On-farm culture systems were developed to enable early treatment decisions. They are based on the use of selective media for differentiation between various groups of mastitis-causing pathogens. Examples include the Minnesota Easy Culture System II BiPlate and Tri-Plate which have been reported to have higher specificity but lower sensitivity. Royster et al. (2014) indicated that such methods are more reliable for the broad classification of infections but not very promising for closely related species identification. Although on-farm culture systems may not serve as replacements for laboratory microbial culturing, they provide acceptable results to make quick treatment decisions for mastitis cases.

This test evaluates the electrical conductivity of milk which can elevated during mastitis due to high sodium chloride concentrations (Khatun et al., 2019; Paudyal et al., 2020). The electrical conductivity test is rapid and non-invasive (Tommasoni et al., 2023). Information gathered in this review indicated that electrical conductivity is commonly used as an in-line indicator of mastitis in automated milking systems. Our literature review revealed the use of the dielectric spectra of raw milk to identify degrees of bovine mastitis. Zhu et al. (2023) reported that the method has a great potential in identifying negative, weakly positive and positive cases of bovine mastitis while providing a feasible, quick, in situ mastitis detection method for monitoring health status of cows. However, Zhu et al. (2023) observed that the accuracy of the dielectric spectra method is that it can be affected by the temperature of the sample as well as the level of SCC in milk. In their case, the method could only be able to identify cows with mastitis when the milk SCC was higher than 500 000 cells/ML. One major drawback of the electrical conductivity method is the limited number of pathogens that it can detect. Jensen and Knudsen (1991) reported that factors such as whether the case is clinical or subclinical and changes in milk composition are among the factors that affect the accuracy of this method in detecting cases of mastitis. Ebrahimie et al. (2018), however, stated that electrical conductivity can give better results in terms of its sensitivity and specificity during inter-quarter comparisons at the cow level and therefore can be a strong indicator of subclinical mastitis. De Mol and Ouweltjes (2001) stated that time-series models for the detection of mastitis based on the historical electrical conductivity of milk can provide results of high accuracy with sensitivity and specificity values close to 100%. Stanek et al. (2024), however, stated that the test can give false negative results especially during the early stages of infection or in cows that exhibit minimal changes in conductivity. Furthermore, high service costs were cited as a major limiting factor for conventional and small parlors. Biggadike et al. (2002) reported that historical electrical conductivity measurements of milk over 14 days are a less costly option for online detection of mastitis. A positive alert of mastitis is announced if the current electrical conductivity is more than 10% higher than the average of the previous 14-day recordings.

Our literature review revealed that the CMT test is the primary reference test of choice for detecting cows with bovine mastitis (Lai et al., 2017; Ferronatto et al., 2018; Teshome Gemechu et al., 2019). However, Lai et al. (2017) cited the reliance on SCC as one of the major limitations of the CMT method. Kandeel et al. (2017) reported that the CMT has a much higher sensitivity and specificity when used at a cut point of trace or higher. The same authors further indicated that when compared to other tests such as the esterase tests, CMT provides the most accurate, practical, and least costly on-farm screening test to predict subclinical mastitis at dry-off. The Wisconsin mastitis test (WMT) is considered a modification of the CMT and was developed to increase the objectivity of measuring milk viscosity. Rodrigues et al. (2009) stated that WMT is advantageous as it is designed for on-farm/cow side screening of subclinical mastitis, can yield results in a few minutes, and results in a semiquantitative measurement of the somatic cell count (SCC). Kandeel et al. (2019) further indicated that CMT provides a faster and more accurate cow-side screening test to predict subclinical mastitis at dry-off and freshening. The sensitivity and specificity of the CMT have been evaluated in multiple studies. Dingwell et al. (2003) evaluated CMT’s ability to detect intra-mammary infections due to major mastitis-causing pathogens (Staphylococcus aureus, Streptococcus spp, and gram-negative organisms) in early lactation. They reported 82.4% and 80.6% for sensitivity and specificity respectively. Nyman et al. (2014) supported this observation by stating that CMT is a useful screening tool for the major inflammatory mastitis-causing pathogens. However, evaluations on CMT’s ability to identify intra-mammary infections due to minor pathogens reported a lower sensitivity (61%) while specificity remained the same (80%). Middleton et al. (2004) reported 70% and 48% for sensitivity and specificity respectively when assessing the ability of CMT to detect intra-mammary infections at dry-off at the cow level for the pathogens tested.

