Abstract

In this research, an efficient, fast, low-cost and easy-to-use liquid-phase microextraction method was established to measure quercetin in onion and tomato before analysis by HPLC instrument. Herein, a rotatable central composite design-response surface methodology and artificial neural network were applied to model, optimize and predict the affecting factors on the microextraction procedure. Here, a minimal level of extractant was applied in the absence of a disperser. The cloudy state was formed by repeatedly suctioning and injecting the mixture of the aqueous solution and extractant with a glass syringe. Due to this procedure, a turbid solution composed of the very fine droplets of extractant dispersed in the aqueous solution was created, the contact surface was significantly enlarged and the quercetin was promptly extracted. The optimum values for the proposed method included 284μL of 1-undecanol as the organic extractive solvent, pH of sample 3.3, the number of air injected nine times and speed and duration of centrifugation 4,000rpm and 5min. The linear range and detection of limit were achieved at 20-4,000 and 6μgL-1, respectively. RSD% was obtained ˂4.93% (n = 5). This model was applied to monitor quercetin in tomato and onion samples.

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