Submitted to: Applied Spectroscopy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/26/2009
Publication Date: 1/5/2010
Citation: Hawkins, S.A., Park, B., Poole, G.H., Gottwald, T.R., Windham, W.R., Lawrence, K.C. 2010. Detection of Citrus Huanglongbing by Fourier Transform Infrared-Attenuated Total Reflection (FTIR-ATR) Spectroscopy. Applied Spectroscopy. 64:100-103.
Interpretive Summary: Citrus plants are highly susceptible to a disease commonly called HLB (Huanglongbing) which is also known as citrus greening disease. This disease has the potential to wipe out most of the citrus groves in the United States. Plants can be infected for up to several years before showing symptoms during which time it may have been passed on to other nearby plants. Currently, the best method for detecting the presence of the disease is a type of DNA testing called PCR (polymerase chain reaction) which is both costly and time consuming. In this paper, a new detection method is described that uses FTIR spectroscopy. This detection method has the potential to detect the presence of the disease before visual symptoms occur and it is both relatively inexpensive and fast.
Technical Abstract: 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 Reflectance 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 which 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.