Classification Of Vegetable Oils Research Articles

Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

Ultra high efficiency/low pressure supercritical fluid chromatography (UHE/LP-SFC) for triglyceride analysis: Identification, quantification, and classification of vegetableoils.

Triglycerides of vegetable oils have been extensively studied. Non-aqueous reversed-phase liquid chromatography and silver-ion chromatography are most frequently used to achieve their separation. In previous works, we presented the use of supercritical fluid chromatography with long columns (75cm) packed with fused-core particles to provide ultra-high-performance separations, with a low-toxicity fluid (carbon dioxide) compared to the usual liquid-phase methods. In the present paper, we describe the quantification of triglycerides with supercritical fluid chromatography and evaporative light-scattering detection. Thanks to the isocratic elution mode, this quantification can be simplified, assuming (a) identical response coefficients for compounds having a close structure, (as only triglycerides are quantified), and (b) constancy of the response coefficient along the analysis (no elution gradient). Therefore, the relative concentrations of triglycerides were easily assessed. Only one calibration curve for one reference compound (in this case triolein) was required. The resulting relative concentrations are in good accordance with the numerous publications available. Relative quantification with UV detection at 210nm is also proposed, facilitated by the very low UV absorption of carbon dioxide and with a calibration curve taking account of the variation of UV response according to double bond number. Nineteen vegetable oils are compared. The identification of triglycerides was carried out based on previous knowledge of these oils, but also with the help of a Goiffon retention diagram, based on the relationship between the logarithm of retention factor and the total double bond number. Finally, cluster analyses were computed, based on evaporative light-scattering detection or UV quantification data. They allow a quick comparison of the triglyceride content between the oils, in the goal to exchange one by the other for certain applications, or to compare a new oil to well-known ones.

Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics

Olive oil is an important food product for human health. The addition of vegetable oils is the most common form of oil adulteration. In this paper, a methodology was developed to identify and quantify adulterations in extra virgin olive oil (EVOO) using a portable near-infrared spectrometer (microNIR). Two different spectral acquisition modes were tested: reflectance and transmittance. Samples sets constitute binary blends of EVOO with soybean, sunflower, corn, and canola oil. Partial least squares regression (PLS) models were built to quantify adulterations in extra virgin olive oil. After, the soybean oil content was checked in 12 olive oils acquired in a local market. Also, the physicochemical properties: acidity, peroxide index, and ultraviolet absorbance of the commercial samples were determined following the standard method of Institute Adolfo Lutz and CEE n. 2568/91. The PLS models' accuracy was 0.5 to 1.8 wt% for transmittance mode and 1.7 to 4.6 wt% for reflectance mode. The commercial olive samples' adulterations, the binary blends of commercial samples, the vegetable oils, and the binary blends EVOO/vegetable oils were evaluated by principal component analysis (PCA) and soft independent modeling class analogy (SIMCA). PCA and SIMCA models distinguished the commercial samples according to the information contained in their labels; besides, it identified the olive oil samples on to dataset with the blends. These results are in excellent agreement with the physicochemical results that corroborate with the limits of regulation.

Raman Spectroscopic Barcode Use for Differentiation of Vegetable Oils and Determination of Their Major Fatty Acid Composition

AbstractIn this study, differentiation of vegetable oils and determination of their major fatty acid (FA) composition were performed using Raman spectral barcoding approach. Samples from seven different sources (sunflower, corn, olive, canola, mustard, soybean and palm) were analyzed using Raman spectroscopy. Second derivative of the spectral data was utilized to generate unique barcodes of oils. Chemometric analyses, namely, principal component analysis (PCA) and partial least square (PLS) methods were used for data analysis. PCA was applied for classification of the samples according to the differences in their levels arising from their barcode data. A successful differentiation based on second derivative barcodes of Raman spectra (2D‐BRS) of vegetable oils was obtained. In addition, PLS method was applied on 2D‐BRS in order to determine the major FA composition of these samples. Coefficient of determination values for palmitic, stearic, oleic, linoleic, α‐linolenic, cis‐11 eicosenoic, erucic and nervonic acids were in the range of 0.970–0.989. Limit of detection and limit of quantification values were found to be satisfactory (0.09–8.09 and 0.30–26.95 % in oil) for these fatty acids . Advantages of both chemometric analysis and spectral barcoding approach have been utilized in the present study. Taking the second derivative of the Raman spectra has minimized background variability and sensitivity to intensity fluctuations. Spectral conversion to the barcodes has further increased the quality of information obtained from Raman spectra and also made it possible to improve the visualization of the data. Converting Raman spectra of oils into barcodes enables simpler presentation of the valuable information, and still allows further analysis such as classification of vegetable oils and prediction of their major fatty acids with high accuracy.

