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,...     

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...     

模型诊断用于近红外光谱建模校正集中奇异样本的识别

由于校正集样本的质量决定校正模型的质量,校正集中奇异样本的检测在多元校正建模中具有非常重要的意义.本研究建立了一种用于近红外光谱多元校正建模时校正集中奇异样本的检测方法.本方法基于奇异样本的定义和偏最小二乘方法的原理,通过考察每个校正集样本在模型的每个因子(或主成分)中对模型的贡献,将与多数样本表现不同的样本识别为奇异样本.采用218个橘汁样本构成的近红外光谱数据进行了分析,结果表明,校正集中存在6个奇异样本,扣除奇异样本后,校正集的交叉验证均方根误差由16.870减小为4.809,预测集的均方根误差从3.688减小为3.332.     

中药材三七提取液近红外光谱的支持向量机回归校正方法

提出近红外光谱的支持向量机回归校正建模方法.以中药材三七渗漉提取液为实际分析对象,对其近红外光谱数据进行预处理和主成分分析后,用支持向量机回归算法建立人参皂苷Rg1,Rb1和Rd以及三七总皂苷的近红外光谱校正模型.以Rg1,Rb1和Rd的HPLC测定值及三七总皂苷的比色法测定值为参照,将本文方法与偏最小二乘回归和径向基神经网络建模方法相比较,结果表明,本文所建模型的预测准确性优于后两者,可推广应用于中药提取过程的近红外光谱分析.     

基于交互式自模型混合物分析的近红外光谱波长变量优选方法

为了提高近红外光谱定量分析的预测精度和建模效率,提出了一种基于交互式自模型的混合物分析的波长优选方法,根据光谱各波长变量的纯度值和标准差值,选择含有用信息的波长变量,并引入相关权函数解决变量间共线性问题.通过依次迭代选择的变量建立定量校正模型,由交互验证均方根预测误差(RMSECV)确定最佳波长变量个数.应用该波长变量优选方法对具有不同葡萄糖含量的两组(四成分葡萄糖水溶液实验和人体血浆实验)近红外光谱数据进行分析,两组数据中分别只选择了全部变量的0.3%建立定量校正模型,其验证集葡萄糖浓度的均方根预测误差(RMSEP)分别减少为669和15 mg/L.与全谱范围及优选波段建立的定量校正模型比较,本方法能够通过波长变量优选最小化冗余信息、提高预测精度及建模效率.     

苹果产地差异对可溶性固形物近红外光谱检测模型影响的研究

为更好地利用近红外光谱预测苹果可溶性固形物含量,减少产地差异对近红外光谱检测模型的影响,以4种不同产地的富士苹果为研究对象,采用基于x-y共生距离的样本划分方法分别对不同产地的苹果选取代表性样本作为校正集,利用偏最小二乘算法,建立和比较单一产地和混合产地下的苹果可溶性固形物近红外光谱检测模型,并结合竞争性自适应重加权算法(CARS)和连续投影算法(SPA)对苹果可溶性固形物的建模变量进行筛选。相比单一产地和其它混合产地模型,混合所有4种苹果产地的校正集样本建立的模型取得了最好的预测结果,另外,结合CARS-SPA筛选的16个特征波长,模型得到了进一步简化,其预测相关系数和预测均方根误差分别为0.978和0.441°Brix。结果表明,利用多个产地的苹果样本建立的混合模型,结合有效特征波长,可提高对苹果可溶性固形物含量的预测精度,减小产地差异对可溶性固形物近红外光谱检测的影响。     

采用多光程长建模方法检测血液成分含量

为了提高近红外光谱血液成分含量分析模型的预测精度,利用多个光程长(optical path length,OPL)共同参与建模的方法进行血糖等6种血液成分的定量分析。通过微米位移机构实现不同光程长血液光谱的测量,由全自动生化分析仪给出生化成分分析结果,并出具化验单。采用偏最小二乘法(PLS2)进行血液的近红外光谱建模及预测。由于血液光谱存在显著的非线性特征,不同光程长的血液样本的等效吸收系数不同,同一波长不同光程长(0.20~1.25 mm)测得的血液光谱互不相关。主动把非线性特性作为一种测量手段引入,不再利用单个的最佳光程长建模,而是用各个血液组分对应的多个最佳光程长的近红外光谱同时参与建立校正模型,进行血液成分的分析预测。研究结果表明,多光程长建模方法用于血液成分含量分析,可提高血液成分校正模型的预测精度。     

