近红外光谱在无机微量成分分析中的应用

由于近红外光谱的独特优势, 在实际复杂样品分析中发挥了重要作用. 但由于近红外光谱的信号相对较弱, 无机离子在近红外光谱中一般没有响应, 因此难以用于微量成分特别是无机微量组分的测定. 总结了近红外光谱技术在环境、土壤、植物及生物样品分析中的应用, 说明了近红外光谱用于无机微量成分分析的原理. 由于近红外光谱技术一般通过多元校正方法进行定性定量分析, 利用组分间的相互作用或组分含量之间的相关性可以实现微量组分或无光谱响应组分的定量分析. 还总结了富集技术在近红外光谱分析中的应用, 利用富集技术可实现稀溶液中金属离子含量的快速测定, 并可以改善分析的灵敏度和检测限.     

近红外光谱分析技术的最新进展与展望

在过去的几十年,近红外光谱是发展最为迅速的分析技术之一。振动光谱基础理论、光谱仪器硬件和化学计量学是现代近红外光谱分析技术的3大支柱,近些年,近红外光谱技术在这3个方面均取得了显著进展。该文结合上述3个方面的应用研究情况,对近红外光谱分析技术的最新进展进行了综述,并对未来发展趋势进行了展望。     

烟草近红外光谱分析网络化及其应用进展

该文回顾了近红外光谱(NIRS)分析技术的应用历程以及"近红外光谱分析+互联网"模式在烟草领域中的应用研究与实践,探讨了在近红外光谱分析网络化环境中,近红外光谱仪器设备存在的硬件差异以及常规化学计量学方法(算法)在建模、数据处理存在的不足对近红外光谱的深度应用产生的影响,并提出了近红外光谱分析云计算应用的解决思路。最后,对大数据时代近红外光谱分析网络化模式的应用前景进行了展望。     

小波变换用于近红外光谱性质分析

以汽油研究法辛烷值分析为例,研究了小波变换在近红外光谱分析中的应用。对近红外光谱的小波特性、小波变换参数以及变量提取方法进行了详细研究。研究结果表明：光谱噪音、有用信息和背景分别分布在小波高、中和低频区域;母小波函数对性质分析结果影响很大;小波变换可以同时扣除光谱背景、去除噪音和压缩变量,具有运算速度快、分析精度高以及无需去噪后处理等优点,在近红外光谱分析中具有很好的应用前景。     

近五年我国近红外光谱分析技术研究与应用进展

评述了近五年来(2014～2018)我国近红外光谱分析技术的研究与应用进展,内容涉及方法研究、软硬件研发、应用特点和趋势等方面,并对今后我国近红外光谱技术的发展方向进行了展望。引用文献97篇。     

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。     

CCD近红外光谱分析技术在测定红烟硝酸中的应用

从化学计量学角度研究了CCD近红外光谱技术在无机物领域分析中的应用。使用偏最小二乘法(PLS)建立了红烟硝酸的密度、水分含量等五项指标的快速测定模型,验证了近红外光谱测定红烟硝酸各项指标的可靠性。试验证明,CCD近红外光谱方法具有重现性好、分析速度快、使用样品量少、可以在线分析等优点,可以用于无机物的常规分析。     

《近红外光谱解析实用指南》

该书是现有惟一介绍近红外光谱吸收谱带与分子结构之间关系的专著。书中给出了300多张近红外光谱图,几乎覆盖了所有在近红外光谱区有吸收的官能团。附录给出了大量的图表和数据以解析分子结构与近红外光谱之间的相关关系,书中还扼要介绍了近红外光谱产生的机理和应用发展史。     

《近红外光谱解析实用指南》

该书是现有惟一介绍近红外光谱吸收谱带与分子结构之间关系的专著。书中给出了300多张近红外光谱图,几乎覆盖了所有在近红外光谱区有吸收的官能团。附录给出了大量的图表和数据以解析分子结构与近红外光谱之间的相关关系,书中还扼要介绍了近红外光谱产生的机理和应用发展史。     

胆酸含量的近红外分析数学模型

本文应用近红外技术研究了快速测定胆酸含量的方法.通过测定胆酸在10000~4000cm-1范围内的近红外透射光谱,基于偏最小二乘(PLS)算法,建立了胆酸含量的数学模型.以校正均方差(RMSEC)和相关系数(R)为指标,确定了用于建模的最优近红外波段和光谱预处理方法,并基于此模型预测了9个样品.结果显示,建模效果良好,...     

NIRS分析技术在饲料品质检测中的应用

简介近红外光谱（Near Infrared Spectroscopy,NIRS)分析技术的优点，原理和应用，并重点对其在饲料品质检测中的应用进行综述（引用文献共51篇）。     

Identification of transgenic foods using NIR spectroscopy: A review

The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA.     

Non-linear regression methods in NIRS quantitative analysis

Due to its speed and precision, near-infrared reflectance spectroscopy (NIRS) has become a widely used analytical technique in many industries. It offers, moreover, a number of other advantages which make it ideal for meeting current demands in terms of control and traceability: low cost per sample analysed; little or no need for sample preparation; ability to analyse a wide range of products and parameters; a high degree of reproducibility and repeatability. NIRS can be built into in-line processes, and - since no reagents are required - produces no waste. However, the major drawback to the use of NIRS for its most traditional application (the generation of prediction equations) is that it is a secondary method, and as such needs to be calibrated using a conventional reference method. For quantitative applications, calibration involves ascertaining the optimum mathematical relationship between spectral data and data provided by the reference method. The model may be fairly complex, since the NIRS spectrum is highly variable and contains physical/chemical information for the sample which may be redundant. As a result, multivariate calibration is required, based on a set of absorption values from several wavelengths. Since the relationship to be modelled is often non-linear, classical regression methods are unsuitable, and more complex strategies and algorithms must be sought in order to model this non-linearity. This overview addresses the most widely used non-linear algorithms in the management of NIRS data.     

