光谱学分析技术因具有快速、 实时、 无损等优势, 被广泛用于土壤养分检测等各个领域, 也是现代农业检测控制中广泛应用的一种有效方法。 实验采集不同养分含量的土壤样本, 分别获取中红外衰减全反射（ATR）和漫反射（DRIFT）光谱数据。 通过不同的光谱预处理算法结合偏最小二乘法（PLS）建立土壤中全碳、 全氮、 碱解氮的定量分析模型, 对土壤养分的ATR和DRIFT光谱分别进行独立模型和叠加模型的分析。 并以土壤养分中的全碳和全氮为例, 发现经过标准正态变量变换处理的两种光谱建立的PLS模型相关系数均有所提高。 全碳的两种光谱模型校正集的R2分别提高至0.826和0.919, 而全氮两种模型校正集R2分别提高至0.841和0.928。 我们进一步对全碳、 全氮各自叠加后的光谱进行分析, 模型的R2分别提高至0.942和0.951。 用同样的方法分析土壤中的碱解氮, 联用两光谱后的R2提高至0.919。 研究结果表明, 两种光谱的联合使用, 为提高模型的相关系数和预测能力提供了一种合理有效的方法。

Due to the advantages of high detection speed, real-time measurement, and non-destructive process, spectroscopy analysis technique is widely used in every field and has becomes one of the most effective methods in modern agricultural control. In this paper, we collected soil samples with different nutrient contents, measured the infrared attenuated total reflection (ATR) and diffuse reflection infrared Fourier transform (DRIFT) data of every sample in order to built models and predicted the content of total carbon, total nitrogen and available nitrogen quantitively in soil by using pretreatment and partial least squares(PLS) methods. It indicated that the determination coefficients of PLS models improved after using standard normal variate transformation (SNV). For the ATR and DRIFT models, the determination coefficients of total carbon were improved to 0.826, 0.919 and 0.841, 0.928 of total nitrogen respectively. By combining with ATR and DRIFT spectra, the prediction accuracy became higher than that of using ATR or DRIFT spectra. The coefficient of determinations of the total carbon, total nitrogen and available nitrogen models were 0.942, 0.951 and 0.919 respectively. It provided a reasonable method to improve the analytical ability in mid infrared spectroscopy.