Classification of vegetable oils according to their botanical origin using n-alkane profiles established by GC–MS

n-Alkane profiles established by gas chromatography-mass spectrometry (GC–MS) were used to classify vegetable oils according to their botanical origin. The n-alkanes present in corn, grapeseed, hazelnut, olive, peanut and sunflower oils were isolated by means of alkaline hydrolysis followed by silica gel column chromatography of the unsaponifiable fractions. The n-alkane fraction was constituted mainly of n-alkanes in the range C8–C35, although only those most abundant (15 n-alkanes, from 21 to 35 carbon No.) were used as original variables to construct linear discriminant analysis (LDA) models. Ratios of the peak areas selected by pairs were used as predictors. All the oils were correctly classified according to their botanical origin, with assignment probabilities higher than 95%, using an LDA model.

Classification of vegetable oils according to their botanical origin using amino acid profiles established by High Performance Liquid Chromatography with UV–vis detection: A first approach

A preliminary study using amino acid profiles to classify oils according to their botanical origin has been performed. Amino acid profiles were obtained from hydrolysis of proteins present in vegetable oils, and established by High Performance Liquid Chromatography (HPLC) with UV–vis detection. Proteins present in hazelnut, corn, soybean, olive, avocado, peanut and grapeseed oils were precipitated with acetone, and the residue was hydrolysed in acid medium. The amino acids obtained were derivatized with o-phthaldialdehyde and separated by HPLC. Peaks corresponding to 18 amino acids were observed using a C18 column and a gradient of acetonitrile–water in the presence of a 5 mM citric/citrate buffer at pH 6.5. The 16 peaks observed in each sample (arginine–serine and phenylalanine–leucine peaks appeared overlapped) were used to construct linear discriminant analysis (LDA) models. Ratios of the peak areas selected by pairs were used as predictors. With a LDA model, the oils were correctly classified with assignment probabilities higher than 99%.

Classification of vegetable oils according to their botanical origin using sterol profiles established by direct infusion mass spectrometry

A simple and quick method to classify vegetable oils according to their botanical origin, based on direct infusion of sterol extracts into a mass spectrometer, was developed. Using mass spectrometry (MS) with either an electrospray ionization or an atmospheric pressure photoionization source, followed by linear discriminant analysis of the mass spectral data, oil samples corresponding to eight different botanical origins were perfectly classified with an excellent resolution among all the categories. An excellent correlation between the sterol profiles obtained by MS and by the official gas chromatography (with flame ionization detection) method was obtained. Thus, the proposed method is a promising alternative for sterol fingerprinting of vegetable oils, with the advantage that prior chromatographic separation is not required.

Classification of vegetable oils according to their botanical origin using amino acid profiles established by direct infusion mass spectrometry

Amino acid profiles, established by direct infusion mass spectrometry, have been used to classify vegetable oils according to their botanical origin. The proteins present in hazelnut, sunflower, corn, soybean, olive, avocado, peanut and grapeseed oils were precipitated with acetone, and the residue was hydrolyzed in acid medium, diluted in a hydrochloric acid/ethanol mixture, and infused into the mass spectrometer. The spectra of the hydrolyzed protein extracts showed [M+H]+ ions of the following amino acids: glycine, alanine, serine, proline, valine, threonine, cysteine, isoleucine + leucine, aspartic acid, lysine, glutamic acid, methionine, histidine, phenylalanine, arginine and tyrosine. These ions were used to construct linear discriminant analysis (LDA) models. The ratios of the ion signal intensities selected by pairs were used as predictors. With the sequential application of three LDA models, the eight botanical origin categories of the samples were well resolved.

