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。     

中红外、近红外和拉曼光谱法测定商品农药制剂中氰戊菊酯和马拉硫磷的含量

利用近红外、中红外和拉曼光谱法定量分析了商品农药制剂中有效成分氰戊菊酯和马拉硫磷的含量.采用偏最小二乘法(Partial least squares,PLS)建立氰戊菊酯和马拉硫磷的定量模型并进行了优化,用独立检验集对模型适应性进行评价.近红外和中红外法测定氰戊菊酯、马拉硫磷定量模型的相关系数分别是0.9981,0.9994和0.9946,0.9998,外部验证集标准差分别是0.082,0081和0.092,0.075,两种方法的定量效果接近;拉曼法氰戊菊酯和马拉硫磷定量模型的相关系数分别为0.9872和0.9993,外部验证集标准差分别为0.254和0.317,预测精度不及近红外和中红外法高.MIR-ATR,NIR和Raman 3种方法均能满足现场检测农药质量的需要.     

傅立叶变换拉曼光谱和红外光谱鉴别塑料

用傅立叶变换拉曼光谱、衰减全反射红外光谱和近红外光谱结合OPUS/Ident软件对添加不同填料、不同助剂的塑料进行鉴别分类。结果表明:分子光谱结合化学计量学鉴别塑料是一种快速可靠的方法。其中拉曼光谱和衰减全反射红外光谱能够直接区分样品,而近红外光谱非常类似,不能直接区分。但是用OPUS/Ident软件中的W ard算法处理这3种光谱后,得到的树形图能够将样品准确分类。     

不同含水量砂岩的中红外光谱特征与预测模型研究

水对岩石具有软化、溶蚀等作用,准确预测岩石的含水量对岩土工程的地下深部开采至关重要。该文将中红外光谱技术应用于砂岩的含水量分析,研究了不同砂岩样品的中红外光谱特征与其含水量之间的关系。通过多种预处理方法提取有效特征向量,构建了针对砂岩含水量的中红外光谱预测模型。结果表明,多元散射校正+偏最小二乘法、归一化特征向量提取+随机森林及一阶微分+支持向量机3种模型在测试集上预测的含水量与实测值之间的相关系数（R2）分别为0.985、0.995和0.951,均方根误差分别为0.074、0.022和0.137,即特征向量提取+随机森林的预测模型效果最佳。该方法通过中红外光谱技术实现了砂岩含水量的无损、快速分析,为地质工程中砂岩的水分预测提供了参考。     

近红外光谱用于过氧化氢含量的定量分析研究

用长波近红外光谱仪（傅里叶变换,InGaAs检测器）和短波近红外光谱仪（光栅分光,CCD检测器）对比研究了25％～30％过氧化氢水溶液中过氧化氢含量的定量分析方法。结果表明,应用短波近红外光谱结合长光程样品池对25％-30％过氧化氢水溶液样品中过氧化氢含量进行定量分析,可以显著减少过氧化氢分解对定量分析的干扰,使定量分析的准确度和重复性显著提高。短波近红外光谱定量分析模型RMSECV和RMSEP分别为0．06和0．05;长波近红外光谱定量分析模型RMSECV和RMSEP分别为0．10和0．09。     

中红外光谱透射比标准物质研制

采用物化性质稳定的单晶硅作为基质,以直流磁控溅射方式轻蒸镀不同厚度的镍、铬混合膜层,实现了中红外波段(500~4000 cm^–1)不同透射比标准物质的研制。研制的中红外光谱透射比标准物质其透射比标称值分别为10%和50%,扩展不确定度优于0.008(k=2)。该标准物质具有量值稳定、光谱平坦等特点,可用于各类傅里叶变换红外光谱设备的透射比校准。     

近红外光谱分析模型传递简易方法研究

本文在不同时间安装的多台同型号近红外光谱仪上建立推进剂校正模型时,由于推进剂样品数量少且难于保存,新到仪器在建模时常遇到代表性样品数量严重不足.为此,提出将2台波长一致性好的近红外光谱仪器上采集的光谱组成一个混合校正样品光谱集,使用偏最小二乘法(PLS)建立模型的方法.结果表明,在用户缺少专业模型传递软件情况下,该方法...     

