Cold-Pressed Walnut-Oil Adulteration with Edible Oils Detection Using Vis-NIR Spectroscopy

Area of Science:

• Analytical Chemistry
• Food Science
• Spectroscopy

Background:

• Edible oil adulteration is a significant concern for food safety and economic fraud.
• Accurate detection methods are crucial for ensuring oil authenticity and quality.

Purpose of the Study:

• To assess UV-Vis-NIR spectroscopy for detecting adulteration in cold-pressed walnut, rapeseed, sunflower, and soybean oils.
• To evaluate various spectral pre-treatment methods and chemometric models for optimal adulteration detection.

Main Methods:

• UV-Vis-NIR spectra (350-1650 nm) were collected from authentic and adulterated edible oils.
• Data pre-processing included normalization, SNV, MSC, and derivative methods.
• Chemometric techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Partial Least Squares Linear Discriminant Analysis (PLS-DA), and Partial Least Squares Regression (PLS-R) were employed.

Main Results:

• PCA revealed distinct separation between authentic and adulterated samples based on spectral regions related to pigments and lipids.
• PLS-DA achieved 100% accuracy in discriminating authentic from adulterated oils using baseline-corrected spectra.
• Partial Least Squares Regression accurately predicted the degree of adulteration, with performance varying by pre-treatment method.

Conclusions:

• UV-Vis-NIR spectroscopy, combined with chemometric analysis, is a powerful and accurate tool for detecting edible oil adulteration.
• PLS-DA demonstrates superior performance for sample classification, while PLS-R effectively quantifies adulteration levels.
• Optimized spectral pre-treatment significantly enhances the reliability of spectroscopic methods in food analysis.