the area under curve AUC parameters. The results demonstrated spectral differences between Huanglongbing samples and healthy ones could be seen. With 785 nm laser irradiation, citrus leaf samples produced strong Auto-fluorescence and Raman peaks. The Auto-fluorescence of HLB leaves was weaker than that of healthy samples in the range of 800-1203 cm ^-1, but stronger in the range of 1206-1800 cm ^-1, and the slope of decline (absolute value) was smaller than that of healthy samples. The similar shapes were found in the Raman spectra of typical HLB samples and healthy ones. But the HLB samples had larger Raman peak intensity and spectral bandwidth at 1257 cm ^-1, 1396 cm ^-1, 1446 cm ^-1, 1601 cm ^-1 and 1622 cm ^-1 than healthy ones. The Raman peak intensity of HLB samples was weaker than that of healthy samples at 1006 cm ^-1, 1160 cm ^-1, 1191 cm ^-1 and 1529 cm ^-1 positions, suggesting that the carotenoid content of HLB samples was lower than healthy ones. The Auto-fluorescence model, the Raman spectral model and the mixed spectral model could distinguish two kinds of samples with the accuracy of 86.08%, 98.17% and 94.75%, respectively. Furthermore, AUCs of Receiver Operating Characteristic Curve (ROC) were calculated. The AUCs for the Auto-fluorescence model, the Raman spectral model and the mixed spectral model were0.9313、0.9991 and 0.9875, respectively. Through further analysis of ROC curve, the identification effect of the Raman spectral model was optimal. Raman spectroscopy could be a new way to explore the rapid diagnosis of citrus HLB.

Key words: Huanglongbing detection, Raman spectra, Auto-fluorescence spectra, PLS-DA, citrus, diagnose

CLC Number:

Dai Fen, Qiu Zeyuan, Qiu Qian, Liu Chujian, Huang Guozeng, Huang Yalin, Deng Xiaoling. Rapid detection of citrus Huanglongbing using Raman spectroscopy and Auto-fluorescence spectroscopy[J]. Smart Agriculture, 2019, 1(3): 77-86.

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真实情况	预测结果		准确率（%）	AUC
真实情况	黄龙病	健康	准确率（%）	AUC
黄龙病	214	6	98.17	0.9991
健康	2	216	98.17	0.9991