基于料酒的三维同步荧光光谱模式识别研究

提出了一种应用同步荧光光谱技术无损快速鉴别料酒品牌的新方法.利用主成分分解法和小波变换法对料酒样品的同步荧光光谱信号进行了压缩处理,分别采用同步荧光光谱数据的第一主成分和小波细节系数为特征变量进行主成分分析和聚类分析,分类结果表明小波系数作为料酒的特征变量对料酒品牌分类正确率更高.利用偏最小二乘和径向基人工神经网络方法...     

二维相关荧光光谱技术

从发展历史、计算方程、一般规则和特有性质等方面系统地介绍了近年来在二维相关荧光光谱技术方面的方法探索和应用进展。以不同的外扰方式,如浓度、激发波长、猝灭以及其他如pH等分类,举例阐述了二维荧光相关光谱的可操作性及其应用,并与普通一维荧光光谱比较,说明了二维荧光相关光谱技术的优势。     

红外、近红外光谱-簇类的独立软模式方法识别植物调和油脂

用IR,NIR光谱法结合簇类的独立软模式(SIMCA)识别方法对植物油脂进行分类识别,建立了识别二元、三元植物调和油脂的测定方法。应用NIRCal5.2软件的SIMCA技术,分别为所制备的植物调和油脂建立了IR和NIR识别模型,并讨论了光谱处理和数据处理方法来提高模型的分类识别效果。分别以各种植物调和油脂的IR和NIR光谱为变量,随机抽取2/3的样本作训练集,建立了各个调和油的主成分分析(Princi-pal component analysis,PCA)模型;1/3作验证集,对所建模型进行验证识别。用聚类分析-主成分分析(CLU-PCA)方法考察调和油的IR,NIR光谱信息与其纯油的主成分分布。结果显示,在4000～10000cm-1光谱范围内,SIMCA可以对15种二元调和油和2种三元调和油的NIR光谱分别聚类并识别;并对10种二元调和油和2种三元调和油的IR光谱分别聚类并识别。IR以4个波数1099,1119,1746与2855cm-1的吸收值作为分析基础,选择不同的主成分数及数据预处理方法。各种油脂的SIMCA分析的分类精度均为100%,调和油的验证识别准确率100%,最低识别比例为1%,且IR识别灵敏度高于NIR。     

X射线荧光光谱法对橡胶鞋底的分类

为建立案件现场常见橡胶鞋底物证的分类方法,利用X射线荧光光谱,对40个不同品牌、不同系列的橡胶鞋底样品中的无机元素进行了检验。并结合多元统计学,分别选用主成分分析法、k均值聚类法及判别分析法等对实验结果进行分析。结果表明,该方法简便快速、结果准确可靠且无损检材,对样品的分类效果较好。利用该方法可以对橡胶鞋底物证进行分类。     

高光谱结合多元统计分析鉴别一次性纸杯样品

为了快速、准确地对现场一次性纸杯物证进行鉴别,提出了一种基于高光谱技术结合PCA、K-Means聚类、Fisher判别分析的识别分类方法。利用高光谱仪对收集的40个不同来源、不同用途的一次性纸杯进行检验,采用主成分分析法对光谱数据进行预处理,从中提取出了11个主成分。借助K-Means算法将40个样品聚为5组,各组分间区分明显。利用Fisher判别分析构建了3组判别函数,经检验函数模型可排除污染客体干扰,分组准确率达100%。     

