超低温冻融对近红外光谱法测定土壤磷、钾含量的影响
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摘要
针对近红外光谱分析技术对土壤全磷、全钾、速效磷、速效钾和有效钾含量的测定分析精度不高、预测偏差较大的研究现状，本研究为提高近红外光谱分析技术对土壤中全磷、速效磷、全钾、速效钾、有效钾含量的分析精度，用液氮对土壤样品进行了超低温冻融处理，分析超低温冻融处理对土壤养分组成及近红外和红外光谱的影响，初步确定了与有关磷、钾养分含量相关的波长或谱带，并运用化学计量学方法分析超低温冻融处理对土壤磷、钾养分的近红外光谱定量模型及预测精度的影响，研究结果如下：
用液氮对235个土壤样品进行冻融处理，测定了处理前后土壤的养分含量及近红外光谱，土壤经冻融处理后，全磷和全钾含量未发生变化，速效磷和速效钾含量显著增加，有效钾含量变化不显著，近红外光谱的吸光度也发生了改变。对长期定位试验站的棕壤和温室土壤的光谱进行比较，初步确定了土壤磷素和钾素较敏感的近红外光谱谱带为1000nm～1890nm、1980nm～2200nm、2200nm～2380nm。经过对土壤养分与近红外光谱相关系数的研究，确定了不同磷、钾养分对近红外光谱吸收的影响谱带，并通过建立近红外定量模型进一步确认谱带的归属，使用这些谱带范围的波长建立的近红外模型明显优于使用全波长范围建立模型的效果。
通过对土壤的近红外光谱定量模型的比较分析，在诸多光谱预处理方法中选择均值中心化和一阶导数对光谱预处理后建模的效果较好。用偏最小二乘回归、人工神经网络和主成分回归三种回归方法建立土壤全磷含量的近红外定量模型，采用偏最小二乘回归法建立的定量模型，土壤进行冻融处理与未处理相比，近红外定量模型的决定系数和预测精度提高了很多（未冻融处理：RC~2=0.949，SEC=0.224，Rp~2=0.942，SEP=0.299，RPD=3.064；冻融处理：RC~2=0.953，SEC=0.214，Rp~2=0.957，SEP=0.273，RPD=3.355）。采用人工神经网络法建立的全磷含量的近红外定量模型，土壤未进行冻融处理的模型预测效果较差，但样品经过冻融处理后，模型预测精度有了很大提高，预测效果也很好（未冻融处理：RC~2=0.967，SEC=0.341， Rp~2=0.952，SEP=0.470，RPD=1.949；冻融处理：RC~2=0.979，SEC=0.153，Rp~2=0.985，SEP=0.168，RPD=5.452）。采用主成分回归建立的土壤全磷含量的近红外定量模型，样品冻融处理后模型效果虽变好，但预测精度较低，没有达到无损定量的要求。
未经过超低温冻融处理的土壤样品，采用偏最小二乘回归、人工神经网络和主成分回归三种回归方法建立的土壤速效磷含量的近红外定量模型的效果均不理想，没有达到对土壤速效磷含量无损定量的要求。土壤样品经冻融处理后，采用三种回归方法建立的近红外定量模型的效果都变好（偏最小二乘回归：RC~2=0.893，SEC=34.007，RP~2=0.918，SEP=30.845， RPD=3.167；人工神经网络： RC~2=0.967， SEC=30.970， RP~2=0.952，SEP=31.325，RPD=3.539；主成分回归：RC~2=0.862，SEC=36.588，RP~2=0.892，SEP=43.276，RPD=2.562），偏最小二乘回归和人工神经网络法建立的近红外模型可以对土壤速效磷含量进行快速无损测定。
使用偏最小二乘回归、人工神经网络和主成分回归三种方法建立的土壤样品冻融前后全钾含量的近红外定量模型的效果都不好，预测偏差较大，都没达到对土壤全钾含量进无损定量的要求。
使用偏最小二乘回归、人工神经网络和主成分回归三种回归方法建立土壤速效钾含量的近红外定量模型，其中采用偏最小二乘回归法建立的近红外模型，土壤进行冻融处理与未处理相比，近红外模型的决定系数和预测精度提高了很多（未冻融处理：RC~2=0.928，SEC=52.038，Rp~2=0.922，SEP=58.701，RPD=2.911；冻融处理：RC~2=0.942，SEC=47.422，Rp~2=0.938，SEP=42.340，RPD=4.035）。采用人工神经网络法建立的近红外定量模型对土壤未进行冻融处理的预测效果较差，但土壤经过冻融处理后建立的模型的预测精度有了很大提高，预测效果也变好（未冻融处理：RC~2=0.878，SEC=71.239，Rp~2=0.867，SEP=93.338，RPD=1.831；冻融处理：RC~2=0.896，SEC=30.556，Rp~2=0.893，SEP=53.814，RPD=3.175），说明土壤冻融处理后使用偏最小二乘回归和人工神经网络方法建立的近红外定量模型可以对速效钾含量进行无损定量检测。采用主成分回归建立的土壤速效钾含量的近红外定量模型，土壤样品经冻融处理后模型效果虽变好，但预测精度较低，没有达到无损定量的要求。
使用偏最小二乘回归、人工神经网络和主成分回归三种方法建立的土壤有效钾含量的近红外定量模型，未经过冻融处理的土壤样品的近红外模型效果不好，预测精度不理想，但对土壤样品进行冻融处理后，使用人工神经网络法建立的土壤有效钾含量的近红外模型较好，RC~2=0.912，SEC=62.047，RP~2=0.917，SEP=42.810，RPD=5.192，说明该模型可以应用于对土壤有效钾含量进行快速测定。
Because of the low accuracy and large prediction deviation using NIRS to measure soilP and K content in present study, the soil samples were carried on ultra-lowtemperature freeze thawing treatment using liquid nitrogen in this study. The effect ofultra-low temperature freeze thawing treatment on soil nutrients, near-infrared and infraredspectroscopy were analyzed. The wavelength and spectroscopy related to soil nutrients wereestablished and the effect of NIR quantitative calibration model and predication accuracywere analyzed using the soil which carried on ultra-low temperature freeze thawingtreatment, the conclusions are as follows:
The soil nutrient and NIR of235soil samples treated ultra-low temperature freezethawing treatment using liquid nitrogen were measured in this study, the result indicated thattotal P and total K contents of treated soil did not change, while available P and K contents oftreated soil increased significantly, effective P content did not change significantly and theNIR absorbance also changed too. The long-term positioning brown soil and greenhouse soilspectrum were also measured, it can be identified the sensitive bands affected by P and Kwere1000nm～1890nm,1980nm～2200nm, and2200nm～2380nm. The effected spectrumof P, K contents to NIR absorption bands were determined through studying the correlationcoefficient between soil contents with NIR and quantitative calibration model were alsoestablished to identify the spectrum band. The result showed that the model established usingthe range of these bands was much better than that using full wavelength range.
Compared to the calibration model, the mean centralization and first derivative were thebest pretreatment methods. The calibration models were established by PLSR, ANN and PCRtechniques to relate NIR spectral data to the total P content. In PLSR model, the coefficient ofdetermination and prediction accuracy are improved after treatment,(before freeze thawingtreatment, Rc~2=0.949, SEC=0.224, Rp~2=0.942, SEP=0.299, RPD=3.064; after freeze thawingtreatment, Rc~2=0.953, SEC=0.214, Rp~2=0.957, SEP=0.273, RPD=3.355). The calibrationmodel established by ANN had low prediction accuracy for unhanded soil, but had higherprediction accuracy for treated soil (before treatment, Rc~2=0.967, SEC=0.341, Rp~2=0.952,SEP=0.470, RPD=1.949; after treatment: Rc~2=0.979, SEC=0.153, Rp~2=0.985, SEP=0.168, RPD=5.452). The calibration model established by PCR had low prediction accuracy, so itcan not get the non-destructive quantitative requirements.
The calibration models were established by PLSR, ANN and PCR techniques to relateNIR spectral data to content of unhandled soil available P, the result showed that all themodels can not reach non-destructive quantitative requirements. However, after treatment, themodels were better.(PLSR: RC~2=0.893, SEC=34.007, RP~2=0.918, SEP=30.845, RPD=3.167;ANN: RC~2=0.967, SEC=30.970, RP~2=0.952, SEP=31.325, RPD=3.539; PCR: RC~2=0.862,SEC=36.588, RP~2=0.892, SEP=43.276, RPD=2.562). The model established by PLSR andANN can non-destructively detect available P content rapidly.
The effect of calibration models established using PLSR, ANN, and PCR for unhandledand treated soils were not very good and had great prediction deviation. These calibrationmodels can not reach our requirements that detect soil available K content non-destructively.
The calibration models were established by PLS, ANN and PCR techniques to relateNIR spectral data to content of unhandled soil available K. In the three calibration models,PLSR was the best to detect available K content. In PLSR model, the coefficient ofdetermination and prediction accuracy were improved after treatment,(before freeze thawingtreatment, Rc~2=0.928, SEC=52.038, Rp~2=0.922, SEP=58.701, RPD=2.911; after freezethawing treatment, Rc~2=0.942, SEC=47.422, Rp~2=0.938, SEP=42.340, RPD=4.035). Theprediction accuracy was low for unhandled soil using ANN calibration model, but it washigher for treated soil.(before freeze thawing treatment, Rc~2=0.878, SEC=71.239, Rp~2=0.867,SEP=93.338, RPD=1.831; after freeze thawing treatment, Rc~2=0.896, SEC=30.556,Rp~2=0.893, SEP=53.814, RPD=3.175). It can be concluded that models using ANN andPLSR can detect available K content non-destructively. Model established using PCR hadlow prediction accuracy and could not reach non-destructive quantitative requirements.
In PLSR, ANN, and PCR models for measuring effective K content of unhandled soil,The results were not very good, but the model established by ANN for treated soil was better,RC~2=0.912, SEC=62.047, RP~2=0.917, SEP=42.810, RPD=5.192, so it can be used to detecteffective K content non-destructively.
用液氮对235个土壤样品进行冻融处理，测定了处理前后土壤的养分含量及近红外光谱，土壤经冻融处理后，全磷和全钾含量未发生变化，速效磷和速效钾含量显著增加，有效钾含量变化不显著，近红外光谱的吸光度也发生了改变。对长期定位试验站的棕壤和温室土壤的光谱进行比较，初步确定了土壤磷素和钾素较敏感的近红外光谱谱带为1000nm～1890nm、1980nm～2200nm、2200nm～2380nm。经过对土壤养分与近红外光谱相关系数的研究，确定了不同磷、钾养分对近红外光谱吸收的影响谱带，并通过建立近红外定量模型进一步确认谱带的归属，使用这些谱带范围的波长建立的近红外模型明显优于使用全波长范围建立模型的效果。
通过对土壤的近红外光谱定量模型的比较分析，在诸多光谱预处理方法中选择均值中心化和一阶导数对光谱预处理后建模的效果较好。用偏最小二乘回归、人工神经网络和主成分回归三种回归方法建立土壤全磷含量的近红外定量模型，采用偏最小二乘回归法建立的定量模型，土壤进行冻融处理与未处理相比，近红外定量模型的决定系数和预测精度提高了很多（未冻融处理：RC~2=0.949，SEC=0.224，Rp~2=0.942，SEP=0.299，RPD=3.064；冻融处理：RC~2=0.953，SEC=0.214，Rp~2=0.957，SEP=0.273，RPD=3.355）。采用人工神经网络法建立的全磷含量的近红外定量模型，土壤未进行冻融处理的模型预测效果较差，但样品经过冻融处理后，模型预测精度有了很大提高，预测效果也很好（未冻融处理：RC~2=0.967，SEC=0.341， Rp~2=0.952，SEP=0.470，RPD=1.949；冻融处理：RC~2=0.979，SEC=0.153，Rp~2=0.985，SEP=0.168，RPD=5.452）。采用主成分回归建立的土壤全磷含量的近红外定量模型，样品冻融处理后模型效果虽变好，但预测精度较低，没有达到无损定量的要求。
未经过超低温冻融处理的土壤样品，采用偏最小二乘回归、人工神经网络和主成分回归三种回归方法建立的土壤速效磷含量的近红外定量模型的效果均不理想，没有达到对土壤速效磷含量无损定量的要求。土壤样品经冻融处理后，采用三种回归方法建立的近红外定量模型的效果都变好（偏最小二乘回归：RC~2=0.893，SEC=34.007，RP~2=0.918，SEP=30.845， RPD=3.167；人工神经网络： RC~2=0.967， SEC=30.970， RP~2=0.952，SEP=31.325，RPD=3.539；主成分回归：RC~2=0.862，SEC=36.588，RP~2=0.892，SEP=43.276，RPD=2.562），偏最小二乘回归和人工神经网络法建立的近红外模型可以对土壤速效磷含量进行快速无损测定。
使用偏最小二乘回归、人工神经网络和主成分回归三种方法建立的土壤样品冻融前后全钾含量的近红外定量模型的效果都不好，预测偏差较大，都没达到对土壤全钾含量进无损定量的要求。
使用偏最小二乘回归、人工神经网络和主成分回归三种回归方法建立土壤速效钾含量的近红外定量模型，其中采用偏最小二乘回归法建立的近红外模型，土壤进行冻融处理与未处理相比，近红外模型的决定系数和预测精度提高了很多（未冻融处理：RC~2=0.928，SEC=52.038，Rp~2=0.922，SEP=58.701，RPD=2.911；冻融处理：RC~2=0.942，SEC=47.422，Rp~2=0.938，SEP=42.340，RPD=4.035）。采用人工神经网络法建立的近红外定量模型对土壤未进行冻融处理的预测效果较差，但土壤经过冻融处理后建立的模型的预测精度有了很大提高，预测效果也变好（未冻融处理：RC~2=0.878，SEC=71.239，Rp~2=0.867，SEP=93.338，RPD=1.831；冻融处理：RC~2=0.896，SEC=30.556，Rp~2=0.893，SEP=53.814，RPD=3.175），说明土壤冻融处理后使用偏最小二乘回归和人工神经网络方法建立的近红外定量模型可以对速效钾含量进行无损定量检测。采用主成分回归建立的土壤速效钾含量的近红外定量模型，土壤样品经冻融处理后模型效果虽变好，但预测精度较低，没有达到无损定量的要求。
使用偏最小二乘回归、人工神经网络和主成分回归三种方法建立的土壤有效钾含量的近红外定量模型，未经过冻融处理的土壤样品的近红外模型效果不好，预测精度不理想，但对土壤样品进行冻融处理后，使用人工神经网络法建立的土壤有效钾含量的近红外模型较好，RC~2=0.912，SEC=62.047，RP~2=0.917，SEP=42.810，RPD=5.192，说明该模型可以应用于对土壤有效钾含量进行快速测定。
Because of the low accuracy and large prediction deviation using NIRS to measure soilP and K content in present study, the soil samples were carried on ultra-lowtemperature freeze thawing treatment using liquid nitrogen in this study. The effect ofultra-low temperature freeze thawing treatment on soil nutrients, near-infrared and infraredspectroscopy were analyzed. The wavelength and spectroscopy related to soil nutrients wereestablished and the effect of NIR quantitative calibration model and predication accuracywere analyzed using the soil which carried on ultra-low temperature freeze thawingtreatment, the conclusions are as follows:
The soil nutrient and NIR of235soil samples treated ultra-low temperature freezethawing treatment using liquid nitrogen were measured in this study, the result indicated thattotal P and total K contents of treated soil did not change, while available P and K contents oftreated soil increased significantly, effective P content did not change significantly and theNIR absorbance also changed too. The long-term positioning brown soil and greenhouse soilspectrum were also measured, it can be identified the sensitive bands affected by P and Kwere1000nm～1890nm,1980nm～2200nm, and2200nm～2380nm. The effected spectrumof P, K contents to NIR absorption bands were determined through studying the correlationcoefficient between soil contents with NIR and quantitative calibration model were alsoestablished to identify the spectrum band. The result showed that the model established usingthe range of these bands was much better than that using full wavelength range.
Compared to the calibration model, the mean centralization and first derivative were thebest pretreatment methods. The calibration models were established by PLSR, ANN and PCRtechniques to relate NIR spectral data to the total P content. In PLSR model, the coefficient ofdetermination and prediction accuracy are improved after treatment,(before freeze thawingtreatment, Rc~2=0.949, SEC=0.224, Rp~2=0.942, SEP=0.299, RPD=3.064; after freeze thawingtreatment, Rc~2=0.953, SEC=0.214, Rp~2=0.957, SEP=0.273, RPD=3.355). The calibrationmodel established by ANN had low prediction accuracy for unhanded soil, but had higherprediction accuracy for treated soil (before treatment, Rc~2=0.967, SEC=0.341, Rp~2=0.952,SEP=0.470, RPD=1.949; after treatment: Rc~2=0.979, SEC=0.153, Rp~2=0.985, SEP=0.168, RPD=5.452). The calibration model established by PCR had low prediction accuracy, so itcan not get the non-destructive quantitative requirements.
The calibration models were established by PLSR, ANN and PCR techniques to relateNIR spectral data to content of unhandled soil available P, the result showed that all themodels can not reach non-destructive quantitative requirements. However, after treatment, themodels were better.(PLSR: RC~2=0.893, SEC=34.007, RP~2=0.918, SEP=30.845, RPD=3.167;ANN: RC~2=0.967, SEC=30.970, RP~2=0.952, SEP=31.325, RPD=3.539; PCR: RC~2=0.862,SEC=36.588, RP~2=0.892, SEP=43.276, RPD=2.562). The model established by PLSR andANN can non-destructively detect available P content rapidly.
The effect of calibration models established using PLSR, ANN, and PCR for unhandledand treated soils were not very good and had great prediction deviation. These calibrationmodels can not reach our requirements that detect soil available K content non-destructively.
The calibration models were established by PLS, ANN and PCR techniques to relateNIR spectral data to content of unhandled soil available K. In the three calibration models,PLSR was the best to detect available K content. In PLSR model, the coefficient ofdetermination and prediction accuracy were improved after treatment,(before freeze thawingtreatment, Rc~2=0.928, SEC=52.038, Rp~2=0.922, SEP=58.701, RPD=2.911; after freezethawing treatment, Rc~2=0.942, SEC=47.422, Rp~2=0.938, SEP=42.340, RPD=4.035). Theprediction accuracy was low for unhandled soil using ANN calibration model, but it washigher for treated soil.(before freeze thawing treatment, Rc~2=0.878, SEC=71.239, Rp~2=0.867,SEP=93.338, RPD=1.831; after freeze thawing treatment, Rc~2=0.896, SEC=30.556,Rp~2=0.893, SEP=53.814, RPD=3.175). It can be concluded that models using ANN andPLSR can detect available K content non-destructively. Model established using PCR hadlow prediction accuracy and could not reach non-destructive quantitative requirements.
In PLSR, ANN, and PCR models for measuring effective K content of unhandled soil,The results were not very good, but the model established by ANN for treated soil was better,RC~2=0.912, SEC=62.047, RP~2=0.917, SEP=42.810, RPD=5.192, so it can be used to detecteffective K content non-destructively.
引文
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