Rapid Detection of Adulteration in Mixing Sesame, Sunflower, and Canola Vegetable Oils by Mathematical Model

• Published in: Food analytical methods 2021-07, Vol.14 (7), p.1456-1463
• Main Authors: Malekahmadi, Roya, Yasini Ardakani, Seyed Ali, Sadeghian, Abolfazl, Eslami, Hadi
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Canola; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Fatty acids; Food Science; Gas chromatography; Linolenic acid; Mathematical analysis; Mathematical models; Microbiology; Polynomials; Sesame oil; Stearic acid; Sunflower oil; Sunflowers; Vegetable oils; Vegetables
• ISSN: 1936-9751; 1936-976X
• DOI: 10.1007/s12161-021-01980-y

The aim of this study was to investigate the application of a mathematical model to rapid detect the adulteration in sesame, canola, and sunflower oils. To hit this target, we combined the refined sesame oil with canola and sunflower oils in different concentrations of 30–60%. Furthermore, fatty acid content of 12 samples of sesame, canola, and sunflower oils was analyzed using the gas chromatography (GC). Chromatograms were analyzed to diagnose and classify the fatty acid types. The results achieved from the experiments were analyzed using Excel 2016. The results showed that decreasing the amounts of sesame oil in different mixture oils reduces the stearic acid content and increases the amount of linolenic acid. For the model development, the mathematical model using the polynomial function was used. Finally, a mathematical formula was successfully designed to determine the amount of sesame oil in the mixture of sesame, canola, and sunflower vegetable oils ( R 2 = 0.995). Finally, application of the mathematical models can be a quick, low-cost, and effective method to detect adulteration in sesame oils.