Prediction of carbon mineralization rates from different soil physical fractions using diffuse reflectance spectroscopy

Soil carbon (C) mineralization rate is a key indicator of soil functional capacity but it is time consuming to measure using conventional laboratory incubation methods. Recent studies have demonstrated the ability of visible-near infrared spectroscopy (NIRS) for rapid non-destructive determination of soil organic carbon (SOC) and nitrogen (N) concentration. We investigated whether NIRS (350-2500 nm) can predict C mineralization rates in physically fractionated soil aggregates (bulk soil and 6 size fractions, n=108) and free organic matter (2 size fractions, n=27) in aerobically incubated samples from a clayey soil (Ferralsol) and a sandy soil (Arenosol). Incubation reference values were calibrated to first derivative reflectance spectra using partial least-squares regression. Prediction accuracy was assessed by comparing laboratory reference values with NIRS values predicted using full hold-out-one cross-validation. Cross-validated prediction for C respired (500 days) in soil aggregate fractions had an R² of 0.82 while that of C mineralized (300 days) in organic matter fractions was 0.71. Major soil aggregate fractions could be perfectly spectrally discriminated using a 50% random holdout validation sample. NIRS is a promising technique for rapid characterization of potential C mineralization in soils and aggregate fractions. Further work should test the robustness of NIRS prediction of mineralization rates of aggregate fractions across a wide range of soils and spectral mixture models for predicting mass fractions of aggregate size classes.     

Near infrared spectroscopy for soil bulk density assessment

Soil bulk density values are needed to convert organic carbon content to mass of organic carbon per unit area. However, field sampling and measurement of soil bulk density are labour-intensive, costly and tedious. Near-infrared reflectance spectroscopy (NIRS) is a physically non-destructive, rapid, reproducible and low-cost method that characterizes materials according to their reflectance in the near-infrared spectral region. The aim of this paper was to investigate the ability of NIRS to predict soil bulk density and to compare its performance with published pedotransfer functions. The study was carried out on a dataset of 1184 soil samples originating from a reforestation area in the Brazilian Amazon basin, and conventional soil bulk density values were obtained with metallic "core cylinders". The results indicate that the modified partial least squares regression used on spectral data is an alternative method for soil bulk density predictions to the published pedotransfer functions tested in this study. The NIRS method presented the closest-to-zero accuracy error (−0.002 g cm⁻³) and the lowest prediction error (0.13 g cm⁻³) and the coefficient of variation of the validation sets ranged from 8.1 to 8.9% of the mean reference values. Nevertheless, further research is required to assess the limits and specificities of the NIRS method, but it may have advantages for soil bulk density predictions, especially in environments such as the Amazon forest.     

近红外光谱法测定玉米秸秆饲用品质

为了对玉米秸秆的饲用品质进行可靠、便捷、快速的分析和评价,该研究以不同品种、密度、氮肥和水分处理的不同发育时期和不同部位玉米秸秆为试验材料,应用近红外光谱（NIRS）技术和偏最小二乘法（PLS）,采用一阶导数+中心化+多元散射校正的光谱数据预处理方法,构建了玉米秸秆体外干物质消化率（IVDMD）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF） 和可溶性糖（WSC）含量的NIRS分析模型。所建立的IVDMD、ADF、NDF和WSC含量的NIRS校正模型决定系数（R2cal）分别为0.9906、0.9870、0.9931和0.9802,交叉验证决定系数（R2cv）分别为0.9593、0.9413 、0.9678和0.9342,外部验证决定系数（R2val）分别为0.9549、0.9353、0.9519和0.9191,各项标准差（SEC、SECV和SEP）为0.935～1.904,相对分析误差（RPD）均大于3。结果表明,各参数的NIRS分析模型可用于玉米秸秆饲用品质的分析和品种选育的快速鉴定。     

