Comparing Different Chemometric Strategies to determine
Flaxseed oil is among the most potent sources of n-3 fatty acids so its adulteration with refined oils may result in an impairment in its quantity of nutrients and its quality overall. The objective of this study was to evaluate different methods of detecting adulteration of flaxseed oil using refined rapeseed oils (RP) by using the technique of differential scanning calorimetry (DSC). On the basis of melting curves, variables like the peak temperature (T) and the peak’s height (h) and the percent of space (P) were measured for adulterated and pure flaxseed oils that had a RP concentration of 5, 10, 20 30, and 50 percent (w/w).
Significant linear relationships (p > 0.05) among and the RP level and the other DSC parameters were detected with the exception of parameter h1 that was the initial peak. To assess the effectiveness of the DSC method to detect adulterations, three chemometric strategies were evaluated: (1) classification models This study highlights the utility of the DSC technique and the importance of a suitable chemometric model to predict the adulteration of flaxseed oil that has been cold-pressed refined oil from rapeseed.
Introduction
Recent studies have shown conclusive evidence that combining the chemometric method with analytical measures are able to produce amazing results in the evaluation of food quality especially its authenticity. This new method provides an accurate and thorough evaluation of food items, allowing to detect any mislabeling or adulteration. 1, 2 , 3 ]. These findings emphasize the necessity to integrate data and utilize multivariate analysis tools for statistical analysis to confirm their authenticity as well as quality of this food chain. The adulteration of expensive edible oils remains a major issue for the edible oil industry as well as consumers’ health, despite the fact that experts had recognized the issue centuries ago 4 ].
This deceitful technique is driven by those who wish to boost their earnings through increasing the number of their product 5 [using the ineffectiveness of methods for assessing the quality of food products, as recommended by Food and Agricultural Organization (FAO) (FAO) 6 ]. linear discriminant analysis (LDA) (LDA) 10 ], multiple linear regression (MLR) [ 2 Multivariate adaptive regression Splines (MARS) [MARS)] 11 ], support vector machine (SVM) [ 12 ], artificial neural networks (ANNs) [ 13 Principle component analysis (PCA) PCA) 14 Orthogonal partial least-squares discriminant analysis (OPLS-DA) (OPLS-DA) 15 And partially least squares regression (PLS) and partial least squares regression (PLS) 16 ].
To for instance, detect adulteration in olive oils, you can use UV-IMS (ultraviolet Ion Mobility Spectrometry) is combined with chemometric tests such as PCA and LDA (LDA and PCA) 17 Near-infrared spectroscopy using the use of chemometric techniques 18 [, DSC together with SVM and SVM combined with DSC. 19 ] were used. The analysis for adulteration within flaxseed oils has been reported by various authors employing different analytical methods combined with a statistical approach, e.g., mid-infrared spectroscopy (MIR) that is a part of the chemometric method of PLS 20 The low-field nuclear magnetic resonance relaxation fingerprints 21 Gas chromatography-mass analysis (GC-MS) combined to PCA as well as recursive support machine (R-SVM) [ 22 HPLC-ELSD profiling of triacylglycerols, chemometrics and triacylgly 23 Dielectric spectroscopy using PCA as well as LDA analysis 10 » and Fourier transform infrared spectrum (FTIR) as well as MLR 24 ].
A majority of these studies stressed the necessity of employing multivariate techniques to detect adulteration. A variety of studies were done to prove the efficacy of the DSC method for adulteration evaluation of various oils and fats, which are relatively expensive and widely regarded as nutritious
