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。     

细胞壁组分在红酵母RS1高耐铝中的作用研究

红酵母RS1是从酸性油茶土壤中分离到的一株高耐铝微生物,能够忍耐高达200 mmol/L以上的铝,前期研究表明RS1高耐铝能力与细胞壁有关,但是其具体机制还不清楚。因此,本文进一步研究了细胞壁组分在RS1高耐铝中的作用,以期为RS1高耐铝的具体机制提供新信息。结果表明,高于70 mmol/L的铝对RS1生长产生抑制作用,0~70 mmol/L铝处理后细胞壁主要组分甘露糖和葡聚糖含量都没有显著改变,而细胞壁磷含量在70 mmol/L铝处理后显著升高。在高铝处理时,糖蛋白抑制剂抗生素衣霉素(tunicamycin)极大加重RS1的铝毒害。由此说明,细胞壁多糖组分含量并不对RS1高耐铝起到主要作用,细胞壁结构修饰如细胞壁磷含量响应和细胞壁N连接的糖蛋白修饰在RS1高耐铝中可能起到一定作用。     

应用同步辐射光谱技术研究退耕草地土壤有机碳官能团变化

  目的  为了明确埋藏古水稻土与现代水稻土有机质官能团红外光谱特征及其变化规律,揭示氧化铁对不同环境条件、不同埋深有机质官能团光谱响应的影响。  方法  采用傅里叶红外光谱测定杉龙岗埋藏古水稻土与现代耕作水稻土原土、去除有机质土、去除氧化铁土的有机质官能团的光谱特征,对比分析土壤有机质官能团的差异及剖面分布特征。  结果  三种处理方式下有机质官能团红外光谱变化特征在同一土壤剖面的红外光谱曲线形态、特征吸收峰和吸收强度基本一致,尤其是与有机质官能团有关的几个典型吸收峰：2925 cm⁻¹（脂族峰）,2854 cm⁻¹（脂族峰）,1634 cm⁻¹（芳香峰）,1401 cm⁻¹（脂族峰）。而去除有机质的差谱图在同一土壤剖面表现为：有机质官能团吸收强度均朝着强度更强的趋势变化,且去除氧化铁的差谱图中有机质官能团吸收强度变化更大。  结论  有机质官能团与含铁矿物之间存在密切联系,去除氧化铁更有利于观察有机质官能团红外光谱特征的变化,且这种变化在埋藏古水稻土中尤为明显,埋藏古水稻土有机质官能团结构稳定性表现为1634 cm⁻¹（芳香峰） > 1401 cm⁻¹（脂族峰） > 2925 cm⁻¹（脂族峰） > 2854 cm⁻¹（脂族峰）。剖面变化特征表现为随土层加深芳香碳增加而脂族碳减少。     

基于SERS的苹果树腐烂病原菌早期侵染检测

为了早期诊断由黑腐皮壳真菌(Valsa mali Miyabe et Yamada)引起的苹果树腐烂病,该研究基于表面增强拉曼光谱(surface-enhanced Raman scattering,SERS)技术,以腐烂病菌丝、病原菌丝侵染的苹果树和健康的苹果树枝作为研究对象,结合S-G平滑和迭代自适应加权惩罚最小二乘法进行拉曼光谱预处理,经解析发现病原菌丝与染菌样本在1 598、1 595 cm^-1和2 930、2 925 cm^-1附近敏感谱峰明显区别于健康样本。重复试验分析发现,病原菌侵染可致寄主特征谱峰偏移以及谱峰强度改变：健康样本在1 286 cm^-1附近的特征峰随病原菌的侵染偏移至1 365 cm^-1附近;健康样本在1 286与1 587 cm^-1附近的谱峰强度比值小于0.5,染菌样本在1 365与1 595 cm^-1附近的谱峰强度比值大于0.5,而菌丝在1 327与1 598 cm^-1附近的谱峰强度比值大于1.0;1 5...     