基于近红外光谱的脐橙产地溯源研究

为研究近红外光谱分析技术鉴别脐橙产地的可行性,该文采用江西、重庆和湖南3个产地脐橙样品1140～1170nm波段的近红外光谱经一阶导数(9点平滑)预处理,分别建立了簇类独立软模式法脐橙产地鉴别模型。在5%显著水平下,模型对3个产地训练集样品的识别率均为100%,拒绝率分别为85.7%、83.3%、100%;对验证集样品的识别率均为100%,拒绝率分别为100%、89.5%、100%,表明簇类独立软模式法模型基本能够判别脐橙产地。将江西、重庆和湖南3个产地的脐橙样品分别赋值0、1、?1,在全波段范围内建立原始光谱脐橙产地的偏最小二乘判别模型,其预测值与真实值的决定系数为0.973,校正标准差为0.110,预测标准差为0.159,模型对训练集和验证集样品的识别率达到100%。因此,应用近红外光谱分析技术可准确、快速地追溯脐橙产地来源。     

Nondestructive determination of lignans and lignan glycosides in sesame seeds by near infrared reflectance spectroscopy

Sesame (Sesamum indicum L.) contains abundant lignans including lipid-soluble lignans (sesamin and sesamolin) and water-soluble lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) related to antioxidative activity. In this study, near infrared reflectance spectroscopy (NIRS) was used to develop a rapid and nondestructive method for the determination of lignan contents on intact sesame seeds. Ninety-three intact seeds were scanned in the reflectance mode of a scanning monochromator. This scanning procedure did not require the pulverization of samples, allowing each analysis to be completed within minutes. Reference values for lignan contents were obtained by high-performance liquid chromatography analysis. Calibration equations for lignans (sesamin and sesamolin) and lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) contents were developed using modified partial least squares regression with internal cross-validation (n = 63). The equations obtained had low standard errors of cross-validation and moderate R2 (coefficient of determination in calibration). The prediction of an external validation set (n = 30) showed significant correlation between reference values and NIRS predicted values based on the SEP (standard error of prediction), bias, and r2 (coefficient of determination in prediction). The models developed in this study had relatively higher values (more than 2.0) of SD/SEP(C) for all lignans and lignan glycosides except for sesaminol diglucoside, which had a minor amount, indicating good correlation between the reference and the NIRS estimate. The results showed that NIRS, a nondestructive screening method, could be used to rapidly determine lignan and lignan glycoside contents in the breeding programs for high quality sesame.     

基于近红外光声光谱的土壤有机质含量定量建模方法

该研究的目的在于应用近红外光声光谱技术结合不同的定量分析方法实现5种不同类型土壤有机质含量的快速估测。对中国中、东部地区5种不同类型土壤风干样本进行光谱扫描,经过多元散射校正、一阶导数、二阶导数及平滑等预处理后,应用逐步多元回归(SMLR)、主成分分析(PCR)、偏最小二乘法(PLS)和偏最小二乘法-反向传播神经网络(PLS-BPNN)等方法建立土壤有机质含量的定量估测模型。结果显示,不同预处理方法对所建土壤有机质含量估测模型的预测精度有较大影响,总体表现为多元散射校正+Norris一阶导数>多元散射校正>Norris一阶导数>标准正态化>Norris二阶导数>吸光度>Savitzky-Golay平滑后一阶导数>Savitzky-Golay平滑后二阶导数。对于4种不同建模方法,均以多元散射校正+Norris一阶导数滤波平滑后的光谱建模精度最高,其中采用PLS-BPNN方法建模效果最好,其次是PLS、SMLR和PCR。采用PLS-BPNN建立有机质校正模型具有极高的预测精度,建模决定系数和均方根偏差分别为0.97和1.88,模型测试决定系数和均方根偏差分别为0.97和1.72。因此,基于多元散射校正+Norris一阶导数光谱建立的PLS-BPNN模型可能是土壤有机质含量估测建模的最优方法。     

