近红外光谱法快速测定涂料固化剂中游离甲苯二异氰酸酯含量

用气相色谱分析值为参照,采用近红外透射光谱（NIR）技术采集相应样品的NIR光谱,研究了涂料固化剂中游离甲苯二异氰酸酯（TDI）含量的快速测定分析方法。 并从120个固化剂样品中挑选出109个代表性的样品建模,选择7320~7250 cm-1和8485~8370 cm-1波段区间,用偏最小二乘法(PLS)和完全交互验证方式建立TDI含量的预测模型。 结果表明,固化剂中游离甲苯二异氰酸酯含量和近红外光谱之间存在较好的相关性,其预测模型的校正集均方差（RMSEC）为0.0815,验证集均方差（RMSEP）为0.0715,模型性能良好。 近红外光谱法可快速准确测定游离甲苯二异氰酸酯（TDI）含量,用于固化剂样品快速分析。     

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,...     

乙醇混合燃料近红外定量分析

采用近红外光谱(NIR)透射法对乙醇混合燃料各成分进行定量分析；其中乙醇体积分数为84．5％～98．2％，汽油体积分数0～15％；通过偏最小二乘法(PLS)建立模型，乙醇含量NIR模型校正集测定系数(R^2)为0．9969，模型校正集标准差(SEE)和预测集标准差(SEP)分别为0．23和0．38，汽油含量NIR模型校正集测定系数为0．9939，模型校正集标准差和预测集标准差分别为0．38和0．39，对含量较小的干扰物质丙酮预测结果也理想；近红外和多元校正技术可作为乙醇混合燃料中成分含量测定简单、快速方法之一。     

啤酒主要成分的近红外光谱法测定

根据近红外光谱的振动吸收强度与有机分子官能团含量的线性关系,用偏最小二乘法,对啤酒的近红外光谱与其中的酒精度、原麦汁浓度以及总酸含量等3种主要成分进行了线性回归,并建立起相关的模型。用该模型对未知啤酒样品中的上述3种成分的含量进行预测,取得了令人非常满意的结果。可望作为啤酒厂的一种快捷而准确的检测方法予以推广。     

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。     

近红外光谱法分析格列齐特片

应用化学计量法处理光谱数据,用偏最小二乘法建立格列齐特片的近红外分析模型。通过光谱预处理和模型的逐步优化最终确定定量分析模型的相关系数为0.991,交叉验证均方差(RMSECV)为0.641,预测均方根误差(RMSEP)为0.980,主因子数为4。选取15个验证样品对模型进行检验,检测结果相对误差在-2.04%～3.52%之间。     

近红外光谱法测定卷烟纸中钙和镁

应用傅里叶变换近红外(FT-NIR)光谱分析技术结合偏最小二乘法(PLS),建立了卷烟纸中钙和镁含量的数学预测模型。结果表明:钙和镁模型的相关系数分别为0.9870和0.9851,内部交叉验证均方差为0.462和0.0082,近红外光谱法预测值与原子吸收光谱法测定值的平均相对偏差各为3.1%和7.4%。该方法简便、快速、不破坏样品,可用于大批量卷烟纸样品中钙和镁的快速测定。     

近红外光谱法测定涂料稀释剂中苯系物含量

建立使用近红外光谱法(NIR)快速测定溶剂型木器涂料稀释剂中甲苯、乙苯、对二甲苯、间二甲苯和邻二甲苯等苯系物含量方法。收集涂料稀释剂样品,使用气相色谱法(GC)测定苯系物含量,并采集其近红外光谱信息,采用偏最小二乘法(PLS)建立NIR光谱与苯系物含量的线性关系模型。苯系物校正均方差(RMSEC)在(0.47~1.40)%之间、相关系数(R2)在0.956~0.988之间;预测均方差(RMSEP)在(0.73~2.32)%之间、相关系数(R2)在0.951~0.986之间。NIR模型预测效果良好,定量方法快速、简单、准确,可在检测涂料的有毒有害物质中推广应用。     

近红外光谱法测定黄芩提取物中黄芩苷含量

近红外光谱技术(NIR)是近年来快速发展的一种新型光谱分析技术,具有快速、高效、无污染、非破坏性以及实时分析等优点~([1]),已在农业、烟草、石油化工、医药等多领域得到广泛应用.尤其在药物分析方面,体现出近红外光谱分析的巨大潜力~([2]).     

多模型共识偏最小二乘法用于近红外光谱定量分析

将多模型共识偏最小二乘法用于近红外光谱定量分析。利用随机抽取的训练子集建立一系列偏最小二乘模型,选取其中性能较好的部分模型作为成员模型,用这些成员模型来预测未知样品。将该方法用于一组生物样本的近红外光谱与样品中人血清白蛋白、γ-球蛋白以及葡萄糖含量之间的建模研究,并与单模型偏最小二乘法了进行比较。结果 PLS对独立测试集中三种组分进行50次重复预测的平均RMSEP分别为0.1066,0.0853和0.1338,RMSEP的标准偏差分别为0.0174,0.0144和0.0416;而本方法重复预测的平均RMSEP分别为0.0715,0.0750和0.0781,RMSEP的标准偏差分别为0.0033,0.2729×10-4和0.0025。     

Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine

It is important to monitor quality of tobacco during the production of cigarette. Therefore, in order to scientifically control the tobacco raw material and guarantee the cigarette quality, fast and accurate determination routine chemical of constituents of tobacco, including the total sugar, reducing sugar, Nicotine, the total nitrogen and so on, is needed. In this study, 50 samples of tobacco from different cultivation areas were surveyed by near-infrared (NIR) spectroscopy, and the spectral differences provided enough quantitative analysis information for the tobacco. Partial least squares regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The quantitative analysis models of 50 tobacco samples were studied comparatively in this experiment using PLSR, ANN, radial basis function (RBF) SVM regression, and the parameters of the models were also discussed. The spectrum variables of 50 samples had been compressed through the wavelet transformation technology before the models were established. The best experimental results were obtained using the (RBF) SVM regression with gamma=1.5, 1.3, 0.9, and 0.1, separately corresponds to total sugar, reducing sugar, Nicotine, and total nitrogen, respectively. Finally, compared with the back propagation (BP-ANN) and PLSR approach, SVM algorithm showed its excellent generalization for quantitative analysis results, while the number of samples for establishing the model is smaller. The overall results show that NIR spectroscopy combined with SVM can be efficiently utilized for rapid and accurate analysis of routine chemical compositions in tobacco. Simultaneously, the research can serve as the technical support and the foundation of quantitative analysis of other NIR applications.     

Quantitative analysis of two adulterants in Cynanchum stauntonii by near-infrared spectroscopy combined with multi-variate calibrations

Authentication of traditional Chinese medicines (TCMs) has become important because they can be adulterated with relatively cheap herbal medicines similar in appearance. Detection of such adulterated samples is needed because their presence is likely to reduce the pharmacological potency of the original TCM and, in the worst cases, the samples may be harmful. The aim of this study was to develop a rapid near-infrared spectroscopy (NIRS) analytical method which was supported by multi-variate calibration, e.g. partial least squares regression (PLSR) and radial basis function artificial neural networks (RBF-ANN), in order to quantify the TCM and the adulterants. In this work, Cynanchum stauntonii (CS), a commonly used TCM, in mixtures with one or two adulterants ?? two morphological types of TCM, Cynanchum atrati (CA) and Cynanchum paniculati (CP), were determined using NIR reflectance spectroscopy. The three sample sets, CS adulterated with CA or CP, and CS with both CA and CP, were measured in the range of 800?C2500 nm. Both PLSR and RBF-ANN calibration models provided satisfactory results, even at an adulteration level of 5 mass %, but the RBF-ANN models with better root mean square error of prediction (RMSEP) values for CS, CA, and CP arguably performed better. Consequently, this work demonstrates that the NIR method of sampling complex mixtures of similar substances such as CS adulterated by CA and/or CP is capable of producing data suitable for the quantitative analysis of mixtures consisting of the original TCM adulterated by one or two similar substances, provided the spectral data are interrogated by multi-variate methods of data analysis such as PLS or RBF-ANN.     

Quantitative aspects of near-infrared Fourier transform Raman spectroscopy

Three fundamental behaviors of vibrational spectroscopy data manipulation routinely associated with Fourier transform infrared (FTIR) spectroscopy are evaluated for near-infrared (NIR) Fourier transform Raman spectroscopy. Spectral reproducibility, spectral subtraction and sensitivity are examined relative to the NIR FT-Raman experiment. Quantitative predictive ability is compared for identical sets of samples containing mixtures of the three xylene isomers. Partial least-squares analysis is used to compare predictive ability. IR performance is found to be better than Raman, though the potential for method development using NIR FT-Raman is shown to be quite promising.     

短波近红外光谱法对蛇床子SFE萃取产物的定量分析

利用中药蛇床子CO2超临界萃取(SFE)的萃取物的短波近红外漫反射光谱(800～1100 nm),以HPLC分析值作参比值,采用化学计量学中的偏最小二乘法(PLS)建立短波近红外漫反射光谱与蛇床子SFE萃取物中主要成分蛇床子素和欧前胡素间定量分析数学模型.实现了快速、无损的测定双组分中药的有效成分.讨论了光谱的预处理方法和主成分数对PLS定量预测蛇床子萃取物中蛇床子素和欧前胡素含量能力的影响,并对预测集样品进行预测.     

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.     

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.     

Rapid analysis of Radix puerariae by near-infrared spectroscopy

A new, rapid analytical method using near-infrared spectroscopy (NIRS) was developed to differentiate two species of Radix puerariae (GG), Pueraria lobata (YG) and Pueraria thomsonii (FG), and to determine the contents of puerarin, daidzin and total isoflavonoid in the samples. Five isoflavonoids, puerarin, daidzin, daidzein, genistin and genistein were analyzed simultaneously by high-performance liquid chromatography-diode array detection (HPLC-DAD). The total isoflavonoid content was exploited as critical parameter for successful discrimination of the two species. Scattering effect and baseline shift in the NIR spectra were corrected and the spectral features were enhanced by several pre-processing methods. By using linear discriminant analysis (LDA) and soft independent modeling class analogy (SIMCA), samples were separated successfully into two different clusters corresponding to the two GG species. Furthermore, sensitivity and specificity of the classification models were determined to evaluate the performance. Finally, partial least squares (PLS) regression was used to build the correlation models. The results showed that the correlation coefficients of the prediction models are R = 0.970 for the puerarin, R = 0.939 for daidzin and R = 0.969 for total isoflavonoid. The outcome showed that NIRS can serve as routine screening in the quality control of Chinese herbal medicine (CHM).