Rapid discrimination of fatty acid composition in fats and oils by electrospray ionization mass spectrometry.

Fatty acids in 42 types of saponified vegetable and animal oils were analyzed by electrospray ionization mass spectrometry (ESI-MS) for the development of their rapid discrimination. The compositions were compared with those analyzed by gas chromatography-mass spectrometry (GC-MS), a more conventional method used in the discrimination of fats and oils. Fatty acids extracted with 2-propanol were-detected as deprotonated molecular ions ([M-H]-) in the ESI-MS spectra of the negative-ion mode. The composition obtained by ESI-MS corresponded to the data of the total ion chromatograms by GC-MS. The ESI-MS analysis discriminated the fats and oils within only one minute after starting the measurement. The detection limit for the analysis was approximately 10(-10) g as a sample amount analyzed for one minute. This result showed that the ESI-MS analysis discriminated the fats and oils much more rapidly and sensitively than the GC-MS analysis, which requires several tens of minutes and approximately 10(-9) g. Accordingly, the ESI-MS analysis was found to be suitable for a screening procedure for the discrimination of fats and oils.     

The Use Of Hplc To Separate Triglycerides In Confectionery Fats Using Ultra Violet Detection

Abstract

The triglycerides of confectionery fats have been separated by reverse phase HPLC using mixtures of either acetonitrile and tetrahydrofuran or acetonitrile and methyl tertiary butyl ether using UV detection at 237 nanometres. The method has been applied to samples of cocoa butter, cocoa butter equivalents, milk fat and other vegetable fats. The fat from a milk chocolate has been separated by the above system.     

Quantitative analysis of triacylglycerol regioisomers in fats and oils using reversed-phase high-performance liquid chromatography and atmospheric pressure chemical ionization mass spectrometry

Positional distribution of fatty acyl chains of triacylglycerols (TGs) in vegetable oils and fats (palm oil, cocoa butter) and animal fats (beef, pork and chicken fats) was examined by reversed-phase high-performance liquid chromatography (RP-HPLC) coupled to atmospheric pressure chemical ionization using a quadrupole mass spectrometer. Quantification of regioisomers was achieved for TGs containing two different fatty acyl chains (palmitic (P), stearic (S), oleic (O), and/or linoleic (L)). For seven pairs of 'AAB/ABA'-type TGs, namely PPS/PSP, PPO/POP, SSO/SOS, POO/OPO, SOO/OSO, PPL/PLP and LLS/LSL, calibration curves were established on the basis of the difference in relative abundances of the fragment ions produced by preferred losses of the fatty acid from the 1/3-position compared to the 2-position. In practice the positional isomers AAB and ABA yield mass spectra showing a significant difference in relative abundance ratios of the ions AA(+) to AB(+). Statistical analysis of the validation data obtained from analysis of TG standards and spiked oils showed that, under repeatability conditions, least-squares regression can be used to establish calibration curves for all pairs. The regression models show linear behavior that allow the determination of the proportion of each regioisomer in an AAB/ABA pair, within a working range from 10 to 1000 microg/mL and a 95% confidence interval of +/-3% for three replicates.     

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.     

Autofluorescence of oral tissue for optical pathology in oral malignancy

Pulsed laser-induced-fluorescence studies of pathologically certified oral tissues are carried out at different excitations and time delays. Among the several excitations used, 325 nm produced noticeably different spectral profile for normal and malignant tissues. Extensive curve analysis was carried out in order to understand changes in biochemical composition of tissue based on spectral profiles. Curve resolution and principal component analysis (PCA) show that the fluorescence intensity changes from normal to malignant tissue samples are not completely explained in terms of simple collagen and NAD(P)H intensity changes. The spectra require at least five components to be fully accounted for. Several discrimination methodologies based on PCA and intensity differences between different emission peaks (resultant peaks of curve analysis) were also evaluated. The results obtained indicate PCA using Mahalanobis distance and spectral residual as discrimination parameters provides best discrimination and can be used for matching unknown samples to standard calibration sets. Intensity ratio of bound NAD(P)H to collagen seems to be more suitable for discrimination between normal and malignant oral tissue, compared to ratio of collagen to total intensity of all the other components together.     