油用牡丹单粒种子含油量NIRS模型的建立

为了建立油用牡丹单粒种子含油量的近红外测定模型,便于高含油量单株的选育,采用索氏抽提法测试了200份油用牡丹凤丹单粒种子的含油量,并应用近红外反射光谱技术(NIRS)采集了200份样品的光谱数据,通过偏最小二乘法(PLS)和主成分回归法(PCR)构建了油用牡丹单粒种子含油量的数学模型。结果表明,索氏抽提法中,均匀粉碎后的油用牡丹籽样品干燥烘焙条件为105℃ 2 h,牡丹籽抽提时间为20 h,测出的含油量变化范围在10%~28%之间,籽油含量基本符合正态分布。NIRS法构建的模型最佳参数为:采用PLS法,光程固定,一阶导数消除背景,数据平滑处理采用Norris derivative filter的方法,平滑参数选用5和3。内部交叉检验校正相关系数r₁为0.980 1、预测相关系数r₂为0.957 6、校正均方根误差(RMSEC)为0.463、预测均方根误差(RMSEP)为0.705。外部检验相关系数达 0.957 6, 平均误差小于3%。本试验所构建的牡丹单粒种子含油量的NIRS模型可靠,可以用于分析油用牡丹单粒种子的含油量。     

基于高光谱和BP神经网络的玉米叶片SPAD值遥感估算

为了进一步提高玉米叶绿素含量的高光谱估算精度,该文测定了西北地区玉米乳熟期叶片的光谱反射率及其对应的叶绿素相对含量（soil and plant analyzer development,SPAD）值,分析了一阶微分光谱、高光谱特征参数与 SPAD的相关关系,构建了基于一阶微分光谱、高光谱特征参数和 BP 神经网络的 SPAD 估算模型,并对模型进行验证;再结合主成分回归（principal component regression,PCR）、偏最小二乘回归（partial least squares regression,PLSR）以及传统回归模型与 BP 神经网络模型进行比较。结果表明：SPAD 值与一阶微分光谱在763nm 处具有最大相关系数（R=0.901）;以763 nm 处的一阶微分值、蓝边内最大一阶微分为自变量建立的传统回归模型可用于玉米叶片 SPAD 估算;将构建传统回归模型时筛选到的光谱参数作为输入,实测 SPAD 值作为输出,构建 BP 神经网络模型,其建模与验模 R2分别为0.887和0.896,RMSE 为2.782,RE 为4.59%,与其他回归模型相比,BP 神经网络模型预测精度最高,研究表明 BP 神经网络对叶绿素具有较好的预测能力,是估算玉米叶片 SPAD 值的一种实时高效的方法。     

Nondestructive determination of oil content and fatty acid composition in perilla seeds by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the oil content and fatty acid composition in intact seeds of perilla [Perilla frutescens var. japonica (Hassk.) Hara] germplasms in Korea. A total of 397 samples (about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for the oil content and fatty acid composition were measured by gravimetric method and gas-liquid chromatography, respectively. Calibration equations for oil and individual fatty acids were developed using modified partial least-squares regression with internal cross validation (n = 297). The equations for oil and oleic and linolenic acid had lower standard errors of cross-validation (SECV), higher R2 (coefficient of determination in calibration), and higher ratio of unexplained variance divided by variance (1-VR) values than those for palmitic, stearic, and linoleic acid. Prediction of an external validation set (n = 100) showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), r2 (coefficient of determination in prediction), and the ratio of standard deviation (SD) of reference data to SEP. The models for oil content and major fatty acids, oleic and linolenic acid, had relatively higher values of SD/SEP(C) and r2 (more than 3.0 and 0.9, respectively), thereby characterizing those equations as having good quantitative information, whereas those of palmitic, stearic, and linoleic acid had lower values (below 2.0 and 0.7, respectively), unsuitable for screening purposes. The results indicated that NIRS could be used to rapidly determine oil content and fatty acid composition (oleic and linolenic acid) in perilla seeds in the breeding programs for development of high-quality perilla oil.     

Modelling soil organic carbon concentration of mineral soils in arable land using legacy soil data

Soil organic carbon (SOC) concentration is an essential factor in biomass production and soil functioning. SOC concentration values are often obtained by prediction but the prediction accuracy depends much on the method used. Currently, there is a lack of evidence in the soil science literature as to the advantages and shortcomings of the different commonly used prediction methods. Therefore, we compared and evaluated the merits of the median approach, analysis of covariance, mixed models and random forests in the context of prediction of SOC concentrations of mineral soils under arable management in the A‐horizon. Three soil properties were used in all of the developed models: soil type, physical clay content (particle size <0.01 mm) and A‐horizon thickness. We found that the mixed model predicted SOC concentrations with the smallest mean squared error (0.05%²), suggesting that a mixed‐model approach is appropriate if the study design has a hierarchical structure as in our scenario. We used the Estonian National Soil Monitoring data on arable lands to predict SOC concentrations of mineral soils. Subsequently, the model with the best prediction accuracy was applied to the Estonian digital soil map for the case study area of Tartu County where the SOC predictions ranged from 0.6 to 4.8%. Our study indicates that predictions using legacy soil maps can be used in national inventories and for up‐scaling estimates of carbon concentrations from county to country scales.     

