Fourier transform infrared (FTIR) spectroscopy combined with linear discriminant analysis (LDA) and back propagation neural network (BPNN) based on wavelet transform (WT) is applied to study citrus osbeck anthracnose and healthy peel. Continuous wavelet transform (CWT) is implemented to the FTIR spectra of anthracnose and healthy peel. By comparison, the decomposition level 10 is obviously different and proposed to extract feature vectors, then three feature regions of level 10 are used to train LDA and BPNN models. The performance of LDA algorithm is better than BPNN. On the other hand, in order to extrude the differences between anthracnose and healthy peel, discrete wavelet transform (DWT) is used to compose all spectra in 1750~950 cm-1 range. Wavelet transform approximation coefficients (DWTAC) and discrete wavelet transform detail coefficients (DWTDC) of level 5 are used to train LDA and BPNN models. Results show that accuracy of both LDA and BPNN based on DWTDC (95%) is better than DWTAC. LDA and BPNN algorithms based on wavelet transform can be successfully used for identifying citrus osbeck anthracnose and healthy peel with FTIR spectroscopy. It also provides technology support to detect citrus anthracnose in early stage quickly and effectively.