高耐铝红酵母RS1耐铝特性初步研究

红酵母RS1是从酸性土壤中筛选出的一株高耐铝微生物,本文对其耐铝特性进行了初步研究.结果发现,RS1可以在pH 2.5的培养基中生长,50 ～ 200 mmol/L A13+处理对RS1产生了毒害作用,其毒害并未对细胞表面形态产生影响.RS1对Al2(SO4)3的忍耐能力高于AlCl3,这种高的忍耐能力是遗传固有的,并不被诱导产生.此外,RS1可以增加体内蛋白含量来适应高的铝毒,其中蛋白型巯基可能在耐铝中表现重要作用.     

不同种类茶叶冲泡前后中红外光谱变化探析

为建立一种快速无损评价茶叶/茶汤化学品质和色泽的方法,本试验选取7种代表性茶叶样本为材料,利用傅里叶变换中红外光谱(FTIR)技术,以开水冲泡方式制备茶汤冻干粉,对茶叶与茶汤共14个样本进行FTIR光谱采集分析。通过聚类分析、光谱曲线拟合、定性分析,并结合常规化学法检测茶多酚、茶多糖、可溶性糖、游离氨基酸、主要儿茶素类和生物碱含量。结果表明,不同茶叶光谱明显不同,由于茶叶加工工艺造成的脂类、儿茶素类、咖啡碱、游离氨基酸及叶绿素含量差异,利用1 800～1 170 cm^-1狭窄区域的光谱特征,可实现茶叶分类。茶叶与对应茶汤的光谱也存在明显差异,体现了茶叶在冲泡过程中的物质变化。主要特征峰区(位)为:2 925/2 855 cm^-1代表脂类饱和烃链-CH₂振动,1 700 cm^-1峰位对应儿茶素类和咖啡碱,1 534 cm^-1对应着叶绿素脱镁过程,1 551/1 556 cm^-1和1 500/1 497 cm^-1为叶绿素中亚甲基桥的伸缩振...     

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。     

基于SERS技术的基底增强效应对比及食源性芽孢快速检测

为了实现食源性芽孢的快速识别,该研究利用表面增强拉曼光谱技术,以产气荚膜梭菌芽孢(C.perfringens spores)、艰难梭菌芽孢(C.difficile spores)和生孢梭菌芽孢(C.sporogenes spores)为研究对象,将3种不同纳米溶胶材料Au NPs、Ag NPs和Au@AgNPs作为表面增强拉曼散射(Surface-enhanced Raman scattering,SERS)基底对食源性芽孢的增强效应进行对比,确定较佳增强效果的纳米材料,进一步开展基于较优纳米溶胶材料为基底的SERS技术对不同种属食源性芽孢的光谱解析及快速识别研究。结果表明,Au@Ag NPs对食源性芽孢的SERS增强效果最较好,其增强因子达3.49×10⁴,且光谱特异性和重现性良好。经拉曼光谱解析,Ca²⁺-DPA的拉曼峰是三种食源性芽孢的共有标志特征峰,拉曼振动峰在1 017、1 440和1 570 cm^-1波段显示且峰强度不同。C.perfringens芽孢在740、787、821、1 203、1 308和1 62...     

接种单细胞微生物对土壤团聚体形成及其稳定性的影响

将根瘤菌(Rhizobium sp.) G-01、肠杆菌(Enterobacter sp.) San8及粘红酵母(Rhodotorula mucilaginosa) R1等3株产胞外多聚物(Extracellular polymeric substances,EPS)的单细胞微生物接种于供试土壤,进行土培试验和盆栽黄瓜试验,研究接种微生物及作物根系对土壤团聚体形成、团聚体组成变化的影响。结果表明,3株单细胞微生物分泌EPS的量分别为2.12 mg (10¹⁰ cells)^-1、0.56 mg (10¹⁰ cells)^-1、172.71 mg (10¹⁰ cells)^-1,其主要组分是多糖、其次是蛋白质,且含有羟基、羧基、羰基等官能团;土培试验后,接种产EPS单细胞微生物处理土壤的大团聚体(>0.25 mm)含量比接种丝状微生物处理提高了71.31%,而盆栽试验后各处理土壤的大团聚体含量(>0.25 mm)比土培试验后土壤显著增加,这可能是由于植...     