NIR-spectrometric analysis of food. Methodical development and achievable performance values

The application of near infrared spectrometry (NIRS) to estimate the distribution of macro nutrients in food provides a rapid method for predicting food quality. Three methods have been developed for quantitative determination of the constituents of foods containing high levels of moisture (meat products, “consumable meals” and potatoes) using reflectance spectrometry in the near infrared region within 1100–2500 nm. Experience concerning these NIRS method developments is reported in order to predict e.g. the fat, crude protein and carbohydrate content. Generally, the performance values achieved so far indicate that the methods may be used as a replacement for conventional expensive and time-consuming wet chemical analysis.     

Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy

The application of supervised pattern recognition methodology is becoming important within chemistry. The aim of the study is to compare classification method accuracies by the use of a McNemar’s statistical test. Three qualitative parameters of sugar beet are studied: disease resistance (DR), geographical origins and crop periods. Samples are analyzed by near-infrared spectroscopy (NIRS) and by wet chemical analysis (WCA). Firstly, the performances of eight well-known classification methods on NIRS data are compared: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN) method, Soft Independent Modeling of Class Analogy (SIMCA), Discriminant Partial Least Squares (DPLS), Procrustes Discriminant Analysis (PDA), Classification And Regression Tree (CART), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) neural network are computed. Among the three data sets, SIMCA, DPLS and PDA have the highest classification accuracies. LDA and KNN are not significantly different. The non-linear neural methods give the less accurate results. The three most accurate methods are linear, non-parametric and based on modeling methods. Secondly, we want to emphasize the power of near-infrared reflectance data for sample discrimination. McNemar’s tests compare classification developed with WCA or with NIRS data. For two of the three data sets, the classification results are significantly improved by the use of NIRS data.     

近红外光谱法测定端羟基环氧乙烷-四氢呋喃共聚醚的羟值

提出了用近红外光谱测定端羟基环氧乙烷-四氢呋喃共聚醚(PET)的羟值,结合主成分回归和偏最小二乘法建立了PET羟值与其近红外光谱之间的关联模型。结果表明,近红外光谱法与化学分析法的测定结果一致;近红外光谱法测定PET羟值的相对误差在5%以内;利用遗传算法选择部分波长建立校正可以降低模型的预测误差。     

水质监测中总磷无损的近红外光谱分析研究

本文对天然水样的近红外光谱与水样总磷（TP）含量进行了相关性研究,并建立了水质总磷的近红外光谱（NIRS）分析方法。结果表明：NIRS方法建立的定量分析模型所得TP含量与国标法得到结果的相关性达到0.908,并且在显著性水平大于0.05的条件下对NIRS方法与国标法的测定结果进行t检验,二者无显著性差异,说明了NIRS方法定量分析地表水中低含量总磷的可行性。     

An enrichment device of silica-based monolithic material and its application to determine micro-carbaryl by NIRS

Silica-based monolithic column material was synthesized and an enrichment device was fabricated with the material by assembling the material inside a glass column.The enrichment device was applied for the determination of micro-carbaryl with near-infrared spectroscopy(NIRS).The aqueous solutions of carbaryl passed through the device and the carbaryl was enriched on the surface of the material where diffuse reflection NIR spectra were measured.These procedures of enrichment and measurement ensured to conc...     

鱼粉近红外光谱模型传递应用研究

为探讨光栅型与傅里叶变换型近红外分析仪之间模型传递的应用效果,选取国产鱼粉为近红外光谱样本,DS2500F型近红外分析仪为源仪器,MPA型近红外分析仪为目标仪器,采用分段直接校正(PDS)方法实现近红外光谱传递。分别建立水分、粗蛋白质、粗脂肪、蛋氨酸和赖氨酸等组分的预测模型,通过交互验证决定系数(R2cv)、交互验证标准误差(RMSECV)、马氏距离(MD)、系统偏差(Bias)、预测均方根误差(RMSEP)和相对分析误差(RPD)等参数,多维度评估光谱传递后所建预测模型的效果。结果表明,DS2500F仪器的近红外光谱传递到MPA型仪器时,所建国产鱼粉的水分、粗蛋白质、粗脂肪、蛋氨酸、赖氨酸的预测模型与MPA型仪器原始预测模型各参数对比无显著差异,预测效果基本一致,说明国产鱼粉在DS2500F仪器上的近红外光谱通过传递可以替代MPA型仪器的原始光谱,间接实现了模型传递,且具有良好的适用性和共享性,可提高近红外预测模型的应用效率。     

Identification of counterfeit drugs using near-infrared spectroscopy

This work was aimed at the investigation of the use of near-infrared spectroscopy (NIRS) for the identification of counterfeit drugs. The identification is based on the comparison of the NIR spectrum of a sample with typical spectra of the authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to observe the diversity of physico-chemical characteristics found among commercial products. The parameters which could affect the spectra of a given drug (especially if presented in solid form) were investigated and the results showed that the first derivative can minimise spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The power of NIRS in distinguishing among similar pharmaceuticals was demonstrated and a protocol is proposed to construct a multivariate model and to include it in a library allowing testing for drug authenticity. The methodology was evaluated with real samples of counterfeit drugs and was able to recognise all those presenting changes in composition as false. The results show unequivocally the potential of NIRS for rapid, on-site and non-destructive identification of counterfeit pharmaceuticals.