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Soil identification and chemometrics for direct determination of nitrate in soils using FTIR-ATR mid-infrared spectroscopy

机译:FTIR-ATR中红外光谱法直接鉴定土壤中硝酸盐的土壤鉴定和化学计量学

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The use of mid-infrared attenuated total reflectance (ATR) spectroscopy enables direct measurement of nitrate concentration in soil pastes, but strong interfering absorbance bands due to water and soil constituents limit the accuracy of straightforward determination. Accurate subtraction of the water spectrum improves the correlation between nitrate concentration and its v_3 vibration band around 1350 cm~(-1). However, this correlation is soil-dependent, due mostly to varying contents of carbonate, whose absorbance band overlaps the nitrate band. In the present work, a two-stage method is developed: First, the soil type is identified by comparing the "fingerprint" region of the spectrum (800-1200 cm~(-1)) to a reference spectral library. In the second stage, nitrate concentration is estimated using the spectrum interval that includes the nitrate band, together with the soil type previously identified. Three methods are compared for estimating nitrate concentration: integration of the nitrate absorbance band, cross-correlation with a reference spectrum, and principal component analysis (PCA) followed by a neural network. When using simple band integration, the use of soil specific calibration curves leads to determination errors ranging from 5.5 to 24 mg[N]/kg[dry soil] for the mineral soils tested. The cross-correlation technique leads to similar results. The combination of soil identification with PCA and neural network modeling improves the predictions, especially for soils containing calcium carbonate. Typical prediction errors for light non-calcareous soils are about 4 mg[N]/kg[dry soil], whereas for soils containing calcium carbonate they range from 6 to 20 mg[N]/kg[dry soil], which is less than four percent of the concentration range investigated.
机译:使用中红外衰减全反射(ATR)光谱技术可以直接测量泥浆中硝酸盐的浓度,但是由于水和土壤成分而产生的强干扰吸收带限制了直接测定的准确性。准确地减去水谱可改善硝酸盐浓度与其在1350 cm〜(-1)附近的v_3振动带之间的相关性。但是,这种相关性是取决于土壤的,主要是由于碳酸盐含量的变化,其吸收带与硝酸盐带重叠。在当前工作中,开发了一种两阶段方法:首先,通过将光谱的“指纹”区域(800-1200 cm〜(-1))与参考光谱库进行比较来识别土壤类型。在第二阶段,使用包括硝酸盐谱带的光谱间隔以及先前确定的土壤类型来估算硝酸盐浓度。比较了三种估算硝酸盐浓度的方法:硝酸盐吸收带的积分,与参考光谱的互相关以及主成分分析(PCA)和神经网络的方法。当使用简单谱带积分时,使用土壤特定的校准曲线会导致测定的矿物土壤的测定误差范围为5.5至24 mg [N] / kg [干土]。互相关技术得出相似的结果。将土壤识别与PCA和神经网络建模相结合可改善预测,尤其是对于含碳酸钙的土壤。轻型非钙质土壤的典型预测误差约为4 mg [N] / kg [干土],而对于含碳酸钙的土壤,其预测误差为6至20 mg [N] / kg [干土],小于调查浓度范围的百分之四。

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