03232naa a2200433 a 450000100080000000500110000800800410001902400930006010000180015324501400017126000090031152019270032065000140224765000160226165000190227765000260229665000240232265000140234665000100236065000300237065000110240065300170241165300230242865300140245165300200246565300260248570000180251170000220252970000220255170000210257370000240259470000200261870000220263870000210266070000180268170000270269970000220272677300500274821542902023-06-06       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1021/acsomega.2c07944?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-as2DOI1 aSOARES, A. C.  aNanoarchitectonic E-tongue of electrospun zein/curcumin carbon dots for detecting Staphylococcus aureus in milk.h[electronic resource]  c2023  aWe 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.  aBeverages  aCalibration  aDairy products  aElectrical properties  aInfectious diseases  aBactéria  aLeite  aProduto Derivado do Leite  aZeína  aCalibração  aDoença infecciosa  aNanofibra  aProduto lácteo  aPropriedade elétrica1 aSOARES, J. C.1 aSANTOS, D. M. dos1 aMIGLIORINI, F. L.1 aPOPOLIN-NETO, M.1 aPINTO, D. dos S. C.1 aCARVALHO, W. A.1 aBRANDAO, H. de M.1 aPAULOVICH, F. V.1 aCORREA, D. S.1 aOLIVEIRA JUNIOR, O. N.1 aMATTOSO, L. H. C.  tACS Omegagv. 8, n. 15, p. 13721-13732, 2023.