Non-destructive detection of multi-component heavy metals in corn oil using nano-modified colorimetric sensor combined with near-infrared spectroscopy

This study attempts to develop a novel nano-modified colorimetric sensor combined with near-infrared spectroscopy (NIRS) for heavy metals (Pb and Hg) detection in corn oil samples. The colorimetric sensor was made of chemical response dyes, and dimethylpyrimidine amine (DPA) with high affinity and porous silica nanospheres (PSNs) were used to modify and improve its sensitivity and stability. Colorimetric sensors sensitive to Pb and Hg for detecting mixed heavy metals (Pb and Hg) were screened using an olfactory visualization system. The colorimetric sensor data were collected using NIRS (899.20–1724.71 nm), and the reflection spectrum data of mixed heavy metals in corn oil samples were analyzed using various partial least squares (PLS) models. These results highlight the accuracy of the sensors for Hg and Pb detection. The ACO-PLS model produced the best detection result at a low concentration (10–100 ppb) of heavy metals. The R p 2 values for predicting Pb and Hg in corn oil containing interfering heavy metals (Mg 2+ , Zn 2+ , CO 2+ , Na 2+ , and K 2+ ) were 0.9793 and 0.9510, and the limit of detection (LOD) were 5 and 7 ppb, respectively. ICP-MS was used to validate the effectiveness and stability of the methods. Finally, the developed method shows great potential for non-destructive detection of multi-component heavy metals in edible oil. • Determination of multi-component heavy metals in corn oil based on modified sensors. • The high efficiency detection of Pb and Hg in corn oil is realized without pretreatment. • Porous silica nanospheres and DPA were used to optimize the sensors performance.