Development of an Innovative Optoelectronic Nose for Detecting Adulteration in Quince Seed Oil

Area of Science:

• Analytical Chemistry
• Food Science
• Sensor Technology

Background:

• Edible oil adulteration is a significant concern for food quality and consumer safety.
• Detecting adulteration often relies on complex and expensive analytical techniques.
• Volatile organic compound (VOC) profiling offers a promising avenue for rapid detection.

Purpose of the Study:

• To develop an innovative odor imaging system for detecting adulteration in edible oils.
• To utilize volatile organic compound (VOC) profiles for identifying mixtures of quince seed, sunflower, and sesame oils.
• To establish a simple, cost-effective method for food quality control.

Main Methods:

• Development of a colorimetric sensor array (CSA) using six pH indicators on a Thin Layer Chromatography (TLC) plate.
• Image analysis to generate difference maps and convert color changes into digital data.
• Application of Principal Component Analysis (PCA) and Support Vector Machine (SVM) with machine learning for classification of adulterated oils.

Main Results:

• The developed system successfully differentiated between pure and adulterated quince seed oil samples.
• Classification error decreased significantly from 37.18% to 1.29% as the number of principal components (PCs) increased from one to five.
• The Support Vector Machine (SVM) classifier, optimized with a random search algorithm, achieved high accuracy in detecting adulterants.

Conclusions:

• The novel odor imaging system is effective and accurate for detecting edible oil adulteration.
• The system offers a simple, cost-effective solution for food quality control and consumer protection.
• This technology has significant potential for widespread application in the food industry.