Application of principal-component analysis on near-infrared spectroscopic data of vegetable oils for their classification

In the near-infrared (NIR) spectra of oil, information about fatty acid composition is concentrated in the range of 1600–2200 nm. Principal-component analysis (PCA) was applied on the standardized full NIR spectral data of this region for vegetable oils to totally capture the NIR spectral pattern. Nine varieties of vegetable oils (soybean, corn, cottonseed, olive, rice bran, peanut, rapeseed, sesame and coconut oil) could be successfully classified from their PCA scores. Examining the contribution of wavelengths to PCA scores showed that wavelengths with a high loading weight were assigned to characteristic absorption regions that correspond to specific fatty acid moieties. This classification is related to the fatty acid composition of an oil, and it can be carried out rapidly and easily after eigenvectors were obtained.     

Comprehensive Triacylglycerol Characterization of Oils and Butters of 15 Amazonian Oleaginous Species by ESI‐HRMS/MS and Comparison with Common Edible Oils and Fats

The Amazon rain forest encompasses an extraordinary source of vegetable oils with many applications, especially for food, pharmaceutical and cosmetics industries. In this work, the main composition of fifteen Amazonian oils and butters are investigated via gas chromatography‐mass spectrometry (GC‐MS) and electrospray ionization high resolution mass spectrometry (ESI‐HRMS). Triacylglycerols (TAG) are characterized by their fragmentation spectra and comparison with the LIPID MAPS database, resulting in a detailed compendium of TAG composition of these samples. Over 70 different TAG are putatively annotated per sample and the occurrence of isomers is remarkable, showing that TAG complexity in these samples is considerably higher than ever reported. The TAG composition of the Amazonian samples are also statistically evaluated using principal component analysis (PCA) for comparison to common edible oils such as soybean, corn, coconut, and olive oil. Some tendencies of grouping are observed: butters with medium chain fatty acids (FA); butters with high oleic FA; and oils with high oleic and high linoleic FA contents. This study provided profiles that ensure Amazonian oils and butters authenticity, quality and also aids in understanding their properties and the best applications for each. Practical Applications: It is expected that this comprehensive set of data on the TAG composition of Amazonian oils and butters will help guide the use and applications of these products, providing consumers with the best benefits from a nutritional perspective. Moreover, adulterations could be more easily detected when a database is available, since the chemical composition of certified samples is investigated in this work. Ultimately, this study can encourage the sustainable production and applications of Amazonian oils and appropriate use of Amazon rain forest resources.     

Application of Infrared Spectroscopy for Characterization of Dietary Omega‐3 Oil Supplements

Fish oil dietary supplements have been linked with health benefits, due to high omega‐3 concentration. The sources of these effects, polyunsaturated fatty acids such as eicosapentaenoic acid and docosahexaenoic acid, are almost exclusively found in seafood products. Our objectives were to characterize the composition of commercial omega‐3 dietary supplements dietary supplements and to generate partial least square regression (PLSR) models using infrared spectroscopy and chemometrics. Fatty acid (FA) composition of oils was determined by FA methyl ester gas chromatography. The supplements encompassed a wide range of FA profiles and delivery methods. Infrared spectral data were collected by portable mid‐infrared Fourier transform infrared (MID FT‐IR) equipment. Principal components analysis (PCA) separated samples based on the type of ester present in the fish oil dietary supplements, showing a strong influence of the 1038 cm^?1 band, which is typically associated with C=C and C–O stretching vibrations. In addition, PLSR was used to correlate the spectra data with GC‐FAME results. PCA using the spectroscopy data allowed for tight clustering of fish oil into distinct classes, depending on the source and processing. PLSR using MID FT‐IR spectra and FA composition generated multivariate models with high correlation coefficient (R ≥ 0.93), and SEP between 0.53 and 2.13 g of FA per 100 g of oil. Our results indicate that IR spectroscopy combined with chemometrics provides for robust screening of FA composition of fish oil supplements, and discriminate types of FAs esterification.     

Comparison of fatty acid profiles and mid‐infrared spectral data for classification of olive oils

The composition of olive oils may vary depending on environmental and technological factors. Fatty acid profiles and Fourier‐transform infrared (FT‐IR) spectroscopy data in combination with chemometric methods were used to classify extra‐virgin olive oils according to geographical origin and harvest year. Oils were obtained from 30 different areas of northern and southern parts of the Aegean Region of Turkey for two consecutive harvest years. Fatty acid composition data analyzed with principal component analysis was more successful in distinguishing northern olive oil samples from southern samples compared to spectral data. Both methods have the ability to differentiate olive oil samples with respect to harvest year. Partial least squares (PLS) analysis was also applied to detect a correlation between fatty acid profile and spectral data. Correlation coefficients (R²) of a calibration set for stearic, oleic, linoleic, arachidic and linolenic acids were determined as 0.83, 0.97, 0.97, 0.83 and 0.69, respectively. Fatty acid profiles were very effective in classification of oils with respect to geographic origin and harvest year. On the other hand, FT‐IR spectra in combination with PLS could be a useful and rapid tool for the determination of some of the fatty acids of olive oils.     