基于红外光谱多元分析的记号笔种类鉴别研究

借助衰减全反射红外光谱结合K近邻算法和层次聚类,建立记号笔墨水种类鉴别的分类模型,为记号笔的种类鉴别提供有效的新方法。研究基于记号笔墨水的外光谱矩阵数据,通过建立主成分和判别分析分析模型和K近邻算法分类模型,实现对模型性能的比较和对模型分类结果的验证。实验结果表明,以水性和油性作为分类标准,模型对样本的区分能力好,其正确率为100%。借助红外谱图进一步分析水性油性样本时,其最强峰与其对应溶剂相符合。借助K近邻算法进行验证性分析,按重要性加权特征给不同的样本施加不同的权重,运用训练样本即为测试样本的方法交互验证,选取K值为1,训练集∶保持集=3∶1,建立分类模型,模型总分类准确率达100%,区分效果良好,不同品牌的油性、水性样本能被聚类为一组。综上,衰减全反射红外光谱结合K近邻算法和层次聚类可作为记号笔墨水种类鉴别的一种快速准确的分析方法。     

健康和病变关节软骨的傅里叶变换红外光谱显微成像及Fisher判别

傅里叶变换红外光谱显微成像(FTIRI)可同时获得样品的红外光谱和形貌信息,其与化学计量学方法结合,可实现生物组织中主成分含量及分布的定量研究.本研究采用FTIRI技术结合主成分分析(PCA)以及Fisher判别算法对正常和病变的关节软骨进行鉴别分析.对关节软骨切片进行实现FTIR扫描及光谱分析,再利用SPSS软件对软骨的光谱(矩阵)进行主成分分析,根据主成分得分矩阵构造分类函数,结合Fisher判别算法对样本进行分类识别.正常和病变的关节软骨样品识别准确率高达95.7％(初始案例)和94.3％(交互验证案例).本方法可准确有效地辨别关节软骨是否发生病变,为监测骨关节炎的发生和修复提供参考.     

光谱结合主成分分析和模糊聚类方法的样品聚类与识别

针对紫外光谱结合化学计量学方法快速测定渣油烃族组成模型适应性问题,对渣油光谱进行主成分分析,以主成分得分作为聚类的特征变量进行模糊聚类,建立了光谱结合主成分分析和模糊聚类方法的样品聚类与识别方法和识别,为光谱结合化学计量分析方法中构正模型的正确选择提供了依据。     

油气化探样品芳烃分析中干扰因素的识别

通过对全国各大油田典型油气的分析,提出各种油气的三维荧光光谱和固定波长同步荧光光谱特征峰和共性峰。利用三维荧光光谱可以判断油气的属性,油气性质的精细分析尚要依靠同步荧光光谱。钻井过程常常加入磺化沥青或原油,三维荧光光谱可以进行很好的干扰识别;钻遇煤层或含煤层段,根据煤的三维荧光等值线图加以判断。人类活动、化石燃料的不完全燃烧均能造成芳烃的污染,在取样和处理分析数据时应该进行校正。湖相和海相沉积物含有丰富的多环芳烃,采用固定能量同步荧光光谱分析效果更佳。     

公共安全视域下精神药物光谱学无损分类

该文对苯二氮类和吩噻嗪类镇静安眠药样品的红外光谱数据进行采集,构建了不同数据分类模型并对比了不同波段光谱数据集对其分析准确率的影响.结果表明,借助贝叶斯判别分析的基于全波段数据集的二阶导数模型分类效果优于其他单一模型,对于苯二氮类和吩噻嗪类两个不同类型精神药物的总体分类准确率达92.7％.借助贝叶斯判别分析的基于全波段...     

Practical aspects of chemometrics for oil spill fingerprinting

Tiered approaches for oil spill fingerprinting have evolved rapidly since the 1990s. Chemometrics provides a large number of tools for pattern recognition, calibration and classification that can increase the speed and the objectivity of the analysis and allow for more extensive use of the available data in this field. However, although the chemometric literature is extensive, it does not focus on practical issues that are relevant to oil spill fingerprinting. The aim of this review is to provide a framework for the use of chemometric approaches in tiered oil spill fingerprinting and to provide clear-cut practical details and experiences that can be used by the forensic chemist. The framework is based on methods for initial screening, which include classification of samples into oil type, detection of non matches and of weathering state, and detailed oil spill fingerprinting, in which a more rigorous matching of an oil spill sample to suspected source oils is obtained. This review is intended as a tutorial, and is based on two examples of initial screening using respectively gas chromatography with flame ionization detection and fluorescence spectroscopy; and two of detailed oil spill fingerprinting where gas chromatography-mass spectrometry data are analyzed according to two approaches: The first relying on sections of processed chromatograms and the second on diagnostic ratios.     