土壤含水量对反射光谱法预测红壤土壤有机质的影响研究

研究土壤含水量对有机质预测的影响,可为野外红壤有机质快速测定提供理论依据。本文在实验室条件下测量了不同含水量红壤的可见光-近红外光谱反射率,运用偏最小二乘回归(PLSR)建立不同含水量的土壤有机质预测模型。结果显示,随土壤含水量的增加,有机质与一阶微分光谱的相关性先增加后下降,含水量为100～150 g/kg时相关系数最大。分380～2 400、380～1 300、1 300～2 400 nm三个波段建立不同含水量的有机质预测模型,模型预测精度均随土壤含水量增加而呈现先增加后下降的趋势。利用1 300～2 400 nm建立有机质预测模型可以有效避开氧化铁影响,建立的模型预测精度最高。本研究认为,当土壤含水量小于200 g/kg时,可以利用在室内控制条件下测定的土壤反射率,建立1 300～2 400 nm波段的PLSR模型,进行红壤土壤有机质含量预测。     

精料补充料中肉骨粉含量的近红外光谱检测

为了保证饲料安全,精料补充料中肉骨粉的检测是十分必要的。该文探讨了精料补充料中肉骨粉含量的近红外光谱分析方法,123个样品作为校正集,采用偏最小二乘法(PLS),分别对光谱进行散射校正和卷积平滑、一阶微分、二阶微分预处理建立校正模型,以最大的决定系数(R²)和最小的标准差(RMSEC)为选择依据,通过比较,以多元散射校正和卷积平滑处理与二阶微分相结合的处理效果最好,其预测值与测量值的决定系数(R²)和标准差(RMSEC)分别为0.9751和0.437。34个样品作为检验集进行外部验证,决定系数(r²)和标准差(RMSEP)分别为0.9749和0.420,平均绝对误差和相对误差分别为0.326和13.89％。结果表明,利用近红外分析技术可以检测精料补充料中肉骨粉的含量。     

光谱特征变量和BP神经网络构建油用牡丹种子含水率估算模型

为了进一步提高种子含水率的高光谱估算精度,该研究测定了156份油用牡丹种子的近红外吸收光谱及其对应的含水率值,分析了近红外吸收光谱、一阶微分光谱、水分吸收特征参数与含水率的相关关系,构建了基于特征波长吸收光谱、特征波长一阶微分光谱、水分特征吸收参数和BP神经网络的油用牡丹种子含水率估算模型,并对模型进行了验证;再结合一元线性回归（SLR,Single Linear Regression）、逐步多元线性回归（SMLR,Stepwise MultipleLinear Regression）、偏最小二乘回归（PLSR,Partial Least Squares Regression）模型与BP神经网络（BPNN,BP Neural Network）模型进行比较。结果表明：1）油用牡丹种子含水率的吸收光谱特征波长位于1 410、1 900、1990 nm,一阶微分光谱特征波长位于1 150、1 950、2 080 nm;2）以DF2080和AD2140为自变量建立的一元线性回归模型预测效果较优,在能够满足水分估算精度的情况下,是最优的选择方法。3）将优选的特征参数作为输入,实测含水率值作为输出,构建BP神经网络模型,其建模与验模R2分别为0.978和0.973,RMSE分别为0.22%和0.242%,而RPD值分别为6.478和5.889,与其他模型相比,BP神经网络模型的建模及预测精度均最高,是估算油用牡丹种子含水率的最优模型,其次为逐步多元线性回归模型。研究结果表明BP神经网络模型对种子含水率具有更好的预测能力,是估算油用牡丹种子含水率的有效方法。     

