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]     

主成分分析-支持向量回归建模方法及应用研究

将主成分分析(PCA)用于近红外光谱的特征提取,并与支持向量回归(SVR)相结合,实现了主成分分析-支持向量回归(PCA-SVR)用于近红外光谱定量分析的建模方法。与单纯的SVR方法相比,不仅提高了运算速度,而且提高了模型的预测准确度。将PCA-SVR方法用于烟草样品中总糖和总挥发碱含量的测定,所得结果的预测均方根误差分别为1.323和0.0477;回收率分别为91.8%~112.6%和88.9%~120.2%。     

Multi‐Channel Surface Acoustic Wave Sensors Based on Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) for Organic Vapors

A multi‐channel surface acoustic wave (SAW) detection system which is employed to detect various organic molecules in a static system was prepared using 315 MHz one‐port quartz resonators and a home‐made computer interface for signal acquisition and data process. The oscillating frequency of the quartz crystal decreases on adsorption of organic molecules on the coating materials. The principal component analysis (PCA) method with SAS software was applied to select the appropriate coating materials onto the SAW crystals for organic vapors, e.g. hexane, 1‐hexene, 1‐hexyne, 1‐propanol, propionaldehyde, propionic acid, and 1‐propylamine. A dataset for a multi‐channel sensor with 19 SAW crystals for 7 analyses was collected after comparing the correlation between the 19 coating materials and the first six principal component (PC) factor. Furthermore, linear discriminate analysis (LDA) with SPSS software and a profile discrimination map were also applied and discussed for the discrimination of these organic vapors. These organic molecules could be clearly distinguished by the six‐channel SAW static sensor. The effect of concentration for various organic vapors was investigated and discussed.     

一类用于多元光度分析的小波基主成分回归法

通过将变尺度小波分解滤噪与特征信息提取相结合,提出一类新的多元光度分析算法－小波基主成分回归（ＰＣＲＷ）方法。该法可有效地减小主成分向量残留噪声所引 误差显著提高多元校正准确性。将其用于分析氯霉素、醋酸对塞米松、尼泊金乙脂体系、得到满意的回收率,与ＰＣＲ法相比,分析结果的总平均相对误差从３．３８％降低到０．８３％。     

偏最小二乘法及主组分回归法用于药物组分的测定

本文研究了多元校准方法——偏最小二乘法(PLS)和主组份回归法(PCR)在药物多组份光度分析中的应用,获得了较满意的结果。而且在系列校准样品的实验设计、交叉证实法确定最佳因子数以及空缺组份体系的分析等方面进行了探讨。     

主成分回归rFIA同时测定磷和硅的应用研究

以钼酸铵-sb^3^ -抗坏血酸为显色体系,利用rFIA(反相流动注射分析)分光光度法,对磷和硅的同时测定进行了实验研究;根据主成分回归法具有用较少的独立成分说明多个变量所提供的信息,有效降低噪声影响的特点,对实验所得数据进行了主成分回归处理,并建立起校正模型;结果表明,该模型在磷和硅的同时测定中是准确可行的。     

主成分回归—极谱分析法测定吡嗪混合物

吡嗪及其甲基衍生物在弱酸性介质中具有良好的极谱波。但它们彼此重叠,无法作单一成分分析。本文在pH=4.8的McIlvane缓冲溶液中对吡嗪、2-甲基吡嚎、2,3-二甲基吡嗪和2,3,5-三甲基吡嗪多组分混合溶液在静汞电极上作微分脉冲极谱测定,所得的极谱重叠波数据用主成分回归法处理,获得了较好的定量分析结果。对于含量在0.15～1μg/ml之间的吡嗪及其甲基衍生物,回收率为95％～105％,相对标准偏差为4％～10％。     

主成分回归-络合滴定法测定混合金属离子

提出了用络合滴定法测定混合金属离子.以汞膜电极为指示电极,银-氯化银电极为参比电极,以标准EDTA溶液滴定混合金属离子溶液.采集-系列指定电位点处滴定剂的体积,并以主成分回归法处理滴定数据.利用本文方法对铜、锌、铅、钻混合溶液进行了测定,结果满意.     

用四种离散变换主组分回归方法分辨重叠吸收光谱

将离散小波变换、小波包变换、傅里叶变换和离散余弦变换与主组分回归方法结合构成4种离散变换主组分回归方法,编制了离散变换主组分回归方法的计算程序。将离散变换主组分回归方法用于处理对硝基甲苯、对硝基酚和对硝基苯胺混合物的重叠紫外吸收光谱数据。结果表明,离散变换主组分回归方法优于主组分回归方法,试样质量浓度的预测值与实际值的相对预测标准误差由3.81%降至约1.11%。     

Chemometrics on Microchips: Towards the Classification of Wines

The coupling of a capillary electrophoresis (CE) microchip with electrochemical detection with a chemometric approach was used for the classification of wines. Cabernet Sauvignon wine samples from 3 different geographical regions were used for verifying the discriminating power of the microchip electrophoretic technique using high separation voltage (3000 V) with analysis time of only 60 seconds. Electrophoretic data were transformed into initial 14 variables, concerning peak's area and peak's height. Principal component analysis was applied to optimize the information given by the electrophoretic data sets. Results of classification using LDA and CART were promising and the preliminary model was able to classify samples on the basis of their geographical origin. While the coupling of microchips with chemometrics approaches is illustrated for a former tentative of the classification of wines it could be readily extended to a wide range of other important applications.     