低场核磁共振结合化学计量学方法快速检测掺假核桃油

以掺假核桃油样品为低场核磁共振检测对象,利用主成分分析法(PCA)和偏最小二乘回归法(PLSR)分析处理Carr-Purcell-Meiboom-Gill(CPMG)序列的核磁共振弛豫数据,旨在探求一种能快速检测核桃油品质的新方法。对几种常见掺假形式(掺入大豆油、玉米油、葵花油)的核桃油样品和纯核桃油样品进行检测和评价。实验结果表明:纯核桃油和掺入不同种类食用油的掺假核桃油在主成分得分图上可以得到很好的区分,且掺假样品随掺假比例在图中呈规律性分布;采用PLSR法对CPMG数据和实际掺假率进行回归,可实现对核桃油掺假水平的准确定量测定。方法快速、无损、准确,在食用油制品的品质控制及评价方面具有很大的应用潜力。     

基于多阶导数拉曼光谱组合技术的矿物油模式分类

为了实现对法庭科学领域重质矿物油物证的快速、准确、无损的鉴定,该文基于光谱分析技术提出了一种多阶导数光谱数据组合分析的方法。收集了80种不同型号、不同厂家的重质矿物油样本,利用傅里叶变换拉曼光谱分析法采集样本的原始光谱数据和导数光谱数据,并通过结合化学计量学构建分类模型。在构建的主成分分析(PCA)结合径向基函数神经网络(RBF)分类模型中,对单独的原始光谱、一阶导数谱和二阶导数谱数据的训练集准确率分别为80.0%、86.7%和86.2%,测试集准确率分别为73.3%、80.0%和72.7%;对组合后的原始光谱+一阶导数谱、原始光谱+二阶导数谱和一阶导数谱+二阶导数谱数据的分类中,训练集准确率分别为97.0%、96.7%和100%,测试集准确率分别为85.7%、90.0%和100%。结果表明,对组合后的导数光谱与原始光谱构建分类模型,准确率更高。其中,基于一阶导数谱+二阶导数谱数据构建的PCA结合RBF分类模型的结果最为理想,准确率达100%。而K最近邻算法模型由于受到样本不均匀的影响,整体分类准确率均较低。利用组合的导数光谱与原始光谱数据构建分类模型能够实现对重质矿物油样本的快速、准确、无损鉴别,可为光谱组合技术在法庭科学及其他分析测试领域的应用提供一定的借鉴和参考。     

Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

Rapid Authentication of Olive Oil by Raman Spectroscopy Using Principal Component Analysis

A method of principal component analysis was employed to authenticate genuine olive oil based on Raman spectroscopy, which can reliably distinguish olive oil from other types of oils and can also accurately identify the level of adulteration in a set of olive oil samples contaminated with 5% or more of other types of oils, such as soybean oil, rapeseed oil, sunflower seed oil, and corn oil. The method is very easy, effective, time-saving, and requires minimal sample preparation. Therefore, the method is a promising technique for the rapid authentication application of olive oil.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resource(s): Additional text and table]     

