Deep learning-based high performance liquid chromatography for food analysis

Abstract

Abstract This paper presents a study on the determination of synthetic pigments using high performance liquid chromatography (HPLC) method combined. A retention value qualitative approach, combined with an uncertainty assessment algorithm for the determination of pigment content, was used for the simultaneous determination of nine synthetic pigments, namely, lemon yellow, amaranthine red, indigo, carmine, sunset yellow, brilliant blue, seductive red, erythrosine, and seductive red, in foodstuffs by reversed-phase high-performance liquid chromatography (RP-HPLC). The sample pretreatment method was optimized, and the chromatographic conditions were set to investigate the UV determination wavelength, mobile phase, column temperature, and wavelength of synthetic pigments. Under the same mobile phase and column conditions, the results indicated that the components’ retention times did not significantly fluctuate with the change in column temperature. The results showed that the chromatographic response signals of lemon yellow, sunset yellow and seductive red were higher at the UV determination wavelength of 254 nm, so 254 nm was finally determined as the UV detection wavelength in this experiment. Mobile phase A: methanol, B: ammonium acetate (0.02mo/L) using gradient elution, the separation degree R>1.5, and the symmetry and stability of the chromatogram were better. The separation of the components was good, and the peak shape was sharp and symmetric when the column temperature was 35 ℃, so the column temperature was chosen to be 35 ℃, and the establishment of the chromatographic conditions was thus completed.