Application of Multivariate Analysis in Mid-Infrared Spectroscopy as a Tool for the Evaluation of Waste Frying Oil Blends

Mid-infrared spectroscopy, in association with multivariate chemometric techniques, was employed for pattern recognition and the determination of the composition of waste frying oils (WFO); data are presented in terms of the percentage of soybean oil, palm oil and hydrogenated vegetable fat in frying oil blends. Principal component analysis (PCA) was performed using spectral data (3,000–600 cm⁻¹) to discriminate between the samples containing 100% soybean oil, 100% palm oil, 100% hydrogenated vegetable fat groups and their blends. Additionally, the results indicated that partial least squares (PLS) models based on mid-infrared spectra were suitable as practical analytical methods for predicting the oil contents in WFO blends. PLS models were validated by a representative prediction set, and the root mean square errors of prediction (RMSEP) were 2.8, 4.7 and 5.5% for palm oil, soybean oil and hydrogenated vegetable fat, respectively. The proposed methodology can be very useful for the rapid and low cost determination of waste frying oil composition while also aiding in decisions regarding the management of oil pretreatment and production routes for biodiesel production.     

Extraction of omega‐6 fatty acids from speciality seeds

‘Omega‐6 vegetable oils’ are a small but important group of vegetable oils used widely in the food, neutraceutical, cosmetic and pharmaceutical industries for their linoleic acid (18:2 n‐6) and more importantly gamma linolenic acid (18:3 n‐6) content. These omega‐6 fatty acids have numerous health benefits recognized worldwide. With linoleic acid being readily available from many dietary sources, one wonders why there is a need to extract the oil from speciality oilseeds, however those that suffer with many of the conditions that omega‐6 fatty acids are said to be beneficial for are frequently advised to take extra supplements of these fatty acids. Due to their wide use as a nutraceutical, omega‐6 fatty acids are in high demand, causing a niche market for extraction of these oils from speciality seeds.     

Application of chemometric methods to synchronous UV fluorescence spectra of petroleum oils

Fluorescence spectroscopy offers the possibility to make qualitative and quantitative analysis of crude oils. Its sensitivity and selectivity permit its use for oils exploration and production. The study describes a novel approach for identification and discrimination of oils by chemometric treatment of synchronous ultraviolet fluorescence (SUVF) spectra which are usually studied by indexes method.Clusters of samples are obtained according to their geographic origin on a 3D plane using SUVF indexes and in the space of principal components for PCA method. Evaluation of oils composition is performed with MCR-ALS algorithm by extracting spectra of families representing aromatic structures.A PLS calibration, built with spectra intensities as input and oils origin as output allows to identify oils chosen for the prediction. Results show that mathematical exploration of full spectral region leads rapidly to classify and predict some oils properties according to their geochemistry.     

Triacylglycerol Composition Profiling and Comparison of High‐Oleic and Normal Peanut Oils

Oilseed plants produce huge amounts of fatty acids (FA) stored as triacylglycerols (TAG) in seeds that give a great variation in their composition. The variety and content of TAG directly affect the nutrition and function of lipids. TAG composition of 12 high‐oleic and normal peanut oil samples were profiled by two‐dimensional liquid chromatography (2D LC) coupled with atmospheric pressure chemical ionization mass spectrometry (APCI‐MS). The statistical evaluation of the TAG profiles determined was conducted on the basis of multidimensional data matrix using Principal Component Analysis (PCA). The technique enabled the differentiation of high‐oleic oils from normal peanut oils—as results illustrated TAG of high‐oleic peanut oil were clearly different from those of normal peanut oils. High‐oleic and normal peanut oils had different profiles mainly in the contents of OOO, OPO and POL. This finding provided theoretical foundation for detecting the adulteration of edible oils and analyzing the nutrition and function of high‐oleic peanut oils.     

