Chia Oil Adulteration Detection Based on Spectroscopic Measurements

Area of Science:

• Food Science
• Analytical Chemistry
• Spectroscopy

Background:

• Chia oil is a valuable source of omega-3 fatty acids.
• High production costs make chia oil susceptible to adulteration with cheaper oils.
• Food fraud detection increasingly utilizes spectroscopic methods.

Purpose of the Study:

• To determine if spectroscopic analysis can detect chia oil adulteration.
• To identify the specific adulterating oils.
• To quantify the extent of adulteration in chia oil.

Main Methods:

• Adulteration of chia oil with sunflower, rapeseed, and corn oils.
• Analysis using Raman, Near-Infrared (NIR), and fluorescence spectroscopy.
• Application of partial least square regression and classification algorithms (including Support Vector Machines - SVM).

Main Results:

• Spectroscopic analysis accurately detected and quantified adulteration levels (R² ≥ 0.95, RMSEP range ≤ 10%).
• Adulterating oils (rapeseed, sunflower, corn) were identified with high accuracy (median true positive rate of 90%).
• Classification models demonstrated high training accuracy, sensitivity, and specificity (>90%), with SVM performing best.

Conclusions:

• Spectroscopic analysis is a viable tool for detecting and identifying adulteration in chia oil.
• The methods used can accurately determine the type and amount of adulterating oil.
• This approach can help ensure the authenticity and quality of chia oil in the market.