Multidimensional calibration spaces in Staphylococcus Aureus detection using chitosan-based genosensors and electronic tongue

Author(s)

Coatrini-Soares, Andrey ; Soares, Juliana Coatrini ; Popolin-Neto, Mario ; de Mello, Suelen Scarpa ; Sanches, Edgar Ap. ; Paulovich, Fernando V. ; Oliveira Jr, Osvaldo N. ; Mattoso, Luiz Henrique Capparelli

• Document type: Journal article
• Source: International Journal of Biological Macromolecules; v. 271, p. 10-pg., 2024-05-29.

Abstract

Mastitis diagnosis can be made by detecting Staphylococcus aureus (S. aureus), which requires high sensitivity and selectivity. Here, we report on microfluidic genosensors and electronic tongues to detect S. aureus DNA using impedance spectroscopy with data analysis employing visual analytics and machine learning techniques. The genosensors were made with layer-by-layer films containing either 10 bilayers of chitosan/chondroitin sulfate or 8 bilayers of chitosan/sericin functionalized with an active layer of cpDNA S. aureus. The specific interactions leading to hybridization in these genosensors allowed for a low limit of detection of 5.90 x 10-19 mol/L. The electronic tongue had four sensing units made with 6-bilayer chitosan/chondroitin sulfate films, 10-bilayer chitosan/chondroitin sulfate, 8-bilayer chitosan/sericin, and 8-bilayer chitosan/gold nanoparticles modified with sericin. Despite the absence of specific interactions, various concentrations of DNA S. aureus could be distinguished when the impedance data were plotted using a dimensionality reduction technique. Selectivity of S. aureus DNA was confirmed using multidimensional calibration spaces, based on machine learning, with accuracy up to 89 % for the genosensors and 66 % for the electronic tongue. Hence, with these computational methods one may opt for the more expensive genosensors or the simpler and cheaper electronic tongue, depending on the sensitivity level required to diagnose mastitis. (AU)

• FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
• Grantee: Osvaldo Novais de Oliveira Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 20/09835-1 - IARA - Artificial Intelligence in the Remaking of Urban Environments
• Grantee: André Carlos Ponce de Leon Ferreira de Carvalho
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 18/18953-8 - Nanostructured films applied in microfluidic biosensors to mastitis detection
• Grantee: Andrey Coatrini Soares
• Support Opportunities: Scholarships in Brazil - Post-Doctoral