基于高光谱吸收特征参数以及光谱吸收指数的藻类叶绿素a反演分离模型研究

利用ASD地物光谱仪对不同浓度下小球藻、聚球藻及其混合藻的反射光谱进行测量,得到3组藻类在不同叶绿素a（Chl-a）浓度下的反射光谱,同时进行了Chl-a浓度测量。利用Matlab软件神经网络模型中的径向基函数对得到的高光谱数据进行高光谱曲线拟合,在拟合结果基础上,提取小球藻在652nm处、聚球藻在609nm处以及混合藻在652nm和609nm处的光谱吸收指数（SpectralAbsorptionFeatureParameter,SAFP）建立了两种单一藻类的定量模型,单一小球藻Chl-a最优定量模型为吸收峰深度模型Chl-a=10.059e713.97H,聚球藻的Chl-a最优定量模型为吸收峰斜率模型Chl-a=3×1012K2＋1×107K＋56.555。并在对两种单一藻类的定量模型研究基础上用光谱吸收指数对该两种藻的混合藻进行了Chl-a浓度分离。通过对比模型计算结果的均方根误差（RootMeanSquareError,RMSE）,除吸收峰对称度模型分离结果不太理想外,吸收峰深度分离模型、吸收峰斜率分离模型、吸收峰光谱吸收指数（SAI）分离模型的分离结果都很好,分离效果最好的为SAI分离模型。     

VNIR Soil Spectroscopy for Field Soil Analysis

The advent of affordable, ground-based, global positioning information (GPS)–enabled sensor technologies provides a new method to rapidly acquire georeferenced soil datasets in situ for high-resolution soil attribute mapping. Our research deployed vehicle-mounted electromagnetic sensor survey equipment to map and quantify soil variability (?50 ha per day) using apparent electrical conductivity as an indirect measure of soil texture and moisture differences. A portable visible–near infrared (VNIR) spectrometer (350–2500 nm) was then used in the field to acquire hyperspectral data from the side of soil cores to a specified depth at optimized sampling locations. The sampling locations were derived by statistical analysis of the electromagnetic survey dataset, to proportionally sample the full range of spatial variability. The VNIR spectra were used to predict soil organic carbon (prediction model using field-moist spectra: R² = 0.39; RPD = 1.28; and air-dry spectra: R² = 0.80; RPD = 2.25). These point values were combined with the electromagnetic survey data to produce a soil organic carbon map, using a random forest data mining approach (validation model: R² = 0.52; RMSE = 3.21 Mg C/ha to 30 cm soil depth; prediction model: R² = 0.92; RMSE = 1.53 Mg C/ha to 30 cm soil depth). This spatial modeling method, using high-resolution sensor data, enables prediction of soil carbon stocks, and their spatial variability, at a resolution previously impractical using a solely laboratory-based approach.     

