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Metalworking fluids (MWFs) are used for cooling and lubrication in a wide range of metal forming and removing processes. During their lifecycle, these fluids are prone to microbial growth, impacting fluid integrity and giving rise to the emergence of opportunistic pathogens. Whereas the former issue results in premature fluids discharges, the latter is linked to occupational health hazards. The lack of rapid and accurate microbial detection and enumeration techniques has impaired effective microbial management of MWF. To meet this need, the following objectives were addressed: (1) Development of a rapid, sensitive, and robust fluorescence-based method for direct detection in MWF using flow cytometry (FCM); (2) Development and validation of a two color staining protocol for target mycobacteria by coupling nucleic acid stains and immunofluorescence labeling with free antibody (FAb) or nano-immunomagnetic particles (NIMPs) and (3) Rapid and specific quantification of M. immunogenum in a mixed community in MWF using an optimized immunofluorescence protocol coupled to cluster analysis-informed visualization analysis. By employing PicoGreenRTM nucleic acid dye and resolving agents, isopropyl alcohol or N, N-dimethylformamide, direct detection of mycobacterial counts in MWF was demonstrated, with a quantitative calibration capturing four orders of magnitude using either FCM or epifluorescent microscopy. The specific detection of mycobacteria (represented by M. parafortuitum or M. immunogenum) in MWF was demonstrated using dual-stain FCM, including DNA stains and immunofluorescence techniques. NIMPs coated with primary and a labeled (Alexa FluorRTM 647) secondary antibody resulted in a 40% recovery efficiency of a pure M. immunogenum culture in MWF. The NIMPs technique and FAb protocols exhibited less than 10% false-positive detection with other MWF bacteria such as Pseudomonas aeruginosa, Pseudomonas pseudoalcaligenes, Citrobacter freundii, and Micrococcus luteus. A tailored immunofluorescence protocol was capable of labeling M. immunogenum in a mixed community in MWF. Final validation by a hierarchical cluster aided visualization analysis increased detection accuracy and yielded a strong correlation (R2>0.999) for M. immunogenum detection in a ternary microbial community in MWF. These results indicate that dual-stain FCM coupled to advanced statistical analysis is a promising detection and quantification platform for aggregating, slow-growing bacteria in complex matrices.