Machine learning-assisted sensor array based on CeO₂ nanozymes for intelligent recognition of multiple synthetic...

Synthetic phenolic antioxidants are commonly utilized in foods, rubbers and plastics to slow down the oxidation process, but excessive intake of these compounds may potentially harm human health.

Hence, we developed a novel nanozyme sensor array utilizing three CeO₂ nanozymes with excellent peroxidase-like activity for rapid discrimination among five synthetic phenolic antioxidants, including propyl gallate (PG), butylated hydroxyanisole (BHA), tertiary butylhydroquinone (TBHQ), butylated hydroxytoluene (BHT), and 3,5-Di-tert-butyl-4-hydroxybenzoic acid (BHT-COOH). Wherein, three CeO₂ nanozymes with different morphologies were synthesized, which exhibited excellent peroxidase-like activity. Interestingly, the synthetic phenolic antioxidants inhibited the expression of CeO₂ nanozymes activity to varying degrees due to their different chemical structures. Inspired by this, we constructed a CeO₂ nanozyme sensor array for precise discrimination of five synthetic phenolic antioxidants through pattern recognitions. Furthermore, the sensor array was integrated with machine learning to develop a dual-stage prediction model featuring classifier-independent concentration discrimination and regression-based quantification. Besides, the identification and concentration discrimination of different synthetic antioxidants in actual samples have been achieved with the support of model, demonstrating good practical applicability.

Therefore, our findings provide a new method for the identification of synthetic phenolic oxidants, providing better oversight of the overuse of synthetic antioxidants.