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

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.

Automated summary

Early diagnosis of cattle diseases, such as mastitis caused by S. aureus, can be effectively achieved through on-site detection methods using immunosensors with high sensitivity. The immunosensors fabricated in this study showed detection limits as low as 2.6 CFU/mL in milk, enabling early detection of mastitis. Additionally, machine learning techniques and rules calibration space were utilized to enhance the selectivity and predictability of the sensors towards various interferents.