NIR Spectroscopy: An Alternative for Soil Analysis

Advances in laboratory instrumentation and chemometrics provide alternatives to traditional methods of conducting soil chemical analysis. One of these is infrared diffuse reflectance spectroscopy in the near-infrared spectral range (NIRS). Herein we report the results of a multinational study to develop useful calibrations associating NIRS spectra with laboratory-measured results for total soil carbon (C), total soil nitrogen (N), δ¹³C, and δ¹⁵N from a single soil site in Mexico subjected to zero- and conventional-tillage regimens with and without crop residues and crop rotations of maize and wheat across 16 years. Modified partial least squares regression (MPLS) was used to obtain useful NIR predictions for total soil C and N, with ratio performance deviation (RPD) values of 6.8 and 2.6, respectively. Corresponding multiple correlation coefficients (RSQs) for C and N were 0.98 and 0.85, with standard errors of prediction (SEPs) of ±0.45 g C kg^–1 and ±0.09g Nkg^–1, respectively. The generation of δ¹⁵N and δ¹³C models produced different NIR recordings in soils with and without crop residues. Application of discriminant partial least squares (DPLS) statistics to the NIR spectral data allowed us to discriminate soils with and without residues. The prediction confidence for stable isotopes was 90% (internal validation) and 94% (external validation). Modified partial least squares regression was used to estimate δ¹⁵N and δ¹³C. Ratio performance deviation, RSQ, and SEP values obtained for δ¹³C and δ¹⁵N were 2.44 and 3.57, 0.83 and 0.81, ±0.5‰ (parts per thousand) and ±0.45‰ in soils with residues and 2.5 and 3.8, 0.93 and 0.92, and ±0.2‰ and ±0.23‰ in soils without residues, respectively. Overall, results obtained with NIRS were comparable to those obtained using conventional analytical methods, a finding that has wide relevance to agricultural soils and environmental studies in tropical locations. However, further testing is necessary to confirm that the calibration models are neither site nor instrument specific.     

基于近红外光谱分析的土壤全氮含量估测研究

应用近红外光谱分析技术对比研究基于土壤风干样本和鲜样来预测全氮含量的可行性。选取水稻土为研究对象,首先分析了不同水分土壤的光谱特征,显示随水分含量增加,吸光度升高,且鲜样的吸光度高于干样。通过比较不同预处理方法,对土壤干鲜样分别采用逐步多元回归(SMLR)和偏最小二乘法(PLSR)建立了相应的近红外模型。结果表明,利用近红外光谱均可预测干鲜土壤样本的全氮含量,特别是利用偏最小二乘法建立的标定模型,预测精度高,反演性较好,鲜样和干样外部验证决定系数分别达到0.89和0.91,相对误差仅为6.92%和5.92%,研究结果可以为田间土壤全氮含量的估测提供技术依据和参考。     

Pasture Vegetation Elemental Analysis by Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a new technique for the analysis of plant material. This study investigates the application of LIBS to pasture-based plant samples. The LIBS measurements were obtained from pelletized pasture samples (100 samples) that had been also analyzed by inductively coupled plasma–optical emission spectroscopy (ICP-OES) following microwave digestion for calibration and comparison purposes. Comparisons for elements sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), boron (B), phosphorus (P), and sulfur (S) showed that LIBS could be used for almost all the standard profile total elements with concentrations down to low mg/kg levels (observed error of Na: 0.024 percent, K: 0.18 percent, Mg: 0.016 percent, Ca: 0.073 percent, P: 0.017 percent, Mn: 31 mg/kg, Fe: 150 mg/kg, Zn: 6.6 mg/kg, and B: 1.1 mg/kg). Elemental analysis at less than mg/kg levels was not possible using LIBS. The elements S and Cu were particularly difficult to analyze with reliability using LIBS at the concentration levels found in the plant samples. Replacing microwave digestion and subsequent ICP analysis with a direct analysis of dried plant samples using LIBS has the potential to improve the productivity and reduce the cost of testing.     

Assessment of Heat-Damaged Wheat Kernels Using Near-Infrared Spectroscopy

Heat damage is a serious problem frequently associated with wet harvests because of improper storage of damp grain or artificial drying of moist grain at high temperatures. Heat damage causes protein denaturation and reduces processing quality. The current visual method for assessing heat damage is subjective and based on color change. Denatured protein related to heat damage does not always cause a color change in kernels. The objective of this research was to evaluate the use of nearinfrared (NIR) reflectance spectroscopy to identify heat-damaged wheat kernels. A diode-array NIR spectrometer, which measured reflectance spectra (log (1/R)) from 400 to 1,700 nm, was used to differentiate single kernels of heat-damaged and undamaged wheats. Results showed that light scattering was the major contributor to the spectral characteristics of heat-damaged kernels. For partial least squares (PLS) models, the NIR wavelength region of 750–1,700 nm provided the highest classification accuracy (100%) for both cross-validation of the calibration sample set and prediction of the test sample set. The visible wavelength region (400–750 nm) gave the lowest classification accuracy. For two-wavelength models, the average of correct classification for the classification sample set was >97%. The average of correct classification for the test sample set was generally >96% using two-wavelength models. Although the classification accuracies of two-wavelength models were lower than those of the PLS models, they may meet the requirements for industry and grain inspection applications.     