A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique: Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis

Pulsed laser‐induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahalanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100, 250 and 500) after carrying out 1st‐order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3% in PCA, and 100 and 93.47% in ANN, respectively. Copyright © 2008 John Wiley & Sons, Ltd.     

A novel detector using a fluorescent sensor array and discrimination of pesticides

A novel, simple, and rapid detector using a fluorescent sensor array for discrimination and quantification of different concentrations (ppb level) of pesticides was proposed in this paper. Employing porphyrin, porphyrin derivatives, and chemically responsive dyes as the sensing elements, the developed sensor array based on a cross-responsive mechanism showed a unique pattern of fluorescence changes upon the reaction that lasted just 10 min. The eigenvalues from raw fluorescence spectra were analyzed via a pattern recognition algorithm, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and back-propagation neural network (BPNN). The results showed that HCA, which were used to assess the feasibility and effectiveness of discrimination of the fluorescent sensor array, revealed a distinct separation between different pesticides. PCA and BPNN were used for automatically predicting the concentration of pesticides, and the recovery was 91.29–109.81 % while the lowest relative standard deviation was up to 3.12 %. It indicates a detector based on the fluorescent sensor array is a rapid and feasible sensing platform for the discrimination and quantitative analysis of pesticides, and also shows the possibilities in the related fields of pesticides identification and detection.     

LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂

应用主成分分析(Principal component analysis,PCA)和聚类分析法(Cluster analysis,CA)对9种(27个)常见食用植物油及100个餐饮废油的低场核磁共振(Low-field nuclear magnetic resonance,LF-NMR)(T2)弛豫特性数据进行分析。结果表明:在正常食用油种类区分方面,主成分分析的效果较优,9种食用油在主成分分布图上按种类正确分组,边界清晰。而在正常食用油与餐饮废油的区分方面,聚类分析效果较优,引入30个待测样本后,聚类分析(127个样品,欧式距离=5)的正确率为94.49%,分析误判率为5.51%,分组效果良好。LF-NMR结合化学模式识别可实现对油脂种类及餐饮废弃油脂的鉴别。     

Subject index

The embryo of the frog Xenopus laecis was examined as a possible model for pyrolysis studies of cells and tissues from higher organisms. Individual cells from embryos in stages 5 and 6 (16–32 cells), and tissue fragments from neural stage embryos, were studied by low-voltage Curie-point pyrolysis—mass spectrometry (Py-MS) in combination with computerized multivariate analysis techniques.The results show that single cells, as well as selected tissue parts, of X. laecis can be analyzed by Curie-point Py-MS and that reproducible differences are found between different parts of the embryo. The biochemical difference patterns expressed in the spectra appear to reflect primarily protein, phospholipid and (poly)hexose moieties, thought to represent intracellular nutrients such as yolk and glycogen. The yolk/glycogen ratio was found to change markedly between the animal and vegetal poles of embryos in stages 5 and 6. Similar changes were found in different tissue regions of neural plate stage embryos, stages 13–14.     

Study of the complexation of Cu(II) by fulvic acids extracted from a sewage sludge and its compost

 The complexation of Cu(II) by two samples of fulvic acids (FA) extracted from the raw sewage sludge (RsFA) of a waste water processing plant and from the composted sludge (CsFA) obtained upon aerobical digestion was studied at pH 6.0. Synchronous molecular fluorescence spectroscopy was used to monitor the association of FA with Cu(II) and a self-modelling mixture analysis method (SIMPLISMA) was used to preprocess the spectral data to calculate the number of components with different quenching profiles as function of the Cu(II) concentration and their spectra. The stability constants and a rough estimation of the binding site concentrations were obtained by a Stern-Volmer analysis, by a non-linear least-square method and by a linear procedure. The analysis of the SyF spectra allowed the identification of two binding sites for both samples. The logarithm of the conditional stability constant corresponding to the 1:1 complex formed between the stronger binding site and Cu(II) is about 4 for both samples. Received: 22 April 1996 / Revised: 5 August 1996 / Accepted: 7 August 1996     

The Use of Chronoamperometry and Chemometrics for Optimization of Conducting Polymer Sensor Arrays

A general approach using principal component analysis was applied to amperometric data obtained from flow injection analysis (FIA) to select conducting electroactive polymers (CEPs) and applied potential waveforms for discrimination of potassium and methylamine. The results were compared with the selection from the application of the PCA approach for cyclic voltammetry data in our previous research. Good agreement between the two methods of selection was found, which indicates that within the general qualitative limits of the approach, PCA can be applied for selection of chemical and electrochemical parameters for FIA detection/discrimination using CEP sensor arrays detector for a range of analytes.     

