基于近红外光谱技术的内燃机油鉴别研究

针对常规近红外光谱技术测试内燃机油时光谱信号响应低,对大分子基团分辨率不高,以及光谱信息与其结构组成之间存在非线性关系等难点,提出了一种基于电压为外扰方式的内燃机油二维近红外光谱测试技术,介绍了近红外光谱具有分形的特征.运用小波变换将近红外光谱分解至不同分辨尺度,然后计算各尺度分量的分形维数(盒维数),用近红外光谱的小波基分形参量替代近红外光谱的采样值.计算结果表明,在不同小波基和不同分解尺度下,内燃机油近红外光谱具有不同的盒维数,得到了近红外光谱在分形意义下的特征信息.以美孚、埃索和壳牌3种内燃机油品种鉴别分类问题为实例,比较研究了近红外光谱采样值与小波基分形参量,K近邻法的交互验证计算结果表明,小波基分形参量的分类效果优于近红外光谱采样值.采用近红外光谱技术测试内燃机油的结构组成信息是可行的.     

《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。     

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。     

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。     

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．     

《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     

《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     

近红外光谱法定量分析维生素E

在９３％～９７．４％浓度范围内,利用维生素Ｅ（ＶＥ）在６０６１～５２４６ｃｍ＾－１处的近红外吸收峰面积积分值和其浓度关系建立的回归方程为：Ｙ＝１０３．４３～０．０７８６２４Ｘ。用此回归方程对已知浓度的样品进行预测,误差及相对误并均在－０．７９％～０．９％内。在较宽浓度范围８０％～９７％之间的ＶＥ,用ＰＬＳ算法选择不同遥数据预处理方法,对近红外吸光度值和其含量的关系建模,度用已知浓度的ＶＥ进行校验。     

Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

Principal component analysis (PCA) is widely used as an exploratory data analysis tool in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited.The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined, compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used.     

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%     

The application of fourier-transformed near-infrared spectra to quantitative analysis by comparison of similarity indices (CARNAC)

A new technique for utilising near-infrared spectroscopic data for quantitative analysis is proposed. The method uses a database of spectra stored in the form of the Fourier transform together with the associated chemical analysis. The spectrum of an unknown sample is compared to each member of the database and a small subset of very similar samples is isolated. The analyte value for the unknown is calculated from the analytical values of the subset. Results are given for the application of the method to the analysis of nicotine in tobacco.     

Discrimination of poly(vinyl chloride) samples with different plasticizers and prediction of plasticizer contents in poly(vinyl chloride) using near-infrared spectroscopy and neural-network analysis.

In the recycling of poly(vinyl chloride) (PVC), it is required to discriminate every plasticizer for quality control. For this purpose, the near-infrared spectra were measured for 41 kinds of PVC samples with different plasticizers (DINP, DOP, DOA, TOTM and Polyester) and different plasticizer contents (0-49%). A neural-network analysis was applied to the near-infrared spectra pretreated by second-derivative processing. They were discriminated from one another. The neural-network analysis also allowed us to propose a calibration model which predicts the contents of plasticizers in PVC. The correlation coefficient (R) and the root-mean-square error of prediction (RMSEP) for the DINP calibration model were found to be 0.999 and 0.41 wt%, respectively. In comparison, a partial least-squares regression analysis was carried out. The R and RMSEP of the DINP calibration model were calculated to be 0.993 and 1.27 wt%, respectively. It is found that a near-infrared spectra measurement combined with a neural-network analysis is useful for plastic recycling.     

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。     

主成分分析在近红外定量分析校正集样本优选中的应用

将主成分分析方法用于近红外定量分析校正集样本的优选,提出一种根据样本光谱第一主成分得分优选校正集样本的方法,并使用本方法从418个烟草样本中优选得到约105个样本的校正集。通过对烟碱成分实际建模与外部验证,与随机法、含量梯度法两种校正集样本挑选方法的性能进行了对比。结果表明,本方法既克服了随机法挑选样本代表性不足的风险,又可避免含量梯度法必须测定所有样本成分含量而造成的人力物力消耗,具有无需编程、操作简单、易于推广的特点。     

人工神经网络用于近红外光谱预测汽油辛烷值

本文对BP人工神经网络(ANN)方法在汽油的辛烷值与其近红外光谱光谱吸光度的关系之间进行关联预报方面进行了研究。采用35个汽油实际样本数据,建立了利用汽油的近红外光谱光谱吸光度预测汽油辛烷值的BP人工神经网络模型。对所有辛烷值的计算结果与实测值进行了比较,得到的预测值与实测值计算误差小于1.55%。     

Chemometrics in near-infrared spectroscopy

Spectroscopy methods of chemical analysis are excellent for the application of chemometric methods, because the measurements at many different wavelengths provide inherently multivariate data. The chemist generally requires three categories of information from specimens under investigation: quantitative data, qualitative data, and fundamental information on the properties of the material. Spectroscopy has long been used for all three purposes; the recent application of chemometric algorithms has assisted greatly in these endeavors. Although there is some overlap, three chemometric methods correspond to the three types of information: multiple regression, discriminant analysis, and principal components analysis. The basis of these chemometric methods and some of their strengths and limitations in application to near-infrared spectroscopy are discussed.     

Cr3+ 掺杂近红外荧光材料的研究进展

近红外光谱检测技术具有非侵入性、便携性、响应速度快、效率高等特点,在食品分析、医学监测、虹膜识别和红外成像等领域具有广阔的应用前景。然而,近红外荧光材料面临发射谱带窄、热稳定性差的问题,极大制约了近红外器件的发展。为此,蓝光激发Cr3+掺杂的近红外发光材料在众多方案中优势显著。本综述总结了近年来Cr3+掺杂的宽带近红外荧光材料的最新研究成果,分析了Cr3+掺杂的近红外荧光材料量子效率、发射谱带、电声耦合效应和NIR pc-LED器件的封装,并阐述了Cr3+离子格位占据、敏化发光、发射谱带和热稳定性调控策略。最后,对高效宽光谱发射近红外发光材料的应用前景进行了展望。