Adulteration detection of Qinghai-Tibet Plateau flaxseed oil using HPLC-ELSD profiling of triacylglycerols and chemometrics

Combination of fingerprint and chemometric analytical approaches to identify the geographical origin of Qinghai-Tibet plateau rapeseed oil

Verification of the geographical origin of rapeseed oil is essential to protect consumers from fraudulent products. A prospective study was conducted on 45 samples from three rapeseed oil-producing areas in Qinghai Province, which were analyzed by GC-FID and GC-MS. To assess the accuracy of the prediction of origin, classification models were developed using PCA, OPLS-DA, and LDA. It was found that multivariate analysis combined with PCA separate 96% of the samples, and the correct sample discrimination rate based on the OPLS-DA model was over 98%. The predictive index of the model was Q2 = 0.841, indicating that the model had good predictive ability. The LDA results showed highly accurate classification (100%) and cross-validation (100%) rates for the rapeseed oil samples, demonstrating that the model had strong predictive capacity. These findings will serve as a foundation for the implementation and advancement of origin traceability using the combination of fatty acid, phytosterol and tocopherol fingerprints.

Identification of Adulteration of Flaxseed Oil From QINGHAI Area Using GC-MS Profiling of Phytosterol Composition and Chemometrics

Flaxseed oil is an important source of vegetable oil with a polyunsaturated fatty acid. It is significant to establish a method to quickly identify adulterated flaxseed oil. In the present study, the qualitative and quantitative analysis of phytosterol of flaxseed oil from different varieties and different production areas in the Qinghai area was first performed by gas chromatography-mass spectrometry (GC-MS) and the phytosterol standard profile of flaxseed oil was established. Then, a combination of similarity evaluation and cluster analysis was used to distinguish pure flaxseed oil from flaxseed oil adulterated with concentrations of 10-50% rapeseed oil, peanut oil, sunflower oil, and sesame oil, and discriminant analysis was used to identify the types of adulterated flaxseed oil. The results showed that similarity evaluation combined with cluster analysis can distinguish pure and adulterated flaxseed oil when the concentration of the adulterant was greater than 10%. Discriminant analysis models accurately identified the types of adulterating oil in flaxseed oil when the concentration of rapeseed, peanut, or sunflower oil was greater than 20%, and that of sesame oil was greater than 30%. This study shows that the determination of the phytosterol composition and chemometrics is a valuable tool to evaluate the purity of flaxseed oil.

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

Detection of walnut oil adulterated with high-linoleic acid vegetable oils using triacylglycerol pseudotargeted method based on SFC-QTOF-MS

Authentication of walnut oil (WO) is challenging due to the adulteration of high-linoleic acid vegetable oils (HLOs) with similar fatty acid composition. To allow the discrimination of WO adulteration, a rapid, sensitive and stable scanning method based on supercritical fluid chromatography quadrupole time-of-flight mass spectrometry (SFC-QTOF-MS) was established to profile 59 potential triacylglycerol (TAGs) in HLOs samples within 10 min. Limit of quantitation of the proposed method is 0.002 µg mL^-1 and the relative standard deviations range from 0.7% to 12.0%. TAGs profiles of WO samples from various varieties, geography origins, ripeness, and processing methods were used to construct orthogonal partial least squares-discriminant analysis (OPLS-DA) and OPLS models that were highly accurate in both qualitative and quantitative prediction at adulteration levels as low as 5% (w/w). This study advances the TAGs analysis to characterize vegetable oils and holds promise as an efficient method for oil authentication.

Effect of Different Extraction Methods on Quality Characteristics of Rapeseed and Flaxseed Oils

This study reports the effect of roasted pretreatment combined with screw press, hydraulic press, and Soxhlet extraction methods on various quality indexes of rapeseed and flaxseed oils, including the oil yield, sensory indexes (color, smell, clarity, viscosity, and colligation score), physicochemical properties (acid value, peroxide value, saponification value, moisture and volatiles), major components (fatty acid composition and triglyceride composition), and minor components (volatile compounds, total phenols, and vitamin E contents). The results indicated that the oil yield, sensory indexes, physicochemical properties, fatty acid composition, volatile compounds, total phenol, and vitamin E contents in vegetable oils have been significantly affected by different extraction methods. The yields of rapeseed and flaxseed oils of Soxhlet extraction method were increased by 30.10%–73.90% and 6.30%–54.40%, respectively, compared with other treatment groups. In addition, roasted pretreatment significantly increased the yields of oils by 4.10%–25.00% and 6.70%–23.15%, respectively, compared with the untreated group. The contents of linolenic acid and vitamin E in rapeseed and flaxseed oils extracted from screw press method were higher. In particular, the linolenic acid content of cold-pressed rapeseed oil extracted by screw press increased by 1.50%–23.80% compared with other treatment groups. In addition, the contents of vitamin E in cold-pressed rapeseed oil and flaxseed oil obtained by screw press increased by 1.22%–78.91% and 3.00%–18.80%, respectively. The Soxhlet extraction could improve oil yield and total phenol content, but the quality of the oil was inferior due to high acid values (0.93–3.36 mg KOH/g) and peroxide values (0.70–5.23 meq O2/kg). Furthermore, the hydraulic press method could extract vegetable oils with excellent sensory scores. The roasted pretreatment gives the rapeseed and flaxseed oils a good smell. The major volatile compounds in rapeseed and flaxseed oils were aldehydes, acids, alcohols, heterocycles, and ketones. Different extraction methods and pretreatment had no significant effect on the compositions and contents of triglycerides. This study provides a basic understanding on the selection of appropriate oil extraction techniques for oil extraction at a large scale.

Saponification Value of Fats and Oils as Determined from 1H-NMR Data: The Case of Dairy Fats

The saponification value of fats and oils is one of the most common quality indices, reflecting the mean molecular weight of the constituting triacylglycerols. Proton nuclear magnetic resonance (1H-NMR) spectra of fats and oils display specific resonances for the protons from the structural patterns of the triacylglycerols (i.e., the glycerol backbone), methylene (-CH2-) groups, double bonds (-CH=CH-) and the terminal methyl (-CH3) group from the three fatty acyl chains. Consequently, chemometric equations based on the integral values of the 1H-NMR resonances allow for the calculation of the mean molecular weight of triacylglycerol species, leading to the determination of the number of moles of triacylglycerol species per 1 g of fat and eventually to the calculation of the saponification value (SV), expressed as mg KOH/g of fat. The algorithm was verified on a series of binary mixtures of tributyrin (TB) and vegetable oils (i.e., soybean and rapeseed oils) in various ratios, ensuring a wide range of SV. Compared to the conventional technique for SV determination (ISO 3657:2013) based on titration, the obtained 1H-NMR-based saponification values differed by a mean percent deviation of 3%, suggesting the new method is a convenient and rapid alternate approach. Moreover, compared to other reported methods of determining the SV from spectroscopic data, this method is not based on regression equations and, consequently, does not require calibration from a database, as the SV is computed directly and independently from the 1H-NMR spectrum of a given oil/fat sample.