Rapid characterization of edible oils by direct matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis using triacylglycerols

Direct matrix-assisted laser desorption/ionization time-of-flight mass spectrometric (MALDI-TOFMS) analysis of solutions of edible fats/oils yielded spectra useful for their rapid differentiation and classification. Results also reflected the individual fatty acid components and their degree of unsaturation. After dissolution in hexane, MALDI-MS analysis revealed spectra showing characteristic triacylglycerols (TAGs), the main fat/oil components, as sodium adduct ions. The Euclidean distances calculated using the mass and intensity values for 20 TAGs were used to evaluate and compare spectra. With cluster analysis, animal fats grouped together differently than vegetable oils and the individual oils grouped together by type. The ion abundances for the individual TAGs and their presumed compositions were used to approximate the overall fatty acid composition of canola, soybean, corn, olive and peanut oil, as well as lard. Using this approach the calculated fatty acid compositions and degree of unsaturation generally fell within about 4% of literature values. When the degree of saturation was compared with values calculated from the package labeling the differences were about 7%.     

Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates

Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.     

Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data—A review

The characterization and authentication of fats and oils is a subject of great importance for market and health aspects. Identification and quantification of triacylglycerols in fats and oils can be excellent tools for detecting changes in their composition due to the mixtures of these products. Most of the triacylglycerol species present in either fats or oils could be analyzed and identified by chromatographic methods. However, the natural variability of these samples and the possible presence of adulterants require the application of chemometric pattern recognition methods to facilitate the interpretation of the obtained data. In view of the growing interest in this topic, this paper reviews the literature of the application of exploratory and unsupervised/supervised chemometric methods on chromatographic data, using triacylglycerol composition for the characterization and authentication of several foodstuffs such as olive oil, vegetable oils, animal fats, fish oils, milk and dairy products, cocoa and coffee.     

Preparation of Low-Diacylglycerol Cocoa Butter Equivalents by Hexane Fractionation of Palm Stearin and Shea Butter

Herein, we prepared 1,3-dipalmitoyl-2-oleoyl glycerol (POP)-rich fats with reduced levels of diacylglycerols (DAGs), adversely affecting the tempering of chocolate, via two-step hexane fractionation of palm stearin. DAG content in the as-prepared fats was lower than that in POP-rich fats obtained by previously reported conventional two-step acetone fractionation. Cocoa butter equivalents (CBEs) were fabricated by blending the as-prepared fats with 1,3-distearoyl-2-oleoyl glycerol (SOS)-rich fats obtained by hexane fractionation of degummed shea butter. POP-rich fats achieved under the best conditions for the fractionation of palm stearin had a significantly lower DAG content (1.6 w/w%) than that in the counterpart (4.6 w/w%) prepared by the previously reported method. The CBEs fabricated by blending the POP- and SOS-rich fats in a weight ratio of 40:60 contained 63.7 w/w% total symmetric monounsaturated triacylglycerols, including 22.0 w/w% POP, 8.6 w/w% palmitoyl-2-oleoyl-3-stearoyl-rac-glycerol, 33.1 w/w% SOS, and 1.3 w/w% DAGs, which was not substantially different from the DAG content in cocoa butter (1.1 w/w%). Based on the solid-fat content results, it was concluded that, when these CBEs were used for chocolate manufacture, they blended with cocoa butter at levels up to 40 w/w%, without distinctively altering the hardness and melting behavior of cocoa butter.     