Our study revealed the lateral flow assay as one of the field tests for bovine mastitis diagnosis. However, few of the articles that were reviewed in our study mentioned this method. However, Sajid et al. (2015) described this method as being cost-effective and rapid. Lateral flow assay relies on antigen-antibody interactions that can be visualized with the help of labeling agents such as colloidal gold. MaChado et al. (2021) described the lateral flow immunochromatographic kit (LFK) as a simple, rapid test that can be carried out in the field. Due to its perceived benefits, the LFK technology is widely used for the determination of bacterial pathogens in clinical samples. Sajid et al. (2015) attributed the recent surge in the use of lateral flow assay (LFA) in different fields of biology to its simplicity, rapidity, cost-effectiveness, and suitability for field deployment. The test utilizes antibody-antigen interactions which are visualized with the help of a labeling agent such as colloidal gold to detect the presence or absence of the analyte of interest in the sample. Upadhyay and Nara (2018) and Yong et al. (2022) reported the use of LFK for milk samples to detect S. aureus enterotoxin and Streptococcus suis serotype 2. Lateral flow assay can rapidly detect the common causes of bovine mastitis such as E. coli, S. aureus, K. pneumoniae, S. agalactiae and S. pyogenes in milk samples from suspected cases with an overall sensitivity of 97% (Cooray, 1994; MaChado et al., 2021). Sayed et al. (2023) reported a sensitivity of 10³/ml for E. coli, S. aureus, K. pneumoniae, S. agalactiae, and S. pyogenes.

The literature reviewed in this study identified the microbiological culture of milk as the popular diagnostic method for bovine mastitis as it is the preferred baseline test for bovine mastitis suspected cases of bovine mastitis. Microbiological culture techniques are generally inexpensive and simple to perform, however, they need to be performed using standardized repeatable methods. One of the articles included in our review by Nagasawa et al. (2020) evaluated microbiological culture against the immune-chromatographic strip (ICS) test. The authors reported high sensitivity and high specificity for the detection of Staphylococcus aureus in clinical mastitic milk. Petrifilm, agar plates, and tube-based systems are examples of culture-based point-of-care tests that were cited in the literature reviewed. Reported sensitivities for gram-positive bacteria ranging from 58.6% to 98% and specificities from 48% to 97% (McCarron et al., 2009; MacDonald, 2013; Leimbach and Krömker, 2018). McCarron et al. (2009) reported that the accuracy of microbiological culture tests depends on observer skills. For example, the ability to detect S. aureus based on hemolysis depends on the population under investigation and the experience of the reader. Taponen et al. (2009 added that limitations of classical bacterial culture include a delay of 24–48 hours to obtain results. In addition, in approximately 25% of milk samples from clinical mastitis cases bacteria are not detected in conventional culture. Nyman et al. (2016) stated that the sensitivity of bacterial culture can be improved by applying repeated sampling or other measures, such as selective agar and biochemical tests. However, such test procedures may take several days and increase analysis costs. Agar-based tests often allow for easy visual identification of sample contamination, which is important to monitor sample quality. Malcata et al. (2020) indicated that in addition to being both time-consuming and labor-intensive, milk culture may not yield bacteria from sub-clinically infected glands if the quantity of pathogens present when samples are taken is low. Murphy et al. (2016) and O’Reilly et al. (2024) stated that the presence of leftover treatment antibiotics in the examined milk may result in a lack of recognition of the target microorganisms from mastitic milk. Lago and Godden (2018) recommended the use of rapid culture plates as they can differentiate between common mastitis pathogens without the requirement for additional enzymatic testing thereby allowing incubation and identification of bacteria to be undertaken on-farm or within a veterinary clinic. Furthermore, rapid-culture plates can be used to make decisions about specific treatments since they are more accurate (Lago and Godden, 2018). In one study, the sensitivity of the bacterial culture test was found to be increased when three-quarter milk sampling occasions were carried out as compared to one sampling occasion.

