Highly sensitive analysis of fatty aldehydes in vegetable oils using a novel coumarin-based fluorescent probe by HPLC for quality control

Abstract

A novel fluorescent probe, 3-(4-((hydroxylamino)methyl)-1H-pyrazol-1-yl)-7-methoxyl coumarin (HAPMC), has been designed and synthesized. The compound exhibits exceptional fluorescence properties due to a coumarin-pyrazolyl fluorophore. Moreover, it incorporates an N-hydroxylamine functional group capable of reacting with aldehydes to yield nitrone derivatives under mild conditions. This unique combination renders it an ideal derivatization reagent for aldehyde analysis. A standard method was established for the quantitative analysis of eight fatty aldehydes by utilizing high-performance liquid chromatography with fluorescence detection (HPLC-FLD) and a pre-column derivatization process, which has low detection limits (0.3–1.2 nM), satisfactory reproducibility (intraday and interday relative standard deviations in the range of 1.29 %-2.92 % and 1.73 %-3.45 %), and an extensive linear range (25–5000 nM) with excellent linearity (R2 ≥ 0.9989). The spiked recoveries of the fatty aldehydes range from 89.6 % to 106.0 %. The greenness and sustainability of our approach are reinforced by evaluations conducted with the AGREE and RGB 12 algorithms. The established method was successfully applied to the qualitative and quantitative detection of fatty aldehydes in nine real vegetable oil samples, including peanut, canola, rice, soybean, olive, rapeseed, corn, sunflower, and blended oils. The fatty aldehydes are known to have adverse effects on human health due to their carcinogenic and mutagenic properties. The findings revealed that hexanal and nonanal were the most prevalent saturated fatty aldehydes, with a notable increase in the total aldehyde concentration observed as the temperature and duration of heating increased. Compared to alternative analytical techniques, this pre-column derivatization method utilizing HAPMC displayed simplicity, reliability, and sensitivity. The results demonstrate the potential for broad application of this approach in various important research domains, including food quality control, environmental monitoring, and medical research.