현장에서의 토양 측정을 위해서는 전처리 과정이 짧을수록 유리하므로 최대한 처리를 하지 않은 토양 시료에 대해 질산태 질소의 측정과 그에 맞는 정량화 방법을 제안하였다. 건조 토양을 분광분석에 그대로 사용하는 경우 산란, 분산되는 빛의 양이 많고 노이즈도 증가하므로 Diffuse reflectance 모드 (Diffuse reflectance infrared Fourier transform spectroscopy: DRIFTS)로 측정하였다. 토양 자체가 나타내는 분광 피크에 의해 질산염의 피크가 가려지는 간섭효과를 보완하기 위해 DRIFTS 스펙트럼에 1차 도함수를 적용하였으며, $1500-1200cm^{-1}$ 영역에서 질산염에 의한 신호의 향상이 확인되었고, 이를 이용해 다변량 회귀분석 모델 (PLSR)을 적용하여 정량화를 수행하였다. 1차 도함수를 이용한 분석모델에서도 각기 다른 종류의 토양을 적용하였을 때 결과치의 신뢰도가 감소하는 결과가 나타났다. 대표적인 토양으로 사질 (sand), 미사질 (sandy loam), 토탄질 (peat), 점토질 (clay) 토양에 대해 각각의 스펙트럼을 특성화하여 해당되는 정량모델을 적용하였다. 그 결과 다양한 종류의 토양에 대한 정량분석의 신뢰도가 향상되었다 ($R^2$>0.95, RPD>6.0). 스펙트럼의 신호처리와 토양 특성별 정량모델의 적용을 통해 현장 시료에 가까운 상태의 토양 질산염을 보다 빠르고 간단하게 평가할 수 있을 것으로 기대되며, 향후에 보다 다양한 조건의 토양에 대해 분광학적 분석을 수행하여 라이브러리가 구축된다면 이러한 기술의 확대 적용이 가능할 것으로 사료된다.
현장에서의 토양 측정을 위해서는 전처리 과정이 짧을수록 유리하므로 최대한 처리를 하지 않은 토양 시료에 대해 질산태 질소의 측정과 그에 맞는 정량화 방법을 제안하였다. 건조 토양을 분광분석에 그대로 사용하는 경우 산란, 분산되는 빛의 양이 많고 노이즈도 증가하므로 Diffuse reflectance 모드 (Diffuse reflectance infrared Fourier transform spectroscopy: DRIFTS)로 측정하였다. 토양 자체가 나타내는 분광 피크에 의해 질산염의 피크가 가려지는 간섭효과를 보완하기 위해 DRIFTS 스펙트럼에 1차 도함수를 적용하였으며, $1500-1200cm^{-1}$ 영역에서 질산염에 의한 신호의 향상이 확인되었고, 이를 이용해 다변량 회귀분석 모델 (PLSR)을 적용하여 정량화를 수행하였다. 1차 도함수를 이용한 분석모델에서도 각기 다른 종류의 토양을 적용하였을 때 결과치의 신뢰도가 감소하는 결과가 나타났다. 대표적인 토양으로 사질 (sand), 미사질 (sandy loam), 토탄질 (peat), 점토질 (clay) 토양에 대해 각각의 스펙트럼을 특성화하여 해당되는 정량모델을 적용하였다. 그 결과 다양한 종류의 토양에 대한 정량분석의 신뢰도가 향상되었다 ($R^2$>0.95, RPD>6.0). 스펙트럼의 신호처리와 토양 특성별 정량모델의 적용을 통해 현장 시료에 가까운 상태의 토양 질산염을 보다 빠르고 간단하게 평가할 수 있을 것으로 기대되며, 향후에 보다 다양한 조건의 토양에 대해 분광학적 분석을 수행하여 라이브러리가 구축된다면 이러한 기술의 확대 적용이 가능할 것으로 사료된다.
Mid-infrared (MIR) spectroscopy, particularly Fourier transform infrared spectroscopy (FTIR), has emerged as an important analytical tool in quantification as well as identification of multi-atomic inorganic ions such as nitrate. In the present study, the possibility of quantifying soil nitrate via ...
