一种基于近红外光谱技术的不同品种及掺假三七的无损鉴别分析研究

基于近红外光谱技术与化学计量学方法,提出了一种不同品种及掺假三七的快速无损鉴别方法.分别采集景天三七、菊三七、血三七、田三七完整、粉末及掺假样品的近红外光谱,采用单一和组合预处理方法消除光谱中的干扰,筛选出最佳的预处理方法;结合主成分分析法建立不同品种以及掺假三七样品的鉴别模型.结果表明:结合主成分分析,采用原始光谱即...     

基于近红外光谱技术与优化光谱预处理的陈皮产地鉴别研究

采集不同产地陈皮内侧和外侧的近红外光谱,采用不同光谱预处理方法进行预处理,筛选得到最佳光谱预处理方法,结合主成分分析法建立了陈皮产地的鉴别模型.实验发现,陈皮原始光谱中存在明显的基线漂移与背景干扰.使用单一光谱预处理可在一定程度上消除干扰的影响.经标准正态变量变换、多元散射校正、一阶导数、二阶导数与连续小波变换预处理后...     

基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明:对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

离散小波变换-遗传算法-交互检验法用于近红外光谱数据的高倍压缩与变量筛选

用遗传算法(GA)与交互检验(CV)相结合建立了一种用于对近红外光谱(NIR)数据及其离散小波变换(DWT)系数进行变量筛选的方法，并应用于烟草样品中总挥发碱和总氮的同时测定。结果表明：NIR数据经DWT压缩为原始大小的3．3％时基本没有光谱信息的丢失；有效的变量筛选可以极大地减少模型中的变量个数，降低模型的复杂程度，改善预测的准确度。     

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.     

基于近红外光谱分析技术的食品包装塑料的定性分析

该文基于近红外漫反射光谱分析技术对食品包装材料聚乙烯、聚丙烯进行定性判别试验研究,选取不同波段范围、采用不同光谱预处理方法,使用主成分分析法(Principal component analysis,PCA)结合SIMCA、贝叶斯判别、K-近邻3种模式识别方法建立定性预测模型,并根据正确识别率比较了各模型预测性能。结果表明:使用SIMCA方法、贝叶斯判别、K-近邻3种方法建立的定性校正模型均在1 050～1 550 nm波长范围内效果较好;采用矢量归一化、标准正态变量变换、中心化、滑动均值滤波、多项式平滑滤波、一阶微分6种光谱预处理方法和上述3种模式识别方法对塑料样品近红外光谱进行了数据处理,其中在1 050～1 550 nm范围内,主成分因子数为3,采用原始光谱建立的K-近邻定性校正模型较优,对样品校正集和预测集的正确识别率均为100%。可为食品包装材料聚乙烯、聚丙烯的快速鉴别研究提供参考。     

鲜辣椒中糖份和维生素C含量的近红外光谱非破坏性测定

用近红外光谱法非破坏测定鲜辣椒中可溶性糖和维生素C含量，可溶性糖含量的化学值与近红外预测值之间的相关系数为0．9024，校正集标准差(SEP)为1．23％，RSD9．3％；VC含量的化学值与近红外预测之间的相关系数为0．9122，校正集标准差(SEP)为24．17，RSD为9．5％，辣椒鲜果中可溶性糖和VC含量与近红外光谱有显著的相关关系。     

小波变换用于近红外光谱数据压缩

近红外光潜数据量大,需要较大数据存储空间和较长的建模时间、本文以成品柴油性质分析为例．将小波变换用于近红外光谱数据压缩处理,详细考察了小波压缩参数,比较了压缩前后潜图差异以及性质分析偏差的变化。研究结果表明．采用Daubechies小波函数(N=2)为母函数．进行3次分解,直接采用其逼近系数(Ca3)作为谱图压缩数据,其重构光谱与原始光谱基本一致直接利用逼近系数进行性质分析,其分析精度与原始光谱数据基本相当,存储空间减少至原来的1／8,且能够明显缩短其建模时间和分析时间。     

连续小波变换-独立成分回归算法及其在多组分分析中的应用

采用连续小波变换（CWT）对光谱数据进行处理，用独立成分分析（ICA）进行特征提取，再用回归分析方法对被测组分进行测定，建立了连续小波变换一独立成分回归（CWT-ICR）方法。方法用于肉样品中水分、脂肪和蛋白质多组分的同时测定，所得结果与化学法测得结果相符。     

近红外光谱无损定量分析吡嗪酰胺片

近红外光谱;径向基神经网络;吡嗪酰胺;定量分析     

Application of near infrared spectroscopy (NIR), X-ray fluorescence (XRF) and chemometrics to the differentiation of marmora samples from the Mediterranean basin

Abstract

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ.     

基于近红外光谱技术的不同产地茯苓块无损鉴别

基于近红外光谱技术,建立了不同产地茯苓块快速无损鉴别方法.利用近红外光谱仪采集了8个不同产地茯苓块的光谱信息;通过单一及组合预处理方法消除光谱中的多种干扰;结合主成分分析方法、软独立模式分类法和Fisher线性判别分析方法分别构建了不同产地茯苓块的鉴别模型.结果 表明:光谱中存在较为明显的背景以及噪声干扰;仅采用主成分...     