Quantification of butanol and ethanol in aqueous phases by reflectometric interference spectroscopy--different approaches to multivariate calibration

This paper presents several methods for analysis of data from reflectometric interference spectroscopic measurements (RIfS) of water samples. The set-up consists of three sensors with different polymer layers. Mixtures of butanol and ethanol in water were measured from 0 to 12,000 ppm each. The data space was characterized by principal component analysis (PCA). Calibration and prediction were achieved by multivariate methods, e.g. multiple linear regression (MLR), partial least squares (PLS) with additional predictors, and quadratic partial least squares (Q-PLS), and by use of artificial neural networks. Artificial neural networks gave the best results of all the calibration methods used. Calibration and prediction of the concentration of the two analytes by artificial neural nets were robust and the set-up could be reduced to only two sensors without deterioration of the prediction.     

基于非负分解方法的质谱成像数据特征提取

质谱成像技术能够在同一个实验里无需标记手段而获得样品表面的分子信息及其分布信息,是当前质谱分析的热点.其分析所得数据量大且复杂,使其特征难以提取.多元统计分析方法,特别是主成分分析法已应用于质谱成像数据的压缩和特征提取.然而由于主成分分析常产生负的数据结果,其意义难以解释且不易分解为单一的特征.本研究开发出一种基于非负分解的质谱成像数据提取方法,能够提取单一的分子特征及其在样品上的分布特征,并将多个单一的特征分布通过红、绿、蓝三色叠加显示,获得轮廓直观的综合特征分布.应用本方法对小鼠脑组织切片质谱成像数据进行分析,可直观分解出灰质区域、白质区域和背景区域,相对主成分分析方法更直观且易于解释.应用本方法对在同一个样品靶上的人膀胱癌变组织和其相邻非癌变组织切片质谱成像数据进行分析,癌变与非癌变组织间差异清晰直观.本研究设计的质谱成像软件可由http://www.msimaging.net获取.     

Pyrolysis-mass spectrometry and gas chromatography-flame ionization detection as complementary tools for soil lipid characterization

Lipid biomarker profiles are a powerful tool for assessing soil microbial community structure, but intensive laboratory work and data analysis are needed to construct profiles from phospholipid fatty acids and other common biomarkers. Pyrolysis mass spectrometry (Py-MS) is a alternative method that provides a rapid and sensitive ‘fingerprint’ of soil lipids and may be sufficient to characterize lipids from various sites. The objective of this work was to evaluate the capacity of pyrolysis metastable atom bombardment time-of-flight mass spectrometry (Py-MAB-TOF-MS) to provide replicable analysis of soil lipids, compared to a routine gas chromatography-flame ionization detection (GC-FID) method. Soils were collected from six agricultural fields under soybean, corn and asparagus production. Soil lipids extracted with 1:2 chloroform:methanol solvent were analyzed with Py-MAB-TOF-MS or transesterified into fatty acids and then analyzed by GC-FID. The two methods were complementary, but distinct: lipid fingerprints, generated from Py-MAB-TOF-MS spectra, included extractable soil lipids from microbial, animal and plant origins plus non-living organic matter in the samples, whereas fatty acid profiles generally represented lipids from soil bacteria and fungi. We conclude that the soil lipid fingerprints generated from Py-MAB-TOF-MS present more variability than lipid biomarker profiles from the GC-FID method because they include a broader group of extractable soil lipids. Further work is needed to identify the molecular fragment masses in Py-MAB-TOF-MS spectra that could precisely identify soil lipids of microbial origin.     

Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms

The aim of this study was to evaluate evolutionary variable selection methods in improving the classification of ¹H nuclear magnetic resonance (NMR) metabonomic profiles, and to identify the metabolites that are responsible for the classification. Human plasma, urine, and saliva from a group of 150 healthy male and female subjects were subjected to ¹H NMR-based metabonomic analysis. The ¹H NMR spectra were analyzed using two pattern recognition methods, principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), to identify metabolites responsible for gender differences. The use of genetic algorithms (GA) for variable selection methods was found to enhance the classification performance of the PLS-DA models. The loading plots obtained by PCA and PLS-DA were compared and various metabolites were identified that are responsible for the observed separations. These results demonstrated that our approach is capable of identifying the metabolites that are important for the discrimination of classes of individuals of similar physiological conditions.