Filters
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.
DSC Phase Transition Profiles Analysed by Control Charts to Determine Markers for the Authenticity and Deterioration of Flaxseed Oil during Storage
An approach of implementing X-bar and R control charts as a statistical control tool to monitor changes in the melting profile of fresh and stored flaxseed oils by differential scanning calorimetry (DSC) was used. Phase transition melting profiles were collected after 0, 2, 4, 6 months of storing flaxseed oils, originated from five different cultivars. Four peaks at around -36, -30, -25, -12 °C were identified using the deconvolution analysis procedure, which enabled data to be collected on peak temperature (T), peak height (h) and the peak area (A), as well as the ratio calculated from these parameters. Control charts of DSC parameters, linked to the second peak (h2, A2) and calculated ratios of those parameters showed an increasing or decreasing trend within the storage time, thus were considered to be indicators of oil deterioration. Since DSC parameters related to the first peak (h1, A1) and third peak (h3, A3) remained unchanged within storage, they were established as the markers of flaxseed oil authenticity.
Differential scanning calorimetry as a tool to assess the oxidation state of cold-pressed oils during shelf-life
AbstractCold-pressed oils are highly prone to the peroxidation process, which causes a rapid decline in quality. Thus, there is a need to develop instrumental methods instead of conventional chemical analysis consuming large quantities of harmful chemicals. Differential scanning calorimetry (DSC) is a valuable analytical tool for assessing the oxidative stability of oils. Cold-pressed flaxseed, camelina and hemp seed oils from different cultivars, which had been stored for six months in room conditions under natural light exposure, were tested. Chemical methods for measuring changes in oxidative stability during storage of oils included determination of peroxide value (PV), p-Anisidine value (p-AV), total oxidation value (TOTOX) value and acid value (AV). Parameters like oxidation induction time (OIT) in isothermal mode (120, 140 °C) and onset temperature (Ton) in non-isothermal mode (heating rate 2, 5 °C/min) were established from DSC curves. Data for OIT and Ton plotted against time showed a strong, significant (p ≤ 0.05) descending trend for all oils. However, flaxseed and hempseed oils revealed a more rapid deterioration during storage compared to camelina seed oils. All DSC results showed promising repeatability of the oxidative characteristics for three types of cold-pressed oils, regardless of their origins in different cultivars. However, the most suitable for monitoring the deteriorative changes in oils during storage was the isothermal test carried out at a temperature of 120 °C, for which the correlations with chemical indicators (PV, p-AV, TOTOX) were highly significant (p ≤ 0.0001). Linear discriminant analysis (LDA) based on the DSC results revealed, that the first discriminating function significantly separated the fresh oils from stored oils. The study showed that, based on a starting point defined for fresh oils, the DSC technique can be used to effectively and ecologically monitor the deterioration of oils by oxidation, instead of harmful chemical analyses.
DSC phase transition profiles analyzed by control charts to determine markers for the authenticity and deterioration of flaxseed oil during storage
An approach of implementing X-bar and R control charts as a statistical control tool to monitor the changes in the melting profile of fresh and stored flaxseed oils by differential scanning calorimetry (DSC) was used. Phase transition melting profiles were collected after 0, 2, 4, and 6 months of storing flaxseed oils, originating from five different cultivars. Four peaks at around −36, −30, −25, and −12 °C were identified using the deconvolution analysis procedure, which enabled the data to be collected at peak temperature (T), peak height (h), the peak area (A), and the percentages of the area (P A), as well as the ratio calculated from these parameters. Control charts obtained for the second peak of the melting profile showed a significant decrease of peak height (h2) from 0.50 to 0.39 W/g and the percentage of the area (P A2) from 50 to 38%, within the storage time (p ≤ 0.05); thus, they were considered to be indicators of oil deterioration. Strong negative correlations of the unstable parameters of DSC with chemical indicators of the oils’ oxidative stability (PV, p-AV, TOTOX) were found. For DSC parameters, related to the first peak (h1, A1) and the third peak (h3, A3), changes were statistically not significant within storage (p > 0.05); thus, they can be used as markers of flaxseed oil authenticity. The study demonstrated that X-bar and R control charts could effectively monitor changes in the specific peaks and calculated ratios from the DSC melting profile of fresh and stored flaxseed oils, serving as reliable indicators of oil deterioration.
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 use the differential scanning calorimetry (DSC) technique to detect adulterations of cold-pressed flaxseed oil with refined rapeseed oil (RP). 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 a RP concentration of 5, 10, 20, 30, 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for h1. 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, Artificial Neural Networks, ANNs); 2) regression models (Multiple Linear Regression, MLR, MARS, SVM, ANNs, 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. These results demonstrate the usefulness of the DSC technique combined with chemometrics for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.