Abstract: Near-infrared reflectance photoacoustic spectroscopy (NIRS) was applied to fast determination of soil organic matter (SOM) by different quantitative methods. The dried samples of five different soil types in the middle and eastern China were selected to assess the quantitative estimation of SOM based on different modeling methods. First derivative, multiplicative scatter correction (MSC) and smoothing algorithms were used to preprocess the original spectra before the calibration models were developed. The results showed that different pre-processing algorithms markedly affected the accuracy of SOM calibration models. The sequence of SOM models with different pre-processing algorithms was MSC+ Norris-gap first derivative smoothing filter (NGFD) > MSC >Norris first derivative smoothing filter > standard normal variate (SNV) > Norris-gap second derivative smoothing filter (NGSD) > LOG > Savitzky-Golay first derivative (SGFD) > Savitzky-Golay second derivative (SGSD). The spectra processed with the combination of MSC and NGFD performed the best among all the pre-processing algorithms. In addition, the calibration model based on PLS-BPNN displayed the highest estimation accuracy with R2 of 0.97 and RMSEP of 1.88, followed by PLS, SMLR and PCR, while the validation model with independent data gave R2 of 0.97 and RMSEP of 1.72, respectively. These results indicated that PLS-BPNN based on MSC-NGFD spectra was a potentially optimal method for SOM estimation.