Huanglongbing (HLB) and citrus variegated chlorosis (CVC) are serious threats to citrus production and have caused considerable economic losses worldwide, especially in Brazil, which is one of the biggest citrus producers in the world. Neither disease has a cure nor an efficient means of control. They are also generally confused with each other in the field since they share similar initial symptoms, e.g., yellowing blotchy leaves. The most efficient tool for detecting these diseases is by polymerase chain reaction (PCR). However, PCR is expensive, is not high throughput, and is subject to cross reaction and contamination. In this report, a diagnostic method is proposed for detecting HLB and CVC diseases in leaves of sweet orange trees using attenuated total reflectance Fourier transform infrared spectroscopy and the induced classifier via partial least-squares regression. Four different leaf types were considered: healthy, CVC-symptomatic, HLB-symptomatic, and HLB-asymptomatic. The results show a success rate of 93.8% in correctly identifying these different leaf types. In order to understand which compounds are responsible for the spectral differences between the leaf types, samples of carbohydrates starch, sucrose, and glucose, flavonoids hesperidin and naringin, and coumarin umbelliferone were also analyzed. The concentration of these compounds in leaves may vary due to biotic stresses. (C) 2012 Elsevier B.V. All rights reserved.