中红外光谱技术应用于蜂蜜掺假预判的研究

由于蜂蜜蜜种多,成分复杂,加之蜂蜜掺假方式繁多,采用传统的方式很难对蜂蜜进行快速准确的鉴别。通过对国内多个地区的蜂蜜进行调研,采集来自全国20个省份多个蜜种的蜂蜜,利用中红外光谱仪对样品进行光谱扫描,采用主成分分析和聚类分析的方法,利用化学计量软件进行模型的建立。该识别模型不仅能较准确地判别蜂蜜是否掺假(准确率为95.36%),还能对添加量在10%以上的掺假方式进行预判,判别准确率为97.78%,符合判别模型的建立要求。利用中红外光谱技术对蜂蜜掺假进行鉴别的方法有效、可行。     

近红外光谱在蛋白质和含酰胺基团聚合物研究中的应用*

近红外光谱(near-infrared spectroscopy,NIR)是一种常用的无损表征手段,但谱带强度弱、交叠情况严重等缺点局限了它的应用范围。本文介绍了几种常见的改善近红外光谱技术不足的方法,如二阶导数法、二维相关光谱法和化学计量法等,并举例阐述了近红外光谱在蛋白质和含酰胺基团聚合物的结构和含量等方面的应用。这些方法对近红外光谱的定性定量分析起了很好的辅助作用,有效地拓宽了近红外光谱技术的应用领域。     

Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

Abstract

American Petroleum Institute (API) gravity is an important parameter in the crude oil industry and the nitrogen compounds are related to the toxic effects of the oil in refineries and the environment. In this paper, 194 crude oil samples with API gravities ranging from 11.4 to 57.5 were used for the purpose of estimating the physicochemical properties: API gravity, total nitrogen content (TNC) and basic nitrogen content (BNC). Initially, infrared spectra in the mid and near regions (MIR and NIR) were collected, then full-spectral partial least squares (PLS) and the orthogonal projections to latent structures (OPLS) chemometric models were developed and validated, as well as models using interval PLS (iPLS), synergy interval PLS (siPLS) and competitive adaptive reweighted sampling PLS (CARSPLS) as variable selection tools. For API gravity and TNC, the best calibration technique is the NIR CARSPLS with a root mean square error of prediction (RMSEP) values of 0.9 and 0.0275?wt%, respectively. For BNC, the best technique is MIR siPLS with a prediction error of 0.0134?wt%. The results were validated based on the evaluation of the figures of merit, a statistical evaluation of the accuracy, characterization of the systematic error and measurement for errors in the residues. The results were satisfactory considering the high variability of the data and the diversity of the samples, demonstrating suitable applicability for practical analysis.     

Rapid Determination of Cotton Content in Textiles by Near-Infrared Spectroscopy and Interval Partial Least Squares

Textile products must be marked by fabric type and composition on the label and cotton is by far the most important fiber in the industry and often needs fast quantitative analysis. The corresponding standard methods are very time-consuming and labor-intensive. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and interval-based partial least squares (iPLS) for determining cotton content in textiles. Three types of partial least square (PLS)-based algorithms were used for experimental measurements. A total of 91 cloth samples with cotton content ranging from 0 to 100% (w/w) were collected and all compositions are commercially available on the market in China. In all cases, the original spectrum axis was split into 20 subintervals. As a result, three final models, i.e., the iPLS model on a single subinterval, the backward interval partial least squares (biPLS) model on the region remaining six subintervals, and the moving window partial least squares (mwPLS) model with a window of 75 variables, achieved better results than the full-spectrum PLS model. Also, no obvious differences in performance were observed for the three models. Thus, either iPLS or mwPLS was preferred considering their simplicity, which suggested that iPLS and mwPLS combined with NIR technique may have potential for the rapid determination of the cotton content of textile products with comparable accuracy to standard procedures. In addition, this approach may have commercial and regulatory advantages that avoid labor-intensive and time-consuming chemical analysis.     

Compositional Analysis of Cement Raw Meal by Near-Infrared (NIR) Spectroscopy

Abstract

A rapid and accurate method is presented to determine CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃ in cement raw meal using near-infrared (NIR) spectroscopy. Multiplicative scatter correction (MSC) was employed to eliminate the scattering signal and partial least squares (PLS) regression was used to build the analysis model. The results demonstrated good performance by this approach for the determination of CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃. NIR spectroscopy exhibits the feasibility to characterize the quality of cement raw meal. Compared with prompt gamma neutron activation analysis (PGNAA) and X-ray fluorescence (XRF), this method is more efficient and safer.     