Garcia et al. (2021) evaluated the diagnostic performance in terms of specificity, sensitivity, and accuracy of two chromogenic culture media for rapid identification of Gram-positive bacteria causing subclinical mastitis in dairy cows. The authors reported a sensitivity of 89.1%, specificity of 96.3%, and accuracy of 95.6% for Streptococcus. agalactia/dysgalactiae identification. Regarding the identification of Streptococcus uberis/Enterococcus spp, a sensitivity of 90.5%, specificity of 92.5%, and accuracy of 92.3% was reported. The advantages of chromogenic plate media as cited by these authors include its rapidity (from 18 to 24 h), the high values obtained for the diagnostic performance in most groups of pathogens, and the ability to differentiate between some species of Staphylococcus spp. and Streptococcus spp. A study by Granja et al. (2021) on bovine milk samples from cows that had tested positive for subclinical mastitis reported average sensitivities of 50%, 94%, and 97% for E. coli, Staphylococcus aureus and Streptococcus agalactiae/dysgalactiae respectively. These authors observed that the diagnostic accuracy of the biplate and triplate chromogenic culture media varied with the pathogen being investigated. Chromogenic plates can be used for rapid decision-making on treatment protocols of the major mastitis-causing pathogens. However, as indicated by Granja et al. (2021), their adoption in mastitis control programs depends on the specific needs of each farm.

Literature reviewed in this study concurred that SCC or the logarithmic transformation of SCC which is known as the somatic cell score (SCS) was the second most mentioned diagnostic test for subclinical mastitis. The advantages of this method are mainly related to the low cost and ease of data collection. In a laboratory setting, SCC can be measured by direct microscopy or by using automated electronic cell counters. However, the latter method is labor intensive and requires thorough training of personnel to gain proficiency while automated electronic cell counters allow for rapid and easy determination of SCC. Portable counters can be used to test SCC in the laboratory or on the farm. At the herd level, SCC data provides an estimation of overall udder health among cows contributing to the bulk tank milk. At the cow level, SCC data can be used to identify the cows that have healthy mammary glands versus those with acute, resolved, or chronic cases of subclinical mastitis. Jashari et al. (2016) reported that when SCC is used as an indicator for intra-mammary infections, sensitivity ranges from 30% to 89% while specificity ranges from 60% to 90%. Narváez-Semanate et al. (2022) reported that in raw milk, in addition to identifying cows that have mastitis, SCC also provides information on biochemical changes in the milk, up to production losses. When using a threshold of 200,000 cells/mL for a single composite SCC, the sensitivity and specificity were 44% and 87%, respectively, for cows infected with any pathogen and 65% and 73%, respectively, for cows infected with major pathogens (Jashari et al., 2016). Adkins and Middleton (2018) further stated that SCC can best detect intra-mammary infections at the quarter level. Schukken et al. (2003) and Zhu et al. (2023) mentioned that a mean SCC of approximately 70,000 cells/mL would indicate that the quarter/s are not infected while a mean SCC of approximately 200,000 cells/mL or greater or an SCS of 4 or higher are often used as a threshold to define infected quarters. However, the SCC threshold was reported to be different within countries of the European community where the established norms are between 400,000 to 750,000 cells mL^-1 as the maximum value, while in Colombia, the maximum accepted count is 800,000 cells mL^-1 (Gómez-Quispe et al., 2015). As reported by these authors, the diagnostic sensitivity of quarter-level SCC for subclinical mastitis can be misleading as it depends to some extent on the pathogen inciting the mastitis. Roberts (2024) indicated that false positives may occur when somatic cell counts are elevated due to factors like injury, stress, or late lactation, while false negatives can arise in mild infections due to lower SCC levels that do not produce visible gel formation. Mohammed et al. (2019) added that SCC is subjective, and can yield variable results due to visual interpretation, and lack of precision in identifying specific pathogens. Other limitations have been identified that impact the use of SCC as a diagnostic tool such as milk SCC levels remaining elevated for some time after an organism has been eliminated, resulting in a false-positive test for intra-mammary infections. In addition, factors other than intra-mammary infections that can cause variations in the SCC were identified namely breed, month of sampling, season, stage of lactation, age of the cow, parity, frequency of milking, nutrition, and animal stress (Middleton et al., 2004; Alhussien and Dang, 2018; Halasa and Kirkeby, 2020; Swinkels et al., 2021).

The present study reported the use of genotypic approaches to supplement phenotypic identification of bovine mastitis, providing a viable option for resolving issues such as false negative results. Taponen et al. (2009) stated that approximately 30% of clinical mastitis samples do not grow in bacterial culture. Therefore, the ability of polymerase chain reaction (PCR) to detect growth-inhibited samples helps to reduce false-negative results (El-Sayed et al., 2017). The high specificity and sensitivity of PCR-based techniques have made it the new gold standard for the diagnosis of mastitis Culture-independent methods for identifying bacterial pathogens in milk have become more common over the last decade (Koskinen et al., 2010). Molecular techniques such as the polymerase chain reaction (PCR) were developed for speedy detection of different mastitis pathogens to counter the limitations of culture and other conventional methods. Commercial PCR assays are available for detecting mastitis-causing pathogen DNA in mammary quarter milk samples, cow-level composite milk samples, and bulk tank milk samples.

