Differentiation of Edible Oils by Type Using Raman Spectroscopy and Pattern Recognition Methods

Area of Science:

• Analytical Chemistry
• Food Science
• Spectroscopy

Background:

• Edible oil authentication is crucial for food safety and quality control.
• Distinguishing between various edible oil types can be challenging due to similar chemical compositions.

Purpose of the Study:

• To investigate the efficacy of Raman spectroscopy combined with pattern recognition for edible oil classification.
• To explore the potential of spectral data in differentiating 15 varieties of edible oils.

Main Methods:

• Collected 286 Raman spectra from 53 edible oil samples across 15 varieties.
• Applied baseline correction using a Whittaker filter to remove fluorescence.
• Utilized pattern recognition techniques: mapping, discriminant development, and clustering.

Main Results:

• Successfully partitioned the 15 edible oil varieties into five distinct groups.
• Grouping was based on the degree of saturation and polyunsaturated to monounsaturated fatty acid ratios.
• Oils within the same group exhibited similar Raman spectra, posing classification challenges.

Conclusions:

• Raman spectroscopy and pattern recognition offer a viable method for edible oil discrimination.
• The approach effectively categorizes oils based on key fatty acid indicators.
• Further refinement may be needed for high-resolution classification within closely related oil groups.