近红外与中红外光谱技术在土壤分析中的应用

传统的土壤分析方法周期长、成本高,不能在短时间内获取所需的土壤信息,如何快速、准确地获取土壤信息成为农业现代化的必然要求。漫反射光谱技术以其快速、廉价、非破坏与无污染等特点,已成为环境研究中获取相关土壤信息的重要技术手段,其中的近红外(NIR,780~2 500 nm)和中红外(MIR,2 500~25 000 nm)光谱技术预测土壤理化特性逐渐成为国内外学者研究的重点领域。文章介绍了近红外和中红外光谱技术的基本原理和分析方法,综述了该技术在土壤分析方面的具体应用,并对此提出了一些观点和展望。     

大豆蛋白的中红外和近红外光谱研究*

大豆蛋白在各领域的应用已得到广泛的关注,因此大豆蛋白及其改性材料在结构性能方面的研究显得越来越重要。中红外光谱(mid-infrared spectroscopy,MIR)和近红外光谱(near-infrared spectroscopy,NIR)正是对蛋白质进行定性定量分析的有力手段。中红外光谱可以有效地分析大豆蛋白在溶液和薄膜中的二级结构以及大豆衍生材料内蛋白质的结构变化情况。近红外光谱则在蛋白质定量分析方面有着独特的优势。本文介绍了运用这两种光谱技术进行研究的一些工作,这些实例表明了中红外和近红外光谱在大豆蛋白研究领域的重要应用价值。     

Application of two-dimensional near-infrared correlation spectroscopy to protein research

The purpose of the present paper is to demonstrate the potential of generalized two-dimensional (2D) near-infrared (NIR) correlation spectroscopy in studies of hydration and structure of proteins. We describe here two examples. The first example is concerned with heat denaturation process of ovalbumin in aqueous solutions and the second one deals with isomerizations with varying pH of serum albumin. New insight has been obtained into the hydration and unfolding process of secondary structures of ovalbumin and serum albumin by studying temperature- or pH-dependent correlation patterns in 2D synchronous and asynchronous spectra. Generalized 2D NIR correlation spectroscopy emphasizes spectral features not readily observable in conventional one-dimensional spectra, enabling to extract subtle but valuable structural changes concerning with the protein denaturation.     

Determination of water in lubricating oils by mid- and near-infrared spectroscopy

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy were used to determine water in lubricating oils with high additive contents that introduce large errors in determinations by the Karl-Fischer and hydride methods. MIR spectra were obtained in the attenuated total reflectance (ATR) mode and exhibited water specific band absorption in the region 3100–3700cm^–1, which facilitated calibration. Multivariate (partial least-squares regression, PLSR) and univariate calibration (based on peak height and band area as independent variables) were tested. Both led to errors of prediction less than 5%. NIRS determinations rely on absorbance and first-derivative spectra, in addition to two different types of multivariate calibration,viz. inverse multiple linear regression (MLR) and partial least-squares regression (PLSR). Both approaches gave similar results, with errors of prediction less than 2%.For none of the proposed approaches any sample pretreatment for recording spectra is required.     

Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect.Near-infrared(NIR) and mid-infrared(MIR) technology combined with supervised pattern recognition based on partial least-squares discriminant analysis (PLSDA) was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L.samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work.Recognition rates of 99.24%,100%and 99.49%for original fingerprint,multiple scatter correct(MSC) fingerprint and second derivative(2nd derivative) fingerprint of NIR spectra were achieved by PLSDA models,respectively.Meanwhile,a perfect recognition rate of 100%was obtained for the above three fingerprint models of MIR spectra.In conclusion.PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces of A.catechu.     

应用近红外光谱技术分析烟丝总糖和还原糖的研究

应用傅立叶变换近红外漫反射光谱仪,对同一品牌通过选择不同批号代表性好的卷烟烟丝样品,建立了卷烟烟丝总糖和还原糖的近红外定量分析数学模型,并对不同批号的分析样品进行检验,近红外预测值的平均相对误差为1.01%和0.77%,接近实测值,说明近红外光谱分析技术可用于卷烟烟丝生产质量指标的检测.     

Morphology, flexural, and thermal properties of sepiolite modified epoxy resins with different curing agents

A bisphenol A-based epoxy resin was modified with 5 wt% organically modified sepiolite (Pangel B40) and thermally cured using two different curing agents: an aliphatic diamine (Jeffamine D230, D230) and a cycloaliphatic diamine (3DCM). The morphology of the cured materials was established by scanning and transmission electron microscopy analysis. The thermal stability, thermo-mechanical properties, and flexural behaviour of the sepiolite-modified matrices were evaluated and compared with the corresponding neat matrix. The initial thermal decomposition temperature did not change with the addition of sepiolite. The flexural modulus of the epoxy matrix slightly increases by the incorporation of the organophilic sepiolite. The flexural strength of the sepiolite modified resin cured with D230 increased by a 10% while the sepiolite modified resin cured with 3DCM resulted in a lower flexural strength compared with the unmodified resin. The reduced flexural strength was attributed to the stress concentrations caused by the sepiolite modifier, which rendered the resins more brittle.     