小波包-局部最相关算法提高土壤有机碳含量高光谱预测精度

高光谱遥感可以实现水稻土排水期有机碳含量的快速预测,但土壤反射率受多种噪声的影响,有机碳光谱信号探测受阻,预测模型性能低下,如何在去除噪声的同时最大限度地保持有机碳光谱信号十分重要。以原状新鲜水稻土为研究对象,采用Bior1.3小波系对反射光谱进行1~7层小波包变换,通过相关分析确定最大分解层;将原始反射率至最大分解层以内的各层光谱相关系数组成相关系数集,采用局部最相关算法(local correlation maximization,LCM)构造土壤有机碳最优光谱;最后基于最优光谱建立有机碳含量偏最小二乘预测模型并进行分析。结果显示:1)随着小波包分解层数的增加,土壤反射率与有机碳含量的相关性不断增强,到第6层达到最高,确定为小波包最大分解层;2)基于LCM构造的最优光谱比未去噪光谱平滑,比小波包去噪光谱保留了更多光谱细节;3)未去噪光谱、小波包去噪光谱和LCM最优光谱有机碳预测模型的验证决定系数分别为0.693、0.727和0.781,均方根误差为1.952、1.840和1.679 g/kg,残留预测偏差为1.85、1.97和2.17。小波包-局部最相关算法在去噪同时有效保持了土壤有机碳光谱信号,可提高水稻土有机碳含量高光谱预测精度。     

Determination by near-infrared spectroscopy of perseitol used as a marker for the botanical origin of avocado (Persea americana Mill.) honey

This paper reports the application of near-infrared (NIR) reflectance spectroscopy to determine the concentration in honey of perseitol, a sugar that is specific to avocado honey. Reference values for perseitol were obtained by high-performance liquid chromatography analysis in 109 honey samples. Although the average concentration of perseitol in honey samples was only 0.48%, accurate prediction equations were successfully developed. The regression model of modified partial least squares was superior to that of principal component regressions. Calibrations based on the first or second derivative of Log(1/R) were equally good (R(2) > 0.95). Using half of the samples for calibration and the second half for validation, the correlation between actual and predicted values of the second half was satisfactory (R(2) = 0.87), the slope did not differ from 1, bias was low (0.005%), and the standard error of prediction was relatively low (0.13%). It was concluded that NIRS analysis may be used to detect to what extent honeybees have harvested avocado nectar but not to authenticate avocado honey as unifloral.     

砂姜黑土有机质含量高光谱估测模型构建

为快速估测砂姜黑土有机质含量,该研究以河南省商水县砂姜黑土为对象,采用光谱指数和遗传算法结合支持向量机构建砂姜黑土有机质估测模型。结果表明,以Savitzky-Golay(SG)平滑后的一阶导数光谱792和1 389 nm两波段组合构建的比值指数表现最好,建模集决定系数为0.81。利用独立的样本验证,预测决定系数和均方根误差分别为0.91和1.56 g/kg。而相同样本经遗传算法筛选敏感波段结合支持向量机回归构建的模型以SG平滑的一阶导数光谱表现最好,建模集和验证集决定系数分别为0.95和0.91,均方根误差分别为1.01和1.69 g/kg。基于遗传算法结合支持向量机回归和光谱指数2种方法构建的有机质含量估测模型均表现出较高的精度,前者稍优于后者,可用于对砂姜黑土有机质含量的有效估测。该研究成果可为砂姜黑土有机质含量的快速定量估算提供依据和参考。     

