In solution-phase, the relationship between the R/G ratio of the fluorescence color alterations and added Al 3+ concentration (0~10 µM) has linearity (R 2 = 0.9686) (Fig. ). The LOD of the RGB method was found to be 82.8 nM. But the fly in the ointment was that the linearity was unsatisfactory. Thus, to improve the linearity of the RGB assay, the solide-phase samples (cotton swab as the support) were prepared. Fig. shows that the fluorescence color was changed from green to yellow as compared with that in the solution, and a reliable and better linear relationship (R 2 = 0.98) between the R/G value and the Al 3+ concentration (0~30 µM) was presented, and the LOD of the RGB platform was 45.4 nM, which confirmed the superiority of the solid-state interface over a liquid surface. Although this method is imperfect at present, our RGB platform constructed by fluorogenic tweezers NPDM and smartphone provides a promising analytical strategy for the portable, semiquantitative, rapid, simple, and in-field visual detection of Al 3+ and other analytes without any complicated and costly apparatus, which is especially suitable for the deprived areas. Thus, as the abstracted unknown sampled signals are introduced to the standard calibration curve, semiquantitative assessment of unknown samples could be ascertained appropriately. In this way, quick and precise assessment of the unknown samples could be obtained through integrating together and phototaking procedures utilizing a smartphone with wide field applications (Fig. ). Aiming to further examine the practicality and reliability of the integrated sensing platform, we utilized the as-proposed method for sensing Al 3+ in real samples (the environment water samples, tea, Chinese wolfberry and milk powder), like river water, bay water, tap water, and green tea. As shown in Table (ESI), the recoveries of Al 3+ ranged from 91.07 % to 102.04 % with the low relative standard deviation (RSD) below 5 %, implying that this portable RGB system based on tweezers NPDM is fully competent for in-field sensing Al 3+ with the high reliability and accuracy in environmental water and food samples. The above results conclude that the intelligent sensing platform has great latent capacity and feasibility for the semiquantitative determination of Al 3+ in real samples.