Bulk tank PCR assays can be used in the same way as bulk tank cultures as an indicator of udder health, milking time hygiene, and storage conditions on the farm. These methods can detect bacterial infections speedily (in hours) as compared to days required by microbial culture techniques. Furthermore, PCR techniques have a high sensitivity which boosts pathogen detection levels. Studies by Taponen et al. (2009) and Bexiga et al. (2011) indicated that PCR techniques could provide a diagnosis for 43% to 47% of mastitic milk samples that are negative based on conventional culture. Gurjar et al. (2012) indicated that PCR may detect bacteria in the presence of preservatives or residual therapeutic antibodies in milk, avoiding the false-negative result caused by a lack of bacterial growth, which is a significant disadvantage of the culture method. Katholm et al. (2012) added to this by stating that the application of PCR to bulk tank samples can be used to monitor bacteria with low prevalence, such as S agalactiae. According to Kelton and Godkin (2014) when using combined milk (e.g., bulk tank or pen) samples, it is recommended to only test for contagious pathogens such as S. aureus, S agalactiae, and Mycoplasma spp. because there is a high probability that these bacteria originated from the mammary gland. Zeinhom et al. (2016) and Mahmmod et al. (2017) indicated that there is a risk of false-positive results because of teat skin contaminants, contaminated teat orifices, contaminated equipment, and carryover of contaminated milk from other cows. Compared to the use of bacterial culture and regular PCR, real time-PCR has extra advantages of speed and accuracy as well as being safe to the environment and the workers since the method does not utilize ethidium bromide, eliminates the need for post-reaction handling (no agarose electrophoresis), and the results are better visualized and digitalized, allowing for data sharing. Koskinen et al. (2010), Keane et al. (2013), Jones et al. (2019) and Mengistie (2022) indicated that when used to identify mastitis pathogens, real-time PCR has a 100% sensitivity and specificity rates. Koskinen et al. (2009) reported higher sensitivity and specificity of the Multiplex real-time PCR kit PathoProof™ when compared with microbial culturing. Amin et al. (2011) highlighted the drawbacks of multiplex PCR with the main drawback being the competition between different sets of primers for PCR substances like deoxynucleotide triphosphates (dNTPs) and Taq polymerase, which reduces the sensitivity as well as the lack of guidelines on how to report multispecies results. Additionally, PCR can detect DNA from dead bacteria. Koskinen et al. (2009) and Koskinen et al. (2010) indicated that despite its short turnaround time for results, PCR is difficult to implement on-farm due to sterility requirements as well as the need for complex equipment and trained personnel. Furthermore, the presence of PCR inhibitors such as calcium, fat, and high protein content in milk requires dedicated DNA extraction protocols to provide high-quality results which limits the application of PCR-based techniques to central laboratories. Nyman et al. (2016) stated that the high analytical sensitivity of PCR and its ability to detect dead bacteria may give false positive results which could potentially lead to an increase in antimicrobial treatment of cows. These data are important to consider if PCR is being used as a follow-up test to assess response to treatment. Hiitio et al. (2015) indicated that PCR analysis may be an effective screening tool, but the results must be carefully scrutinized before treatment decisions. Hiitio et al. (2016) recommend waiting for at least 2 to 3 weeks after the onset of mastitis or treatment or until the quarter milk SCC returns to normal levels before using PCR to assess response to treatment.

Other molecular typing methods used for the identification of bovine mastitis pathogen at the species level include ribotyping (Zadoks et al., 2011), amplified fragment length polymorphism (AFLP) (Leung et al., 2004), restriction fragment length polymorphism (RFLP) (Ombarak et al., 2018), pulsed-field gel electrophoresis (PFGE) typing (Freitag et al., 2017), and multiple-locus variable-number tandem repeat analysis (MLVA) (El-Sayed and Kamel, 2021), and at both species and strain levels include transfer DNA intergenic spacer length polymorphism (Chakraborty et al., 2019) and DNA sequencing of housekeeping genes (Ali et al., 2021). Another genotypic method is microarray technology, which can detect common species of mastitis-causing pathogens in as little as 6 h, with a sensitivity of 94.1% and a specificity of 100% (Lee et al., 2008).