Novel,Rapid Identification,and Quantification of Adulterants in Extra Virgin Olive Oil Using Near‐Infrared Spectroscopy and Chemometrics

A new, rapid Fourier transform near infrared (FT‐NIR) spectroscopic procedure is described to screen for the authenticity of extra virgin olive oils (EVOO) and to determine the kind and amount of an adulterant in EVOO. To screen EVOO, a partial least squares (PLS1) calibration model was developed to estimate a newly created FT‐NIR index based mainly on the relative intensities of two unique carbonyl overtone absorptions in the FT‐NIR spectra of EVOO and other mixtures attributed to volatile (5280 cm^?1) and non‐volatile (5180 cm^?1) components. Spectra were also used to predict the fatty acid (FA) composition of EVOO or samples spiked with an adulterant using previously developed PLS1 calibration models. Some adulterated mixtures could be identified provided the FA profile was sufficiently different from those of EVOO. To identify the type and determine the quantity of an adulterant, gravimetric mixtures were prepared by spiking EVOO with different concentrations of each adulterant. Based on FT‐NIR spectra, four PLS1 calibration models were developed for four specific groups of adulterants, each with a characteristic FA composition. Using these different PLS1 calibration models for prediction, plots of predicted vs. gravimetric concentrations of an adulterant in EVOO yielded linear regression functions with four unique sets of slopes, one for each group of adulterants. Four corresponding slope rules were defined that allowed for the determination of the nature and concentration of an adulterant in EVOO products by applying these four calibration models. The standard addition technique was used for confirmation.     

Organogelation of plant oils and hydrocarbons by long-chain saturated FA,fatty alcohols,wax esters,and dicarboxylic acids

Conversion of oils into gels generally involves altering the chemical characteristics of the liquid. We describe here the gelling of vegetable oils, essential oils, and hydrocarbons at ambient temperature, without changing the chemical characteristics of the liquids, using saturated FA having carbon chain lengths of 10 to 31. The gelling ability of the added FA increased linearly with their chain lengths. Structure-function studies demonstrated that the carboxyl group, position of an additional hydroxyl group, and acyl chain length played an important role in gelation. Long-chain saturated fatty alcohols, wax esters, and dicarboxylic acids also had the ability to gel plant oils and hydrocarbons.     

Comparative analysis of triacylglycerols from Olea europaea L. fruits using HPLC and MALDI‐TOFMS

MALDI‐TOFMS and HPLC are two analytical methods that were used to characterize triacylglycerols (TAG) of the Meski, Sayali, and Picholine Tunisian olive varieties. The HPLC chromatograms of the oils showed the presence of 15 TAG species, among which triolein (OOO) was the most abundant (21–48%). In the Sayali cultivar, OOO was the predominant TAG species followed by POO and LOO. However, the minor TAG molecules were represented by LnLO and LnLP. MALDI mass spectra produced sodiated ([M + Na]⁺) and potassiated ([M + K]⁺) TAG molecules; only the major TAG were potassiated [OOO + K] ([OOO + K]⁺, [POO + K]⁺, and [LOO + K]⁺). In contrast to the HPLC chromatograms, the MALDI mass spectra showed 13 peaks of TAG. The major peak was detected at m/z 907, which corresponds to OOO with an Na⁺ adduct. The results from both HPLC and MALDI techniques predict the fatty acid composition and their percentages for each olive variety. Practical applications: TAG are the main components in vegetable oils. These biomolecules determine the physical, chemical, and nutritional properties of the oils. The nutritional benefits of TAG are related to DAG (moderate plasma lipid level) and esterified FA, which are intermediate biosynthetic molecules of TAG. TAG analysis is necessary to discriminate between oils of different origin, since some oils have similar FA profiles. Olive products, oils, and table olives, are the main diet sources of TAG in the Mediterranean countries. In this work, chromatographic and spectrometric methods were used for TAG analysis and characterization of Tunisian olive varieties.     

Manoeuvring the physicochemical and nutritional properties of vegetable oils through blending

Modification of vegetable oils is carried out to make them suitable according to their specific end use as most of the vegetable oils in original forms do not meet the recommended dietary allowance of saturated (SFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids. Vegetable oils are modified using a variety of techniques including hydrogenation, interesterification, fractionation, and blending. However, blending is the most widely accepted method for improving the physicochemical properties, nutritive value and oxidative stability of vegetable oils because it is simple, cost-effective, non-destructive, and does not involve chemical treatments. Blending vegetable oils with contrasting fatty acid compositions or blending omega 3 fatty acids and antioxidants rich minor oils with major oils are two common strategies to formulate blends. Blended oil with balanced fatty acids could play substantial role in improving the consumers' health. However, while designing vegetable oil blends, it is important to keep in mind the intended application of the formulated blend, consumer's demands and also food laws. This review paper covers the literature related to blending of vegetable oils with a focus on effect of vegetable oils blending on their physicochemical and nutritional properties, health benefits and utility in food industries.     

Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils

It was previously demonstrated that Fourier transform near infrared (FT‐NIR) spectroscopy and partial least squares (PLS1) were successfully used to assess whether an olive oil was extra virgin, and if adulterated, with which type of vegetable oil and by how much using previously developed PLS1 calibration models. This last prediction required an initial set of four PLS1 calibration models that were based on gravimetrically prepared mixtures of a specific variety of extra virgin olive oil (EVOO) spiked with adulterants. The current study was undertaken after obtaining a range of EVOO varieties grown in different countries. It was found that all the different types of EVOO varieties investigated belonged to four distinct groups, and each required the development of additional sets of specific PLS1 calibration models to ensure that they can be used to predict low concentrations of vegetable oils high in linoleic, oleic, or palmitic acid, and/or refined olive oil. These four distinct sets of PLS1 calibration models were required to cover the range of EVOO varieties with a linoleic acid content from 1.3 to 15.5 % of total fatty acids. An FT‐NIR library was established with 66 EVOO products obtained from California and Europe. The quality and/or purity of EVOO were assessed by determining the FT‐NIR Index, a measure of the volatile content of EVOO. The use of these PLS1 calibration models made it possible to predict the authenticity of EVOO and the identity and quantity of potential adulterant oils in minutes.     

Monitoring PV in corn and soybean oils by NIR spectroscopy

NIR spectroscopy was used successfully in our laboratory to monitor oxidation levels in vegetable oils. Calibration models were developed to measure PV in both soy and corn oils, using partial least squares (PLS) regression and forward stepwise multiple linear regression, from NIR transmission spectra. PV can be measured successfully in both corn and soy oils using a single calibration. The most successful calibration was based on PLS regression of first derivative spectra. When this calibration was applied to validation sample sets containing equal numbers of corn and soy oil samples, with PV ranging from 0 to 20 meq/kg, a correlation coefficient of 0.99 between titration and NIR values was obtained, with a standard error of prediction equal to 0.72 meq/kg. For both types of oil, changes occurred in the 2068 nm region of the NIR spectra as oxidation levels increased. These changes appear to be associated with the formation of hydroperoxides during oxidation of the oils.     

Fatty acid profiles of 80 vegetable oils with regard to their nutritional potential

The current concern for fat intake in western countries has raised the question of the individual fatty acid (FA) impact on health. This important issue has strengthened the awareness of nutritionists and food manufacturers for the control of the FA profile of food products. The aim of this review is to provide a classification of the FA profiles of 80 vegetable oil sources, according to their nutritional potential. The first part of the review focuses on lipoprotein metabolism, and on the impact of each dietary FA on blood lipid composition (LDL‐cholesterol, HDL‐cholesterol and circulating triacylglycerols). In the second part of the review, the oil sources are clustered by similar FA profiles, and the classification is discussed with regard to the individual FA action on blood lipid composition. Apart from the major vegetable seeds, the clustering highlighted some interesting nutritional oil sources containing mainly α‐linolenic acid (camelina, linseed, perilla and stock oils), or interesting amounts of the two essential FA (purslane, chia, raspberry seed, sea buckthorn seed and salicorn oils). Furthermore, this classification provides a useful tool for the formulation of the FA profile of food products.     

Predicting temperature-dependence viscosity of vegetable oils from fatty acid composition

The viscosities of 12 vegetable oils were experimentally determined as a function of temperature (5 to 95°C) by means of a temperature-controlled rheometer. Viscosities of the oil samples decreased exponentially with temperature. Of the three models [modified Williams-Landel-Ferry (WLF), power law and Arrhenius] that were used to describe the effects of temperature on viscosity, the modified WLF model gave the best fit. The amounts of monounsaturated FA or polyunsaturated fatty acids (PUFA) highly correlated (R ²>0.82) with the viscosities of the oil samples whereas and the amounts of saturated or unsaturated FA. An exponential equation was therefore used to relate the viscosity of these vegetable oil samples to the amounts of monounsaturated FA or PUFA. The models developed are valuable for designing or evaluating systems and equipment that are involved in the storage, handling, and processing of vegetable oils.     