基于代谢组学技术的快速蒸发电离质谱与智能手术刀联用实时鉴别果汁掺伪方法研究

近年来,果汁掺伪问题日益突出。作为一种新型的实时鉴别方法,智能手术刀(IKnife)与快速蒸发电离质谱(REIMS)联用技术无需样品前处理即可解决这一问题。该文采用代谢组学指纹识别技术建立橙汁、苹果汁和葡萄汁的实时鉴别方法,并对其掺假水平进行预测。主成分分析-线性判别分析联用法建立的判别模型能够区分不同类型的果汁,排除20%交叉验证法的正确率为97.28%,未知样品实时鉴别正确率高达100%。筛选出不同果汁中的标志物,并对部分化合物进行鉴定。结果表明,此方法能够成功区分掺有10%-50%苹果汁和葡萄汁的橙汁。通过对比分析REIMS单极质谱和串联质谱(REIMS/MS)的检测结果,两种方法建立的偏最小二乘分析模型均能准确预测掺假水平(所有模型的R 2和Q 2均大于0.82)。与传统的REIMS方法相比,REIMS/MS的预测值更加精确。     

Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines

Near-infrared (NIR) spectroscopy is a non-destructive measurement technique for many chemical compounds that has proved its efficiency for laboratory and industrial applications (including petroleum industry). Motor oil classification is an important task for quality control and identification of oil adulteration. Type of motor oil base stock is a key factor in product price formation. In this paper we have tried to evaluate the efficiency of different methods for motor oils classification by base stock (synthetic, semi-synthetic and mineral) and kinematic viscosity at low and high temperature. We have compared the abilities of seven (7) different classification methods: regularized discriminant analysis (RDA), soft independent modelling of class analogy (SIMCA), partial least squares classification (PLS), K-nearest neighbour (KNN), artificial neural network - multilayer perceptron (ANN-MLP), support vector machine (SVM), and probabilistic neural network (PNN) - for classification of motor oils. Three (3) sets of near-infrared spectra (1125, 1010, and 1050 items) were used for classification of motor oils into three or four classes. In all cases NIR spectroscopy was found to be effective for motor oil classification when combined with an effective multivariate data analysis (MDA) technique. SVM and PNN chemometric techniques were found to be the most effective ones for classification of motor oil based on its NIR spectrum.     

Comparison of single-reflection near-infrared and attenuated total reflection mid-infrared spectroscopies to identify and monitor hydrocarbons spilled in the marine environment

The capability of single-reflection near-IR (NIR) spectroscopy to differentiate, characterize and monitor the fate of a set of hydrocarbons spilled in the marine environment was compared with that of multiple-reflection horizontal attenuated total reflection mid-IR (ATR-MIR) spectroscopy. Multivariate pattern recognition techniques [principal component analysis (PCA), multivariate polynomial regression, cluster analysis and potential curves] were applied to unravel the major trends of the weathering processes of four generic types of crude oils and two heavy fuel oils spilled under controlled conditions for almost 4 months. A chemical interpretation of the NIR spectra related the weathering processes and the PCA loadings, which had not already been done in the literature. Weathering for both light and heavy products was characterized by a contrast among the linear aliphatic structures (more volatile and easy to degrade) and the branched and aromatic structures (more recalcitrant). Potential curves were applied to model each product and determine objectively whether unknown samples could be classified correctly. Polynomial regression on the PCA scores was employed to evaluate the time elapsed from the oil spillage to its sampling; this represents a new approach to assess the age of a hydrocarbon lump. In general, NIR spectroscopy yielded good results when light crude oils were studied, whereas ATR-MIR spectroscopy led to satisfactory results for both light and heavy products.     

多环芳烃指纹用于渤海采油平台原油的鉴别

采用气相色谱/质谱方法,对渤海海上4个不同区块、5个平台的6口油井原油进行了烷基化多环芳烃系列化合物和美国环保署（EPA）优先控制多环芳烃系列化合物的准确定性定量分析。通过多环芳烃原始指纹谱图、多环芳烃组分分布模式和特征比值的比较对上述原油进行鉴别。结果证明不同区块的原油中多环芳烃指纹信息不尽相同,即使在同一平台不同油井中所产的原油其指纹也存在一定差异。为确保原油鉴别的准确性,分析过程中必须在仪器的稳定性和样品前处理方面实施严格的质量控制措施。