废旧涤/棉混纺织物近红外定量分析模型的建立及预测

将中红外光谱筛选出的598个纯涤、纯棉及涤/棉混纺样本采用GB/T 2910.11-2009法测定其涤、棉准确含量,其中校正集样本252个,验证集样本346个。使用便携式近红外光谱仪获取样本的原始近红外光谱(NIRS)。校正集样本依据回归系数的分布趋势和范围选取最佳建模谱区,并采用差分一阶导、S-G平滑和均值中心化相结合的方法对原始光谱进行预处理,利用偏最小二乘法(PLS)建立涤/棉混纺织物中涤含量的近红外(NIR)定量分析模型。同时分析了样本颜色对NIRS的影响,探讨了斜线光谱样本、奇异样本和不同组织结构织物对模型预测效果的影响。结果表明:利用PLS法建立的涤/棉混纺织物定量分析模型最优组合包含1个光谱区间和9个主成分因子,校正集相关系数(RC)为0.998,标准偏差(SEC)为0.908。为验证所建模型的有效性和实用性,对346个未参与建模的涤棉样本进行了预测,并将预测结果与国标法测定值进行方差分析,两种方法结果无显著差异,预测正确率达97%以上。模型的建立为废旧涤/棉混纺织物快速、无损分拣提供了基础数据库。     

近红外光谱定量校正模型的建立及应用

介绍了近红外光谱定量校正模型的建立步骤,从样品集的选择,光谱的采集,定量校正模型的建立和验证,到模型适用性判据的建立.在此基础上,剖析了影响近红外光谱分析结果的因素,并讨论了在实际应用过程中应注意的关键性问题.     

PDS用于不同温度下的近红外光谱模型传递研究

采用合适的计算方法可降低测定环境对近红外光谱校正模型稳健性的影响。该文以喷气燃料为研究对象,考察了分段直接校正算法对所建模型预测结果的影响,通过选择转移样品数及窗口宽度,建立了最佳的校正模型和光谱转移参数。结果表明,在20℃下建立近红外光谱校正模型,直接预测30℃下喷气燃料的密度,预测集样品均方根误差（RMSEP）为0.2031,而30℃近红外光谱采用分段直接校正算法模型转移后,预测集样品均方根误差（RMSEP）降低为0.1354,预测结果得到明显改善,有效地解决了样品温度对近红外光谱分析结果的影响。     

A local pre-processing method for near-infrared spectra,combined with spectral segmentation and standard normal variate transformation

Pre-processing of near-infrared (NIR) spectral data has become a necessary part of chemometrics modeling and is widely used in many practical applications. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve subsequent qualitative or quantitative analysis. Herein, a localized version of standard normal variate (SNV) is proposed, in which the correction parameters are estimated from local spectral areas. The method of determining the optimal spectral segmentation is also presented. Compared with full range methods, the local method demonstrates advantages in spectral linearity correction, model interpretation and prediction accuracy. Several benchmark NIR data sets were studied in our experiments; the proposed method achieved comparable performance against proven full range methods, with the reduction of prediction errors being statistically significant in many cases.     

Simultaneous Spectrophotometric Estimation and Validation of Three Component Tablet Formulation Containing Pioglitazone Hydrochloride,Metformin Hydrochloride and Glibenclamide

Abstract

Simultaneous estimation of active ingredients in multicomponent pharmaceutical products normally requires the use of separation techniques, such as HPLC, HPTLC, or GC, followed by their quantitation. Presented here are two spectrophotometric methods that do not require prior separation for simultaneous estimation of three drugs—pioglitazone HCl, metformin HCl, and glibenclamid—in a tablet formulation. Shimadzu UV 1700, capable of multicomponent analysis, was used for quantitation. In method I, absorbance of the sample solution was measured at 285 nm and 300 nm for the estimation of pioglitazone HCl and glibenclaimide, and at 237 nm for estimation of metformin hydrochloride, respectively. Method II is based on a multiwavelength spectroscopic method. Recording the absorbances of standard solutions at 237 nm, 268 nm, 280 nm, and 300 nm were processed by means of statistical calculations and results of the sample solution were obtained. All three drugs obey Beer's law in the concentration ranges used for the methods. The result of the analysis for both methods were tested and validated for various parameters according to ICH guidelines. The utility of the developed methods has been demonstrated by an analysis of commercial formulation containing all the three drugs.     

Generalized two-dimensional correlation spectroscopy-- Theory and applications in analytical field

Since the theory of generalized two-dimensional (2-D) correlation spectroscopy was proposed, it has been keenly concerned in scientific research and its analytical method has been widely applied in various analytical fields. The mathematical process to construct generalized 2-D correlation spectroscopy and the physical meaning of 2-D correlation spectral map are described, and three examples in the fields of chemical analysis and molecular biology are provided, such as the component analysis of organic solvent, the analysis of biological molecules in the solvent with different pH values and structural analysis of protein. The theory and analytical method of generalized 2-D correlation spectroscopy are also detailedly commented.     

