Microfluidic E-tongue to diagnose bovine mastitis with milk samples using Machine learning with Decision Tree models

Autor: Andrey Coatrini-Soares, Juliana Coatrini-Soares, Mario Popolin Neto, Suelen Scarpa de Mello, Danielle Dos Santos Cinelli Pinto, Wanessa Araújo Carvalho, Michael S. Gilmore, Maria Helena Oliveira Piazzetta, Angelo Luiz Gobbi, Humberto de Mello Brandão, Fernando Vieira Paulovich, Osvaldo N. Oliveira Jr, Luiz Henrique Capparelli Mattoso
Rok vydání: 2022
Předmět:
Zdroj: Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
DOI: 10.5281/zenodo.7686519
Popis: We report an electronic tongue based on impedance spectroscopy to detect Staphylococcus aureus and diagnose bovine mastitis in milk samples. This was achieved with optimized sensing units made with layer-by-layer films and by treating the capacitance data with machine learning algorithms employing decision trees models. These films were made with chitosan, chondroitin sulfate, sericin and gold nanoparticles /sericin, whose molecular-level interaction with S.aureus depended on the architecture according to PM-IRRAS measurements. The limit of detection in blank milk varied from 3.41 to 2.01 CFU/mL depending on the sensing unit. This sensitivity was complemented with the selectivity provided by combining the electrical responses of the four sensing units. Indeed, with machine learning it was possible to determine multidimensional calibration spaces (MCS) that could generate rules to explain how the milk samples could be discriminated. With a 7-dimension MCS, distinct S.aureus concentrations could be distinguished from possible interferents with a 100 % accuracy. In crude milk samples, 94 % accuracy was obtained with a 6-dimension MCS in multiclass classification for milk from different udders of a mastitis infected cow, including samples diluted 50-fold, in addition to milk from an infected cow treated with Bronopol and from a healthy cow. It is significant that in a ternary classification with these crude milk samples, a 2-dimension MCS could distinguish between milk from an infected cow, treated with Bronopol and from a healthy cow with 100 % accuracy. The combination of electronic tongues and machine learning – as in this proof-of-concept study - is promising for diagnosis of mastitis at a low cost.
This work is supported by São Paulo Research Foundation (FAPESP) (Grants # 2018/18953-8 and 2018/22214-6), Brazilian National Council for Scientific and Technological Development (CNPq) (Grant # 402287/2013-4, 304044/2019-9 and # 303796/2014-6), FAPEMIG (CVZ PPM 00691/17 and RED-00282/16), SISNANO-MCTI, the Financier of Studies and Projects (FINEP), INEO and Rede Agronano/Embrapa. Participation of SSdM and MSG were made possible through the Harvard-wide Program on Antibiotic Resistance (NIH Grant AI083214). We thank Prof. Paulo Machado (ESALQ-USP/ Clínica do Leite) and Mr. Augusto Lima (Clínica do Leite) for crude milk samples with Bronopol. 6. References
Databáze: OpenAIRE
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