Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed...

Area of Science:

• Food Science and Technology
• Analytical Chemistry
• Chemometrics

Background:

• Flaxseed oil is a rich source of omega-3 fatty acids, making its quality and nutritional value critical.
• Adulteration of flaxseed oil with cheaper refined oils diminishes its health benefits and economic worth.
• Accurate methods are needed to detect adulteration and ensure the authenticity of cold-pressed flaxseed oil.

Purpose of the Study:

• To compare various chemometric models for detecting adulteration of flaxseed oil with refined rapeseed oil (RP).
• To evaluate the effectiveness of differential scanning calorimetry (DSC) in identifying adulterated samples.

Main Methods:

• Differential scanning calorimetry (DSC) was used to analyze pure and adulterated flaxseed oil samples (5-50% RP).
• Key thermal parameters (peak temperature, height, area) were extracted from DSC melting curves.
• Chemometric models including Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANNs), Support Vector Machine (SVM), and Partial Least Squares (PLS) were employed for analysis.

Main Results:

• Significant linear correlations were found between RP concentration and DSC parameters.
• The LDA model achieved the highest classification accuracy (99.5%), followed by ANN, SVM, and MARS.
• ANN demonstrated the best performance in regression analysis (R=0.996), while PLS showed superior results over OPLS-DA for predicting adulteration levels.

Conclusions:

• Differential scanning calorimetry (DSC) is a valuable technique for detecting refined rapeseed oil adulteration in flaxseed oil.
• The choice of chemometric model significantly impacts the accuracy of adulteration detection.
• LDA and ANN models show high potential for accurate classification and regression, respectively, in identifying adulterated flaxseed oil.