The findings of the present study agree with the report by Barreiro et al. (2017) indicating that matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry is utilized for the genus and species identification of mastitis pathogens. This technology is becoming widely adopted in many diagnostic and research laboratories. MALDI-TOF is a high-throughput technology that uses a protein fingerprint and a database of reference spectra to determine bacterial species. This test has been validated as an accurate secondary test for some mastitis-causing pathogens. Although this method has been reported to be a rapid technique for the speciation of bacteria, the initial equipment setup is costly and, for the most part, it requires the organism to be cultured first. One study recommended that MALDI-TOF be used in a culture-independent fashion to identify bacterial species in experimentally inoculated milk samples. However, direct-from-milk MALDI-TOF is not currently recommended owing to the high likelihood of false negative results in cows infected with mastitis-causing pathogens. Thompson (2022) stated that MALDI-TOF MS is highly accurate and can yield results with sensitivity and specificity of up to 100% when used to identify mastitis-causing pathogens. The same author added that the ability of MALDI-TOF MS to analyze protein profiles at the molecular level makes it superior to traditional biochemical methods for distinguishing bacterial species. Zhang et al. (2022) stated that MALDI-TOF MS is extremely fast, providing results in minutes, and once set up, it is cost-effective, capable of processing large sample volumes, and highly precise in pathogen identification. However, the test’s accuracy depends on comprehensive databases. Proper sample preparation is essential to ensure reliable results, and MALDI-TOF MS cannot assess antimicrobial susceptibility, requiring additional tests for antibiotic resistance analysis (Solntceva et al., 2021).

The milk microbiome composition is an important determinant of mammalian health and plays an important role in udder health by interacting with the immune and metabolic functions of the cow, the spread of virulence factors, and the spread of antimicrobial resistance genes (Jiménez et al., 2015; Mediano et al., 2017; Derakhshani et al., 2018; Sakwinska and Bosco, 2019). One of the advantages of microbiome techniques such as metagenomic analyses is the ability to identify fastidious/anaerobic bacterial organisms, which are difficult to cultivate by routine methods. Improved access to genome-based, culture-independent methods has generated great interest in defining the bovine milk microbiome (Bonsaglia et al., 2017; Metzger et al., 2018a, b). Kuehn et al. (2013) added that the application of culture-independent methods has been especially used as diagnostic methods to better understand the nature and biological relevance of culture-negative samples. Parente et al. (2020) indicated that intramammary infections affect the microbiota composition of milk collected directly from the cistern compared to the microbiota present in healthy animals. Hoque et al. (2019) reported greater diversity in the milk microbiota in animals with clinical mastitis compared to healthy animals. According to Derakhshani et al. (2018) and Hoque et al. (2020), the microbiome of bovine milk is influenced by different factors such as breed, farm management, feeding, milk yield, lactation stage, and parity. Furthermore, the accuracy of results obtained from microbiome techniques can be compromised in cases where the microbial biomass of the milk sample is low (Rodrigues et al., 2017; Pang et al., 2018; Vasquez et al., 2019).

Mukherjee et al. (2023) stated that measuring the levels of specific enzymes such as N-acetyl-beta-D-glucosaminidase (NAGase) and lactate dehydrogenase (LDH) in milk is an effective approach for detecting mastitis. Novac and Andrei (2020) added that increased enzyme levels are associated with mastitis. Gross and Bruckmaier (2019) reported that enzyme-based tests are simple, reliable, and allow for early detection of subclinical mastitis, and are non-invasive since they only utilize milk samples for analysis. Kalmus et al. (2013) reported that the haptoglobin test can give reliable results because a constant increase in the haptoglobin concentration was found in the milk along with increasing quantities of bacterial DNA. The esterase activity test is a qualitative test that converts the results of an enzymatic reaction into an estimated SCC. However, Chakraborty et al. (2019) indicated that enzyme-based diagnostic tests are not accurate since they can differ in other disorders as well as in mastitis-affected cows. Pumipuntu et al. (2017) assessed the deoxyribonuclease (DNase) test for Staphylococcus aureus identification in milk samples. The authors reported a sensitivity of 53% and a specificity of 42%. Based on their findings, the authors did not recommend the DNase test for screening of Staphylococcus aureus in milk. Just like with SCC, factors that are not related to mastitis such as stress, inflammatory conditions, dietary changes, and lactation stage can cause variations in enzyme levels. This can potentially affect the specificity and sensitivity of these tests thereby providing false positive/negative results and inconsistent outcomes due to variations in enzyme activities among different cows (Martins et al., 2019). Kumari et al. (2018) stated that although acute phase proteins may be useful, currently they are not an economically feasible option for diagnosing subclinical mastitis.