基于连续统去除法的南疆水稻土有机质含量预测

监测土壤有机质含量状况,可为土壤肥力诊断及土壤资源的合理开发利用提供科学依据。本研究通过对南疆191个水稻土样品的反射率数据进行连续统去除处理后,构建了有机质连续统去除光谱指数并提取了850~1 380、1 380~1 550、1 730~2 150、2 150~2 380 nm 4个波段的吸收特征参数,据此建立了多种定量反演模型。结果表明:经连续统去除后,有机质的吸收特征得到了有效放大,不同有机质含量的连续统去除曲线在850~1 380 nm,其有机质含量与连续统去除值呈正相关,与吸收面积呈负相关,而在1 730~2 150 nm波段则呈现相反的规律。反射率连续统去除值与有机质含量的相关性要优于反射率与之的相关性,而反射率一阶微分与连续统去除一阶微分与有机质的相关性差异不明显。不同有机质光谱指数模型之间的建模参数与预测能力差异不大,但均只具备初略估测有机质的能力。吸收特征参数模型中,仅有850~1 380 nm波段的面积归一化最大吸收深度(NMAD850~1380nm)所建模型具有较好的定量预测能力。以反射率、反射率连续统去除、反射率一阶微分、反射率连续统去除一阶微分所建的PLSR模型均具有较好的预测能力,相对分析误差均大于2.00。所有模型中,连续统去除一阶微分(CR′)模型的决定系数与相对分析误差最高,分别为0.91、2.58,均方根误差最低,其值为5.62,具有最好的预测能力。     

The use of Vis-NIR spectral reflectance for determining root density: evaluation of ryegrass roots in a glasshouse trial

This paper reports the use of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350–2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46–5.02 mg dry root cm^?3) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76–5.02 mg dry root cm^?3) than pots of the Ramiha soil (0.46–3.84 mg dry root cm^?3). Each soil had visually distinct reflectance spectra in the range 470–2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r² cross‐validation = 0.78, ratio of prediction to deviation (RPD) = 2.14, root mean squares error of cross‐validation (RMSECV) = 0.60 mg cm^?3; with removing outliers, r² cross‐validation = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm^?3). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils.     

褐潮土的光谱特性及用土壤反射率估算有机质含量的研究

本文通过ASDFR便携式光谱仪对132个风干土壤样品的光谱反射率进行了实验室测定。根据土样光谱反射率变化,获得了褐潮土土壤剖面的不同诊断层反射光谱特征。结果表明,在400～1200nm范围之间,土壤有机质含量与土壤光谱反射率有较好的相关性。利用导数光谱方法建立了预测土壤有机质含量的方程,提出了预测北京地区褐潮土有机质光谱的最佳波段。在波长447nm处采用反射率和A值(反射率倒数的对数)所建立的预测方程的预测精度较高。采用反射率的一阶微分建立的预测方程的最佳波段在516nm处。而A值一阶微分光谱在615nm处相关性最好。作为一项参考指标用光谱分析法评价土壤中有机质含量,以期对精准农业中土壤养分或肥力的预测具有一定的指导作用。     

Use of near‐ and mid‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils

The presence of relatively inert organic materials such as char has to be considered in calibrations of soil C models or when calculating C‐turnover times in soils. Rapid and cheap spectroscopic techniques such as near‐infrared (NIRS) or mid‐infrared spectroscopy (MIRS) may be useful for the determination of the contents of char‐derived C in soils. To test the suitability of both spectroscopic techniques for this purpose, artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals) and forest‐floor Oa material were produced. The total C content of these mixtures (432 samples) ranged from 0.5% to 6% with a proportion of char‐derived C amounting to 0%, 20%, 40%, 50%, 60%, or 80%. All samples were scanned in the visible and near‐IR region (400–2500 nm). Cross‐validation equations for total C and N, C and N derived from char (C_char, N_char) and Oa material were developed using the whole spectrum (first and second derivative) and a modified partial least‐square regression method. Thirty‐six samples were additionally scanned in the middle‐IR and parts of the near‐IR region (7000–400 cm^–1 which is 1430–25,000 nm) in the diffuse‐reflectance mode. All properties investigated were successfully predicted by NIRS as reflected by RSC values (ratio of standard deviation of the laboratory results to standard error of cross‐validation) > 4.3 and modeling efficiencies (EF) ≥ 0.98. Near‐infrared spectroscopy was also able to differentiate between the different coals. This was probably due to structural differences as suggested by wavelength assignment. Mid‐IR spectroscopy in the diffuse‐reflectance mode was also capable to successfully predict the parameters investigated. The EF values were > 0.9 for all constituents. Our results indicated that both spectroscopic techniques applied, NIRS and MIRS, are able to predict C and N derived from different sources in soil, if closed populations are considered.