Nanoarchitectonic E‑Tongue of Electrospun Zein/Curcumin Carbon Dots for Detecting Staphylococcus aureus in Milk

• Published in: ACS omega 2023-04, Vol.8 (15), p.13721-13732
• Main Authors: Soares, Andrey Coatrini, Soares, Juliana Coatrini, dos Santos, Danilo Martins, Migliorini, Fernanda L., Popolin-Neto, Mario, dos Santos Cinelli Pinto, Danielle, Carvalho, Wanessa Araújo, Brandão, Humberto Mello, Paulovich, Fernando Vieira, Correa, Daniel Souza, Oliveira, Osvaldo N., Mattoso, Luiz Henrique Capparelli
• Format: Article
• Language: English
• ISSN: 2470-1343; 2470-1343
• DOI: 10.1021/acsomega.2c07944

We report a nanoarchitectonic electronic tongue made with flexible electrodes coated with curcumin carbon dots and zein electrospun nanofibers, which could detect Staphylococcus aureus­(S. aureus) in milk using electrical impedance spectroscopy. Electronic tongues are based on the global selectivity concept in which the electrical responses of distinct sensing units are combined to provide a unique pattern, which in this case allowed the detection of S. aureus through non-specific interactions. The electronic tongue used here comprised 3 sensors with electrodes coated with zein nanofibers, carbon dots, and carbon dots with zein nanofibers. The capacitance data obtained with the three sensors were processed with a multidimensional projection technique referred to as interactive document mapping (IDMAP) and analyzed using the machine learning-based concept of multidimensional calibration space (MCS). The concentration of S. aureus could be determined with the sensing units, especially with the one containing zein as the limit of detection was 0.83 CFU/mL (CFU stands for colony-forming unit). This high sensitivity is attributed to molecular-level interactions between the protein zein and C–H groups in S. aureus according to polarization-modulated infrared reflection-absorption spectroscopy (PM-IRRAS) data. Using machine learning and IDMAP, we demonstrated the selectivity of the electronic tongue in distinguishing milk samples from mastitis-infected cows from milk collected from healthy cows, and from milk spiked with possible interferents. Calibration of the electronic tongue can also be reached with the MCS concept employing decision tree algorithms, with an 80.1% accuracy in the diagnosis of mastitis. The low-cost electronic tongue presented here may be exploited in diagnosing mastitis at early stages, with tests performed in the farms without requiring specialized laboratories or personnel.