The determination of peroxide value by fourier transform infrared spectroscopy

A rapid method for the quantitative determination of peroxide value (PV) of vegetable oils by Fourier transform infrared (FTIR) transmission spectroscopy is described. Calibration standards were prepared by the addition oft-butyl hydroperoxide to a series of vegetable oils, along with random amounts of oleic acid and water. Additional standards were derived through the addition of mono- and diglyceride spectral contributions, as well as zero PV spectra obtained from deuterated oils. A partial least squares (PLS) calibration model for the prediction of PV was developed based on the spectral range 3750–3150 cm⁻¹. Validation of the method was carried out by comparing the PV of a series of vegetable oils predicted by the PLS model to the values obtained by the American Oil Chemists Society iodometric method. The reproducibility of the FTIR method [coefficient of variation (CV)=5%)] was found to be better than that of the chemical method (CV =9%), although its accuracy was limited by the reproducibility of the chemical method. The method, as structured, makes use of a 1-mm CaF₂ flow cell to allow rapid sample handling by aspiration. The spectrometer was preprogrammed in Visual Basic to guide the operator in performing the analysis so that no knowledge of FTIR spectroscopy is required to implement the method. The method would be suitable for PV determinations in the edible oil industry and takes an average of three minutes per sample.     

Seed roasting improves the oxidative stability of canola (B. napus) and mustard (B. juncea) seed oils

Animal fats and partially hydrogenated vegetable oils (PHVO) have preferentially been used for deep‐frying of food because of their relatively high oxidative stability compared to natural vegetable oils. However, animal fats and PHVO are abundant sources of saturated fatty acids and trans fatty acids, respectively, both of which are detrimental to human health. Canola (Brassica napus) is the primary oilseed crop currently grown in Australia. Canola quality Indian mustard (Brassica juncea) is also being developed for cultivation in hot and low‐rainfall areas of the country where canola does not perform well. A major impediment to using these oils for deep‐frying is their relatively high susceptibility to oxidation, and so any processing interventions that would improve the oxidative stability would increase their prospects of use in commercial deep‐frying. The oxidative stability of both B. napus and B. juncea crude oils can be improved dramatically by roasting the seeds (165 °C, 5 min) prior to oil extraction. Roasting did not alter the fatty acid composition or the tocopherol content of the oils. The enhanced oxidative stability of the oil, solvent‐extracted from roasted seeds, is probably due to 2,6‐dimethoxy‐4‐vinylphenol produced by thermal decarboxylation of the sinapic acid naturally occurring in the canola seed.     

Discrimination of various poly(propylene) copolymers and prediction of their ethylene content by near‐infrared and Raman spectroscopy in combination with chemometric methods

Near‐infrared (NIR) diffuse reflectance (DR) spectra and Fourier‐transform (FT) Raman spectra were measured for 12 kinds of block and random poly(propylene) (PP) copolymers with different ethylene content in pellets and powder states to propose calibration models that predict the ethylene content in PP and to deepen the understanding of the NIR and Raman spectra of PP. Band assignments were proposed based calculation of the second derivatives of the original spectra, analysis of loadings and regression coefficient plots of principal component analysis (PCA) and principal component regression (PCR) (predicting the ethylene content) models, and comparison of the NIR and Raman spectra of PP with those of linear low‐density polyethylene (LLDPE) with short branches. PCR and partial least squares (PLS) regression were applied to the second derivatives of the NIR spectra and the NIR spectra after multiplicative scatter correction (MSC) to develop the calibration models. After MSC treatment, the original spectra yield slightly better results for the standard error of prediction (SEP) than the second derivatives. A plot of regression coefficients for the PCR model shows peaks due to the CH₂ groups pointing upwards and those arising from the CH₃ groups pointing downwards, clearly separating the bands due to CH₃ and CH₂ groups. For the Raman data, MSC and normalization were applied to the original spectra, and then PCR and PLS regression were carried out to build the models. The PLS regression for the normalized spectra yields the best results for the correlation coefficient and the SEP. Raman bands at 1438, 1296, and 1164 cm^?1 play key roles in the prediction of the ethylene content in PP. The NIR chemometric evaluation of the data gave better results than those derived from the Raman spectra and chemometric analysis. Possible reasons for this observation are discussed. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 616–625, 2003     

Estimation of stereospecific fatty acid distribution in vegetable oils from liquid chromatography data

The fatty acid distribution in triacylglycerols (TG), the major components of vegetable oils, affects the physical properties of oils and their physiological effects as components of the human diet. A computational scheme is proposed for estimating the stereospecific distribution of fatty acids in TG on the basis of the data from HPLC analysis, using a model for TG synthesis. A new model for the synthesis is proposed which, in contrast to the widely used model published independently by Vander Wal and Coleman, enables us to simulate the asymmetrical fatty acid distribution at the sn‐1 and sn‐3 stereochemical positions. The computational scheme combined with either literature models or the new model was validated by its application to several TG profiles and comparison of the resulting fatty acid distribution at stereochemical positions with experimental data from the literature. The stereospecific composition of TG was calculated using the new model for nine vegetable oils.