A new application of X-ray scattering using principal component analysis – classification of vegetable oils

This work describes an innovative technique based on X-ray scattering applied to classify complex organic matrices of different vegetable oils. This new, very simple and rapid procedure is based on taking a 1 mL aliquot of sample, depositing it on the thin Mylar™ film of a sample cell and submitting it to irradiation for 100 s in a conventional EDXRF equipment. The resulting spectra are processed through Principal Component Analysis (PCA), a chemometric tool, and a very convenient classification is obtained as a function of the origin of the oil. This classification can be used to verify oil adulterations, for example. The X-ray fluorescence spectrometer is a common bench equipment, with a Rh tube as source of the X-rays. Vegetable oils from corn, canola, soybean, sunflower and olive (extra virgin and others) were analysed and classifications were obtained using information from the scattered radiation. For the corn, canola, soybean and sunflower oil analyses, the PC1 × PC4 score plots show distinct groups for each oil. These differences related to the variable fatty acid contents in the samples. Considering the discrimination between extra virgin and other olive oils, the PC1 × PC2 scores plot shows two groups of samples. The main difference between them can be attributed to the total absence of water in extra virgin olive oils, since this first extraction is performed at room temperature without any kind of reagent. The technique is very useful to rapidly classify vegetable oil samples at very low cost, with the possibility of performing the measurements in situ with a portable instrument.

Classification of Vegetable Oils by Principal Component Analysis of FTIR Spectra

Principal component analysis (PCA) of infrared spectra of different known vegetable oils is used to determine the identity of several unknown vegetable oils. The experiment requires access to an FTIR spectrometer with 1-cm-1 resolution and peak-finder capability, and a software package that can perform principal components analysis. Students acquire IR spectra of a series of known vegetable oils, choose spectral features to be analyzed by PCA, and create scatter plots of principal component scores of each oil. The unknowns are then analyzed, plotted, and identified based on their proximity to the knowns in principal component space. The PCA data analysis extracts the useful information from a highly correlated data set; it is easier to identify the unknown oils by looking at the plot of principal component scores than by looking at the IR spectra. This practical application of PCA in an instrumental laboratory introduces students to chemometrics and allows them to experience first-hand the utility of a mu...

Classification of vegetable oils by high‐resolution 13C NMR spectroscopy using chromatographically obtained oil fractions

Abstract13C NMR spectra of oil fractions obtained chromatographically from 109 vegetable oils were obtained and analyzed to evaluate the potential use of those fractions in the classification of vegetable oils and to compare the results with the NMR analysis of complete oils. The oils included the following: virgin olive oils from different cultivars and regions of Europe and north Africa; “lampante” olive, refined olive, refined olive pomace, hazelnut, rapeseed, high‐oleic sunflower, corn, grapeseed, soybean, and sunflower oils; and mixtures of virgin olive oils from different geographical origins. Oils were divided into two sets of samples. The training set (98 samples) was employed to select the variables that resulted in significant discrimination among the different oil classes. By using stepwise discriminant analysis, more than 98% of correct validated assignments were obtained; these results were confirmed when applied to the test set (11 blind samples). Results suggest that the use of oil fractions considerably increases the discriminating power of NMR in the analysis of vegetable oils.

Classification of vegetable oils by linear discriminant analysis of Electronic Nose data

The purpose of this work was to attempt to classify edible vegetable oils by chemometric treatment of the data obtained from an array of gas sensors. A commercial Electronic Nose (FOX 2000) comprising six metal oxide semiconductor sensors was used to generate a pattern of the volatile compounds present in the samples. Linear discriminant analysis (LDA) was applied to the patterns generated to achieve several classification tasks. The procedure for obtaining the signals and the chemometric treatment are rapid and simple, and provide classification and prediction capabilities higher than 95%.

Classification of Vegetable Oils by FT-IR

The Fourier transform infrared (FT-IR) spectra of 27 brands of 10 types of cooking oils and margarines were measured without temperature control. Attempts to predict the vegetable source and physical properties of these oils failed until wavelength selection and multiplicative signal correction (MSC) were applied to the FT-IR spectra. After pretreatment of the data, principal component analysis (PCA) was totally successful at oil identification, and partial least-squares (PLS) models were able to predict both the refractive indices [standard error of estimation (SEE) 0.0002] and the viscosities (SEE 0.52 cP) of the oils. These models were based predominately on the FT-IR detection of the cis and trans double-bond content of the oils, as well as small amounts of defining impurities in sesame oils. Efforts to use selected wavelengths to discriminate oil sources were only partially successful. These results show the potential utility of FT-IR in the fast detection of substitution or adulteration of products like cooking oils.