Rapid Simultaneous Determination of Andrographolides in Andrographis paniculata by Near-Infrared Spectroscopy

The potential of near-infrared spectroscopy (NIRS) for the quality control of traditional Chinese medicine has been evaluated. Seven quantitative parameters, andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, and alcohol-soluble extract of Andrographis paniculata, were evaluated by NIRS. The reference values of andrographolides were determined by high-performance liquid chromatography, and the others were obtained using the standard methods of the 2015 Chinese Pharmacopoeia. The predicted values were determined by a quantitative model using NIRS based on partial least square regression. Different spectral preprocessing methods, spectral ranges, and optimum number of factors were selected to optimize the models. All models were estimated by the combination of various parameters, including the correlation coefficient of calibration for andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, alcohol-soluble extract (values of 0.980, 0.984, 0.989, 0.983, 0.987, 0.988, 0.979, respectively), root mean square error of calibration (values of 0.156, 0.038, 0.050, 0.029, 0.604, 0.431, 0.135, respectively), root mean square error of prediction (values of 0.169, 0.041, 0.050, 0.033, 0.280, 0.493, 0.140, respectively), root mean square error of cross-validation (values of 0.626, 0.114, 0.158, 0.046, 1.145, 0.774, 0.508, respectively), and ratio of standard deviation to standard error of prediction (values of 4.583, 4.690, 4.796, 4.899, 4.899, 4.690, 5.099, respectively). The results show that the calibration models by NIRS are reliable and can be applied for the quantification for seven parameters from A. paniculata for quality control in traditional Chinese medicine production and processing.     

Investigation of Water Interaction with Polymer Matrices by Near-Infrared (NIR) Spectroscopy

The interaction of water with polymers is an intensively studied topic. Vibrational spectroscopy techniques, mid-infrared (MIR) and Raman, were often used to investigate the properties of water–polymer systems. On the other hand, relatively little attention has been given to the potential of using near-infrared (NIR) spectroscopy (12,500–4000 cm⁻¹; 800–2500 nm) for exploring this problem. NIR spectroscopy delivers exclusive opportunities for the investigation of molecular structure and interactions. This technique derives information from overtones and combination bands, which provide unique insights into molecular interactions. It is also very well suited for the investigation of aqueous systems, as both the bands of water and the polymer can be reliably acquired in a range of concentrations in a more straightforward manner than it is possible with MIR spectroscopy. In this study, we applied NIR spectroscopy to investigate interactions of water with polymers of varying hydrophobicity: polytetrafluoroethylene (PTFE), polypropylene (PP), polystyrene (PS), polyvinylchloride (PVC), polyoxymethylene (POM), polyamide 6 (PA), lignin (Lig), chitin (Chi) and cellulose (Cell). Polymer–water mixtures in the concentration range of water between 1–10%(w/w) were investigated. Spectra analysis and interpretation were performed with the use of difference spectroscopy, Principal Component Analysis (PCA), Median Linkage Clustering (MLC), Partial Least Squares Regression (PLSR), Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) and Two-Dimensional Correlation Spectroscopy (2D-COS). Additionally, from the obtained data, aquagrams were constructed and interpreted with aid of the conclusions drawn from the conventional approaches. We deepened insights into the problem of water bands obscuring compound-specific signals in the NIR spectrum, which is often a limiting factor in analytical applications. The study unveiled clearly visible trends in NIR spectra associated with the chemical nature of the polymer and its increasing hydrophilicity. We demonstrated that changes in the NIR spectrum of water are manifested even in the case of interaction with highly hydrophobic polymers (e.g., PTFE). Furthermore, the unveiled spectral patterns of water in the presence of different polymers were found to be dissimilar between the two major water bands in NIR spectrum (ν_s + ν_as and ν_as + δ).     

近红外光谱分析技术在快速分析上的应用

简要介绍了近红外光谱的原理、特点,综述了近红外光谱在农业、食品、制药、石油化工、高分子等领域快速分析上的研究及应用现状,并对近红外光谱的应用前景进行了展望．     

Study of the thermal decomposition of a sepiolite by mid- and near-infrared spectroscopies

Near-infrared (NIR) and mid-infrared (MIR) spectroscopies were used to characterize the hydroxyl groups present in a sepiolite and its calcination products at 250, 500 and 800 °C. Calcining the sepiolite at 250 °C was found to result in its dehydration through the loss of coordination water. Raising the calcination temperature to 500 °C caused the sepiolite structure to fold and hydroxyl groups in octahedral layers to be removed. Finally, calcination at 800 °C resulted in a phase change leading to enstatite, as confirmed by XRD spectroscopy.     

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.     

Nondestructive Quantitative Analysis of Analgini Powder Pharmaceutical by Near-Infrared Spectroscopy and Artifical Neural Network Technique

ABSTRACT

This paper demonstrates the usefulness of near-infrared (NIR) spectra and artificial neural network (ANN) in nondestructive quantitative analysis of pharmaceuticals. Real data sets from near-infrared reflectance spectra of analgini powder pharmaceutical were used to build up an artificial neural network to predict unknown samples. The parameters affecting the network were discussed. A new network evaluation criterion, the degree of approximation, was employed. The overfitting was discussed. Owing to the good nonlinear multivariate calibration nature of ANN, the predicted result was reliable and precise. The relative error of unknown samples was less than 2.5%