Dual-Mode Optical Sensor Array for Detecting and Identifying Perillaldehyde in Solution Phase and Plant Leaf with Smartphone.

Promising techniques for detecting and quantifying active components in the plants and foods have received global concern in smart agriculture. Dual-mode optical assays are becoming more attractive and popular thanks to robust and reliable analysis parameters. We herein unveil a novel turn-on and dual-mode sensor array comprising three kinds of reactive indicators including ring-closed rhodamine-hydrazine, squaraine-hydrazine, and 2,4-dinitrophenylhydrazine for evaluating perillaldehyde. Significant colorimetric and fluorescent changes were triggered through reacting primary amine/hydrazine with the active aldehyde group in perillaldehyde, thus turning on the chromogenic responses of all the indicators. Optimal colorimetric sensing showed good responses to perillaldehyde ranged up to 100 mM in ethanol. Dramatic fluorescence enhancement was also exhibited, illustrating good selectivity as well as high sensitivity (detection limit ∼20.0 μM). Inspired by rapid chemical reactions and distinct optical changes, distinct sensor array strips loaded with the optimal solid-state reactive indicators were developed for evaluating the perillaldehyde content in the perilla frutescence leaves. Smartphone-enabled readout system and digital data processing were further performed for chemometric analysis. A good correlation was obtained and the semiquantitative evaluation of the perillaldehyde content could be achieved within 15 min, possessing the significant features of naked-eye recognition, easy operation, and disposability. To the best of our knowledge, present work demonstrated the use of chromogenic sensing strips to evaluate the active perillaldehyde content in solution and vapor phases for the first time. Taken together, these characteristics also indicate that the present turn-on sensor array has great potential applications in the precise detection and evaluation of perillaldehyde in the forthcoming smart agriculture.