Open AccessLactoperoxidase potential in diagnosing subclinical mastitis in cows via image processing
Author(s) -
Emmanuelle P E Silva,
Edgar P. Moraes,
Katya Anaya,
Yhelda M O Silva,
Heloysa Aline Pinheiro Lopes,
Júlio C. Andrade Neto,
Juliana Paula Felipe de Oliveira,
Jackson Araújo de Oliveira,
Adriano H. N. Rangel
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0263714
Subject(s) - lactoperoxidase , context (archaeology) , mastitis , principal component analysis , multivariate statistics , somatic cell count , linear regression , mathematics , artificial intelligence , pattern recognition (psychology) , medicine , statistics , biology , computer science , pathology , biochemistry , enzyme , peroxidase , paleontology , pregnancy , genetics , lactation , ice calving
This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals (32 from Jersey, 7 from Gir, and 9 from Guzerat cow breeds), totalizing a dataset of 144 sequential images was collected and analyzed. In this context, this methodology was developed based on the lactoperoxidase activity to assess mastitis using recorded images of a cuvette during a simple experiment and subsequent image treatments with an R statistics platform. The color of the sample changed from white to brown upon its exposure to reagents, which is a consequence of lactoperoxidase enzymatic reaction. Data analysis was performed to extract the channels from the RGB (Red-Green-Blue) color system, where the resulting dataset was evaluated with Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Second-Order Regression (SO). Interesting results in terms of enzymatic activity correlation (R 2 = 0.96 and R 2 = 0.98 by MLR and SO, respectively) and of somatic cell count (R 2 = 0.97 and R 2 = 0.99 by MLR and SO, respectively), important mastitis indicators, were obtained using this simple method. Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland.