小白菜的中红外光谱特征及其在氮营养诊断中的应用

采用不同中红外光谱（漫反射光谱、 衰减全反射光谱和光声光谱）对不同氮处理的小白菜进行表征。结果表明,小白菜不同的红外光谱具有明显不同的光谱特征,主要表现于峰形、 峰位以及相对峰强度;中红外漫反射光谱的主要吸收峰分别为2800~3800 cm-1、 2200~2500 cm-1、 1500~1700 cm-1和1000~1500 cm-1,衰减全反射光谱由于水的强烈干扰可用的吸收峰主要在1200~1500 cm-1,而中红外光声光谱的主要吸收峰分别为2800~3800 cm-1、 1500~1700 cm-1、 1200~1500 cm-1和1000~1200 cm-1。漫反射光谱、 衰减全反射光谱和光声光谱的主成分分析表明,不同氮处理主成分分布特征明显不同,且第一主成分（PCA1）均与施氮量显著相关,相关系数（r）分别为0.9103、 0.8527和0.9366。这三种光谱利用主成分分布均可实现小白菜氮营养快速诊断,且光声光谱由于其独特的原位逐层扫描功能而表现出更好的诊断效果,在植物营养诊断中表现出较强的应用潜力。     

红外光谱在土壤学中的应用

红外光谱技术在土壤学中已得到较广泛的应用,它能够综合地反映土壤体系的物质组成及其相互作用,为研究土壤中物质循环及其作用过程提供了新的手段。本文回顾了近年来红外光谱技术在土壤学中的应用,包括透射光谱在土壤定性分析中的应用,并重点介绍红外反射光谱与化学计量学相结合的光谱建模技术发展情况及其在土壤定量分析中的应用。同时本文探讨了基于光声效应的红外光声光谱技术,红外光声光谱非常适合用于土壤这种复杂、非透明体系的研究,能够克服传统透射和反射光谱中存在的缺陷,测定快速方便,并具有较高的灵敏度和测量精确度,具有很大的应用潜力。     

苏北滨海土壤碳酸钙含量反射光谱估算研究

土壤属性的快速、精确测定是实现现代精细农业的基础。本研究分析了江苏省北部滨海土壤的属性特征以及碳酸钙的可见-近红外反射光谱特征,探讨利用可见-近红外光谱估算滨海土壤碳酸钙含量的可行性,比较不同光谱反射率数据集、不同预处理方法以及不同建模方法定量反演的优劣。结果表明:(1)苏北滨海土壤有机质含量较低、碳酸钙含量较高,其光谱曲线在2 340 nm处有较明显的碳酸钙吸收特征;(2)滨海土壤碳酸钙含量与土壤的可见-近红外波段反射率呈正相关,且碳酸钙含量高低对于土壤的近红外波段反射率的影响高于可见光波段;(3)可见-近红外反射光谱可用于估算滨海土壤碳酸钙含量。就建模结果而言,381~2 459 nm波段反射光谱数据集、log(1/R)预处理、偏最小二乘回归三者结合的效果比较理想。     

应用拉曼和中红外衰减全反射光谱测定溶液和土壤中的硝酸盐

为比较拉曼光谱和红外光谱在溶液和土壤中硝酸盐含量定量分析的适用性,采用两种光谱对溶液和土壤中的NO3–-N含量(0~200 mg/L)进行快速测定。结果表明,溶液中硝酸盐的拉曼特征峰在1 047 cm–1处,该特征峰强度与NO3–-N浓度成正比,对1 035~1 060 cm-1波段拉曼光谱峰面积和NO3–-N含量进行线性回归,决定系数R2为0.995 4;溶液中硝酸盐的中红外衰减全反射光谱特征吸收峰在1 350 cm–1,吸收峰与NO3–-N含量成正比,特征吸收区1 200~1 500 cm–1峰面积与NO3–-N含量的决定系数R2为0.991 1,表明两种光谱都可用于溶液中硝酸盐的测定。对于土壤样品,红外光谱在1 250~1 500 cm–1处有硝酸盐吸收峰,且吸收峰与NO3–-N含量成正比,峰面积与NO3–-N含量之间的决定系数R2为0.968 4;而对于拉曼光谱,硝酸盐的拉曼峰因受较强干扰导致吸收峰不明显,峰面积与NO3–-N含量之间的决定系数R2仅为0.000 9,表明中红外衰减全反射光谱可用于土壤中硝酸盐的测定,而拉曼光谱则很困难。因此,拉曼光谱和中红外衰减全反射光谱都可用于溶液中硝酸盐的测定,且前者灵敏度要高于后者;中红外衰减全反射光谱可用于土壤中硝酸盐的测定,而拉曼光谱难以用于土壤中硝酸盐定量分析,这为硝酸盐的快速测定提供理论依据和技术支持。     

不同傅里叶变换红外光谱法研究土壤光谱特征的比较与应用

为了将傅里叶变换红外光谱技术更好地应用于土壤中官能团的定量分析,通过比较3种不同傅里叶变换技术下土壤特征吸收峰的差异,来选取最佳的光谱技术应用于土壤的相关研究。采用透射(T-FTIR)、衰减全反射(ATR-FTIR)和漫反射(DR-FTIR)3种光谱技术分别对有机物(苯甲酸、硬脂酸)和土壤(辽东栎)进行了分析。并将辽东栎和草地土壤样品按不同比例混合,使用T-FTIR和DR-FTIR对混合土样进行光谱测定,用于定量分析研究。结果表明:(1)有机物样品在3种光谱技术中均出现特征吸收峰,苯甲酸在1 600～1 400 cm^-1出现苯环C=C骨架特征峰,硬脂酸在2 900～2 800 cm^-1显现甲基的特征峰。有机物中羧酸-COOH内羰基-C=O在1 720～1 680 cm^-1出现伸缩振动吸收峰,羟基-OH分别在1 430～1 410 cm^-1,940～930 cm^-1附近出现面内和面外弯曲振动吸收峰。有机物在T-FTIR技术中需要用溴化钾(KBr)对其稀释。(2)辽东栎土壤样品在T-FTIR和DR-FTIR技术测试中发现,其土壤谱图中有较多有效特征吸收峰,土壤样品在T-FTIR技术中也需要用KBr进行稀释并使样品均匀的分布在锭片中; 而ATR-FTIR技术测试中仅出现个别有效特征吸收峰,不利于对土壤谱图鉴别与进一步分析。(3)辽东栎和草地土壤样品按不同比例混合的测试结果表明:T-FTIR和DR-FTIR技术测试中质量分数与峰面积比呈正相关,线性拟合分别为R²=0.70和R²=0.88。土壤在3种不同红外技术中,DR-FTIR光谱具有较好的土壤特征吸收峰,对土壤样品可以不用KBr稀释。测试步骤简单易操作,可用于土壤样品定量分析研究。     

基于红外衰减全反射光谱的温室土壤盐分特征研究

与传统的露地耕作土壤不同,温室土壤在耕作过程中受较多的人为调控,但在调控的过程中产生了多种问题,如大量施肥导致的土壤盐渍化[1]。因此,研究如何表征温室土壤的离子特征并预测温室土壤的发展是当前设施农业发展所面临的重要问题。常规农化分析只从不同的角度分析温室土壤的特征,很难实现整体上的综合表征。红外光谱能够综合反应土壤的理化性质,在温室土壤研究中有具有明显的特点。常规的透射光谱在研究土壤时存在制样时间长和难以定量的缺点,而衰减全反射红外光谱(ATR-FTIR)则可克服这一缺点,结合化学计量学的方法,能够实现土壤的定性与定量分析[2,3]。红外光谱包含大量化学键信息,在分析时需进行合理的降维,抽取     

中红外光声光谱法测定土壤顶空氨气浓度

利用中红外光声光谱对土壤顶空氨气(NH3)浓度进行测定。结果表明,在动镜速率为0.16 cm/s,分辨率为4 cm–1时获得的NH3光谱噪音较少且信号强度高。利用选定的参数采集不同浓度NH3光谱,其中位于900~1 000 cm–1的特征吸收峰适于定量分析。分别选用该特征吸收的峰高、峰面积和第一主成分与NH3浓度进行回归建模,线性决定系数(R2)均达到0.9以上,表明三者都呈显著线性相关,中红外光声光谱可以用于快速测定土壤顶空中NH3浓度,这为土壤氮素循环研究提供了新手段。     

干旱区典型盐碱土壤含盐量估算的最佳高光谱指数研究

盐碱土的表层土壤含盐量是土壤盐碱化程度分级的主要因素之一。对西北干旱区不同含盐量和含水率的典型盐碱土进行了分析,运用统计方法分析了5种高光谱指数与土壤含盐量的定量关系,并通过对比研究,确定了能提高盐碱土壤含盐量估算精度的最佳光谱指数及定量预测模型。结果表明,不同的指数所选择出的对土壤含盐量最敏感的波段基本都位于近红外(NIR)和短波红外波段(SWIR),说明盐类矿物在近红外和短波红外波段(VNIR-SWIR)区域会产生特有的光谱特征。所选定的最佳光谱ND指数结构简单,易于计算,且对土壤中的水分不敏感,该技术对于大面积土壤含盐量制图具有较广的应用前景。     

不同粒径土壤的光谱特征差异分析

通过野外采集土样,研磨后获取不同粒径(2、1、0.5、0.25、0.15和0.05 mm)土壤,在暗室内进行光谱测定。分别采用光谱数据微分变换和连续统去除的方法,研究不同粒径土壤的光谱特征差异,探索不同粒径处理对土壤光谱特征的影响。结果表明:不同粒径土壤的平均光谱反射率在全波段、可见光、红外波段都随土壤粒径的减小而增加;土壤光谱反射率与土壤粒径呈负相关关系。微分变换放大了某些在原始光谱数据中比较隐晦的信息。可见光、全波段平均反射率均在土壤粒径0.25 nm时,呈现出更明显的增长趋势,而红外波段则在土壤粒径0.15 mm时,增长趋势更为明显;土壤光谱在450和490 nm处的吸收峰值随土壤粒径的减小呈多项式分布;土壤全波段光谱反射率随着粒径的减小呈幂函数增长。     

基于高光谱的中国干旱区棉花遥感估产（英）

该文测定了棉花盛蕾期至吐絮后期各时期冠层的高光谱反射率以及产量,并对棉花产量与冠层光谱植被指数进行相关分析。结果表明：棉花各生育期可见光波段、近红外波段及短波红外波段光谱反射率与产量间分别呈显著负相关、显著正相关与显著负相关。根据棉花冠层光谱波形特征,利用植被红边波段560 nm反射峰、670 nm吸收谷、近红外波段890 nm反射峰、980和1?210 nm两个弱水汽吸收谷、短波红外1?650和2 200 nm反射峰,设计归一化差值光谱指数,并与棉花产量进行相关分析,上述波段组合定义的归一化差值光谱指数与产量在各生育期均达显著或极显著相关。VARI_700抗大气植被指数在各生育期均达极显著相关。     

可见-近红外与中红外光谱预测土壤养分的比较研究

对土壤养分的快速和准确测定有助于适时指导施肥。为进一步研究可见-近红外（350～2500 nm）与中红外光谱（4000～650 cm-1）对土壤养分的预测能力,以贵州省500个土样为例,对光谱进行Savitzky-Golay（SG）平滑去噪处理,再用标准正态化（SNV）方法进行基线校正,然后分别应用偏最小二乘回归（PLSR）和支持向量机（SVM）两种方法进行建模,探讨了可见-近红外和中红外光谱对土壤全氮（TN）、全磷（TP）、全钾（TK）和碱解氮（AN）、有效磷（AP）、速效钾（AK）共六种土壤养分的预测效果。结果表明：（1）无论基于可见-近红外光谱还是中红外光谱,PLSR模型的预测精度整体均优于SVM模型。（2）中红外光谱对TN、TK和AN的预测精度均显著高于可见-近红外光谱,可见-近红外和中红外光谱均可以可靠地预测TN和TK（性能与四分位间隔距离的比率（RPIQ）大于2.10）,中红外光谱可相对较可靠地预测AN（RPIQ=1.87）;但两类光谱对TP、AP和AK的预测效果均较差（RPIQ<1.34）。（3）当变量投影重要性得分（VIP）大于1.5时,PLSR模型在中红外光谱区域预测TN和TK的重要波段多于可见-近红外光谱区域,TN的重要波段主要集中于可见-近红外光谱区域的1910和2207 nm附近,中红外光谱区域的1 120、1 000、960、910、770和668 cm-1附近;TK的重要波段主要集中于可见-近红外光谱区域的540、2176、2225和2268 nm附近,中红外光谱区域的1 040、960、910、776、720和668 cm-1附近。因此,中红外光谱技术结合PLSR模型对土壤养分预测效果较好,可快速准确预测土壤TN和TK,可为指导适时施肥提供技术支撑。     

基于近红外光谱的土壤全氮含量估算模型

土壤全氮是诊断土壤肥力水平和指导作物精确施肥所需的重要信息,建立土壤全氮的近红外光谱估测模型并对建模波段进行优化选择对于土壤养分信息快速获取和精确农业发展具有重要意义。该研究以中国中、东部地区5种主要类型土壤为研究对象,利用近红外光谱仪采集土壤样品的光谱信息,结合近红外区域分子振动特点选取全谱、合频、一倍频、二倍频和N-H基团及其组合的8个波段,采用多元散射校正等多种预处理方法组合进行处理,结合偏最小二乘法(PLS)对每个波谱区域进行定标建模。结果表明,利用4000～5500cm-1波谱区域结合附加散射校正处理过的原始光谱建立的模型精度表现最好,其内部互验证决定系数达到0.90,均方根误差为0.16。经不同类型土壤的观测资料检验,模型验证决定系数为0.91,均方根误差为0.15,相对分析误差RPD为3.40,表明模型具有极好的预测能力。因此,利用近红外光谱可以实现土壤全氮的快速估测,且以合频波段(4000～5500cm-1)为建模区域可以得到更好的预测效果。     

Mid-infrared spectroscopy for topsoil layer identification according to litter type and decompositional stage demonstrated on a large sample set of Austrian forest soils

When forest soils are investigated, the identification of litter and soil layers is a key step. Mid-infrared spectroscopy seems to be promising for this due to its capability to provide fingerprint information. In this study, a large data set of mid-infrared spectra of different forest soils (L, FH and Ah horizons of coniferous, mixed and deciduous forests) was available together with general analyses data (among others organic carbon, total nitrogen and carbonate contents). This set was used for devising models for the identification of different soil layers and to follow the characteristics of different forest types in the different layers. Canonical discriminant functions were calculated using nine mid-infrared bands occurring in both organic layers and mineral soil layers. The discriminant analysis of the topsoil layers showed that aliphatic mid-infrared bands dominated the first function and yielded a clear separation of mineral and organic layers. Further, this analysis reflected the higher chemical diversity in the organic layers visible by a high scattering of the calculated data points. Two tests with two separate data sets showed a reliable and reproducible performance of the model. Separations of forest types were best possible in the organic layers, in which the deciduous forests were separated best from mixed and coniferous forests. Also for these discriminations, aliphatic bands were dominating. Advanced degradation of soil organic matter resulted in small distances of equidistant forest-type group-centers in the 5-10 cm layers of the mineral soils. Dominating molecular moieties in the discriminant functions in these layers were carbonates and aliphatic bands. The applied methodology suggests mid-infrared spectroscopy as an appropriate tool for soil layer identification, allowing for unequivocal discrimination between organic and mineral soil layers. Discriminations of forest types delivered information about dominating mid-infrared bands in the single layers and allowed conclusions about functional groups dominating these differences.     

Quantifying soil organic carbon fractions by infrared-spectroscopy

Methods to quantify organic carbon (OC) in soil fractions of different stabilities often involve time-consuming physical and chemical treatments. The aim of the present study was to test a more rapid alternative, which is based on the spectroscopic analysis of bulk soils in the mid-infrared region (4000-400 cm⁻¹), combined with partial least-squares regression (PLS). One hundred eleven soil samples from arable and grassland sites across Switzerland were separated into fractions of dissolved OC, particulate organic matter (POM), sand and stable aggregates, silt and clay particles, and oxidation resistant OC. Measured contents of OC in each fraction were then correlated by PLS with infrared spectra to obtain prediction models. For every prediction model, 100 soil spectra were used in the PLS calibration and the residual 11 spectra for validation of the models. Correlation coefficients (r) between measured and PLS-predicted values ranged between 0.89 and 0.97 for OC in different fractions. By combining different fractions to one labile, one stabilized and one resistant fraction, predictions could even be improved (r=0.98, standard error of prediction=16%). Based on these statistical parameters, we conclude that mid-infrared spectroscopy in combination with PLS is an appropriate and very fast tool to quantify OC contents in different soil fractions.     

Interpretation of infrared spectra for OM characterization of soil structural surfaces of Bt‐horizons

The surfaces of macropores or aggregates can act as hot spots for biogeochemical processes and solute transport during preferential flow. For the characterization of organic matter (OM) at macropore surfaces non‐destructive methods have been applied such as diffuse reflectance infrared Fourier transform spectroscopy (DRIFT). However, effects of organic components on DRIFT signal intensities are often difficult to distinguish from those of mineral components. Here, DRIFT spectra from intact earthworm burrow walls and coated cracks were re‐evaluated to improve the interpretation of C–H and C=O bands. We compared DRIFT and transmission Fourier transform infrared (FTIR) spectra of entire samples that were from the same pedogenetic soil horizon (Bt) but different in mineral composition and texture (i.e., glacial till vs. loess). Spectra of incinerated samples were subtracted from the original spectra. Transmission FTIR and DRIFT spectra were almost identical for entire soil samples. However, the DRIFT spectra were affected by the bulk mode bands (i.e., wavenumbers 2000 to 1700 cm^?1). These bands affected spectral resolution and reproducibility. The ratios between C–H and C=O band intensities as indicator for OM quality obtained with DRIFT were smaller than those obtained from transmission FTIR. The results demonstrated that DRIFT and transmission FTIR data required separate interpretations. DRIFT spectroscopy as a non‐destructive method for analyzing OM composition at intact surfaces in structured soils could be calibrated with information obtained with the more detailed transmission FTIR and complementary methods. Spectral subtraction procedure was found useful to reduce effects of mineral absorption bands. The improved DRIFT data may be related to other soil properties (e.g., cation exchange capacity) of hot spots in structured soils.     

基于漫反射光谱的茶园土壤硝态氮检测

该文研究了充分利用土壤漫反射光谱在可见-近红外波段的有效信息,研究快速准确检测土壤硝态氮含量的新方法。试验选取89个风干土壤样本,经粉碎过直径1 mm筛孔后,使用 FieldSpec 3便携式光谱仪（光谱波长范围：400～2 500 nm）,获取其漫反射光谱。检查各土样的原始光谱的有效性并进行平均,经偏最小二乘法partial least squares（PLS）聚类分析后,选取其中的63个样本构成校正集建立模型,10个样本构成预测集进行模型验证。通过一阶微分与滑动平均滤波相结合的预处理方法,用15个主成分建立的主成分+神经网络模型为最好,其校正模型的回判相关系数为0.9908,均方根误差（RMSEC）为1.4528,预测模型的相关系数为0.7179。研究结果表明,利用可见-近红外光谱技术可以准确地检测茶园土壤硝态氮含量。     

Evaluating soil quality in tropical agroecosystems of Colombia using NIRS

Near infrared reflectance spectroscopy (NIRS) analysis was used to discriminate soils of different agroecosystems in Colombia, with different contents and qualities of organic matter, chemical, and biological properties.Correlations were sought between absorbance in wavelength classes as determined by NIRS, and a set of variables describing soil quality grouped into three classes: (i) chemical variables (Ca, Mg, K, exchangeable Al, total P, P-Bray II); (ii) organic variables (total C, total N, , , respirometry and carbon content in different fractions separated by the LUDOX physical methods) and (iii) NIRS variables quantifying the absorptions in the near infrared region separated into 101 classes of wavelength).For each group of variables, a principal component analysis (PCA), associated with discriminant analysis, was run. Each class of variables separated the different soil-use systems (***P<0.001) similarly. Co-inertia analyses among the different groups of variables verified the sensitivity of the NIRS in detecting significant changes in the soil chemical and organic composition, as well as in microbial activity.These results show the high potential of the NIRS for evaluating soil quality in large areas, rapidly, reliably and economically, thereby facilitating decision-making with respect to soil management and conservation.