Characterization of Automobile Plastics by Principal Component Analysis and Near-Infrared Spectroscopy

Based on near-infrared diffuse reflection spectroscopy, multivariate calibration models for discarded automobile plastic were constructed using principal component analysis and clustering analysis to rapidly characterize four widely employed materials: polypropylene, polyethylene, acrylonitrile butadiene styrene, and polymethylmethacrylate with an accuracy rate of 97%. The method was shown to rapidly discriminate waste automobile plastic.     

Use of Principal Component and Cluster Analysis for the Comparison of Reversed-Phase HPLC Columns

Abstract

The retention of ethoxylated nonylhylphenyl surfactants was determined in reversed-phase high-performance liquid chromatography (RP-HPLC) using various supports (C1, C2, C6, C8, C18, polyethylene-coated silica, and polyethylene-coated alumina). The retention data matrix was evaluated both by principal component analysis and cluster analysis. The retention characteristics of both polyethylene-coated supports were similar to that of C1, the retention capacity of RP columns for surfactants increased with the increasing length of the covalently bonded hydrocarbon chain. The retention of ethoxylated surfactants depended nonlinearly on the length of ehtyleneoxide chain suggesting that the polar ethyleneoxide chains are in folded state under the chromatographic separation.     

主成分分析同时单点pH滴定法研究

本文将主成分分析用于多组分同时单点ｐＨ滴定法中,讨论了方法原理,指定ｐＨ值的选择,建立了主成分分析常数矩阵,对四组分醇酮氧化酸试样及模拟样进行了多次测定,均获得了满意结果。     

Simultaneous Determination of Two Antibiotics in Tablets by Spectrophotometry and Principal Component Regression (PCR) Analysis. An Advanced Undergraduate Experiment Involving Chemometrics

An advanced analytical chemistry laboratory experiment involving the simultaneous determination of the antibiotics sulfamethoxazole and trimethoprime in commercial tablets is described. It involves the following steps: (1) preparation of nine calibration mixtures and the recording of their absorption spectra in the region 200–320 nm, (2) dissolution of the tablet contents and the recording of spectrophotometrics data, and (3) processing the data with the multivariate calibration technique of principal component regression (PCR). The theory of PCR is discussed, and a Visual Basic program is made available for data processing. The latter program allows students to obtain and save relevant statistical information (root-mean-square deviation, correlation coefficient, and relative error of prediction), as well as calibrate spectral factors and spectral residuals for each test sample. This program helps to illustrate the PCR technique in detail. The reagents used are of low cost and nontoxic; the experiment is simple and provides students with insight into a the real practice of integrating chemistry, instrumentation and computer techniques.     

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。     

近红外光谱技术结合主成分分析法用于子宫内膜癌的诊断

应用近红外光谱技术结合化学计量学方法研究了子宫内膜癌组织近红外光谱特征提取和早期诊断的可行性. 测定了154 例子宫内膜组织切片的近红外光谱, 选取适宜的波段和光谱预处理方法进行主成分分析, 很好地区分了癌变、增生和正常子宫内膜组织切片, 并且分辨出处于不同分化期的组织切片, 为子宫内膜癌的早期诊断提供了可靠依据. 该法快速、简便, 有望发展成为一种新型的肿瘤无创诊断方法.     

主成份分析法测定多组份体系

主成份分析法同多元线性回归法、因子分析法^[1],偏最小二乘法^[2]等都是对多组份体系进行仪器分析信号解析的分析方法,该法的特点是能将维数较大的数据矩阵降维,而保留其全部有效信息,Geladi等给出了确定主成份的方法^[3],但利用主成份对未知混合物试样进行浓度测定的报道尚不多见,本文以主成份分析法对光度法多组份体系进行了研究,采用迭代法求出各个主成份向量,利用所建立的数学模型对未知样品进行浓度测定,方法较简便。     

主成分回归残差神经网络校正算法用于近红外光谱快速测定汽油辛烷值

根据汽油辛值预测体系本身的非线性特点,提出主成分回归残差神经网络校正算法（principal component regression residual artificial neural network,PCRRANN)用于近红外测定汽油辛烷值的预测模型校正,该方法给合了主成分回归算法（PC）,与经典的线性校正算法（PLS（Partial Least Square),PCR, 以及非线性PLS（NPLS,Non-linear PLS)等相比,预测明显的改善,文中还讨论了PCR主成分数及训练参数对预则模可能的影响。