Study of the temperature-dependent near-infrared spectra of water by two-dimensional correlation spectroscopy and principal components analysis

Near-infrared spectroscopy (NIR) is an important analytical tool in monitoring properties of systems for that water is a major constituent. For such objects of analysis a quality of information extracted from the NIR spectra depends essentially on used methods of analysis of a massive absorbance of water. Correctly chosen method should be able to identified rational number of overlapped components hidden under the broad band of water. The resolved components have to be justified on grounds of the structure of water and by relation to the properties a hydrogen-bonded network of water molecules. The interest in the correlation is imposed by a fact that hydrogen bonds of water around nonpolar group are significantly strengthened and weakened around polar groups. Intensity variations classified in this context could be valuable source of information on varying properties of the solute molecules embedded in water environment. Therefore, there is a big interest in methods that have a power for detailed analysis of the intensity changes in the broad NIR spectra. Two-dimensional correlation spectroscopy (2DCOS) and principal component analysis (PCA) are our proposition. In the analysis of the temperature-dependent NIR spectra of water by means of the two methods we have focused on the interpretation of the 2DCOS results through the concept of linear and nonlinear relationships. Moreover, a cascaded curve fitting procedure has been employed. Presented approach should be very instructive of how to interpret the features of the 2D results that frequently is not a straightforward task.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

Understanding the thermal stability of human serum proteins with the related near-infrared spectral variables selected by Monte Carlo-uninformative variable elimination

Understanding the thermal stability of the proteins in human serum is essential since human serum is the important source of pharmaceutical proteins. Near-infrared (NIR) spectroscopy was applied to the investigation of thermal changes in secondary structure and hydration of human serum proteins. However, as a multicomponent system, the overlap of the broad NIR bands makes the structural analysis very difficult directly using the spectra of serum samples. Therefore, continuous wavelet transform (CWT) was used to improve the resolution of NIR spectra, and Monte Carlo-uninformative variable elimination (MC-UVE) method was applied to the selection of the variables associated with the proteins for the structural analysis. The variables (5956, 5867, 5815, 5747, 4525, 4401, 4359 and 4328 cm^-1) related to protein secondary structures and those (7074, 6951, 6827 and 6700 cm^-1) connected with water species were selected. Then, the thermal stability was analyzed through the intensity variations of the selected variables with temperature from 30 ℃ to 80 ℃. It was found that the variation of the spectral variables related to both α-helix and β-sheetchanges apparently around 60 ℃, indicating the beginning of the thermal denaturation and the transition from α-helix to β-sheet. Moreover, an obvious change was found around 60 ℃ for the content of the water specie S₃, i.e., the water cluster containing three hydrogen bonds. The result demonstrates that MC-UVE can identify the protein-related NIR spectral variables, and the water species may be a marker for investigation of the structural change of proteins in biochemical systems.     

ASTM clustering for improving coal analysis by near-infrared spectroscopy

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.     

The spatial effect of near-infrared spectroscopy and its application to the study of supramolecular chemistry

An unlooked-for experimental observation that in near-infrared spectroscopy (NIR) the absorption peak of the second overtone of aniline adsorbed by 13X molecular sieve nearly disappeared led us investigate a fundamental question: the behavior of NIR when the outside space surrounding a molecule is too small to allow the molecule to vibrate freely. Through NIR of various organic compounds adsorbed by different porous inorganic materials like 13X molecular sieve, silica gel and active aluminium oxide, and NIR of supramolecular cyanuric acid-melamine, we can reasonably confirm a theoretical inference that in the micro-environment above, all intensities of NIR absorbance decrease, and the second overtone decreases more than the first overtone does. Furthermore, one distinct feature of NIR, higher sensitivity to the size of micro-environmental space as compared with mid-infrared (MIR), and its potential application to the study of supramolecular structure are outlined by our experiments.     

Feasibility of Rapid Diagnosis of Colorectal Cancer by Near-Infrared Spectroscopy and Support Vector Machine

The feasibility of diagnosing colorectal cancers based on the combination of near-infrared (NIR) spectroscopy and supervised pattern recognition methods was investigated. A total of fifty-eight colorectal tissues were collected and prepared. The spectra were first preprocessed by standard normalize variate (SNV) and first derivatives of Savitzky-Golay polynomial filter for removing unwanted background variances. The information of CH-stretching overtones and combination regions proved to be the most valuable. Four pattern recognition methods including K-nearest neighbor classifier (KNN), perceptron, Fisher discriminant analysis (FDA), and support vector machine (SVM) were used for constructing classifiers. In terms of the total accuracy, sensitivity and specificity, the SVM classifier achieved the best performance; the sensitivity and specificity were 92.8% and 86.7%, respectively. These findings suggest that NIR spectroscopy offers the possibility of constructing a simple, feasible and sensitive method for diagnosing colorectal cancer, avoiding the need of laborious visual inspection from experts.     

