测定并分析了碳酸钙（CaCO3）的中红外光声光谱及光谱特征, 利用中红外光声光谱并结合主成分回归（PCR）、 偏最小二乘回归（PLSR）和人工神经网络（GRNN）三种分析方法建立回归模型, 分析了土壤CaCO3的含量。 结果表明CaCO3具有丰富的中红外吸收, 最强吸收峰波数在1 450 cm-1, 且干扰少, 可以作为土壤CaCO3的特征吸收峰; 三种回归建模方法所建模型线性都很好, PLSR和GRNN最好, 相关系数（R2）均大于0.9, PCR次之, 为0.847; 验证样本预测能力PLSR和PCR最佳, R2大于0.9; GRNN次之, 为0.882。 偏最小二乘回归在校正和预测过程中的结果都非常好, RPD值均大于3.0, 具有较强的适用性。

The mid-infrared photoacoustic spectra of CaCO3 was determined and characterized, and multi-calibration methods of principal component regression (PCA), partial least squares regression (PLSR), and GRNN artificial neural network were applied to quantitative analysis of soil carbonate. The results showed that abundant absorptions were found in the mid-infrared photoacoustic spectra of CaCO3, especially the very strong band at the wavenumber of 1 450 cm-1, in which there was few interferences, and could be used as spectral indicator of soil carbonate; the calibration results were good or excellent with the three chemometric methods, in which PLSR and GRNN modeling were excellent with a R2 more than 0.9, and PCA modeling was good with a R2 of 0.847; the validation results showed that PLSR and PCA modeling were excellent with higher R2 values (>0.9), and GRNN was also very satisfied with a R2 of 0.882. Totally, PLSR modeling was the best with RPD values more than 3.0, indicating its strong potential in the prediction of soil carbonate.