Comparison of FTIR spectra between Huanglongbing (citrus greening) and other citrus maladies.

Fourier transform infrared (FTIR) spectroscopy has the ability to quickly identify the presence of specific carbohydrates in plant materials. The presence of the disease huanglongbing (HLB) in the leaves of infected citrus plants has a distinctive spectrum that can be used to distinguish an infected plant from a healthy plant. However, many citrus diseases display similar visible symptoms and are of concern to citrus growers. In this study several citrus diseases (citrus leaf rugose virus, citrus tristeza virus, citrus psorosis virus, and Xanthomonas axonopodis ) and nutrient deficiencies (iron, copper, zinc, manganese, and magnesium) were compared with HLB using FTIR spectroscopy to determine if the spectra alone can be used to identify plants that are infected with HLB instead of another disease. The results indicate that the spectra of some diseases and deficiencies more closely resemble those of apparently healthy plants and some share the carbohydrate transformation that has been seen in the spectra of HLB-infected plants.

Detection of citrus Huanglongbing by Fourier transform infrared-attenuated total reflection spectroscopy.

Citrus Huanglongbing (HLB, also known as citrus greening disease) was discovered in Florida in 2005 and is spreading rapidly amongst the citrus growing regions of the state. Detection via visual symptoms of the disease is not a long-term viable option. New techniques are being developed to test for the disease in its earlier presymptomatic stages. Fourier transform infrared-attenuated total reflection (FT-IR-ATR) spectroscopy is a candidate for rapid, inexpensive, early detection of the disease. The mid-infrared region of the spectrum reveals dramatic changes that take place in the infected leaves when compared to healthy non-infected leaves. The carbohydrates that give rise to peaks in the 900-1180 cm(-1) range are reliable in distinguishing leaves from infected plants versus non-infected plants. A model based on chemometrics was developed using the spectra from 179 plants of known disease status. This model then correctly predicted the status of >95% of the plants tested.