A Porous FeNi-CNF Nanozyme with Boosted Peroxidase-like Performance: Gallic Acid Determination and Machine Learning-Assisted Identification of Teas

Gallic acid (GA) modulates the flavor, aging, and fermentationdegree of traditional Chinese tea. Herein, hierarchically porous FeNi-CNF (PFeNi-CNF) nanozymes were fabricated via electrospinning and carbonization for GA detection, utilizing differential thermal decomposition kinetics of carbon precursors. The hierarchical porous structure optimizes mass transfer and facilitates active site formation, which endows PFeNi-CNF with 3.22-fold peroxidase-like activity compared to FeNi-CNF (calculated based on the absorbance values), as well as outstanding long-term stability and cycling durability. A highly selective and sensitive GA colorimetric sensor was developed with an LOD of 0.02 μM and a linear range of 0.05-3 μM. Machine learning model-assisted tea sample analysis (based on R, G, B, H, S, V values) was performed (highest accuracy of 99%), providing a fast, accurate, and convenient GA sensing platform for detection, recognition, and prediction of biosensing and food technology.