食源性致病菌的表面增强拉曼光谱检测技术研究进展

食源性致病菌引起的疾病的快速管控与预防是当前各国面临的食品安全监管难题之一,受到社会各界的广泛关注。目前常用的食源性致病菌检测方法存在步骤复杂、耗时、灵敏度低或选择性差等局限,发展快速、可靠的食源性致病菌检测方法仍是食品安全和公众健康的热点研究领域。表面增强拉曼光谱(SERS)作为一种新型的光谱快检技术,具有灵敏度高、选择性好、快速、无损检测等优点,在食源性致病菌检测方面表现出广阔的应用前景。该文简要介绍了SERS技术的背景、增强机制并总结了用于致病菌检测的活性纳米结构,全面综述了近5年来利用直接或间接SERS法以及SERS与其他技术结合检测食源性致病菌的研究进展,同时讨论了这些方法的优缺点。最后,提出当前SERS技术在实际应用中面临的挑战,并展望了SERS在食源性致病菌检测领域未来可能的发展趋势。     

Generalized two-dimensional correlation spectroscopy——Theory and applications in analytical field

After correlation analysis for general spectro- scopy, two-dimensional (2-D) correlation spectroscopy is obtained by extracting the information contained in the spectra in two dimensions, which is the function of two dependent spectral variables. 2-D correlation spectroscopy is initially regarded as an analytical data treatment method in the study of molecular interaction by using sinusoidal infrared sign[1]. In 1993, it was extended to generalized 2-D correlation spectroscopy, which used mo…     

糖胺聚糖分析测定的研究进展

糖胺聚糖是一类重要的生物大分子。综述了利用分子光谱法、高效液相色谱法、电泳法、质谱法和电化学等方法对其进行分析测定的研究进展。     

Comparative Study using Raman and Visible Spectroscopy of Cretan Extra Virgin Olive Oil Adulteration with Sunflower Oil

The adulteration of extra virgin olive oil with low-quality and inexpensive seed oil is a serious problem in the industry. In recent years, the characterization of extra virgin olive oil adulteration with various techniques has been successfully implemented. In this work, a comparative study of Raman and visible spectroscopy is presented. These methods are rapid, noninvasive, and no sample pretreatment is required. We used both methods to study Cretan extra virgin olive oil adulterated with sunflower oil. Statistical analysis based on partial least square regression was used to determine the detection limits of the methods. Raman spectroscopy was superior in comparison to visible spectroscopy with adulteration detection limits of 3.5 and 5.5%, respectively, for the same samples. These results indicate that both techniques are suitable for olive oil quality control.     

Development of diagnostic models for canine osteoarthritis based on serum and joint fluid mid‐infrared spectral data using five different discrimination and classification methods

Osteoarthritis (OA) is an insidious joint disease that gradually leads to cartilage loss and the morphological impairment of other joint tissues. Therefore, early diagnosis and timely therapeutic intervention are of importance. Although there are a few diagnostic techniques used in clinics, these methods have various drawbacks. Infrared spectroscopy has emerged as an important analytical technique with wide applications in a variety of areas including clinical diagnosis. Research has shown that the presence of OA is associated with biochemical changes that are presumed to be reflected in serum or joint fluid. Hence, OA may be detected provided that serum or joint fluid is measured by infrared spectroscopy and appropriate data analysis methods are used to extract the diagnostic information from the infrared spectra. In this work, 5 discrimination and classification methods ([1] principal component analysis coupled with linear discriminant analysis, [2] principal component analysis coupled with multiple logistic regression, [3] partial least squares discriminant analysis, [4] regularized linear discriminant analysis, and [5] support vector machine) were used to build OA diagnostic models based on mid‐infrared spectra of serum and joint fluid. Useful diagnostic models were developed, indicating that infrared spectroscopy coupled with multivariate data analysis methods is very promising as a simple and accurate approach for OA diagnosis. The results also showed that models built from the 5 methods were different, as were the models' predictive performances. Therefore, choice of appropriate data analysis methods in model development should be taken into account.     

ZnO-Bi2O3二元陶瓷粉体电化学行为研究

采用电化学阻抗谱和电位-电容测试及Mott-Schottky分析技术研究了ZnO-Bi2O3粉体电极在0.5 mol/L NaCl溶液中的电化学行为. 研究表明, ZnO-Bi2O3粉体表现为n型半导体; 随着Bi2O3含量的增加或混合时间的延长, 粉体的阻抗增大, 空间电荷层电容Csc减小, 载流子浓度ND减小; 经高温烧结成二元压敏陶瓷, 随着混合时间的延长, 电阻片综合电性能越好. 该方法可有效评价二元陶瓷粉体混合均匀性.     

Expanding the analytical toolbox for identity testing of pharmaceutical ingredients: Spectroscopic screening of dextrose using portable Raman and near infrared spectrometers

In the pharmaceutical industry, dextrose is used as an active ingredient in parenteral solutions and as an inactive ingredient (excipient) in tablets and capsules. In order to address the need for more sophisticated analytical techniques, we report our efforts to develop enhanced identification methods to screen pharmaceutical ingredients at risk for adulteration or substitution using field-deployable spectroscopic screening. In this paper, we report our results for a study designed to evaluate the performance of field-deployable Raman and near infrared (NIR) methods to identify dextrose samples. We report a comparison of the sensitivity of the spectroscopic screening methods against current compendial identification tests that rely largely on a colorimetric assay. Our findings indicate that NIR and Raman spectroscopy are both able to distinguish dextrose by hydration state and from other sugar substitutes with 100% accuracy for all methods tested including spectral correlation based library methods, principal component analysis and classification methods.