Mid-infrared (MIR) spectroscopy, particularly Fourier transform infrared spectroscopy (FTIR), has emerged as an important analytical tool in quantification as well as identification of multi-atomic inorganic ions such as nitrate. In the present study, the possibility of quantifying soil nitrate via diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) without change of a sample phase or with least treated samples was examined. Four types of soils were spectrally characterized in terms of unique bands of soil contents and interferences with nitrate bands in the range of $2000-1000cm^{-1}$. In order to reduce the effects of soil composition on calibration model for nitrate, spectra transformed to the 1st order derivatives were used in the partial least squared regression (PLSR) model and the classification procedure associated with input soil types was involved in calibration system. PLSR calibration models for each soil type provided better performance results ($R^2$>0.95, RPD>6.0) than the model considering just one type of soil as a standard.
Mid-infrared (MIR) spectroscopy, particularly Fourier transform infrared spectroscopy (FTIR), has emerged as an important analytical tool in quantification as well as identification of multi-atomic inorganic ions such as nitrate. In the present study, the possibility of quantifying soil nitrate via diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) without change of a sample phase or with least treated samples was examined. Four types of soils were spectrally characterized in terms of unique bands of soil contents and interferences with nitrate bands in the range of $2000-1000cm^{-1}$. In order to reduce the effects of soil composition on calibration model for nitrate, spectra transformed to the 1st order derivatives were used in the partial least squared regression (PLSR) model and the classification procedure associated with input soil types was involved in calibration system. PLSR calibration models for each soil type provided better performance results ($R^2$>0.95, RPD>6.0) than the model considering just one type of soil as a standard.
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제안 방법
For soil nitrate determination with the least sample treatment (drying and grinding), DRIFTS spectra was used to the prediction of soil nitrate in this study. This paper presents the characterization of spectral absorption peaks of nitrate in the MIR range and the feasibility of a calibration soil nitrate contents in solid samples using DRIFTS.
This paper presents the characterization of spectral absorption peaks of nitrate in the MIR range and the feasibility of a calibration soil nitrate contents in solid samples using DRIFTS. The calibration was based on a partial least square regression (PLSR) model and included an identification of soil types (thus differently applied depending on soil types) to reduce spectral interferences from soil CaCO3 and organic matter (OM).
The spectral characterization was carried out by means of peak searching with FTIR spectral library (IR Mentor Pro 6.5, Bio-Rad Laboratories Inc., USA).
Partial least square regression (PLSR) model was used in estimate of soil nitrate. In order to assess the performance of calibration models, the training set was cross-validated using some parameters such as determination coefficient (R2), root mean square errors (RMSE), and ratio of prediction to deviation (RPD) values.
This study focused on the feasibility to apply diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) to quantitative determination for nitrate in dry soils, less treated and almost raw samples which are available to real-time sensing. Four different types of soils were spectrally characterized in terms of unique bands of soil contents and interferences with nitrate bands in the range of 2000-1000 cm-1.
데이터처리
Based on soil types, different calibration equations were developed to minimize spectral interferences of nitrate caused by soil composition such as minerals and organic matter. The suitable range of wavenumber for calibration model was selected by means of the Pearson’s correlation coefficient (r) between absorbance spectrum and wavenumber. Partial least square regression (PLSR) model was used in estimate of soil nitrate.
이론/모형
The measured spectra were smoothed by means of a 21-points Savitzky-Golay (1964) smoothing filter and their first order derivatives were obtained by calculating polynom. The spectral characterization was carried out by means of peak searching with FTIR spectral library (IR Mentor Pro 6.
The suitable range of wavenumber for calibration model was selected by means of the Pearson’s correlation coefficient (r) between absorbance spectrum and wavenumber. Partial least square regression (PLSR) model was used in estimate of soil nitrate. In order to assess the performance of calibration models, the training set was cross-validated using some parameters such as determination coefficient (R2), root mean square errors (RMSE), and ratio of prediction to deviation (RPD) values.
성능/효과
The results of each calibration are described in Table 3. The validation parameters indicated a good performance of the calibration system including classification procedure in association with soil type (sandy loam, sand, peat, and clay). The prediction of FS, AH, WA, and ZE showed a reliable results with R > 0.
The validation parameters indicated a good performance of the calibration system including classification procedure in association with soil type (sandy loam, sand, peat, and clay). The prediction of FS, AH, WA, and ZE showed a reliable results with R > 0.94 and RPD > 6.5, indicating a strong statisticalcorrelation between measured and predicted values. However, the prediction results for ZE were relatively poorer than others, giving lower RPD (=6.
참고문헌 (11)
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