菲林B近红外分光光度法测定维生素C

在pH 3的三氯乙酸酸性介质中,菲林B可以定量地将还原型维生素C氧化成脱氢型维生素C,利用脱氢型维生素C在920 nm处有最大吸光度,测定其含量,建立了一种测定维生素C的新方法,并研究了影响反应的各种因素。该方法对维生素C的检出限为0.17 mg/L;线性范围为0.5~10 mg/L,对水果中维生素C含量测定的RSD<2.31%;回收率为99.7%~101.1%,比2,4-二硝基苯肼分光光度法测定结果的相对偏差<±1.6%。     

Discrimination of industrial products by on-line near infrared spectroscopy with an improved dendrogram

Near infrared(NIR) spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques.Moreover,since each brand contains a large number of samples,an improved dendrogram was proposed to show the classification of different brands.The results suggest that NIR spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis(HCA) performs well in discrimination of the different brands,and the improved dendrogram could provide more information about the difference of the brands.     

Characterization of nano FeIII-tannic acid modified polyacrylate in controlled-release coated urea by Fourier transform infrared photoacoustic spectroscopy and laser-induced breakdown spectroscopy

Polyacrylate polymer (PA) has been widely applied in coating products for decades. Recently, it has been used in controlled-release fertilizers. Nano Fe^III-tannic acid modified PA (PA-Fe) provides a better nutrient controlled release performance than conventional PA. In this work, a preliminary database of molecular and elemental information about the polymer was obtained using FTIR-PAS (Fourier transform infrared photoacoustic spectroscopy) and LIBS (laser-induced breakdown spectroscopy), respectively. The PA-Fe polymer contained more hydrophobic groups (–CH₃) and fewer hydrophilic groups (–COOR, –COOH) than PA. More elements were detected for PA-Fe than PA. LIBS was useful to identify and classify PA and PA-Fe samples using principal component analysis. The combination of spectroscopic results and a film formation process model explained the lower nutrient release rate of PA-Fe. These results showed the strong analytical capabilities of FTIR-PAS combined with LIBS for identifying and characterizing modified PA.     

Comparative analysis of smokeless gunpowders by Fourier transform infrared and Raman spectroscopy

Fourier Transform Infrared (FTIR) and Raman spectroscopic techniques were used to perform a comparative study of the spectral profiles of single-base, double-base and triple-base smokeless gunpowders. Preliminary results based on visual comparison of the spectra point out that spectra obtained by both vibrational techniques were useful for a rapid identification of gunpowders containing dinitrotoluene as one of the major components and triple-base gunpowders. Additionally, the Raman spectra of gunpowders with diphenylamine in its primary composition showed a characteristic band, assigned to 2-nitro-diphenylamine, allowing the identification of this type of gunpowders.     

Discrimination of ethylene/vinyl acetate copolymers with different composition and prediction of the content of vinyl acetate in the copolymers and their melting points by near-infrared spectroscopy and chemometrics

Near-infrared (NIR) diffuse reflectance spectra have been measured by use of a rotating drawer for pellets of 12 kinds of ethylene/vinyl acetate (EVA) copolymers with vinyl acetate (VA, the comonomer) varying in the 7–44 wt % range. They are unambiguously discriminated from one another by a score plot of the principal component analysis (PCA) Factor 1 and 2, based upon the NIR spectra pretreated by multiplicative scatter correction (MSC). Principal component (PC) weight loadings for Factor 1 show that the discrimination relies largely upon bands due to the overtone and combination modes arising from the VA unit. We have found one “outlier” in the score plot and elucidated its spectral characteristics based upon PC weight loadings for Factor 2. Partial least-squares (PLS) regression has been applied to propose calibration models which predict the VA content in EVA. The models have been prepared for three kinds of pretreatment, the first derivative, the second derivative, and MSC; and four kinds of wavelength regions. The NIR spectra in the 1100–2200 nm region after the MSC treatment has given the best correlation coefficient and standard error of prediction (SEP) of 0.998 and 0.70%, respectively. The calibration models, prepared by NIR diffuse reflectance spectroscopy for the pellet samples, are compared with previously reported models by NIR transmission spectroscopy for the flowing molten samples, and with those by Raman spectroscopy for the pellet samples. PLS regression has also allowed us to predict melting points of the copolymers with the correlation coefficient and SEP of 0.997 and 0.78°C, respectively. © 1998 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 36: 1529–1537, 1998     

Investigation into classification/sourcing of suspect counterfeit Heptodin™ tablets by near infrared chemical imaging

Near infrared chemical imaging (NIR-CI) analysis was performed on 55 counterfeit Heptodin™ tablets obtained from a market survey and an additional 11 authentic Heptodin™ tablets for comparison. The aim of the study was to investigate whether NIR-CI can be used to detect the counterfeit tablets and to classify/source them so as to understand the possible number of origins to aid investigators and authorities to shut down counterfeiting operations. NIR-CI combined with multivariate analysis is particularly suited to compare chemical and physical properties of samples, since it is a quick and non-destructive method of analysis. Counterfeit tablets were easily distinguished from the authentic ones. Principal component analysis (PCA) and k-means clustering were performed on the data set. The results from both analyses grouped the counterfeit tablets in 13 main groups. The main groups found with both methods were quite consistent. Out of the 55 tablets only 18% contained the correct active pharmaceutical ingredient (API), i.e., the anti-viral drug lamivudine. The remaining 82% of counterfeit tablets contained talc and starch as main excipients. The API containing tablets classified into three main groups, based mainly on the amount of lamivudine present in the tablet. The group which had close to the correct amount of lamivudine sub-classified into three groups. From the analysis carried out, it is likely that the counterfeit tablets originate from as many as 15 different sources.     

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz ¹H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley & Sons, Ltd.     

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.