Contact
    Start Publications Detection of Staphylococcus aureus in milk samples using ...
    ←  Publication Finder
    KSV NIMA

    Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space.

    Year: 2022

    Journal: Sens. Actuator Rep., Volume 4, NOV

    Authors: Soares, Juliana Coatrini; Soares, Andrey Coatrini; Popolin-Neto, Mario; Paulovich, Fernando Vieira; Oliveira Jr, Osvaldo N.; Caparelli Mattoso, Luiz Henrique

    Organizations: CNPq [103266/2020-8, 113757/2018-2]; Sao Paulo Research Foundation (FAPESP) [2018/22214-6, 2018/18953-8]; INEO; SISNANO - MCTI; Qualification Program of the Federal Institute of Sao Paulo (IFSP); Natural Sciences and Engineering Research Council of Canada (NSERC); Agronano Network

    Keywords: Staphylococcus aureus; Mastitis; immunosensor; nanostructured film; machine learning

    Early diagnosis of cattle diseases such as mastitis caused by Staphylococcus aureus (S. aureus) can be made effective if on-site detection methods with portable instruments are available. In this work, we fabricated immunosensors based on a layer-by-layer (LbL) film of chitosan and carbon nanotubes coated with a layer of antibodies to detect S. aureus. Using electrical and electrochemical impedance spectroscopies, detection was possible in buffer solutions and in milk with limits of detection which could be as low as 2.6 CFU/mL for milk, sufficient to detect mastitis at early stages. This high sensitivity is ascribed to the specific interactions involving the antibodies, as demonstrated with polarization-modulated infrared reflection absorption spectroscopy (PMIRRAS). The selectivity of the immunosensor was verified by distinguishing S. aureus-containing samples from possible interferents found in milk, for which the interactive document mapping (IDMAP) was employed. Because the interferents affected the spectra, in spite of this distinguishability, we treated the data with a machine learning technique with decision tree models. A multidimensional calibration space was then obtained with rules that permit interpretability and predictability in detecting S. aureus in matrices with high variability as in milk.
    View publication