Visual classification for sesame oil adulteration detection and quantification of compounds used as adulterants using flavor compounds targeted array sensor in combination with DD-SIMCA and PLS

Sesame oil is a kind of vegetable oil, which is loved by people all over the world due to its unique aroma, edible and medicinal value. Driven by interests, counterfeit and shoddy sesame oil products often appear on the market. This study is based on the color change caused by the competitive coordination of Zn2 + at the phase interface between four polyphenol dyes (Alizarin Red S, bromocatechol red, pyrogallol red and catechol violet) and volatile authenticity markers (VAM) in sesame oil. The DD-SIMCA model was constructed based on the RGB values of each sensing point after the reaction of real sesame oil and adulterated sesame oil with four polyphenol dyes. The results of the DD-SIMCA classification model show that the accuracy of sesame oil classification can reach 100%, and the result of the array sensor is much better than that of the single dye. Furthermore, the PLSR quantitative analysis model is used to verify that the RGB value obtained by the sensor is linearly related to the adulteration concentration. Therefore, this study established a visual array sensor for rapid authenticate of sesame oil adulteration based on the flavor compounds.