The literature reviewed in this study revealed biosensors as an emerging technology that was developed for on-site mastitis testing to offer less lengthy approaches to conventional diagnostic methods (Kour et al., 2023). Recent advances in nanotechnology and biotechnology have led to the development of analytical tools called biosensors, capable of converting the biological compounds in a sample into electrical signals that can detect the presence of particular cells and markers with high sensitivity upon proper tuning and amplification. Wang et al. (2015) identified single-stranded oligonucleotides, antibodies, and artificial binding proteins as the most used recognition elements in sensing bacterial contamination. Duarte et al. (2015) and Ashraf and Imran (2018) mentioned infrared thermography, biosensors, and nanotechnology methods as the novel emerging diagnostic technologies that have been developed in recent years for improving the diagnosis of mastitis both at microbial and biomarker levels. New biomarkers for high sensitivity and specificity, rapid and efficient with a “cow-side” application, are among the development prospects for novel E. coli mastitis diagnosis techniques (Duarte et al., 2015; Sharifi et al., 2018; Li et al., 2019). For example, as stated by Ceciliani et al. (2014), proteomic research has yielded data on protein expression patterns that can be used to find novel therapeutic targets such as bacterial immunogenic proteins for vaccines and diagnostic biomarkers for bovine mastitis. Haxhiaj et al. (2022) reported that proteomic techniques for accurate biomarkers are feasible for early identification of mastitis and treatment efficacy, as well as the discovery of novel targets for alternative therapy development. Farmanullah et al. (2021) reported various in silico analysis tools that can be applied to screen for gene expression in mastitis. The same authors reported that through in silico analysis, it was observed that mastitis reduces the expression of fat metabolism and immune system-related genes, whereas it increased the expression of inflammatory genes. Huma et al. (2020) reported significant increases of interleukin-1∝, interleukin-8 and haptoglobin in both blood serum and milk whey in subclinical and clinical mastitis cows. These advancements, however, are still not suitable for routine diagnosis of bovine mastitis.

Angelopoulou et al. (2019) highlighted the importance of culture-independent techniques for mastitis diagnosis as these techniques assist in providing evidence-based treatment for mastitis through accurate identification of the mastitis-causing organism(s). However, the method is not always readily available in most mastitis diagnostic laboratories. Oikonomou et al. (2012) reported that sequencing and analysis of hypervariable regions within the 16S rRNA gene can provide comparably expeditious and cost-effective methods for appraising bacterial diversity and abundance. Jiménez et al. (2015) added that this technique is an effective tool for pathogen discovery and offers a breakthrough from the limitations of conventional culture methods. Ajitkumar et al. (2012) indicated that real-time PCR amplification of 16S rRNA gene fragment, spanning the variable region V5 and V6 with high-resolution melt analysis (HRMA) can be used as a powerful, fast and low-cost tool for the differentiation of clinically important bacterial mastitis pathogens. This allows the supply of timely information to physicians in a clinical laboratory setting. In one study, 33 samples that were identified by classical aerobic culture techniques as culture negative, pyrosequencing was able to detect sequences from bacteria that are known to cause bovine mastitis. This confirms that conventional methods for bacterial identification based on biochemical utilization, enzymatic profiles and serotyping are insufficient for identifying closely related bacterial strains. Forsman et al. (1997) recommended 16S rRNA sequencing as an objective and accurate method for the proper identification of Staphylococcus species isolated from bovine mastitis However, McMillan et al. (2017) reported that 16s rRNA gene sequencing yields compositional data sets and thus fails to provide resolution to species/strain level. Furthermore, it cannot differentiate between living and dead microorganisms. It was observed by these authors that the usefulness of 16S rRNA gene sequencing is limited when applied to certain staphylococcal species owing to the high degree of gene similarity. Lange et al. (2015) stated that this limitation can be overcome by using housekeeping genes to differentiate staphylococcal species and reported an accuracy of 95% following partial 16S rRNA sequencing for the species identification of coagulase-positive and coagulase-negative staphylococci isolated from bovine mastitis.