Characterization of Compost Leachate Fractions Using NMR Spectroscopy

A distilled water leachate was prepared from a hardwood-leaf compost. The ¹³C NMR (nuclear magnetic resonance) spectra of organic acids isolates from this leachate provided support for the hypothesis that the composition of the dissolved organic carbon (DOC) in the leachate is very similar to that in natural soil pore waters. The leachate DOC was adsorbed by a model mineral surface, and the organic coating on the surface was characterized by solid-state ¹³C NMR spectroscopy.     

Potential of Near- and Mid-infrared Spectroscopy in Biofuel Production

For many decades, near-infrared spectroscopy (NIRS) has been used to determine the composition of animal feedstuffs and grains. More recently, mid-infrared spectroscopy (mid-IR) has also been examined for similar determinations. These spectroscopic methods offer the potential for rapid and accurate determination of organic constituents, such as fiber components and protein, of forages, by-products, and grains at reduced cost and greatly increased speed (minutes instead of hours or days). Because they are nonchemical in nature, they result in a large reduction (90% or more) in the chemical wastes associated with standard chemical-based assay methods. The same components of interest for biofuel production (cellulose, hemicellulose, lignin, starch, protein, oil, etc.) are those that have already been determined by NIRS/mid-IR for evaluating grains and animal feedstuffs. Therefore, these techniques would appear to be a natural match for evaluating feedstocks for biofuels, and the literature shows that efforts in this direction are being successfully tested and instituted. For this discussion, an overview of where such efforts are and the potential for NIRS/mid-IR in producing biofuels will be covered. For example, while there are similarities between the needs of the biofuels industry and the analysis of animal feedstuffs, there are also both practical and technical differences between the two that will likely impact how NIRS/mid-IR is developed for biofuels. As an example, grain analysis for protein is performed on a large scale by government agencies such as the Canadian Grain Commission and U.S. Grain Inspection Service, while at least in the United States, animal feedstuff analysis is performed by state or independent laboratories for individual farmers. For biofuels, this might well result in most analysis being performed by the large corporations converting the feedstocks to biofuels, as opposed to the individual producer having analysis performed at an independent laboratory. Similarly for animal feedstuffs, measurements of fiber (neutral detergent fiber or NDF, acid detergent fiber or ADF, and lignin) and protein are carried out. These fiber measurements often consist of more than one type of fiber component with some being computed by difference (hemicellulose = NDF – ADF) and are empirical at best. Whether such empirical estimates will be sufficient for assessing biofuels or whether new spectroscopic methods for directly measuring the components of interest (cellulose, etc.) will need to be developed is a question to be answered when components other than starch for ethanol or oil for biodiesel become common.     

激光诱导击穿原子光谱在土壤分析中的应用

土壤分析是土壤学研究及应用的前提和基础,传统化学土壤分析方法逐渐不能适应现代土壤学海量信息数据快速获取的需求。激光诱导击穿光谱作为一种全新的、反映土壤组成元素原子信息的光谱技术,其无需对样品进行复杂前处理,可实现原位、快速、多元素连续在线检测,每条光谱记录土壤样本独一无二的特征,可以视为土壤“指纹”,成为现代土壤分析的有效技术之一。首先介绍和分析了激光诱导击穿原子光谱的原理、光谱获取的主要影响因素、光谱数据处理的化学计量学方法等;然后阐述和梳理了基于激光诱导击穿光谱技术在土壤分析方面的应用成果和进展,包括土壤鉴定、土壤养分评估、土壤重金属测定、微观/介观尺度土壤原位表征等;最后讨论和总结了激光诱导击穿光谱技术在土壤分析中所面临的挑战及其应用展望。