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.