偏最小二乘-近红外漫反射光谱法测定西米替丁药片

研究了应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对西米替丁片剂药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了波长间隔和主成分数对PLS定量预测能力的影响,预测了未知样品。     

Chemometrics and near-infrared spectroscopy: Avoiding the pitfalls

The use of chemometrics in quantitative near-infrared (NIR) spectroscopy is reviewed from the standpoint of avoiding pitfalls that may lead to misleading or overly optimistic results. Using the NIR analysis of glucose in six-component mixture samples as an example, a set of guidelines is presented to help the analyst develop and implement a successful calibration.     

Synchronous ATR infrared and NIR-spectroscopy investigation of sepiolite upon drying

A new environmental cell allowing for the independent synchronous collection of the near- and mid-infrared spectra (12,000–600 cm⁻¹) in the diffuse reflection and attenuated total reflection (ATR) modes, respectively, is reported. The cell is employed to study in real time the dehydration of the phyllosilicate mineral sepiolite, Mg₈Si₁₂O₃₀(OH)₄(OH₂)₄·wH₂O, in both its natural form and after in situ deuteration at ambient. The spectra are obtained under dynamic purging with dry N₂ and compared to those of the same material conditioned over saturated salt solutions. Sepiolite is an important industrial mineral with a modulated structure of alternating tunnels and ribbons. Its mild drying is associated with pronounced vibrational spectral changes due to the removal of surface and zeolitic H₂O and the concomitant structural relaxation of the ribbons. Detailed assignments are provided for the fundamental, combination and overtone spectrum of H₂O confined in the tunnels of sepiolite, SiOH groups on the external surface of the particles, and Mg₃OH groups in the 2:1 ribbons. The spectra are discussed in comparison to those of palygorskite (modulated phyllosilicate with narrower ribbons and tunnels), talc (trioctahedral magnesian phyllosilicate without modulation) and high-surface area silica. It is demonstrated that sepiolite exhibits three discrete states of zeolitic hydration at ambient temperature: Besides the previously known hydrated (w = 7–8) and dry (w = 0–1) states which dominate the spectra above 30% and below 3% relative humidity, respectively, a hitherto unknown intermediate (w = 4–5) is found in the 3–10% range. The new state is most conveniently identified in the near-infrared by a ν₀₂ Mg₃O-H stretching mode at 7205 cm⁻¹ (ν₀₁ = 3686 cm⁻¹, X = 83.5 cm⁻¹) and a characteristic H₂O combination band at 5271 cm⁻¹ (D₂O: 3908 cm⁻¹).     

A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine

Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction – the boxcar signal transfer (BST) – was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set.     

Classification of Liquor Using Near-Infrared Spectroscopy and Chemometrics

The authenticity of Chinese liquor concerns consumer health and economic issues. The traditional characterization methods are time-consuming and require experienced analysts. The use of near-infrared (NIR) spectroscopy and chemometrics to classify Chinese liquor samples was investigated using 128 liquors. The spectral region between 5340 cm^?1 and 7400 cm^?1 was found to be most informative. Principal component analysis was employed to characterize liquor and principal components were extracted as inputs of training classifiers. Several supervised pattern recognition methods including K-nearest neighbor, perceptron, and multiclass support vector machine were used as algorithms of constructing classifiers. The initial principal components and all spectral variables were used as the input of training models. In terms of the misclassification ratio, the support vector machine approach was the most accurate. The results indicated that near-infrared spectroscopy and chemometrics are an alternative to conventional methods for the characterization of liquor.     

Application of near-infrared spectroscopy in the sugar industry for the detection of betaine

One problem in industrial molasses desugarization is the lack of a fast analytical method for process control. At the moment, control of the chromatographic production process is achieved by detecting refractive index and conductivity. However, since elution of some components takes place only in a narrowly defined time frame, the data gained are insufficient for effective online product quantification. Near-infrared (NIR) spectroscopy was applied to this process by development of a simple method for detection of betaine. Compared to chemometric models currently used, the developed method demonstrates the advantage of requiring only a small calibration set. Additionally, it can easily be transferred to other processes without further re-calibration. Based on the NIR spectrum of betaine, a characteristic peak in the spectrum could be assigned to the molasses compound betaine. A calibration was developed by using dissolved betaine in pure water. Afterwards, the calibration was tested for samples from a molasses desugarization process. The method was than successfully transferred to a complete chromatographic cycle of the industrial molasses desugarization process.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.